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Using twenty years of earnings data on Finnish twins, we find that about 40% of the variance of women’s and little more than half of men’s lifetime labour earnings are linked to genetic factors. The contribution of the shared environment is negligible. We show that the result is robust to using alternative definitions of earnings, to adjusting for the role of education, and to measurement errors in the measure of genetic relatedness.
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Heritability of lifetime earnings
Ari Hyytinen
&Pekka Ilmakunnas
&Edvard Johansson
&Otto Toivanen
Received: 21 August 2017 /Accepted: 12 March 2019 /Published online: 14 May 2019
#The Author(s) 2019
Using twenty years of earnings data on Finnish twins, we find that about 40% of the variance
of womens and little more than half of mens lifetime labour earnings are linked to genetic
factors. The contribution of the shared environment is negligible. We show that the result is
robust to using alternative definitions of earnings, to adjusting for the role of education, and to
measurement errors in the measure of genetic relatedness.
Keywords Earnings inequality.Heritability.Twins .Genetics
1 Introduction
The determinants of earnings and income inequality, intra- and intergenerational income mobility,
and sibling correlations of earnings are subject to a broad research program (e.g., Solon 1999;
Sacerdote 2011; Jäntti and Jenkins 2015). The determinants of sibling correlations include shared
The Journal of Economic Inequality (2019) 17:319335
Electronic supplementary material The online version of this article (
09413-x) contains supplementary material, which is available to authorized users.
*Pekka Ilmakunnas
Ari Hyytinen
Edvard Johansson
Otto Toivanen
Department of Economics, Hanken School of Economics, P.O. Box 479, 00101 Helsinki, Finland
Finland and Helsinki GSE, Helsinki, Finland
Aalto University School of Business, P.O. Box 21210, FI-00076 Aalto, Espoo, Finland
Faculty of Social Sciences, Business and Economics, Åbo Akademi University, Tuomiokirkontori 3,
20500 Turku, Finland
Aalto University School of Business, P.O. Box 21240, FI-00076 Aalto, Helsinki, Finland
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environmental factors, such as common family background, neighbourhood and peers, and
genetically inherited traits. For example, Björklund and Jäntti (2012) estimated using Swedish
data that shared environmental and genetic factors explain 4060% of inequality in a number of
productive traits, including cognitive and non-cognitive skills, schooling and long-run earnings.
Heritability measures the extent to which genetic variation between individuals account for
differences in a particular outcome, in a particular population, characterized by a particular mix
of genetic and environmental influences that prevailed at the time of measurement (Plomin
et al. 2014). Earnings can be transferred genetically through several channels. There is a large
literature in economics (e.g. Heckman et al. 2006) showing that (heritable) non-cognitive
aspects of personality, such as various personality traits or addictions, and cognitive skills can
have an influence on, for example, occupational choices, labor supply, work effort, and risk
taking, which all influence earnings. Inherited cognitive skills and non-cognitive traits also
affect schooling choices and thereby earnings through the returns to education.
Our contribution is fourfold: First, we provide new evidence on the genetic heritability of
lifetime (labour) earnings and total lifetime earnings (incl. capital income). In contrast to the
existing heritability literature that has mostly relied on relatively short-term proxies for lifetime
earnings, our evidence is based on twenty years of data on a large number of monozygotic
(MZ) and dizygotic (DZ) twin pairs.
Our measure of earnings refers to earnings in the form of
wages, salaries and self-employment income, but excludes social transfers, such as unemploy-
ment benefits. Total lifetime earnings also include capital income, which consists of taxable
dividends, interest income and capital gains. The information on earnings and income comes
from tax registers and is therefore not subject to self-reporting error. The twin cohorts that we
use are old enough that we can use data on the various types of income in their prime working
age to measure lifetime earnings.
Second, we examine heritability of earnings by gender. Analysis separately by gender is
important, as it is well known that mens and womens earnings develop differently over their
working careers, for example because of womens more frequent career breaks or because of
gender differences in risk preferences and in other socio-psychological factors (e.g.
Killingsworth and Heckman 1986; Bertrand 2011). Also the influence of shared or nonshared
environment vs. heritability may differ by gender e.g. in career choices.
The literature on the heritability of economic outcomes has been criticized in the past
(Goldberger 1979) and more recently (Manski 2011) of being not only policy irrelevant
but also harmful, as heritability research has been misused for political and other ends.
We share the concern of potential misuse, but disagree with the implied suggestion that
genetic heritability of economic outcomes ought not to be studied at all. Heritability is a
descriptive statistic in genetic research (Plomin et al. 2014). It does not imply immuta-
bility. Showing that a policy intervention can reduce economic inequality (what could
be) is not the same thing as learning about the genetic and environmental origins of
inequality, as they existed (what is) in the economy that generated the data researchers
use - like e.g. Plomin et al. (2014) emphasise. Consistent with this, Björklund and Jäntti
(2012) argue that genetic inheritance cannot be neglected if we pursue a deeper under-
standing of the influences of family background.
We explore the origins of variation in lifetime earnings in Finland, as it existed during the
period from 1990 to 2009. This institutional environment is of broader policy interest, because
a robust finding in the recent literature is that the relatively equitable Nordic countries have
The few recent exceptions are Björklund et al. (2005) and Benjamin et al. (2012).
320 A. Hyytinen et al.
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high intergenerational mobility, exceeding clearly that of the UK and US (Black and Devereux
2011). Consistent with this, the correlation of incomes among siblings is much lower in the
Nordic countries than in the U.S. (Solon 1999;Jänttietal.2002; Black and Devereux 2011).
However, the question of the persistence of economic outcomes across generations is far from
solved (Lucas and Pekkala Kerr 2013). How much of the variation in lifetime earnings is
related to genetic variation is therefore worthwhile to know, not least because it provides a
useful benchmark against which other estimates and other (possibly less equitable) countries
can be compared. In this spirit, Landersø and Heckman (2017), for example, compare how
intergenerational mobility and its determinants differ between Denmark and the US.
Our main findings are as follows: Using accurate administrative data on twinsprime
working-age work and capital income and standard behavioural genetics designs, we docu-
ment that genes explain a reasonably high share of variation in the twins(age-adjusted)
lifetime earnings (54% for men; 39% for women), whereas the shared environment explains
very little. Our results thus echo those reported for Sweden by Benjamin et al. (2012), as they
also find that the shared environment explains a small fraction of variation in long-term
These findings are in line with the much broader literature on the relative impor-
tance of shared and non-shared environment in explaining variation in many kinds of complex
traits (phenotypes), suggesting that environmental influence for most traits is typically non-
shared (Plomin 2011).
In auxiliary analyses, we also explore how sensitive the estimates of heritability are to the
removal of the effect of education on lifetime earnings and total earnings. Education is one
channel through which earnings can betransferred genetically. We focus on education, because
schooling is known to have high intergenerational persistence, depends on genetic endow-
ments (e.g. Behrman and Taubman 1989;Milleretal.2001;Braniganetal.2013), and is a key
driver of permanent income. We show that in the relatively equitable economic and institu-
tional environment of Finland, the share of variance of lifetime earnings explained by
education is clearly less than a tenth (in our data). This comparison suggests that the variation
in lifetime earnings that can be attributed to genetic variation is not negligible and warrants
attention. The results of our auxiliary analyses also suggest - but do not conclusively show -
that removing the effects of education on the lifetime earnings of the cohorts we study does not
change these heritability estimates.
We also provide estimates of group heritability by analysing the importance of
heritability of earnings at different points of the earnings distribution. It is possible that
e.g. certain personality traits have a particularly strong impact on top (or bottom)
earnings, leading to variation in earnings heritability across the earnings distribution.
However, if the difference between top (or bottom) earnings and the earnings of the
whole sample is heritable, the same genetic factors are related to earnings at all parts of
the earnings distribution. Group heritability allows measuring how much genetics ac-
count for of the mean difference in lifetime earnings between those who are at the tails of
the earnings distribution and the rest of the population. It hence allows highlighting
whether and why individuals with very high or very low earnings differ as a group from
the rest of the population (DeFries and Fulker 1985; Plomin et al. 2014). We find that the
heritability of mean earnings in the tails of lifetime earnings distribution broadly follows
similar patterns as that of individuals at large. Group heritability suggests therefore that
Relaxing some of the assumptions of the standard variance decomposition reduces the share of income
explained by genetic heritability; see Björklund et al. (2005).
