Religion nurtures some forms of
prosociality, education does not
René Bekkers
Religion nurtures some forms of prosociality, education does not
René Bekkers
Center for Philanthropic Studies, VU University Amsterdam
[email protected]
September 21, 2013
COMMENTS WELCOME – PLEASE DO NOT DISTRIBUTE
Abstract
Evidence from a US sample of twins shows that positive associations between prosocial behavior
and the level of education, common in random population samples, also appear between twins,
but not within monozygotic twin pairs. This finding suggests that the relation between prosocial
behavior and level of education is not due to environmental influence. In contrast, monozygotic
twins with different levels of religiosity do show significantly different levels of prosocial
behavior: the more religious twin from the same pair shows more prosocial behavior than the
less religious twin. The associations of church attendance with prosocial behavior were limited to
contributions to religious organizations, but the association of the strength of religiosity extended
to contributions to non-religious organizations. Prosocial self-identity partly explains this
relationship.
1
Voluntary contributions to public goods in the form of volunteer work effort and monetary
donations constitute substantial proportions of the economy (List & Price, 2011; Salomon,
Anheier, List, Toepler & Sokolowski, 1999) and are important for the well being of nations
(Putnam, 2000). Two ubiquitous correlates of volunteering and philanthropy are education and
religion: higher educated and more religious individuals contribute money and time at
disproportionately high levels relative to their prevalence in human populations (Musick &
Wilson, 2008; Bekkers & Wiepking, 2011a).
This study investigates to what extent the positive associations between education and
religion on the one hand and volunteering and philanthropy on the other reflect an environmental
influence of education and religion. Evidence from a vast number of studies in behavioral
genetics shows genetic components in almost all domains of human behavior (Plomin, DeFries,
McClearn & McGuffin, 2008; Turkheimer, 2000), including education, religion and prosocial
behavior (Bradshaw & Ellison, 2008; Cesarini et al., 2009; Rietveld et al., 2013). However, the
costly search for effects of specific genes through Genome Wide Association Studies (GWAS)
has mostly yielded negative results thus far (see, for instance, Verweij et al., 2010 on personality;
Hatemi et al., 2011 and Benjamin et al. 2012 on economic and political preferences; and Rietveld
et al., 2013 on educational attainment). Previous results from smaller studies linking complex
phenotypes to specific candidate-genes are likely to have been false positives (Chabris et al.,
2012; Wahlsten, 2013). As Koenig & McGue (2011) put it: “There is no God gene.”. The gap
between the moderate to substantial estimates of genetic effects from biometric models and the
negligible effects of specific genes in GWAS has been labeled the problem of ‘missing
heritability’ (Manolio et al., 2009; Zuk et al., 2012).
2
In a diagnosis of the missing heritability problem, Wahlsten (2013, p.286) states that
there “is now a need…to restrict the funding of further searches for elusive genes that account
for so little variance in normal behaviors”. Whether one agrees with this statement depends on
funding priorities, and de gustibus non est disputandum. However, a case can be made in favor
of a change of the course of research into a search for primarily environmental influences based
on two less controversial criteria: cost-effectiveness and policy relevance.
There is now a strong consensus that the effective identification of genetic effects on
complex social behavior phenotypes will require very large samples of biological and social
survey data (Beauchamp et al., 2011; Chabris et al., 2012, 2013). Even though the costs of
sequencing individual DNA have declined strongly, the samples required to identify specific
genetic effects with sufficient power are very large and will still impose considerable costs. The
current study takes a cost-effective approach by relying on publicly available data.
Rather than searching for specific genes, the study seeks to identify specific
environmental influences. The resistance against the use of genomic data in the social sciences is
based in part on a firm belief in the power of culture and institutions as socializing agents
(Freese, Li & Wade, 2003; Bradshaw & Ellison, 2009). Social scientists typically assume that
institutions in society such as religion and the educational system have environmental influences
on social behavior. Individuals in different societies and social groups often show markedly
different behaviors which seem hard to explain from minute biological differences. Critics of
biosocial research could take the discouraging results of genetic association studies as further
support for the position that discoveries of genetic effects do not lead to obvious policy
implications (Goldberger, 1979; Jencks, 1980; Manski, 2011).1 In contrast, discoveries of
1
Some critics even maintain that genetic effects simply do not exist (Chaufan & Joseph, 2013).
3
environmental effects do seem to have more obvious policy implications. If – say – the
environmental effect of an additional year of schooling on prosocial behavior is a 10% increase
in number of hours volunteered or providing support to friends, it could be argued that increasing
the compulsory education age will make society a better place.
Social scientists have been slow to use behavioral genetics to identify environmental
influences of education and religion even though it is clear that genomic data will transform our
understanding of social behavior (Freese, 2011; Bates & Lewis, 2012). Twin studies have also
shown that engagement in religiosity, religious attitudes, volunteering and helping behaviors all
have at least some heritability (D’Onofrio, Eaves, Murrelle, Maes & Spilka, 1999; Rushton et al.,
1984, 1986; Son & Wilson, 2011). Several studies have found an association between a variant
of the oxytocin receptor gene (OXTR) and prosocial value orientation and prosocial behaviors
(for a review, see Ebstein, Knafo, Mankuta, Chew & Lai (2012).
