The Malmö Study - KTH

The Malmö Study
Sofia Sandgren ‡
The Malmö Study is one of the longest longitudinal individual databases
existing. In this chapter its history and substance is presented in more detail
than in the other articles in the thesis. The main sources on historical
background are Fägerlind (1975), Tuijnman (1989), and Furu (2000). I discuss
my choice of variables for the econometric chapters from the wealth of
information available in the Malmö data set. Some composite variables are
presented in Section C. They were created to test Eliasson’s (1998) so called
platform hypothesis of cumulative learning at school and on the job. Even
though some variables (for instance recurrent education) appear to contribute
significantly to earning, the material collected so far is not up to capturing a
possible interactive (cumulative) effect. For the time being we regard this work
as an ongoing effort, but nevertheless present data created as an illustration of
future uses of the material for deeper studies on the determination of earnings
and job careers. This work has been done in co-operation with Gunnar Eliasson.
A: A bit of history
A Licentiate Student at Lund University initiated the study in 1937. His name
was Siver Hallgren, and he needed the data for his thesis on the relation
between social environment and ability in young children. He developed a
group test on ability, which was – after being thoroughly tested during 1937 –
done by all the third graders in the county of Malmö in 1938. 1 A total of 1,542
pupils were included in the study, of which 1,342 were normal-aged (born
1928). 171 pupils were over-aged, and 29 were under-aged; the oldest pupil was
born in 1925, and the youngest in 1930. A vast amount of background
‡ Department of Infrastructure, Royal Institute of Technology, 100 44 Stockholm
E-mail: sofia.sandgren@vinnova.se
1 Malmö is the third biggest city in Sweden, located in the South. The cohort includes pupils in the city of
Malmö and surrounding municipalitites. In 1938 the population was 151,247 (verbal information from Statistics
Sweden, December 2004).

The Malmö Study
information on the pupils was collected, as well as on the teachers’ opinion of
their cognitive ability. Today, all people in the study are either retired or dead,
but their individual histories have been followed at intervals until now.
Siver Hallgren completed his thesis in 1939, and Torsten Husén was his
opponent at the defence. Siver himself did a follow-up of the study in 1942, to
see how useful the ability test had been in predicting the pupils’ scholastic
achievements. In 1948 Torsten Husén was engaged in the construction of ability
tests for the military enlistment, which were carried out at the age of twenty and
were compulsory for all men in Sweden. He realised that the men in the Malmö
Study must be going through the tests at this time. Husén obtained the military
enlistment data sets for the Malmö men, and combined these with the data sets
from 1938. With these data he had an excellent opportunity to study the
development of ability during adolescence, i.e. how different forms of
schooling affect IQ, as measured in the tests. One result was that on average,
those who went through the higher levels of education improved their IQ, while
the other often experienced a decreased IQ, the correlation between IQ1938 and
IQ1948 being 0.7. (Husén, 1950)
When Siver Hallgren died at a young age in 1961, his widow gave the
material to Torsten Husén, who at the time was professor of applied educational
research at the Stockholm School of Education. In 1964 Husén, together with a
group at his department, carried out the first questionnaire follow-up, while at
the same time collecting data from registers. In 1971, 1984 and 1994 further
questionnaires were distributed and collected. The response rate has generally
been between 72 and 75 percent.
Register data has been collected at intervals, fourteen times: 1948, 1953,
1958, 1963, 1968, 1969, 1971, 1978, 1980, 1982, 1984, 1986, 1991, and 1993.
For the five first occasions, the county tax registers were the source of
information, and the people involved collected data more or less manually. For
1969 and 1971 data was bought from Statistics Sweden, while from 1978 and
onward data was bought from SPAR (a special State Register established at the
State Computing Center (DAFA) in 1978).
Today the material is located at the Institute of International Education at
Stockholm University, under the guidance of Albert Tuijnman, who took over
the chair from Ingemar Fägerlind, who took over the chair from Torsten Husén.
2