Heritability of lifetime earnings 321
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earnings at the extreme parts of and in the rest of the distribution are, at least in part,
related to the same genetic factors (Plomin and Kovas 2005;Shakeshaftetal.2015).
Our findings bear on two ongoing debates. First, they bear on the determinants of
intergenerational mobility at the top of the earnings and income distributions in equitable
Nordic countries. For example, Björklund et al. (2012) show using Swedish data that inter-
generational transmission of mens long-term income is quite low in general and similarly
modest in the top 10% of the distribution, except in the very top 1 and 0.1%. Björklund et al.
(2012) argue that the strong intergenerational transmission at the very top percentiles is related
to inherited wealth. Second, our analyses also bear on the related debates of the heritability of
inequality (e.g., Bowles and Gintis 2002) and of the determinants of changes at the tails of the
income distribution (e.g., Piketty and Saez 2003; Björklund et al. 2012;Chettyetal.2014).
Our findings suggest that the importance of genetic variation in explaining variation in long-
term earnings should not be overlooked.
The remainder of this paper is organized as follows. In the next section, we discuss the
existing evidence. We then present in section three the Finnish twin and register data and how
we measure lifetime labour earnings and total lifetime (market) earnings. The fourth section
describes our main results. The final section concludes.
2 Prior evidence on heritability of earnings and income
The economic literature that uses twin data to analyse the determinants of the variance of
earnings and income began with Taubman (1976).
A great advantage of twin data is that it
allows measuring how genetic, shared environmental and individual-specific (non-shared
environmental) factors contribute to the variance of earnings. The relative contributions of
these factors can under certain assumptions be identified, because MZ and DZ twins have a
shared (family) environment, but unlike MZ twins, DZ twins share, like non-twin siblings,
only one-half of their genes, on average. Greater similarity in outcomes between the MZ twins
is therefore indicative of the importance of genes.
According to the standard behavioral genetics decomposition (Posthuma et al. 2003), the
genetic heritability of an outcome, such as lifetime earnings, is twice the difference of the
correlations of the lifetime earnings between MZ and DZ twins, i.e., h2=2(rMZ rDZ) and the
fraction of variance explained by the shared environment is c2=rMZ h2=2rDZ rMZ.The
fraction explained by non-shared environment is 1 h2c2=1rMZ. This simple decompo-
sition assumes i) that genes and environment have additive effects, ii) that MZ twins experi-
ence environments that are similar to those of DZ twins, iii) that there is no correlation between
genetic factors and the shared environment (i.e., within-pair genetic differences are not
correlated with the within-pair environmental differences; see e.g. Stenberg (2013) who
stresses the importance of this assumption for the interpretation of heritability estimates),
and iv) that there is no assortative mating. The last assumption would not hold if the genotypes
of the parents were correlated (Posthuma et al. 2003).
Table 1reports from a number of prior studies the sibling correlations of earnings
(or income) for MZ and DZ twins as well as the (implied) heritability estimates that can
There also are papers that use (non-twins) siblings and/or adoption data (Björklund et al. 2006,2007;Plugand
Vijverberg 2003; Sacerdote 2002,2007) and papers that focus on the intergenerational mobility and elasticity of
incomes (see Solon 1999; Björklund and Jäntti 2009 for reviews).
322 A. Hyytinen et al.
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be obtained from the standard additive variance decomposition. This decomposition
requires that the correlation of lifetime earnings within the MZ twin pairs, rMZ,should
be bigger than that of the DZ twin pairs, rDZ,andthat2rDZ should be at least as big as
rMZ. The standard decomposition results in the so-called ACE model, where A, C, and
E stand for additive genetic, shared (common) environment, and nonshared environ-
ment components, respectively. When 2rDZ <rMZ,wehaveinTable1set for simplicity
the estimate of the variance share of the shared environment (c2) to zero, and subtracted
the negative estimate from the heritability estimate. This effectively gives the so-called
Table 1 Earlier studies on the genetic heritability of income
Source Income measure Gender Country rMZ rDZ h2c2e2
Taubman (1976) Log(annual income) M USA 0.54 0.30 0.48 0.06 0.46
Ashenfelter and Krueger (1994) Log(hourly wage) M,W USA 0.56 0.36 0.40 0.17 0.44
Ashenfelter and Rouse (1998) Log(hourly wage) M,W USA 0.63 0.37 0.52 0.11 0.37
Johnson and Krueger (2005) Log(annual income) M,W USA 0.38 0.13 0.38 0.00 0.62
Schnittker (2008) Log(annual income) M,W USA 0.40 0.26 0.28 0.12 0.60
Miller et al. (1995) Log(avg. occup. income) M,W Australia 0.68 0.32 0.68 0.00 0.32
Miller et al. (1997) Log(avg. occup. income) M Australia 0.59 0.56 0.07 0.52 0.41
Miller et al. (1997) Log(avg. occup. income) W Australia 0.56 0.28 0.55 0.01 0.44
Miller et al. (2006) Log(annual income) M,W Australia 0.50 0.14 0.50 0.00 0.50
Isacsson (1999) Avg. of 3 year log
M,W Sweden 0.680.460.440.240.32
Björklund et al. (2005) Avg. of 3 year log
M Sweden 0.360.170.360.000.64
Björklund et al. (2005) Avg. of 3 year log
W Sweden 0.310.120.310.000.69
Cesarini (2010) Log(3-year avg. income) M Sweden 0.49 0.29 0.40 0.09 0.51
Benjamin et al. (2012) Avg. of 20 year log
M Sweden 0.630.270.630.000.37
Benjamin et al. (2012) Avg. of 20 year log
W Sweden 0.480.220.480.000.52
Benjamin et al. (2012) Avg. of 5 year log
M Sweden 0.510.200.510.000.49
Benjamin et al. (2012) Avg. of 5 year log
W Sweden 0.300.
Benjamin et al. (2012) Log(annual income) M Sweden 0.41 0.16 0.41 0.00 0.59
Benjamin et al. (2012) Log(annual income) W Sweden 0.27 0.14 0.25 0.02 0.73
Ørstavik et al. (2014) Annual income M Norway 0.55 0.30 0.50 0.05 0.45
Ørstavik et al. (2014) Annual income W Norway 0.45 0.17 0.45 0.00 0.55
Avg . U.S. 0.50 0.28 0.41 0.09 0.50
Avg . AUS 0.58 0.32 0.45 0.13 0.42
Avg. SWE 0.44 0.22 0.40 0.05 0.56
h2= 2*(rMZ-rDZ), c2=r
2-c2refer to the standard additive behavioral genetics variance
decomposition. In the cases where this decomposition gives a negative value for c2, it has been set to zero,
and the corresponding value has been deductedfrom h2. Earnings (income) data refer to a cross-section in the US
and Australian studies. Ashenfelter and Rouse (1998) average the income over time for those twins (25% of the
sample) who were interviewed more than once. They do not show the correlations, but those are reported in
Harding et al. (2005, fn. 4). In Miller et al. (1995,1997) the earnings measure is the average full time income
from the occupation of employment, measured at the level of 2-digit, gender-specific occupational groups (i.e., it
is not measured at the level of individuals). Johnson and Krueger (2005) use household rather than individual
income. Isacsson (1999) and Björklund et al. (2005) use incomes from 3 years over a 7-year period and Cesarini
(2010) from 3 yearsover a 5-year period. Benjamin et al. (2012) use data from consecutive years. They also show
the correlations for 10-year and 3-year average log incomes, which are not reported here. Most of the multi-year
studies adjust the incomes for age. M = men, W = women
Heritability of lifetime earnings 323
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ADE model, where D stands for non-additive genetics (dominance) effects (see also
section 4.1.below).