Genetic influences on religion and prosocial behavior are likely to be correlated. In the
only study of its kind among a sample of Minnesota Twins (n = 165 MZ pairs and n = 100 DZ
pairs), Koenig et al. (2007) find that although the variance in prosocial behavior accounted for by
additive genetic effects was not large (10%), the majority of these effects was accounted for by
the genetic effects on religiousness (73%). In addition, of the shared environmental variance in
prosocial behavior (28%), half was accounted for by religiousness. The level of education was
not examined in this study.
The current study uses publicly available data on monozygotic twins from a US
population survey and applies models from behavioral genetics to examine the environmental
influences of religion and education on prosocial behaviors. Three questions are addressed. The
first question is to what extent religion and education have environmental effects on prosocial
4
behavior, both formal and informal. What is the contribution of achieving a higher level of
education and an additional occasion of religious service attendance to formal and informal
prosocial behaviors? Formal prosocial behaviors such as charitable giving and volunteering for
organizations are specifically investigated because they tend to have stronger relationships with
education and religion than informal prosocial behaviors such as providing social support to
family and friends (Wilson & Musick, 1997).
A second question addressed in the current study is how prosocial behavior is associated
with the level of education. Previous studies in this area have reached different conclusions. One
study using a relatively small sample of monozygotic twin pairs from New Zealand (n = 85)
found that while between twins a higher level of education was associated with a higher level of
volunteering, differences in education within twin-pairs were inversely related to the intensity of
volunteering (Gibson, 2001). This finding suggests that the environmental effects of education
are positive and stronger than the negative genetic effects of education. In contrast, a study using
a larger sample of US twins (n = 673) found a weakly positive relationship between education
and volunteering (Son & Wilson, 2010).
Three explanations of environmental effects on prosocial behavior
The third question addressed in the current study is how the environmental effects of
education and religion can be explained, a question left unanswered by previous research. The
associations between prosocial behavior, religion and education could be due to environmental
as well as genetic effects. Previous studies that have sought to explain the relationship between
education, religion and prosocial behavior (a survey is provided in Bekkers & Wiepking, 2011b)
have confounded environmental and genetic effects.
5
The current study eliminates genetic effects of education and religion by restricting the
analysis to MZ twins. If education and religion have purely genetic effects, they act like sorting
machines that rank individuals based on their genetic endowments – their natural talents for
religion and education. If, in contrast, education and religion have institutional effects that add
prosociality to equally talented individuals, we should observe enhanced helping, giving and
volunteering among the twins with higher levels of education and religiosity than their co-twins.
How can achieving a higher level of education and more intense religiosity change the
environment of individuals such that they engage more intensively in prosocial behavior? Three
hypotheses on the environmental influence of religion and education on prosocial behavior are
tested: the resources hypothesis, the social norms hypothesis, and the prosocial role identity
hypothesis.
Resources. The resources hypothesis predicts that prosocial behavior is related to
achievement in education because of the resources that achievement in education brings: higher
income and wealth through higher status jobs and more extensive social networks of high-status
individuals (Wilson & Musick, 1997; Bekkers, 2006). The value attached to educational
credentials in society could be such a uniform influence. Regardless of the specific institution
where individuals received their education the labor market rewards diplomas with higher status
jobs and a higher income level. Note that schooling as a form of investment in human capital
could also reflect genetic effects; this possibility is discussed below. The resources hypothesis
does not explain the relationship between religion and prosocial behavior as religiosity does not
bring along financial advantages.
Social norms. The social norms hypothesis is an alternative explanation for the effect of
education on prosocial behavior. In the social sciences social norms are conceptualized as the
6
expectations that society holds of the behavior of members of social groups (Durkheim, 1897).
Classical accounts of the ‘socialization effects’ of education in sociology (Durkheim, 1956) and
political science (Lipset, 1963; Hyman & Wright, 1979) assume that exposure to prosocial norms
for a longer period of time inculcates prosocial norms prescribing individuals to take
responsibility for public welfare. If social norms explain the environmental effects of education
on prosociality, higher educated individuals should feel a stronger social obligation to contribute
money and time to public welfare. In our case, social responsibility norms should be associated
with giving and volunteering among MZ twins, and statistically controlling for these norms
should reduce the association between religion, education and prosocial behavior.
The social norms hypothesis also applies to the effect of religion on prosocial behavior.
All the major world religions prescribe kindness and generosity to others; in the Christian
tradition, kindness and generosity to strangers and taking responsibility for society as a whole are
important (Wuthnow, 1991). Forbes and Zampelli (2012) even state that “churches and religions
foster a culture of benevolence more than any other institution”. Exposure to such norms through
religious socialization is likely to foster prosocial behavior. Some dispute this relationship and
claim that religion only makes people nice to coreligionists (Galen, 2012). Indeed the correlation
of measures of religiosity with contributions of time and money to religious organizations are
stronger than the correlation with contributions to non-religious organizations (for reviews on
volunteering and philanthropy see Musick & Wilson, 2008 and Bekkers & Wiepking, 2011b,
respectively). Several studies on religion and secular giving find no or even negative
relationships, especially when giving is anonymous and cannot produce social rewards (Galen,
2012).
