Heritability of lifetime earnings
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 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.
Keywords Earnings inequality.Heritability.Twins .Genetics
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) 17:319–335
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10888-019-
09413-x) contains supplementary material, which is available to authorized users.
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
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 40–60% 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 men’s and women’s earnings develop differently over their
working careers, for example because of women’s 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.
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 twins’prime
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
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 men’s 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 −h2−c2=1−rMZ. 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.
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 0.300.200.200.100.70
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
ADE model, where D stands for non-additive genetics (dominance) effects (see also
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.
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.
We focus on the youngest cohort of our data, born in 1950–1957. 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 1972–1977, 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 individual’s 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
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
MZ DZ MZ DZ
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
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
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 1990–2009, 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.
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
where INCji is a measure of the lifetime earnings of twin iin twin pair j,INCji'is the
corresponding measure for co-twin i’from 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
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
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 i’of a pair of twins is an explanatory variable for twin i’s
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.
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 twins’lifetime
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.
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.
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
ACE AE ADE ACE AE ADE
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*
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
= 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
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 33–40 years old in 1990 and 52–59 years old in
2009. To investigate the issue further, we estimated the models successively for those at age
40, those at ages 39–41, those at ages 38–42, and those at ages 35–45. 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
women’s 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.
Education and heritability of lifetime earnings To “net out”the 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 women’s 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 1990–2009. 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
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 people’s 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.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), 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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