The following preliminary observations can be made: First, the US estimates for the
importance of the genetic component, h2, are close to those reported for Sweden. Second,
the genetic component accounts for as much as 40% of earnings (or income) variation. Third,
consistent with prior behavior-genetic findings (Plomin 2011), the shared environment (c2)
accounts for a relatively small fraction, say at most 10% or so, of the variance of the earnings
(or income). Fourth, the individual-specific non-shared environmental factors (e2) account for
roughly half of the variation in earnings (or income).
A particular challenge in prior work has been that the object of primary interest, lifetime
earnings or income, has often been measured using poor proxies.
This issue has been widely
discussed in the literature on intergenerational mobility. The use of short run income may lead,
for example, to a gross underestimation of the strength of the intergenerational links (e.g.,
Mazumder 2005; Haider and Solon 2006; Nilsen et al. 2012). As Table 1shows, most of the
prior work uses a single cross-section and short-term income measures, such as annual
earnings or hourly salary. Notable exceptions are the studies by Isacsson (1999) and Björklund
et al. (2005), which both use three years of earnings data on Swedish twins over a spell of
seven years, and Benjamin et al. (2012), who use up to 20 years of Swedish earnings data.
Besides the studies that focus on the heritability of income, there are a number of papers
that apply variance decompositions to twin data in order to determine the importance of
genetic and environmental factors for the variation of economic outcomes (see also Sacerdote
2011 for a review). This branch of the literature includes Behrman and Taubman (1989)and
Miller et al. (2001), who investigate the genetic heritability of education, Miller et al. (1996)
and Schnittker (2008), who focus on occupational status and socioeconomic position, and
Nicolaou et al. (2008), who examine the effect of genetic heritability on the likelihood of
becoming an entrepreneur. More recent work has extended the literature by studying the
genetic heritability of the formation of preferences (Cesarini et al. 2009; Simonson and Sela
2011), financial decision-making (Barnea et al. 2010;Cesarinietal.2010), and savings
(Cronqvist and Siegel 2015).
3.1 Data sources
Our twin data are based on the Older Finnish Twin Cohort Study (of The Department of Public
Health in University of Helsinki) that was matched to the Finnish Longitudinal Employer-
Employee Data (FLEED) of Statistics Finland using personal identification numbers (see also
Hyytinen et al. 2013 and the online appendix).
The Finnish Cohort Study was established in 1974 and was initially compiled from
the Central Population Registry of Finland. Initial twin candidates were persons born
before 1958 with the same birth date, municipality of birth, sex, and surname at birth
(Kaprio et al. 1979; Kaprio and Koskenvuo 2002;Kaprio2013). A questionnaire was
mailed to these candidates in 1975 to determine zygosity and to collect baseline data (see
Income variation can also be decomposed into its permanent and transitory components (e.g. Moffitt and
Gottschalk 2012; see also Björklund et al. 2009; Bingley and Cappelari 2012).
324 A. Hyytinen et al.
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also the online appendix). The response rate was 89%. Two follow-up surveys were then
subsequently done in 1981 and 1990.
We linked the twin data to FLEED using personal identifiers (see also Hyytinen et al.
2013). FLEED is constructed from a number of different administrative registers on individ-
uals, firms and establishments that are collected or maintained by Statistics Finland. Impor-
tantly for this study, FLEED includes information on salaries and other income, taken directly
from tax registers. This means that our earnings data are not biased by underreporting or recall
error, nor do the data suffer from top-coding. The earnings data used in this study cover the 20-
year period from 1990 to 2009.
3.2 Sample
We focus on the youngest cohort of our data, born in 19501957. This cohort obtained their
primary and secondary schooling in the old, more selective, Finnish school system (for a nice
description, see Pekkarinen et al. 2009). In the old system, there was a tracking of students to
vocational and academic tracks after the fourth grade at the age of 11. In 19721977, the
system was reformed so that a comprehensive school was established where all students obtain
nine years of common education. The youngest twins in our data were 15 years old when the
reform started, so they were not affected by it. Our sample contains nearly all same-sex DZ and
MZ twins of this cohort of the Finnish population. Most of the attrition is due to death (e.g., of
fatal diseases or accidents) and migration.
We examine men and women separately. There are many reasons to expect that the
development of lifetime earnings is different between the genders. For example, women have
more career breaks than men due to family reasons, which create bigger variation in earnings
across individuals among women than among men. There are gender differences also in many
choices that affect earnings, like risk taking or educational and occupational choices. To the
extent that these choices are affected by inherited personality traits, also heritability of earnings
should differ by gender.
In our estimation sample, male MZ twins lived together, on average, 20.7 years before
they moved apart. For male DZ twins, the corresponding average is close, 0.7 years less.
The difference is a bit larger for female twins: Female MZ twins lived together, on
average, 20.3 years before they moved apart. Female DZ twins moved apart on average
1.8 years earlier.
3.3 Variable definitions and descriptive statistics
Our measure for the lifetime earnings (i.e., work income) of an individual is the average
of (the logarithm of) the individuals wage and salary earnings and self-employment
income, converted to euros, deflated to year 2000 euros using the consumer price index,
and calculated over the sample period; see also Böckerman et al. (2017), who use a
similar measure of long-run income, calculated from the FLEED data. The findings of
Haider and Solon (2006) for the U.S. and those of Böhlmark and Lindquist (2006)for
Sweden suggest that this long-term sample average ought to be a reliable measure for the
lifetime earnings. In particular, because we use a sample of individuals born between
1950 and 1957, we observe the earnings of individuals who are at their prime working
age: The individuals are from 33 to 40 years old at the beginning of our sample period in
1990 and from 52 to 59 years old at the end of the sample period in 2009. This window
Heritability of lifetime earnings 325
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matches quite nicely the periods when annual earnings is a good proxy for the long-term
earnings, especially for men. Total lifetime earnings are calculated in a similar way, but
the difference is that it also includes capital income (i.e., taxable dividends, interest
income and capital gains). We use total lifetime earnings in the robustness analysis.
Table 2reports the means and standard deviations of (unadjusted) earnings and total
lifetime earnings, age, and education years based on standard degree times, separately for
MZ and DZ twins by gender. As the table shows, the average age (in 1990) is 36 years and the
average amount of schooling is twelve years. Average lifetime earnings of men is around
23,000 euros per year, whereas for women, it is 17,000 euros per year.
Since we observe the individuals at different stages of their life cycles, we adjust the
earnings for age and year for our empirical analysis. We obtain the adjusted income from a
regression of the log of annual real earnings on a constant, calendar year dummies and a third
order polynomial of age, separately for men and women. The age-adjusted lifetime earnings
are then computed as the within-individual average of the residuals from these regressions.
Table 2also reports within-twin pair correlations, using the adjusted lifetime earnings
measures. With these correlations, the standard additive variance decomposition can be applied
to lifetime earnings, using the formulas presented in section 2for the shares of genetic
heritability, shared environment and non-shared environment. The estimate of shared environ-
ment, c2, would be negative for both genders, as 2rDZ <rMZ. One potential reason for this is the
Table 2 Descriptive statistics
Females Males
Work income ()
Average 17,081.61 17,071.24 23,439.91 22,832.58
Standard deviation 12,121.26 13,515.52 18,796.77 15,728.47
Log(work income)
Average 8.43 8.35 8.62 8.59
Standard deviation 2.50 2.62 2.79 2.78
Tot al in come ()
Average 18,524.47 18,471.72 25,919.55 25,149.70
Standard deviation 17,079.37 16,169.20 26,376.08 24,217.23
Log(total income)
Average 8.57 8.48 8.68 8.65
Standard deviation 2.66 2.66 2.92 2.89
Age 1990 (years)
Average 36.25 36.26 36.30 36.41
Standard deviation 2.26 2.25 2.31 2.24
Education 1990 (years)
Average 12.10 11.86 12.30 11.91
Standard deviation 2.39 2.39 2.63 2.56
Correlations of age adjusted 0.414 0.176 0.543 0.198
average log(work income), rMZ and rDZ
Correlations of age adjusted 0.405 0.197 0.533 0.209
Average log(total income), rMZ and rDZ
Number of twin pairs 646 1219 516 1158
Number of persons 1292 2438 1032 2316
Income and log(income) are within-person averages for 19902009, and their averages are across persons.