7
Prosocial role-identity. The prosocial role-identity hypothesis is an alternative
explanation of the effect of religion on prosocial behavior. To be a true believer means putting
your money where your mouth is; a good member does not merely espouse generosity and
kindness as values, but views helpfulness as an integral part of her self-identity. Indeed persistent
blood donors and volunteers are more likely to have such a prosocial role-identity (Lee, Piliavin,
& Call, 1999).
Analytical strategy
The analytical strategy of the current paper is based on the fact that two MZ twins from
the same pair share almost 100% of their genes with each other. As a result, any differences
between MZ twins from the same pair must be due to environmental effects. A regression model
with fixed effects at the level of twin pairs can show to what extent differences in one
characteristic (i.e., prosocial behavior) between MZ twins from the same twin pair are correlated
with other characteristics (i.e., measures of education and religion).2 This model is commonly
denoted as the ‘twin fixed effects model’ (Kohler, Behrman & Schnittker, 2011). If we ignore the
pair structure, however, genetic effects are statistically allowed to influence the differences
between twins. In our case, a regression model with between effects estimates whether twins
with a higher score on the level of education or religiosity report more prosocial behavior. A
comparison of the between and fixed effects estimates for a given shows to what extent the
differences among MZ twins are due to environmental influences.
Using data from MZ twins we can estimate to what extent the associations of prosocial
behavior with measures of education and religiosity are due to environmental influences, but we
2
The twin fixed effects model has been used in economics to estimate the influence of schooling on income since
the 1970s (Behrman & Taubman, 1976; Ashenfelter & Kreuger, 1994; Ashenfelter & Rouse, 1998; Isacsson, 1999;
Miller, Mulvey & Martin, 1995; Bonjour et al., 2003).
8
cannot tell how much of the total variance in prosocial behavior is due to environmental effects.
Our analytical strategy only works if the assumption that there are environmental effects is
satisfied. While precise estimates of the magnitude of environmental effects are difficult to
obtain, biometric models strongly suggest that environmental effects on education, religion, and
prosocial behaviors are substantial.3 The best fitting models show unique environmental effects
in the .32 - .88 range and shared environmental effects ranging from .00 to .47.4 An inspection of
co-twin cross-tables shows considerable variance among twins from the same twin pair in the
level of education, religiosity, and prosocial behavior.
A second assumption that needs to be satisfied is that relationships between prosocial
behaviors, the level of education and religiosity are positive in the MIDUS data, as in previous
research. Indeed positive relationships emerge from the MIDUS data between the level of
education, the frequency of church attendance and donating money to organizations and hours
volunteered (see Supplementary Information, Figures 1 and 2).5
3
Conventional models decompose the variance among MZ and same-sex DZ twins into genetic, shared
environmental and unique environmental sources of variance (Rijsdijk & Sham, 2002). The strong assumptions
underlying the tripartite decomposition are likely to be violated (Stenberg, 2013). Biometric modeling shows
heritability estimates (a2) that are far from unity for all of the characteristics investigated (see Supplementary
Information, Table 2). Additive genetic effects account for 30% of the variance in the level of education, 33% of the
variance in the total amount donated to organizations, and 23% of the variance in the level of religiosity. Most of the
variance in prosocial behavior is due to unique environmental effects; it should be noted that this component of
variance includes measurement error. The best fitting models of the prosocial behaviors investigated here do not
include shared environmental effects. For philanthropic behaviors AE models had the best fit to the data, including
only additive genetic and unique environmental effects. These estimates are similar to a previous study using
experimental methods in Sweden (Cesarini et al., 2009). For the hours spent volunteering, the amount spent on
financial assistance to family and friends and the hours spent helping family and friends DE models had the best fit
to the data, including only dominant genetic and unique environmental effects.
4
The estimates should be interpreted with great caution (Stenberg, 2013). Genetic effects may drive individuals to
self-select into specific environments (genotype-environment correlation; Plomin, DeFries, & Loehlin, 1977; see
Haworth et al., 2011 for an illustration in the case of education), MZ twins may encounter more similar
environments than DZ twins (Horwitz, Videon, Schmitz & Davis, 2003); MZ twins do not have 100% identical
DNA (Bruder et al., 2008); MZ discordance may be due to de novo mutations (Wahlsten, 2013) or epigenetic
influences (Landecker & Panofsky, 2013); twins may also influence each other (Kohler, Behman & Schnittker,
2011); and gene-environment interactions may be at work (see Verhulst & Hatemi, 2013). Each of these processes
lead to biases in the variance decomposition parameters.