The Malmö Study
Many theses and publications have been based on the Malmö material, mainly
within the educational, psychological and medical fields (Furu, 2000). The
Malmö data set is extremely rich and this thesis only uses part of the material. I
will therefore describe the main variables I have decided to use rather than other
variables available, and also briefly present some other variables in the material
that relate to my problem. In doing this I also discuss the validity of several
variables assumed to capture complex phenomena, not least ability. The Malmö
material is particularly suited for that. First, the data set includes several
measures of the same thing, or rather measures of different dimensions of the
same thing, such as both an IQ measure and the teacher’s assessment of the
cognitive ability of the student. It is therefore interesting to speculate whether
non conformance means that entirely different dimensions have been measured
or that it depends on deficiencies in the measurement instruments (tests or
assessments). Second, the same phenomenon has been measured at different
times on the same individuals. Stability of measures over time is an interesting
phenomenon in itself.
I wish to emphasise that all means, correlation coefficients and other data
given below are calculated on the whole population of 1,542 individuals (or as
many there is data on). In the other articles in this thesis, I often give means and
coefficients calculated on subsets of the population, so the means and
frequencies might not be the same.
B: Statistics
Earnings
Measures on earnings were collected for fourteen different years: 1948, 1953,
1958, 1963, 1968, 1969, 1971, 1978, 1980, 1982, 1984, 1986, 1991, and 1993.
This data is collected through tax registers, and include – apart from earnings
from gainful employment - pensions, sickness/study allowances, and
unemployment benefits. Unfortunately, the figures concern assessed income,
i.e. net earnings after deductions but before exemptions. This amount could,
from the mid-seventies, be rather different from gross earned income. Therefore
supplementary information on gross earnings in 1968, 1971, 1974, 1978, 1982,
1986, 1990 and 1993 have been bought from Statistics Sweden.
3

SEK
The Malmö Study
For the last survey, regarding year 1993, earnings on 1,237 individuals
were gathered. Of the original 1,542 individuals, 275 were no longer alive in
1993. Thus, earnings information is available for 98% of those still alive. For
the years until 1968, earnings are rounded up or down to nearest 1000 SEK, and
thereafter to the closest 100 SEK.
For the years 1948, 1953, 1958, 1963, 1968, 1969, and 1971 there is also
data on family income and capital income. For 1978, 1980, 1982, 1984 and
1986 there is data on ownership of property and other assets, and for the years
1991 and 1993 there is data on family income, property and other assets. 1986
is the year when most individuals reported family wealth exceeding 200,000
SEK – 247 men and women did so. The mean value of assets for this year
(among those who reported asset values exceeding 200,000 SEK) was 643,000
SEK, while the richest individual, a woman, reported assets amounting to
3,318,000 SEK.
When plotting earnings against educational level the higher educational
groups obviously earn more than the lower ones. In the figure below all
earnings are given in 1993 year’s value, for men and women together:
500000
450000
400000
350000
300000
250000
200000
150000
100000
50000
0
Figure 1: Earnings by educational level, 1993 years value
1950 1960 1970 1980 1990 2000
Year
4
< Primary school
Primary school
Vocational school
Lower sec. School
Upper sec. School
Academic education

Schooling
The Malmö Study
There are several schooling variables. Unfortunately none gives the exact
number of years each individual has been to school. The two variables I have
made most use of are 1) Acquired level of schooling, and 2) Number of years of
formal schooling. Both of these variables were collected through the
questionnaire in 1964, even though the former has been supplemented with
information from the school registers.
The Swedish school system in the 1930s offered several different
educational paths. The shortest way was to attend primary school (‘folkskola’)
only, for as long as it was compulsory, which differed between different regions
in Sweden; in Malmö seven years. Thereafter one could attend lower secondary
school (‘realskola’), which was partly overlapping with primary school, and
could begin after four, five or six years in the latter. The children who had some
distance to the closest lower secondary school were those who took five or six
years in primary school before transferring. Those who went further on to upper
secondary school (‘gymnasium’) normally took only four years in lower
secondary, but the exam was taken after five years. After three or four years in
upper secondary school a matriculate exam (‘studentexamen’) was achieved;
normally a student had been to school for twelve or thirteen years at this point.
University was the next educational step. A large part of the adolescents took a
vocational education, and this was normally taken after primary school, but
could also be taken after lower secondary school. There were also other ways to
go, for example quite a few of the girls went to a girls’ schools instead of lower
secondary school. After the matriculate exam, or upper secondary school, many
participated in some sort of correspondence course, or shorter courses at
technical or commercial institutes (one or two years). A few of the educations
which today are academic were not so during this time, for example teacher’s
college.
Acquired level of schooling: data available for 1373 persons. There are
six levels: discontinued primary school; primary school; vocational school;
lower secondary school; upper secondary school and, university.
Number of years of formal schooling: data available for 1013 persons.
The answers are grouped into four categories, as follows: less than 8 years; 8-10
years; 11-14 years; and, more than 14 years.
5