Because of missing observations, the total income numbers for females are based on 638 MZ and 1209 DZ twin
pairs and for males on 513 MZ and 1141 DZ twin pairs
326 A. Hyytinen et al.
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presence of dominant (non-additive) genetic factors, which tend to make outcomes more
similar for MZ twins relative to DZ twins. The data are suggestive of dominance effects, if
2rMZ 4rDZ > 0, which is the case in our data for the lifetime earnings of both genders. A
negative estimate of c2may be added to the estimate of genetic heritability (h2), giving a
baseline heritability estimate of 54% for men and 41% for women. These estimates are a bit
higher than what we reported in Table 1for other countries. This observation is consistent with
the view that the shorter-term earnings measures lead to lower heritability estimates: A low
within-pair correlation suggests that the unshared environmental effects are important, but it
may also mirror measurement error at the level of individuals.
4 Empirical analysis
4.1 Method: DeFries-Fulker variance decomposition
We measure the importance of genetic factors and shared environment for lifetime earnings
separately for both genders using the regression model proposed by DeFries and Fulker
(1985), and further developed by Waller (1994), Kohler and Rodgers (2001) and Rodgers
and Kohler (2005), among others. This model and its closely related variants have also been
used in earlier economics research (see, e.g., Miller et al. 1996,2001). The most basic version
of the DeFries and Fulker (DF) model is a regression model that relies on the (above-
mentioned) assumptions of the standard behavioral genetics decomposition, i.e., the ACE-
model (Posthuma et al. 2003). The ACE model can be written as
INCji ¼β0þβ1INCji0þβ2Rjþβ3INCji0Rj
þεji ð1Þ
where INCji is a measure of the lifetime earnings of twin iin twin pair j,INCji'is the
corresponding measure for co-twin ifrom the same twin pair j,Rjis the coefficient of genetic
relatedness (R= 1.0 for MZ twins and R= 0.5 for DZ twins), and εji is an error term. If the
assumptions of the additive genetic model are satisfied, β1and β3are unbiased estimates of c2
and h2, respectively (DeFries and Fulker 1985; Rodgers and McGue 1994). An alternative way
to think about the DF model is that it is a regression-based method to match moments, i.e., to
fit the parameters of the decomposition model to the observed MZ and DZ correlations.
Alternative versions of the DF regression model can also be considered.
One possibility is
to drop the shared environmental term from Eq. (1) by imposing β1= 0. The term is often
dropped also when the estimate for β1is statistically not significant in the ACE model. The
resulting model is called the AE-model.
If the estimate for the variance share of the shared environmental factors turns out to be
negative, the ACE model is not consistent with the decomposition. One potential reason for the
negative estimate for the variance share of the shared environmental factors is that genetic
effects are not additive, but of a dominant form. To be more specific, genetic effects on a trait
are the sum of all effects of single genes and their interaction. Genes can have different effects
due to genetic variation at a single base pair in the genome or to larger genetic structural
variation. The variants at a locus in a gene are known as alleles. If the effect of carrying no, one
We restrict our attention to DF-regression models, which assume that additive genetic effects are to some extent
always present (i.e., take a degree of precedence over dominant genetic factors).
Heritability of lifetime earnings 327
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or two alleles (as humans have two DNA strands) is additive on the trait, these are summed as
additive genetic effects. Non-linear effects at a single locus are termed dominance, while
interactions between loci result in effects that are termed epistasis. Additive effects are
transmitted from parents to children, while effects due to dominance are not correlated between
generations. Broad sense heritability refers to all kinds of genetic contributions, including
additive, dominant, and epistatic. Narrow sense heritability refers solely to the additive genetic
factors. (See Posthuma et al. 2003.)
The data are suggestive of dominance effects, if 2rMZ -4r
DZ >0. Such effects can be
accommodated in the DF-model by reformulating it as
INCji ¼β0þβ2Rjþβ3INCji0Rj
þεji ð2Þ
where Djis the coefficient of dominant genetic relatedness (with D= 1 for MZ twins and
D= 0.25 for DZ twins; Waller 1994,Rodgersetal.2001). This model is the ADE-model.
In (2), β3estimates narrow-sense heritability, β4the dominance effect, and β3+β4
estimates broad-sense heritability (Waller 1994). As noted in Section 3and as can be
seen from Table 2, our data for lifetime earnings is suggestive of dominance effects, as
2rMZ is higher than 4rDZ for both genders.
In (1) and (2), the value for twin iof a pair of twins is an explanatory variable for twin is
outcome. However, it is not possible a priori to decide which of the twins is twin iand which is
twin i. The DF regression analysis is therefore performed in the double entry form, i.e. each
twin pair is entered into the data twice: The first observation uses the outcome of the first twin
as the dependent variable and that of the co-twin as the explanatory variable. The second
observation reverses the roles. This procedure means that standard errors shouldbe clustered at
twin pair level for correct inference (see Kohler and Rodgers 2001), which we do.
4.2 Results
Table 3presents the results of DF-regressions for the ACE, AE, and ADE models for lifetime
earnings for women and men. As can be seen from the table, the estimate for the variance
component of the shared environment (c2=β1) is negative in the ACE model for both genders.
This finding and the fact that 2rMZ is higher than 4rDZ for both genders suggest that alternative
models ought to be considered and that dominance effects may be present (Waller 1994; Rodgers
et al. 2001). The AE and ADE models suggest a similar degree of genetic heritability. In the ADE-
model, broad heritability refers to the sum β3+β4and is 41% for women. The AE model is
consistent with this, suggesting that the estimate of h2(=β3) is 39% for females. Based on the
Akaike information criterion (AIC), AE is marginally preferred to ADE for females. The 95%
confidence interval for the heritability estimate h2from the AE model is (32%, 47%). For men, the
AE and ADE models suggest that the estimate of h2is about one half: the former suggests that the
estimate of h2is 49% and the latter that the broad heritability is 54%. Based on the AIC criterion,
the ADE model is preferred. The 95% confidence interval for h2from this model is (45%, 64%).
These findings are in line with what we found from the simple decompositions based on Table 2.
In sum, we find that genes explain a reasonably high share of variation in the twinslifetime
earnings (54% for men; 39% for women), whereas the shared environment explains very little.
Even if no preferred model was used, all models suggest qualitatively similar heritability:
heritability estimates for women are in range 39%50% and for men in range 50%70%.
328 A. Hyytinen et al.
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When interpreting Table 3, it is useful to recall that heritability measures the extent to which
genetic variation between individuals account for differences in a particular outcome, in a
particular population, characterized by a particular mix of genetic and environmental influ-
ences that prevailed at the time of measurement (Plomin et al. 2014). Thus, to put the
heritability numbers into a perspective, we can consider a simple univariate regression in
which lifetime earnings is regressed on schooling years. In these simple models (not reported),
the coefficient of determination (R2) is 0.05 for women and 0.06 for men in the pooled sample
of DZ and MZ twins. These numbers are much smaller than the share of lifetime earnings
explained by genes, suggesting that genes explain a rather significant part of population
variation. The reason is that earnings can be transferred genetically through several channels,
such as aspects of personality and cognitive skills. These traits and skills are partly genetically
inherited and influence earnings because of their association with work effort, risk taking,
schooling choices, occupational choices, and labor supply.
4.3 Robustness
We have checked the robustness of the results displayed in Table 3in seven ways. We
considering each of them in turn without reporting the results in tables:
First, we ran the DF-regressions using a larger sample that included both twin pairs born
between 1945 and 1949 and those born between 1950 and 1957. The results for earnings were
very similar to those obtained with our baseline sample based on the younger cohort.
Second, the main results reported in Table 3are robust to not doing the age adjustment, i.e.
using mean of log real earnings as the dependent variable.