5
In addition, the level of education also has a positive relationship with the amount spent on financial assistance to
family and friends. The number of hours spent giving emotional and practical support to family and friends shows a
9
Results
Environmental effects of education and religion on prosocial behavior
Table 1 analyzes differences in philanthropy among monozygotic twins using fixed
effects models. The results of between effects models – ignoring the pair structure – of the total
amount donated in the first column (reported in Supplementary Information Table 3) show
relations of education, religious affiliation, and church attendance that are commonly found in
the literature (Bekkers & Wiepking, 2011b): higher educated individuals, Protestants, and more
frequent church attendees give higher amounts.
The results in Table 1, excluding genetic effects by design, show no significant
relationship between the level of education, religious affiliation, strength of religiosity and the
amount donated. The relationship with the frequency of church attendance, however, is
significant and sizeable: one additional church visit is associated with a $23 increase in the
amount donated per year. Excluding donations to religious organizations, however, we do not
observe a positive relationship between church attendance and the amount donated. Thus the
higher level of prosociality by active church goers is limited to religious donations. The level of
education is not related to the amount donated to ‘secular’ organizations.6
negative relationship with level of education. The number of hours spent giving emotional and practical support to
friends and family shows a weakly negative relationship with the frequency of church attendance.
6
Lundborg (2013); shows that schooling has non-linear associations with health. Schnittker & Behrman (2012)
report linear associations between education and civic engagement. The average amounts donated and hours
volunteered (Supplementary Information, Figure 1) show almost linear associations with the level of education.
Robustness checks of the twin fixed effects models (Supplementary Information, Table 7) however show deviations
from linearity: those with a college degree give higher amounts than one would expect from an extrapolation of the
trend in the lower categories. For volunteering the trends are fairly linear (but negative).
10
In the analysis of the number of hours volunteered no relationship with the level of
education is found when we exclude genetic effects in the fixed effects model.7 Consistent with
Gibson’s (2001) finding among New Zealand twins the relationship in the fixed effects model is
even slightly negative, but not strong, nor significant. Consistent with previous research using
another US dataset (Forbes & Zampelli, 2012), the analysis does show a strongly positive
relationship of strength of religiosity with the number of hours volunteered. A one standard
deviation increase in strength of religiosity is associated with a 31 hour increase in the number of
hours volunteered per year. A similar estimate emerges from the analysis excluding religious
volunteering.
The dollar amount spent on financial assistance and the number of hours spent on
practical and emotional support to family and friends (see Supplementary Information, Table 4)
are not associated with the level of education or the religion variables in the fixed effects model.
Table 2 reports results of fixed effects regressions of four prosocial behaviors on the level
of education, strength of religiosity and the frequency of church attendance (Model 1) and
models including measures of social responsibility norms, prosocial role identity and financial
resources (Model 2). The inclusion of the three mediating variables strongly reduces the
coefficient for the strength of religiosity in the analysis of the number of volunteer hours (by
64%). The measure of prosocial role-identity is responsible for this reduction. Among MZ twins
the more religious twin volunteers more because she sees herself more strongly as a giving and
7
Schnittker & Behrman (2012) report a non-significantly positive relationship between education and the natural log
of hours volunteered in an analysis of the same data. Re-analysis of the model reported in Table 1 using logtransformed variables for hours volunteered (Supplementary Information, Table 8) show stronger relationships of
social responsibility norms with the log-transformed hours volunteered than with the absolute number of hours
volunteered. Also the relationship of the level of education with amounts donated is more positive, the value of
assets has a less positive relationship with amounts donated and hours volunteered, and church attendance has a
more pronounced relationship with hours volunteering at the expense of strength of religiosity.
11
sharing person than her less religious co-twin. The relationship with church attendance is
unaffected in Model 2.