The Malmö Study
The following figure shows how the sample is distributed over the
different levels of education. Differences between men and women are not
large, and not shown here; some more women completed primary school, and
slightly less achieved the two highest levels of education, than among the men.
Figure 2: Acquired level of schooling. N=1373
< Primary school
Primary school
Vocational school
Lower sec. School
Upper sec. School
Academic education
There is also more detailed information on what type of education each
individual has acquired, divided into 42 alternatives. For example, peoples’
college, vocational school, commercial school, upper secondary school,
technical school, or medical university. This kind of information is available for
1,012 persons. The subjects studied by the 47 persons who have academic
education are distributed as follows:
Medicine 5 persons
Law 6 persons
Business school 6 persons
Technical college 10 persons
Faculty of philosophy 20 persons
This detailed information, together with the information about acquired
level of education, can be used to convert the length of each individual’s
6

The Malmö Study
education into years of education. Number of years in school is then known for
1,373 men and women.
I have in most cases used the information on achieved level of education,
instead of years of education. I believe levels to be a more accurate measure of
the investment in human capital, since it gives more information on the
outcome of the investment. In some cases, the input might be the same, for
example nine years in school. However, the result can be very different,
vocational school or lower secondary school, two educational choices involving
approximately the same number of years.
Adult education: Furthermore, we know if the individual has participated
in adult education between 1960 and 1982. Surprisingly many have answered
that they did participate in adult education, for example 542 men and women
said they did between 1964 and 1971. However, only three of these men
reported their occupational category to be adult students. I thus conclude that
the studies were mainly temporary and not part of a professional educational
program. For example, 95 of the 309 men who had participated in adult
education did so by way of in-service training, 48 in correspondence courses,
and 21 in radio or tv-courses. The most common subjects were foreign
languages, economics or other courses.
Ability
The ability measures in the Malmö material are unusually good for this kind of
study. Most of them were collected at a young age – ten years/third grade –
when the individuals’ intellectual capacity had not yet been too affected by
schooling and environmental factors. The educational system in Sweden was
still undifferentiated up to fifth grade. Four alternative ability measures were
collected in third grade:
• IQ38 - scores from the ability test transformed to intelligence
quotas. The test consisted of four subtests: word opposites, sentence
completion, perception of identical figures, and disarranged sentences.
• Teacher’s rating of ability - the teachers were asked to give each
student a rating of general cognitive ability on a five point scale.
7

The Malmö Study
• Teacher’s rating of class standing - the teachers were also asked
to give each student a rating of his/her relative standing in class, on a three
point scale were 1 was given to the fifteen percent weakest, and 3 to the fifteen
percent strongest. The 70 percent in between received the number 2.
• Grades - there are grade point averages for third to seventh
grade from primary school, but since many pupils left for lower secondary
school in fifth grade there are many missing values from fifth grade and after.
For some reason there are no grades for the pupils who went to private schools.
71 pupils (4.6%) went to private schools in 1938.
• IQ48 - for men there are also IQ scores from the age of twenty,
from the military registrations. This data is available for 653 of the 834 men in
the sample.
The average IQ-score in 1938 for these children was 98, and the
distribution is pictured in Figure 2. Teachers’ ratings are available for 1,416
individuals, and the correlation coefficient between these two variables is 0.7.
Hence, even if the two ability measures given by the teachers often follow each
other, there are exceptions. An able student in a class with a very high average
level of ability might have a four or five on the general ability rating, but only a
two in relation to his class, while a not so able student in a class with a low
level of average ability might receive a 3 on class standing but only a 3 on
general ability. In Table 1 means and standard deviations for the ability
variables are given, grade being the grade point average from third and fourth
grade. As can be seen, the girls have obtained somewhat higher scores on all
variables except for grades.
8