Third, we used the (logarithm of) total lifetime earnings as the income measure. This
includes, in addition to earnings, also capital income, which consists of taxable dividends,
interest income and capital gains. Information on capital income, and hence on total lifetime
earnings, is available from 1993 to 2009. This income measure gave almost the same results as
earnings. The preferred model for women was again AE, indicating heritability estimate 40%
for total lifetime earnings. For men, the ADE model produced heritability estimate 53%.
Table 3 ACE, AE, and ADE -regressions
Variable (coefficient) Females Males
INC (β1)0.062 0.148*
= shared environment c2(0.082) (0.083)
R(β2)0.135 0.132 0.135 0.095 0.082 0.095
(0.136) (0.137) (0.136) (0.140) (0.146) (0.140)
INC×R (β3)0.476*** 0.392*** 0.289** 0.691*** 0.490*** 0.247*
= heritability h2(0.116) (0.039) (0.143) (0.118) (0.040) (0.144)
INC×D (β4)0.124 0.296*
(0.165) (0.167)
Constant (β0)0.132 0.129 0.132 0.118 0.107 0.118
(0.106) (0.106) (0.106) (0.112) (0.110) (0.112)
AIC 17,368.57 17,368.31 17,368.57 15,947.27 15,954.44 15,947.27
N(pairs) 1865 1865 1865 1674 1674 1674
β3+β40.413*** 0.543***
= broad heritability (0.047) (0.049)
Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion
Heritability of lifetime earnings 329
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Fourth, we considered how measurement error in additive genetic relatedness, R, affects our
results. This variable includes some measurement error, as it is equal to 0.5 only in expectation
for the DZ twins. Visscher et al. (2006) report that the standard deviation of genetic relatedness
of (non-MZ) siblings is 0.036. Using 0.0013 as the variance of the noise in Rfor the DZ twins
(and zero for MZ twins), the reliability of the Rvariable is 1 sDZ0.013/Va r (R), where sDZ is
the share of DZ twins and the variance of Ris calculated over both MZ and DZ twins. It turned
out that this reliability measure (and a corresponding measure calculated for the interaction of
Rand earnings, assuming no measurement error in earnings) is very close to one. What this
high reliability means is that the standard OLS estimation gave the same results as a method
that accounts for errors-in-variables.
Fifth, we used alternative definitions of the outcome variable, using earnings for years when
the individuals were close to 40. It has been argued that for men (but not necessarily for
women) annual earnings in the age interval from early 30s to early 50s are a good proxy for
lifetime earnings (Haider and Solon 2006; Böhlmark and Lindquist 2006). In our data, the
twins are mostly in this interval, as they are 3340 years old in 1990 and 5259 years old in
2009. To investigate the issue further, we estimated the models successively for those at age
40, those at ages 3941, those at ages 3842, and those at ages 3545. In each case, the
earnings variable was average of the logarithm of real earnings for the corresponding age
interval. The results showed that with narrower age intervals, heritability h2was lower, but it
increased with the widening of the interval. For men the h2estimates for the fourintervals were
43%, 45%, 47%, and 53%, respectively, for the preferred ADE model, and for women 27%,
34%, 34%, and 37% respectively, for the preferred AE model.
Sixth, as an additional check we estimated the DF-regressions separately for each year in
our data. The results showed that, like in Benjamin et al. (2012,seeTable1above), the
heritability estimate is on average lower if annual data are used. The average heritability
estimate for men was 41.9% when calculated from the annual data, using the ADE model.
There was quite a bit of variation over the years, as the standard deviation of annual estimates
is 4.3%. For women, the average of the annual estimates (from the AE model) was 26.8%,
which is also below the corresponding long-term estimate. The standard deviation of the
womens annual estimates was 3.2%.
Finally, we tested formally whether the difference in heritability between women and men
is statistically significant. We re-estimated the models for earnings so that women and men
were pooled and the models included as additional variables also a dummy for females as well
as all the variables interacted with the female dummy. The difference between men and women
was statistically significant at the 10% level in the (preferred) AE and ADE models.
4.4 Auxiliary analyses
In this subsection, we provide a summary of two auxiliary analyses that we have
conducted (see the online appendix for details). In the first of them, we analyze how
sensitive the estimates of heritability are to the removal of the effect of education on
lifetime earnings. In the second of the two auxiliary analyses, we explore group herita-
bility, i.e., how much of the mean difference in lifetime earnings between those who are
at the higher or lower tails of the earnings distribution (probands) and the whole
population can be attributed to genetics.
We used the eivreg procedure in Stata 15.
330 A. Hyytinen et al.
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Education and heritability of lifetime earnings To net outthe effect of education on
earnings, we deduct the estimated effect of education from the age-adjusted earnings of each
individual directly before performing the DF estimation. We produce the estimated effect by
using a standard way to estimate returns to education with data on twins (e.g. Ashenfelter and
Krueger 1994). The results do not differ much from those estimated without adjusting lifetime
earnings for education. We again find that the heritability of lifetime earnings is about 40% for
women and 50% for men.
Group heritability Group heritability measures the genetic influence on the difference be-
tween proband and population means, whereas the usual heritability estimate refers to genetic
influence on individual differences in a sample. If strong group heritability is found, it implies
that both the extreme earnings and the earnings of the rest of the distribution are heritable and,
specifically, that the genetic contributions at the extremes and in the intermediate (normal)
range are not independent from one another (Plomin and Kovas 2005). The method that we
use to study group heritability is the DeFries-Fulker extremes analysis (DeFries and Fulker
1985;LaBudaetal.1986;Bishop2005; Plomin et al. 2014). We find that while there are some
gender differences, the group heritability of lifetime earnings is overall fairly high for both
genders and that the group heritability estimates are in line with the usual heritability estimates.
These findings mean that the genetic contributions at the extremes and in the intermediate
(normal) range are not independent from one another (Plomin and Kovas 2005). In sum, while
earnings may be transferred genetically through several channels and while the specific
channels may be different at the different points of the earnings distribution, our findings
suggest that the degree of heritability is by and large similar in the tails and in population at
large, and possibly linked to related genetic factors.
We have documented that about 40% of the variance of womens lifetime earnings - as
measured over a 20-year period in their prime working age - is due to genetic factors. For
men the corresponding share is a bit more than half. Consistent with the prior epidemiological
and behavioral genetics literature on the heritability of complex traits (Plomin 2011), the
contribution of the shared environment is negligible. Controlling for the effect of education on
lifetime earnings does not change these findings. The heritability of the earnings at the upper
and lower tails of lifetime earnings distribution follows broadly similar patterns. The relatively
high estimates of group heritability indicate that earnings at the extreme parts of and in the rest
of the distribution are related to the same genetic factors.
Our findings suggest two lessons for contemporary debates. First, we provided evidence on
the genetic and environmental origins of lifetime earnings inequality, as they existed in a
relatively equitable Nordic economy in 19902009. This piece of descriptive evidence is
useful to know, as it demonstrates the importance of genetic variation for the lifetime earnings
in a country where income inequality is perceived to be moderate by international standards. It
does not, of course, imply that things could not be or could not have been otherwise. Second,
our findings suggest that the genetic heritability of lifetime earnings is somewhat higher for
men, especially at the lower end of the earnings distribution. This result bears on the debate on
the documented differences between men and women in various economic outcomes during
Heritability of lifetime earnings 331
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adulthood (e.g., Goldin 2014). Most prominent explanations for them appear to be trends and
influences affecting the workings of and outcomes in the labor market (e.g., occupational
preferences, fertility) and the apparently long-lasting effects of childhood environment and
family background (e.g. Autor et al. 2016;Chettyetal.2016).
The findings from our auxiliary analyses have implications for what mechanisms are at
work. On the one hand, a number of non-cognitive traits, cognitive skills, and other socio-
psychological factors have a genetic component and may partly rationalize why genetic factors
explain variation in earnings. Our auxiliary analysis suggests (but does not prove) that
whatever drives the explanatory power of genetic factors, they remain there when the effect
of education, which is a key determinant of peoples long-term earnings, is netted out.