Table 1: Regressions of total amount donated and hours volunteered among monozygotic twins
(fixed effects models)
Total amount
Excluding
Total hours
Excluding
donated
donations to
volunteered
religious
religion
Level of education
volunteering
87.354
29.673
-2.781
-1.988
(SE)
(61.582)
(22.202)
(6.456)
(4.082)
Protestant
432.231
50.378
-38.166
-40.238*
(289.669)
(104.436)
(30.373)
(19.201)
870.034
30.370
-51.280
-58.804
(582.945)
(210.172)
(61.126)
(38.642)
172.762
-28.917
31.944**
33.840***
(SE)
(142.496)
(51.375)
(14.942)
(9.446)
Church attendance
23.168***
2.251
-0.105
-0.320
(4.648)
(1.676)
(0.487)
(0.308)
Constant
-618.808
-90.130
112.483
85.766*
(SE)
(476.405)
(171.761)
(49.954)
(31.580)
ni
617
617
617
617
nj
329
329
329
329
(SE)
Other religion
(SE)
Strength of religiosity
(SE)
* significant at 10%; ** significant at 5%; *** significant at 1%
12
215.977*
(140.436)
22.817***
Strength of religiosity
(SE)
Church attendance
-59.847
(96.052)
6.598***
Sharing person
(SE)
Household income ($k)
(103.597)
90.052
(5.121)
21.892***
(154.277)
281.485*
(64.856)
67.441
Model 2
(SE)
Social responsibility
(4.604)
(61.717)
(SE)
(SE)
88.660
Level of education
Model 1
Donations
(1.651)
2.295
(50.361)
-26.677
(22.132)
29.749
Model 1
2.369***
(35.216)
-56.412
(37.982)
28.482
(1.878)
1.948
(56.563)
-5.015
(23.778)
25.982
Model 2
religion
Excluding donations to
(.481)
-.104
(14.687)
29.115**
(6.454)
-2.870
Model 1
.008
(9.780)
24.870**
(10.548)
-4.638
(.521)
.212
(15.707)
13.369
(6.604)
-7.551
Model 2
Volunteer hours
(.306)
-.313
(9.339)
30.668***
(4.104)
-2.086
Model 1
13
-.035
(6.026)
8.861
(6.450)
1.636
(.321)
-.177
(9.678)
21.681**
(4.069)
-1.896
Model 2
volunteering
Excluding religious
Table 2: Within-MZ twin fixed effects reduced form regression coefficients (Model 1) including mediators (Model 2)
1.063*
(0.543)
-357.143
(714.916)
561
-303.685
(438.474)
617
329
Assets (x $1,000)
(SE)
Constant
(SE)
ni
nj
* significant at 10%; ** significant at 5%; *** significant at 1%
315
(1.784)
(SE)
329
617
(157.238)
-58.277
315
561
(262.112)
103.233
(0.199)
0.301
(0.654)
329
617
(45.855)
86.379*
315
561
(72.792)
-55.390
(.055)
.095*
(.182)
329
617
(29.159)
57.914*
315
561
14
(44.848)
-3.474
(.034)
.043
(.112)
The inclusion of household income and the value of assets in the analysis of the total
amount donated to organizations reduces the coefficient for the level of education from the first
model, which was non-significant to begin with, and not strongly so (by 24%). The coefficient
for the strength of religiosity becomes somewhat stronger (by 30%) in the second model.
Differences in social responsibility norms do not explain variation in prosocial behavior within
MZ twin pairs. Neither do denominational differences emerge from the twin fixed effects models
as they do from between effects models (see Supplementary Information, Table 3) and other
research using unrelated individuals, in which Protestants and members of other religions give
more than Catholics or the non-religious (Bekkers & Wiepking, 2011b).
Twin fixed effects models of the dollar amount spent on financial assistance and the
hours spent on support for family and friends (see Supplementary Information, Table 4) show
that neither the level of education, nor religious denomination, nor the frequency of church
attendance, nor the strength of religiosity are related to these informal prosocial behaviors.
Conclusion
Evidence from the twin sample of the 1995/1996 MIDUS survey shows that positive
relationships between the level of education and voluntary contributions of money and time,
commonly found in survey data among population samples, are not found between monozygotic
twins. Achieving a higher level of education is not a life event that changes people’s
environment such that a higher level of giving and volunteering or financial help and support for
family and friends emerges. This finding indicates that relationships between the level of
education and prosocial behavior are due to genetic effects.
15
Religion, however, does seem to nurture some prosocial behaviors. The evidence is
supports the existence of an environmental religion-prosociality linkage for formal prosocial
behaviors, but not for informal prosocial behaviors. A higher frequency of church attendance is
associated with higher amounts donated to religious organizations, but not secular organizations.
An additional occasion of religious service attendance is associated with a $23 increase in the
amount donated to religion and a (non-significant) $2 increase in the amount donated to other
causes. The strength of religiosity is positively associated with the number of hours volunteered
for both religious and other organizations and the relationship is equally strong for both types of
volunteering. Further analyses revealed support for the interpretation that religiosity is associated
with volunteering through a prosocial self-image.
Discussion
The finding that the level of education is associated with prosocial behavior primarily through
genetic effects that predispose individuals to reach a certain level of education, and not through
the environmental changes that occur as a result of reaching that level of education raises the
question how these genetic effects can be explained. The genetic effects underpinning both the
level of education and engagement in formal prosocial behavior are likely to appear in at least
three groups of phenotypes: cognitive ability, personality, and social networks. In a wellfunctioning meritocratic educational system, cognitive ability is a necessary precondition to
achievement. Controlling for cognitive ability, the predictive power of the level of education for
charitable giving and volunteering diminishes considerably but does not disappear altogether
(Bekkers, 2005, 2006; Hauser, 2000; Wiepking & Maas, 2009). In addition, genetic influences
on personality traits are likely to determine academic achievement While the MIDUS does
16
include personality measures that allow for tests of these influences, the survey did not measure
cognitive ability. Neither does the survey include measures of the size and composition of social
networks, which are often invoked as explanations of the influence of the level of education on
civic engagement (Musick & Wilson, 2008). The formation of social networks is partly a result
of genetic effects (Fowler, Dawes & Christakis, 2008).