IQ38
400
350
300
250
200
150
100
50
Teacher’s rating of general
ability
Teacher’s rating of class
standing
Grades, average points 3 rd
& 4 th grade
0
The Malmö Study
Figure 3. Distribution of IQ38
-59 60-69 70-79 80-89 90-99 100-109 110-119 120-129 130-
IQ
Table 1.
Ability variables
Means and Standard Deviations
All
Men
Women
Std. dev.
Std. dev.
Std. dev.
98,1 16,37 97,7 16,02 98,5 16,8
2,92 1,23 2,89 1,21 2,96 1,25
2,01 0,53 2,00 0,51 2,02 0,54
3,50 0,56 3,52 0,57 3,47 0,53
When doing a correlation matrix of these ability measures and the level
of education, Grade turns out to be the ability variable with the highest
correlation with acquired level of schooling.
9

IQ38 0.55557
The Malmö Study
Table 2.
Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0
Number of Observations
Sclevel IQ38 TRa • TRb ∗

Social background
The Malmö Study
There are many different background variables in the material, even more for
the men since additional information was collected at the military enlistment.
Below is a list of some of them, with an (M) for those only available for the
men. These variables have been presented to illustrate the wealth of information
available in the material:
- parents’ income in 1929, 1933, 1937 and 1942
- place of residence at birth (M), 1948 (M),
- family size 1938
- nr of children at home, in 1938
- father’s education
- social class, in 1938
- one adult at home, i.e. one or two parents in the family
- father’s occupation, in 1938
- parent’s attitude to schooling and occupational choice
Parents’ income is available for several years. In Article 5 we have used
parents’ income (family income) in 1937. This is the year for which there is
information on most individuals, 1,437 of 1,542. When studying those with
missing information on parents’ income, I reached the conclusion that the
reason probably was that they did not have any income this year. For example,
many of the 105 children were in the schools’ welfare registers, more of these
children had single parents, and the reported incomes for surrounding years
were very low.
At birth, 79% of the men (for whom there is information) lived in
metropolitan areas 2 , probably Malmö. Approximately eight percent were born
in rural areas, and twelve in other towns. In 1964, 79% of the men lived in big
cities, nine percent in rural areas, and eleven percent in other cities or towns.
Eleven men had moved abroad (slightly more than one percent). For 1964, this
kind of information is available for 1,468 individuals (of whom 36 were
deceased), and among the women 73% lived in big cities, twelve percent in
2 = Stockholm, Gothenburg, Malmö
11

The Malmö Study
rural areas, and eleven in other cities or towns. 26 women (almost four percent)
had moved abroad.
The average number of children at home in 1938 was 2.51, while 99
children had a single parent. 85 children had foster children in their homes in
1938. Only 8.4 percent of the children had a father with lower secondary
education or higher.
Social class in 1938 was assigned to one of four categories (category four
being the ‘highest’), on the basis of four items of information: father’s
occupation, family income in 1937, number of children at home, and
appearances in the social welfare registers of the Malmö schools. On this basis
34% were placed in social class 1, 34% in social class 2, 18% in social class 3,
and 14% in social class 4. 163 of the 1,542 children were found in the welfare
records in 1938.
In the 1964 survey, the respondents were asked about their parent’s
attitude to schooling. Of the 1,074 persons who responded to this question, 18%
said that they felt that their parents had the opinion that they should leave
school as soon as possible, 17% felt that they thought it was a good thing to go
to school, without any clear expression about what kind, and 42% had parents
who had wanted them to take as much schooling as possible. The remaining
23% had not felt any clear opinion about schooling at home.
Below are tables of correlation for a few of the background variables and
acquired level of formal schooling, separately for men and women.
12

The Malmö Study
Table 3.
Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0
Number of Observations
MEN Schooling Father’s Social One
Father’s
level
education class parent
education
0.42530