Moreover, genetic heritability plays a quite similar role in the upper and lower tails of the
earnings distribution as it does in the population at large. This suggests that, for example for
men, shared family background, which includes e.g. bequests, appear not to dominate
variation in the sample in general or at the upper tail in particular. Our data are, however,
not big enough for us to explore whether this also holds at the very top (1, or 0.1) percentiles.
The available Swedish evidence suggests that it does not (Björklund et al. 2012).
We conclude by acknowledging the limitations of our analysis. Our decompositions
allowed genes and environment to have additive and dominant (non-additive) effects, but
we assumed that MZ twins experience environments that are similar to those of DZ
twins, that there is no assortative mating, and that there is no correlation between genetic
factors and the shared environment. Our heritability estimates would be upwards biased
if MZ twins experience more similar environments than DZ twins do. The evidence on
this appears to be somewhat mixed and context specific, and in any case, most environ-
mental influence for many traits and outcomes appears to be non-shared (Plomin 2011).
On the other hand, assortative mating increases the similarity of parents, which in turn
increases the genetic similarity of DZ twins (but not those of MZ twins, because they
share their entire DNA, irrespectively of the similarity of their parents). This biases the
heritability estimates downwards and inflates the estimates of the shared environment. It
is much harder to sign the bias if there is correlation between genetic factors and the
shared environment (Stenberg 2013).
Acknowledgements We would like to thank anonymous referees, Anders Björklund, Markus Jäntti, Jaakko
Kaprio, Tomi Kyyrä, Tuomas Pekkarinen, Roope Uusitalo, as well as seminar participants at the Summer
Meeting of the Finnish Economists (Jyväskylä), EALE Conference (Bonn), EEA Conference (Gothenburg),
VATT (Helsinki), and SOFI (Stockholm) for useful comments. The usual caveat applies. We are thankful to
Professor Jaakko Kaprio (University of Helsinki) for access to the twins data (Older Finnish Twin Cohort Study
of the Department of Public Health in the University of Helsinki), to Statistics Finland for access to the register
data (Finnish Longitudinal Employer-Employee Data FLEED), and to the Research Services unit of Statistics
Finland for linking of the data sets. The Ethics Committee of Statistics Finland has given permission to use the
data and all data work has been carried out following the terms and conditions of confidentiality of Statistics
Funding Open access funding provided by Aalto University. This research has been financially supported by
the Academy of Finland (project 127796), the Strategic Research Council (project Work, Inequality, and Public
Policy, 293120), Jenny and Antti Wihuri Foundation, and Palkansaajasäätiö Foundation. The opinions expressed
in the article are those of the authors and do not necessarily reflect the views of the funding sources.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
332 A. Hyytinen et al.
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License (, which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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... Nisén et al. (2013), studying birth cohorts from the 1950s, found that the heritability of education was somewhat smaller and almost the same across genders (around 42%), but that the shared environmental factors were stronger for women (54% vs. 37% of men). In the case of lifetime labour earnings, heritability was about 40% among women and over 50% among men in the birth cohorts from the 1950s, while the contribution of the shared environment was negligible (Hyytinen et al., 2019). ...
... The results differ from those of earlier studies in Finland studying older cohorts but are similar to those in Norway involving more recent cohorts with similar institutional settings (Silventoinen et al., 2004;Nisén et al., 2013;Ørstavik et al., 2014;Lyngstad, Ystrøm and Zambrana, 2017). For income, the result is in line with a previous Finnish study (Hyytinen et al., 2019). There have been no previous studies on genetic influences in ISEI in Finland, and, to our knowledge, very few elsewhere. ...
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To what extent are differences in education, occupational standing, and income attributable to genes, and do genetic influences differ by parents’ socioeconomic standing? When in a children’s life course does parents’ socioeconomic standing matter for genetic influences, and for which of the outcomes, fixed at the different stages of the attainment process, do they matter most? We studied these research questions using Finnish register-based data on 6,529 pairs of twins born between 1975 and 1986. We applied genetically sensitive variance decompositions and took gene–environment interactions into account. Since zygosity was unknown, we compared same-sex and opposite-sex twins to estimate the proportion of genetic variation. Genetic influences were strongest in education and weakest in income, and always strongest among those with the most advantaged socioeconomic background, independent of the socioeconomic indicator used. We found that the shared environment influences were negligible for all outcomes. Parental social background measured early during childhood was associated with weaker interactions with genetic influences. Genetic influences on children’s occupation were largely mediated through their education, whereas for genetic influences on income, mediation through education and occupational standing made little difference. Interestingly, we found that non-shared environment influences were greater among the advantaged families and that this pattern was consistent across outcomes. Stratification scholars should therefore emphasize the importance of the non-shared environment as one of the drivers of the intergenerational transmission of social inequalities.
... Using long panels of earnings for monozygotic and dizygotic twins, Hyytinen et al. (2019) estimate that the heritability of lifetime earnings is 53% for men and 39% for women (see Table 1). Their findings are broadly in line with other studies of the heritability of earnings and income, such as Sacerdote (2011) and Björklund and Jäntti (2020). ...
... Two such contributing factors may be socioeconomic status (SES) and intelligence. Different aspects of SES, such as educational attainment and income, typically display twin heritabilities of 40% [11,12]. The heritability of intelligence increases linearly, from 40% in childhood to 80% in late adulthood but declines to about 60% after age 80 years [13]. ...
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Background: Previous studies have reported associations between attention-deficit/hyperactivity disorder (ADHD) and lower socioeconomic status and intelligence. We aimed to evaluate the causal directions and strengths for these associations by use of a bi-directional two-sample Mendelian randomization (MR) design. Methods: We used summary-level data from the largest available genome-wide association studies (GWAS) to identify genetic instruments for ADHD, intelligence, and markers of socioeconomic status including the Townsend deprivation index, household income, and educational attainment. Effect estimates from individual genetic variants were combined using inverse-variance weighted regression. Results: A genetically predicted one standard deviation (SD) increment in the Townsend deprivation index conferred an odds ratio (OR) of 5.29 (95% confidence interval (CI) 1.89-14.76) for an ADHD diagnosis (p<0.001). A genetically predicted one SD higher education level conferred an OR of 0.30 (95% CI 0.25-0.37) (p<0.001), and a genetically predicted one SD higher family income provided an OR of 0.35 (95% CI 0.25-0.49; p<0.001). The associations remained after adjustment for intelligence whereas the lower odds of an ADHD diagnosis with higher intelligence did not persist after adjustment for liability to greater educational attainment (adjusted OR 1.03, 95% CI 0.68-1.56; p=0.87). The MR analysis of the effect of ADHD on socioeconomic markers found that genetic liability to ADHD was statistically associated with each of them (p<0.001) but not intelligence. However, the average change in the socioeconomic markers per doubling of the prevalence of ADHD corresponded only to 0.05-0.06 SD changes. Conclusions: Our results indicate that an ADHD diagnosis may be a direct and strong intelligence-independent consequence of socioeconomic related factors, whereas ADHD appears to lead only to modestly lowered socioeconomic status. Low intelligence seems not to be a major independent cause or consequence of ADHD.
... Out-of-home placement aggregates in families, 7 and many parental risk markers 8,9 are at least moderately heritable, including low socioeconomic status, [10][11][12] antisocial behaviors, 13,14 and psychiatric disorders. 15 Sibling comparison designs wherein risks of long-term outcomes are compared between biological full siblings who were differentially exposed to outof-home care allow researchers to account for time-stable unmeasured familial confounders, including shared early-life environments and around half of cosegregating genes. ...