The reduced association between the level of education and prosocial behaviors is similar
to findings in research on the influence of schooling on health. Positive associations between
schooling and health are significant in analyses allowing for genetic effects but disappear in
within-MZ analyses (Behrman et al., 2011; Fujiwara & Kawachi, 2009).
The implications of the present findings for conventional theories on the influence of
religion on prosocial behavior are noteworthy as well. Previous research among unrelated
individuals has found that Protestants donate higher amounts to charity than Catholics, and that
this difference can partly be explained by social responsibility norms. Within monozygotic twin
pairs, however, no reliable differences in amounts donated emerge.
While the twin fixed effects model has been heralded as ‘one step closer to the holy grail
of causality’ (Vitaro, Brendgen & Arsenault, 2009), the associations reported here should in no
way be interpreted as causal. The hypotheses all assume that education and religion produce
prosocial behavior, but it is possible that reverse causality is at work, or that omitted variables
bias the estimates. It is well known that low birth weight and perinatal complications,
unmeasured in the current study, are risk factors for externalizing behavior and aggression
(LaPrairie, Schechter, Robinson & Brennan, 2011). Such risk factors may be environmental
influences that co-determine educational achievement, religiosity, as well as prosocial behavior.
The association between religiosity and prosocial behavior could also reflect a causal influence
17
of prosocial behavior on religiosity rather than vice versa. Volunteering may encourage church
attendance and religiosity, for instance when co-volunteers draw volunteers to their churches.
Indeed the MIDUS data suggest that such a process is at work, and that it is due to genetic
effects. Respondents who quit volunteering between the first and the second wave are less
frequently attending church and report lower strength of religiosity in the second wave than in
the first wave. Respondents who started volunteering are more frequently attending church in the
second wave. However, these results only emerge if the twin pair structure is ignored in between
effects models. In twin fixed effects models changes in volunteering are not related to changes in
the frequency of church attendance or strength of religiosity.
It is well known that measurement error results in a downward bias of within-twin fixed
effects coefficients for these variables (Griliches, 1979; Behrman & Rosenzweig, 1999; Behrman
et al., 2011). Indeed the standard errors for education and religion variables are much larger in
the twin fixed effects models than in between effects models (see Supplementary Information
Tables 3 and 4). The common strategy to instrument education (or religion) by co-twin reports
(Ashenfelter & Rouse, 1998) is not possible with the current data because twin respondents in
the MIDUS survey did not report on their co-twin’s level of education. The disappearance of
associations between the level of education and formal prosocial behavior in the twin fixed
effects models cannot easily be explained by measurement error. The measurement error for the
level of education is relatively small, given that the test-retest correlation between M1 and M2 –
ten years apart – is .87. Furthermore, the test-retest correlation for the level of education is higher
than for the frequency of church attendance (.72) similar to the test-retest correlation for the
strength of religiosity (.84). If anything one would expect the association of prosocial behaviors
with the frequency of church attendance to decline even more than the association with the level
18
of education, and the reverse pattern is found. Also the association of prosocial behavior with the
strength of religiosity does not decline in the twin fixed effects model. In fact, the reverse is the
case: associations between the strength of religiosity and prosocial behavior in between effects
models are weaker than in the twin fixed effects models.
The twin fixed effect model has been used here to examine associations of education and
religion with prosocial behavior as a result of environmental effects. Previous studies have
primarily investigated genetic effects of religion, e.g. on group identification (Weber, Johnson,
Arcenaux, 2011) and community integration (Lewis & Bates, 2013). The current study
demonstrates that twin fixed effects models can also be used to study environmental effects of
education and religion. Given the ubiquity of education and religion as correlates of outcomes
commonly studied in the social sciences, such as political participation, subjective well being,
and health, twin fixed effects models are a promising design to examine environmental
influences on these outcomes.
19
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27
Materials and Methods
Participants in this study were respondents to the first wave of the MIDUS survey, funded by the
MacArthur Foundation (Brim et al., 1996). The first survey (M1) was fielded in 1995/1996
among a sample of US twins in addition to a nationally representative sample, both selected
through random digit dialing. A second survey (M2) was fielded in 2004-2006.
Twins were identified in the recruitment procedure for the initial survey through a
screening question among 50.000 potential respondents within the MIDUS sampling frame: the
English-speaking, non-institutionalized US population ages 25–74 with telephones. In 14.8% of
the cases potential respondents reported the presence of a twin in the family, i.e. either the
potential respondents themselves, their spouse/partner, sibling, child, or parent. After permission
to contact the twins was obtained (in about 60% of the cases), the twins were invited to
participate for whom phone numbers were obtained from the potential respondents. Only
English-speaking respondents aged 25-74 living in the US who were physically and mentally
able to complete the interview were allowed to participate. The strict eligibility criteria, missing
phone numbers of (co-)twins and refusals to participate more than halved the resulting sample to
1,914 twins.
Zygosity was determined using self-reports of similarity in hair and eye color,
complexion, confusion by peers, doctor’s assessments and results of tests of zygosity. 25
respondents for whom zygosity could not be determined are excluded from the analyses.