Current family situation
The Malmö Study
A set of variables capturing the current family situation have been collected:
- aspiration for children’s education, 1964, 1971,
- number of children in custody
- number of children born, 1942-1971
- year of birth for first child
- place of residence, 1964, 1971
The women were between the ages of 15 and 38, and the men between the ages
of 17 and 42, when giving birth to their first child. The average number of
children born was only 1.7, varying between 0 and 8 for the men and between 0
and 7 for the women.
Work life variables
The amount of variables describing the individuals’ work career and situation
are also rich. For example:
- occupational category, 1942 – 1983
- occupational status, 1942 – 1983
- number of employers since school, 1964, 1971
- supervisor or not, 1964, 1971, 1984
- number of employees supervised, 1964, 1984
- terms of employment, 1964, 1971
- unemployment or not 1971, 1984
- length of unemployment, 1984
- kind of employment, 1984
- change of occupation, 1984
- change of workplace, 1984
- number of working-hours, 1984
- employed in public or private sector, 1984
- length of present employment, 1984
In 1971 91% (838 persons) of those who answered the survey had worked for at
most 2 employers during the last six years, while not even one percent (eight
14

The Malmö Study
persons) had worked for more than four employers. Only 90 persons (nine
percent) had been unemployed at least once since 1965. In 1984, 454 persons
(259 men and 195 women, 47%) reported that they had never changed
occupation, while 515 persons had. 208 men and 40 women (37 and 9%,
respectively) were in 1964 supervisors of some kind, 17 men supervising more
than 50 employees.
Mortality
For the fifth article of the thesis, we have supplemented with information from
the National death cause register. We thus have information on mortality until
31 December 2003, and cause of death until 31 December 2001. At the end of
2003, 515 men and women were dead, and we know the cause of death for 449
of these persons. The main causes of death were cancer and circulatory
diseases, 176 and 168 persons respectively.
C: Created variables
No single valued measure can be a good representation of a multidimensional
variable, such as for instance “innate talent” or “ability”. In addition, the
information in the Malmö study has been defined and collected for other
purposes than the economic analysis of the previous chapters. Thus we have
both a validity and a reliability problem. The Malmö material is, however, very
rich in composition and makes possible the construction of proxy measures to
capture different characteristics of men and women during their working life.
To understand better how the men and women of the cohort learn and earn by
moving between occupations, but also between gainful employment,
unemployment and recurrent education, some new variables have been created.
This “enhanced information” should make possible a study on how these
characteristics has affected both career choice and performance, and lifetime
earnings. Below follows a presentation of these variables and a detailed
description of how they have been constructed.
To begin with, lifetime earnings – L – have been constructed, to be used
also in Articles 3 and 5; for a detailed description of how it has been created,
see them. It is a measure of the sum of earnings for each individual, from the
age of fourteen, through the age of 65.
15

The Malmö Study
Recurrent education (RE) is based on questions asked in the
questionnaires 1964, 1971 and 1984 and gives information about potential adult
education between the years 1964 and 1984. We expect that the return to
education diminishes with age because there will be fewer income years before
retirement. To capture this effect we assume that an ordinary working-life
consists of 45 years (which is the case for a majority of these men, see
Appendix in Article 3), and that the age of retirement is 65. For example, if a
person has been through adult education at the age of 40, s/he has 25 years left
to capture the benefits of this education. 25 is then divided by 45 (the length of
the working life), and this individual is assigned 0.555 for this spell of
education. The same procedure is used for all observed spells of educational
information in adult life, and finally the figures are added together.
There are two sorts of unemployment information. Firstly, there is the
occupational information, where those individuals who have been unemployed
for the main part of the year supposedly have reported their occupation as
unemployed. However, these variables miss those who have been unemployed
only parts of the year. Secondly, there is information from the questionnaires in
1971 and 1984 on unemployment. This information has been used in the
following way; if someone has been unemployed once between 1964 and 1971,
s/he has been assigned the value 1, and the value 2 if s/he has been unemployed
two or more times between the same years. The same procedure is used
between the years 1971 and 1984. These numbers are then added together,
giving a measure – UNE - of unemployment spells between 1964 and 1984.
Furthermore, we create the dummy EE to capture ‘clean’ recurrent
education, or rather, voluntary spells of adult education, not forced upon the
individual because s/he is unemployed. The individual is assigned the value 1 if
s/he has been through recurrent education without any unemployment during
the same period, otherwise EE is zero.
The mandatory retirement age in Sweden is 65 years. However, many
men and women retire earlier, either voluntarily, or because of bad health. No
difference has been made between different reasons for earlier retirement, but a
proxy variable for health – RH – has been constructed from a measure on the
age of retirement – RA - divided with 65. However, there is more information
on potential bad health. From the occupational information, there is data on
16