Importance Children who are placed in out-of-home care may have poorer outcomes in adulthood, on average, compared with their peers, but the direction and magnitude of these associations need clarification. Objective To estimate associations between being placed in out-of-home care in childhood and adolescence and subsequent risks of experiencing a wide range of social and health outcomes in adulthood following comprehensive adjustments for preplacement factors. Design, Setting, and Participants This cohort and cosibling study of all children born in Finland between 1986 and 2000 (N = 855 622) monitored each person from their 15th birthday either until the end of the study period (December 2018) or until they migrated, died, or experienced the outcome of interest. Cox and Poisson regression models were used to estimate associations with adjustment for measured confounders (from linked population registers) and unmeasured familial confounders (using sibling comparisons). Data were analyzed from October 2020 to August 2021. Exposures Placement in out-of-home care up to age 15 years. Main Outcomes and Measures Through national population, patient, prescription drug, cause of death, and crime registers, 16 specific outcomes were identified across the following categories: psychiatric disorders; low socioeconomic status; injuries and experiencing violence; and antisocial behaviors, suicidality, and premature mortality. Results A total of 30 127 individuals (3.4%) were identified who had been placed in out-of-home care for a median (interquartile range) period of 1.3 (0.2-5.1) years and 2 (1-3) placement episodes before age 15 years. Compared with their siblings, individuals who had been placed in out-of-home care were 1.4 to 5 times more likely to experience adverse outcomes in adulthood (adjusted hazard ratio [aHR] for those with a fall-related injury, 1.40; 95% CI, 1.25-1.57 and aHR for those with an unintentional poisoning injury, 4.79; 95% CI, 3.56-6.43, respectively). The highest relative risks were observed for those with violent crime arrests (aHR, 4.16; 95% CI, 3.74-4.62; cumulative incidence, 24.6% in individuals who had been placed in out-of-home care vs 5.1% in those who had not), substance misuse (aHR, 4.75; 95% CI, 4.25-5.30; cumulative incidence, 23.2% vs 4.6%), and unintentional poisoning injury (aHR 4.79; 95% CI, 3.56-6.43; cumulative incidence, 3.1% vs 0.6%). Additional adjustments for perinatal factors, childhood behavioral problems, and traumatic injuries, including experiencing violence, did not materially change the findings. Conclusions and Relevance Out-of-home care placement was associated with a wide range of adverse outcomes in adulthood, which persisted following adjustments for measured preplacement factors and unmeasured familial factors.
... A szocioökonómiai státusz családi halmozódásáért részben genetikai ténye zők felelősek. A kvantitatív genetikai vizsgálatok eredményei arra utalnak, hogy a jövedelem (Hyytinen, Ilmakunnas, Johansson, & Toivanen, 2019) és az iskolai végzettség (Branigan, McCallum, & Freese, 2013) varianciájának 30-60%-át genetikai tényezők magyarázzák. A molekuláris genetikai vizs gálatok (Hill és mtsai, 2019; Lee és mtsai, 2018) eredményei mindkét fenotípust konkrét genetikai variánsokhoz is kötötték. ...
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A kognitív epidemiológia az intelligencia és az egészségi állapot összefüggésének tudo- mánya. A modern, sokszor több százezer fős, teljes populációkon végzett kognitív epide- miológiai vizsgálatok eredményei alapján a magasabb premorbid intelligencia gya- korlatilag valamennyi mentális betegség, illetve pszichiátriai probléma alacsonyabb kockázatával függ össze. A magasabb premorbid intelligencia a halálozás, a szív- és ér- rendszeri betegségek, a metabolikus betegségek, a rossz egészség-magatartás és számos kisebb népegészségügyi jelentőségű betegség előfordulásával is negatívan függ össze; a légzőszervi betegségekkel és a dohányzáshoz nem köthető daganatokkal azonban gyen- ge vagy hiányzik az összefüggés. A mentális betegségekkel való összefüggést nem, a szo- matikus betegségekkel és a mortalitással való összefüggést azonban részben mediálják a felnőttkori szocioökonómiai státusz mutatói. A speciális vizsgálati elrendezések – úgymint ikerkontroll-vizsgálatok, pszeudoexperimentális vizsgálatok, valamint a mendeli ran- domizáció módszerét használó molekuláris genetikai vizsgálatok – eredményei arra utal- nak, hogy az intelligencia és az egészség közötti kapcsolat jelentős részét genetikai ténye- zők közvetítik, de a szomatikus egészségre a magasabb intelligencia következményeként elérhető jobb szocioökonómiai státusz is szerény hatást gyakorol. Cognitive epidemiology is the science of the relationship between intelligence and health. Modern studies of cognitive epidemiology, often with samples of several hundreds of thousands of individuals, have revealed that higher premorbid intelligence is associated with a lower risk of virtually all of mental illnesses and psychiatric problems. Higher premorbid intelligence is also associated negatively with the incidence of mortality, circulatory illness, metabolic illness, poor health behavior and many diseases of lower epidemiological significance, but its relationship to respiratory illness and non-smoking related cancers is weaker or non-existent. Indicators of adult socioeconomic status do not mediate the association between intelligence and mental illness, but they do partially mediate the relationship with somatic illness and mortality. Studies with special designs -twin control studies, pseudo-experimental studies and molecular genetic studies using Mendelian randomization – suggest that the relationship between intelligence and health is heavily mediated by genetic factors, but somatic health may be modestly but causally improved by better social status as a consequence of higher intelligence.
... 10 The importance of such mechanisms is underlined by twin studies that have consistently reported that income phenotypes are moderately to considerably heritable (40-60%). 11 These findings are expected because heritable characteristics, such as cognitive abilities, impulsivity and personality traits, increasingly predict differences in social outcomes as societies move towards meritocratic systems. [12][13][14] Moreover, there is considerable evidence that psychiatric disorders are also heritable (30-80%) [15][16][17][18] and molecular genetic studies have recently estimated genetic correlations between income and many psychiatric and neurodevelopmental disorders 13,19 Genetically informative research designs that can explicitly account for unmeasured familial risk factors and thereby isolate the residual, and potentially causal, effects of environmental risk markers (i.e. ...
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Background Childhood family income has been shown to be associated with later psychiatric disorders, substance misuse and violent crime, but the consistency, strength and causal nature of these associations remain unclear. Methods We conducted a nationwide cohort and co-sibling study of 650 680 individuals (426 886 siblings) born in Finland between 1986 and 1996 to re-examine these associations by accounting for unmeasured confounders shared between siblings. The participants were followed up from their 15th birthday until they either migrated, died, met criteria for the outcome of interest or reached the end of the study period (31 December 2017 or 31 December 2018 for substance misuse). The associations were adjusted for sex, birth year and birth order, and expressed as adjusted hazard ratios (aHRs). The outcomes included a diagnosis of a severe mental illness (schizophrenia-spectrum disorders or bipolar disorder), depression and anxiety. Substance misuse (e.g. medication prescription, hospitalization or death due to a substance use disorder or arrest for drug-related crime) and violent crime arrests were also examined. Stratified Cox regression models accounted for unmeasured confounders shared between differentially exposed siblings. Results For each $15 000 increase in family income at age 15 years, the risks of the outcomes were reduced by between 9% in severe mental illness (aHR = 0.91; 95% confidence interval: 0.90–0.92) and 23% in violent crime arrests (aHR = 0.77; 0.76–0.78). These associations were fully attenuated in the sibling-comparison models (aHR range: 0.99–1.00). Sensitivity analyses confirmed the latter findings. Conclusions Associations between childhood family income and subsequent risks for psychiatric disorders, substance misuse and violent crime arrest were not consistent with a causal interpretation.
... All measurable human differences have genetic correlations. Researchers have found that income, marital status, health insurance coverage, homophobia, military service, frequency of bread eating, and dog ownership are all heritable (Beaver et al., 2015;Fall et al., 2019;Hasselbalch et al., 2010;Hyytinen et al., 2019;Trumbetta et al., 2007;Wehby & Shane, 2019;Zapko-Willmes & Kandler, 2018). Obviously, human genes do not code for whether someone enrolls in health care coverage or joins the military. ...
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The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group people with similar self-reported symptoms (i.e., like-goes-with-like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory-building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the DSM is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system.