Data on twins were used only for complete twin pairs; data from triplets, quadruplets etc.
were excluded from the analyses. Ten respondents classified as same sex dizygotic (DZ) twins
but self-reporting different sex were excluded. Also two respondents classified as belonging to a
28
monozygotic (MZ) twin pair but self-reporting different genders were excluded. The resulting
sample of twins includes 335 MZ pairs (n = 670) and 290 DZ pairs (n = 580).
Donations. Donations to organizations were measured with the following question: “On
average, about how many dollars per month do you or your family living with you contribute to
each of the following people or organizations? If you contribute food, clothing, or other goods,
include their dollar value. (If none, enter "0".)” After this introduction, donations to three
categories of organizations were measured: (1) to religious groups; (2) to political organizations
or causes; (3) to any other organizations, causes, or charities (including donations made through
monthly payroll deductions)? Amounts donated per month were multiplied by 12 to obtain the
total amount donated per year. The sum of these contributions is the variable for the total amount
donated to organizations. A separate variable was created excluding donations to religion to see
if the relationship between religion and philanthropy would also hold for ‘secular giving’. The
test-retest correlation of the total amount donated measured in dollars is .25; for the logtransformed amounts the test-retest correlation is .44. For donations to organizations other than
religion the test-retest correlation of the dollar amounts is .29; for the log-transformed amounts it
is .39.
Volunteering. The questions on volunteering in M1 and M2 asked about four types of
formal volunteer work: ‘hospital, nursing home, or other health care-oriented work’, ‘school or
other youth-related volunteer work’, ‘volunteer work for political organizations or causes’, and
‘volunteer work for any other organization, cause or charity’. While these questions did not
explicitly identify religious organizations, respondents could report volunteering for religious
organizations in the question about any ‘other’ organizations. A separate variable was created
excluding potentially religious volunteering by computing the sum of hours volunteered in the
29
first three types. The test-retest correlation of the total number of volunteer hours is .38; for the
log-transformed hours the test-retest correlation is .46. For the hours volunteered in organizations
other than religious organizations the test-retest correlation is .28; for the log-transformed
variable it is .36.
Financial support was measured in M1 and M2 with five questions following the
introduction: “On average, about how many dollars per month do you or your family living with
you contribute to each of the following people or organizations? If you contribute food, clothing,
or other goods, include their dollar value. (If none, enter "0".)” The beneficiaries of financial
support distinguished in the questions were “your parents or the people who raised you”; “to
your in-laws”;” to your grandchildren or grown children”; “to any other family members or close
friends?”; “to any other individuals (not organized groups), including people on the street asking
for money”. Amounts donated per month were multiplied by 12 and added to obtain the total
amount of financial assistance provided to others per year. While the final category of the
questions on financial assistance includes unknown individuals, the amounts reported in this
question were included in the total financial assistance variable because the category also
includes friends at a distance and acquaintances. The test-retest correlation of the amount of
financial support measured in dollars is .08; for the log-transformed amounts the test-retest
correlation is .30.
Support provided was measured with the question “On average, about how many hours
per month do you spend giving informal emotional support (such as comforting, listening to
problems, or giving advice) to each of the following people?” and “On average, about how many
hours per month do you spend providing unpaid assistance (such as help around the house,
transportation, or childcare) to each of the following people?” If none, or if the question did not
30
apply to the respondent (in case the respondent did not have a spouse or partner for example), the
respondent was instructed to enter "0".)” The questions then listed the following categories of
beneficiaries: ‘your spouse or partner’, ‘your parents or the people who raised you’, ‘your inlaws’, ‘your children or grandchildren’, ‘any other family members or close friends’ and ‘anyone
else (such as neighbors or people at church)’. The numbers of hours providing emotional support
(in response to the first question) or practical assistance (in response to the second question)
were summed into a variable measuring the total number of hours providing support and
assistance. The test-retest correlation of the total number of hours spent on support and
assistance is .25; for the log-transformed hours the test-retest correlation is .39.
Level of education was reported by the respondents in twelve categories, ranging from 1
(no school/some grade school) to 12 (postdoctoral degree).8 The test-retest correlation is .87.
Religious affiliation was self-reported in response to a question ‘What is your religious
preference?” A very detailed set of response categories was offered; recoded here to four groups:
protestant; catholic; other religious preference (including Jewish, Buddhist, Hindu, Muslim, and
other preferences); the reference category was formed by non-religious respondents (indicating
‘no religious preference’, ‘agnostic’, or ‘atheist’).
Frequency of church attendance was measured in M1 with the question “How often do
you usually attend religious or spiritual services?” and in M2 with the question “Within your
religious or spiritual tradition, how often do you: attend religious or spiritual services?”
Responses were recoded such that a frequency of church attendance per year was obtained
(‘never’ = 0; ‘less than once per month’ = 5; ‘1-3 times per month’ = 20; ‘once a week’ = 50;
8
Because the level of education may have non-linear effects and one study on the relationship between education
and health in fact shows such non-linear effects (Lundborg, 2008), additional analyses were conducted replacing the
linear education variable by a set of dummy variables. These analyses yielded qualitatively similar results.