The Malmö Study
those who have been sick for entire years. Each person has been assigned the
value 1 for each year of sick-leave, and the years have been added together to
the variable SICK.
Career performance: all different occupations have been divided into
five different groups, ranked according to occupational position. The levels are:
1. Manual work, demanding little formal education
2. Simple white-collar work
3. More advanced white-collar work
4. Civil servants – high level
5. Supervisors, professionals
A proxy for career performance would then be the rank number above, the
lowest being 1 and the highest 5. Each individual has been assigned a number
for the category when entering the job market, i.e. his/her first job – J-ENTRY –
as well as for the category when exiting the job market – J-EXIT. Finally, a
measure of the individual’s career performance has been constructed, dividing
J-EXIT with J-ENTRY – JC. A higher value signals a higher positional mobility
- the individual started low but ended high. A low value signals less positional
mobility, but we don’t know if it is because the individual started low and
ended low, or started high and ended high.
MOBILITY is a measure of the individuals’ occupational mobility during
their working lives, assigning values between 0.2 and 1 for each occupational
change, depending on how big the change is. If the individual has completely
changed occupational field, s/he will be assigned the value of 1 for that change,
while if s/he only changes from for example selling cars to selling computers,
s/he only gets 0.2 for that change. For this variable the codes for occupational
category have been used, which are given on a five-digit scale.
The variable SUPER tells whether the individual has been a supervisor
(=1) or not (=0) during the years 1964, 1971 and 1984.
Reliable and comparable variables require that the individuals have
answered all the questions in all questionnaires distributed. Hence, the attrition
becomes rather large – the subsample with more or less complete information
consists of 555 individuals. In Table 4, summary statistics are given for that
subsample:
17

Variable
The Malmö Study
Table 4.
Summary statistics - created variables
Mean Std Dev Min Max
Recurrent Education –
RE
0.911 0.589 0 1.622
Unemployment spells
- UNE
0.189 0.529 0 3
Dummy
education
recurrent 0.728 0.445 0 1
Retirement age – RA 61.362 3.118 45 66
Retirement health –
RH
0.978 0.041 0.692 1.015
Sickness
SICK
spells – 0.708 1.983 0 14
Mobility 3.123 1.933 0 10.6
Job entry position – J-
ENTRY
1.627 0.799 1 5
Job exit position – J-
EXIT
2.077 1.141 1 5
Job career – JC 1.454 0.975 0.333 5
Supervisor position –
SUPER
0.964 1.123 0 3
Lifetime earnings – L 5,450,092 3,561,307 456,419 5.36e+07
Sex in percent
men 58.6
women 41.4
18

D: Remarks
The Malmö Study
Most of the above variables have been used in the various chapters of my
thesis. Some have been prepared for studies not yet completed. I have presented
them all to illustrate the wealth of information embodied in the Malmö data set,
and also to illustrate the unusual problem we have had to choose the best
variables for the econometric models.
References:
Furu, M. 2000 ”The Malmö Study” in Seven Swedish Longitudinal Studies
Stockholm: Forskningsrådsnämnden
Eliasson, G. 1998 “Developments in industrial technology and production –
competence requirements and the platform theory of on-the-job learning” paper
presented to the 4 th Agora Tessalonici Seminar on The Low-skilled on the
European Labour Market, october 29-30 1998 Tessalonika: Cedefop
Fägerlind, I. 1975 Formal Education and Adult Earnings: A Longitudinal Study
on the Economic Benefits of Education. Stockholm: Almqvist & Wiksell
International
Husén, T. 1950 Testresultatens prognosvärde. Stockholm: Geber
Tuijnman, A. 1989 Recurrent education, earnings and well-being. Stockholm:
Almqvist & Wiksell International
19