Background A large literature demonstrates associations between socioeconomic status (SES) and health, including physiological health and well-being. Moreover, gender differences are often observed among measures of both SES and health. However, relationships between SES and health are sometimes questioned given the lack of true experiments, and the potential biological and SES mechanisms explaining gender differences in health are rarely examined simultaneously. Purpose To use a national sample of twins to investigate lifetime socioeconomic adversity and a measure of physiological dysregulation separately by sex. Methods Using the twin sample in the second wave of the Midlife in the United States survey (MIDUS II), biometric regression analysis was conducted to determine whether the established SES-physiological health association is observed among twins both before and after adjusting for potential familial-level confounds (additive genetic and shared environmental influences that may underly the SES-health link), and whether this association differs among men and women. Results Although individuals with less socioeconomic adversity over the lifespan exhibited less physiological dysregulation among this sample of twins, this association only persisted among male twins after adjusting for familial influences. Conclusions Findings from the present study suggest that, particularly for men, links between socioeconomic adversity and health are not spurious or better explained by additive genetic or early shared environmental influences. Furthermore, gender-specific role demands may create differential associations between SES and health.
The population of individuals with cognitive impairment and dementia is growing rapidly, necessitating etiological investigation. It is clear that individual differences in cognition later in life have both genetic and multi-level environmental correlates. Despite significant recent progress in cellular and molecular research, the exact mechanisms linking genes, brains, and cognition remain elusive. In relation to cognition, it is unlikely that genetic and environmental risk factors function in a vacuum, but rather interact and cluster together. The purpose of the present study was to examine whether aspects of individual socioeconomic status (SES) explain the cognitive genotype-phenotype association, and whether neighborhood SES modifies the effects of genes and individual SES on cognitive ability. Using data from non-Hispanic White participants in the 2016 wave of the Health and Retirement Study, a national sample of United States adults, we examined links between a polygenic score for general cognition and performance-based cognitive functioning. In a series of weighted linear regressions and formal tests of mediation, we observed a significant genotype-phenotype association that was partially attenuated after including individual education to the baseline model, although little reductions were observed for household wealth or census tract-level percent poverty. These findings suggest that genetic risk for poor cognition is partially explained by education, and this pathway is not modified by poverty-level of the neighborhood.
This commentary critiques Betthäuser, Bourne and Bukodi's (2020) paper which finds that cognitive ability does not substantially mediate class of origin effects on educational and occupational outcomes. From these results, they conclude that cognitive ability is only of minor importance for social stratification, reasserting their view of the primacy of class origins for social stratification. The central issue surrounding cognitive ability in social stratification is its effects on socioeconomic attainments vis-à-vis socioeconomic origins, not the extent that cognitive ability mediates classorigin effects. Their analytical strategy of estimating the extent that cognitive ability mediates class origineffects is misleading because: it ignores the only moderate associations of socioeconomic origins with educational and occupational outcomes; the stronger direct effects of cognitive ability; the associations of parents’ ability with their own socioeconomic attainments; and the genetic transmission of cognitive ability and other traits relevant to social stratification from parents to their children.
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This paper examines the sources of differences in social mobility between the US and Denmark. Measured by income mobility, Denmark is a more mobile society, but not when measured by educational mobility. There are pronounced non-linearities in income and educational mobility in both countries. Greater Danish income mobility is largely a consequence of redistributional tax, transfer, and wage compression policies. While Danish social policies for children produce more favorable cognitive test scores for disadvantaged children, they do not translate into more favorable educational outcomes, partly because of disincentives to acquire education arising from the redistributional policies that increase income mobility. © 2016 The Authors. The Scandinavian Journal of Economics published by John Wiley & Sons Ltd on behalf of The editors of The Scandinavian Journal of Economics.
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This study uses an extraordinary Swedish data set to explore the sources of the intergenerational transmission of socioeconomic status. Merging data from administrative sources and censuses, we investigate the association between sons' and daughters' socioeconomic outcomes and those of their biological and rearing parents. Our analysis focuses on children raised in six different family circumstances: raised by both biological parents, raised by the biological mother without a stepfather, raised by the biological mother with a stepfather, raised by the biological father without a stepmother, raised by the biological father with a stepmother, and raised by two adoptive parents. Relative to the existing literature, the most remarkable feature of our data set is that it contains information on the biological parents even when they are not the rearing parents. We specify a simple additive model of pre-birth (including genetic) and post-birth influences and examine the model's ability to provide a unified account of the intergenerational associations in all six family types. Our results suggest substantial roles for both pre-birth and post-birth factors.
Boys born to disadvantaged families have higher rates of disciplinary problems, lower achievement scores, and fewer high school completions than girls from comparable backgrounds. Using birth certificates matched to schooling records for Florida children born 1992–2002, we find that family disadvantage disproportionately impedes the pre-market development of boys. The differential effect of family disadvantage on boys is robust to specifications within schools and neighborhoods as well as across siblings within families. Evidence supports that this is the effect of the postnatal environment; family disadvantage is unrelated to the gender gap in neonatal health. We conclude that the gender gap among black children is larger than among white children in substantial part because black children are raised in more disadvantaged families. D Autor, D Figlio, K Karbownik, J Roth, M Wasserman. Family disadvantage and the gender gap in behavioral and educational outcomes. American Economic Journal: Applied Economics 2019, 11(3): 1–45
The returns to entrepreneurship are monetary and non-monetary. We offer new evidence on these returns using a large sample of male twins. Our within-twin analysis suggests that OLS estimates are downwards, and panel data estimates upwards biased. The within-twin estimates imply that entrepreneurs earn a negative earnings premium. Our within-twin analysis of non-monetary returns shows that entrepreneurs work longer hours and have greater responsibilities, but also have a greater control over their work.
Researchers in a variety of important economic literatures have assumed that current income variables as proxies for lifetime income variables follow the textbook errors-invariables model. In our analysis of Social Security records containing nearly career-long earnings histories for the Health and Retirement Study sample, we find that the relationship between current and lifetime earnings departs substantially from the textbook model in ways that vary systematically over the life cycle. Our results can enable more appropriate analysis of, and correction for, errors-in-variables bias in any research that uses current earnings to proxy for lifetime earnings.
We use the Young Finns Study (N = ∼2000) on the measured height linked to register-based long-term labor market outcomes. The data contain six age cohorts (ages 3, 6, 9, 12, 15 and 18, in 1980) with the average age of 31.7, in 2001, and with the female share of 54.7. We find that taller people earn higher earnings according to the ordinary least squares (OLS) estimation. The OLS models show that 10 cm of extra height is associated with 13% higher earnings. We use Mendelian randomization, with the genetic score as an instrumental variable (IV) for height to account for potential confounders that are related to socioeconomic background, early life conditions and parental investments, which are otherwise very difficult to fully account for when using covariates in observational studies. The IV point estimate is much lower and not statistically significant, suggesting that the OLS estimation provides an upward biased estimate for the height premium. Our results show the potential value of using genetic information to gain new insights into the determinants of long-term labor market success.
We show that differences in childhood environments play an important role in shaping gender gaps in adulthood by documenting three facts using population tax records for children born in the 1980s. First, gender gaps in employment rates, earnings, and college attendance vary substantially across the parental income distribution. Notably, the traditional gender gap in employment rates is reversed for children growing up in poor families: boys in families in the bottom quintile of the income distribution are less likely to work than girls. Second, these gender gaps vary substantially across counties and commuting zones in which children grow up. The degree of variation in outcomes across places is largest for boys growing up in poor, single-parent families. Third, the spatial variation in gender gaps is highly correlated with proxies for neighborhood disadvantage. Low-income boys who grow up in high-poverty, high-minority areas work significantly less than girls. These areas also have higher rates of crime, suggesting that boys growing up in concentrated poverty substitute from formal employment to crime. Together, these findings demonstrate that gender gaps in adulthood have roots in childhood, perhaps because childhood disadvantage is especially harmful for boys.
This article establishes that a low-dimensional vector of cognitive and noncognitive skills explains a variety of labor market and behavioral outcomes. Our analysis addresses the problems of measurement error, imperfect proxies, and reverse causality that plague conventional studies. Noncognitive skills strongly influence schooling decisions and also affect wages, given schooling decisions. Schooling, employment, work experience, and choice of occupation are affected by latent noncognitive and cognitive skills. We show that the same low-dimensional vector of abilities that explains schooling choices, wages, employment, work experience, and choice of occupation explains a wide variety of risky behaviors.