31
‘more than once a week’ = 75). The test-retest correlation of the frequency of church attendance
is .72.
Strength of religiosity was measured in M1 and M2 with eight questions on a 0 (not at
all) to 3 (very) scale: ‘how religious are you?’; “how spiritual are you?”; “how important is
religion in your life?”; “how important is spirituality in your life?”; “how important is it for you-or would it be if you had children now--to send your children for religious or spiritual services or
instruction?”; “how closely do you identify with being a member of your religious group?”;
“how much do you prefer to be with other people who are the same religion as you?” and “how
important do you think it is for people of your religion to marry other people who are the same
religion?”. The reliability of the scale was .90 in M1 and .91 in M2. The test-retest correlation
for strength of religiosity was .84.
Social responsibility was a factor score composed of responses to the following question
in M1 (the question was not repeated in M2). “Here is a list of hypothetical situations. Please rate
how much obligation you would feel if they happened to you, using a 0 to 10 scale where 0
means ‘no obligation at all’ and 10 means ‘a very great obligation.’ If the situation does not
apply to you, please think about how much obligation you would feel if you were in this
situation.” From the variety of situations, those involving contributions to public benefit were
selected as a measure of social responsibility: “To pay more for your health care so that everyone
had access to health care”, “To volunteer time or money to social causes you support”, “To
collect contributions for heart or cancer research if asked to do so”, and “To vote for a law that
would help others worse off than you but would increase your taxes”. The reliability is .80.
32
Prosocial self-identity is measured in M1 and M2 with the response on a 1 (disagree
strongly) to 7 (agree strongly) response to the question “People would describe me as a giving
person, willing to share my time with others.” The test-retest correlation of this variable is .42.
Household income is the sum of all sources of income (personal earnings income, social
security retirement benefits, government assistance programs, pensions, investments, child
support, or alimony) of household members (the respondent, spouse/partner and other household
members) in the 12 months preceding the interview. Incomes exceeding $300k (M1: 1.2%; M2:
1.4%) are capped. Small proportions of respondents (M1: 2.1%; M2: 4.4%) reported zero
income. For sizeable minorities of respondents (M1: 10.7%; M2: 17.5%) total household income
was missing due missing values on income components. The total amount was divided by 1,000
to obtain a measure in thousands of dollars. The test-retest correlation for the dollar amounts is
.41; for the log-transformed variables it is .47.
Assets is the total net worth of the respondent and spouse or partner measured by the
following question: “Suppose you (and your spouse or partner) cashed in all your checking and
savings accounts, stocks and bonds, real estate, sold your home, your vehicles, and all your
valuable possessions. Then suppose you put that money toward paying off your mortgage and all
your other loans, debts, and credit cards. Would you have any money left over after paying your
debts or would you still owe money?” with response options ‘would have money left over’,
‘would still owe money’, ‘debts would just about equal assets’; and a follow-up question “How
much would that be (that you had left over, or would owe)? Again, please write down the correct
letter from the list on the previous page. (Your best estimate is fine. If your debts would just
about equal your assets, enter "B - $0/none".)” Assets exceeding $1m were capped; substantial
minorities of respondents (M1: 16.2%; M2: 20.8%) refused to answer this question or had
33
missing values. Original amounts reported in hundreds of dollars were divided by 10 to obtain a
measure in thousands of dollars. The test-retest correlation for the dollar amounts is .56; for the
log-transformed variables it is .50.
Descriptive statistics of the measures used in the current study are presented in
Supplementary Information Table 1.
34
Supplementary Information
Table 1: Descriptive statistics among twins and non-twins
Table 2: Concordance, correlation and sources of variance in education, religion and prosocial
behaviors among monozygotic and same-sex dizygotic twins
Figure 1. Levels of prosocial behavior by the level of education
Figure 2. Levels of prosocial behavior by the frequency of church attendance
Figure 3.
Table 3. Regressions of total amount donated among monozygotic twins (between effects models)
Table 4: Within-twin fixed effects regression of hours spent on support to family and friends
among monozygotic twins
Table 5: Between effects reduced form regression coefficients among MZ twins including
mediators
Table 6: Between effects reduced form regression coefficients of assistance and support (Model
1) among MZ twins including mediators (Model 2)
Table 7: Within-twin fixed effects regression of prosocial behaviors including non-linear
education variables
Table 8: Within-twin fixed effects regression of log-transformed variables for amounts donated
and hours volunteered
1
3,480
3,487
3,487
3,487
2,969
Age (25-75)
Protestant (0-1)
Catholic (0-1)
Other religion (0-1)
Frequency of church attendance per
3,485
3,487
3,487
3,487
Level of education
Donates money (0-1)
Amount donated ($)
Amount donated other than religion ($)
2,902
Religiosity (factor score)
year (0-75)
3,487
Female (0-1)
n
292.55
868.09
55.38
6.63
-.02
25.78
23.92
21.08
50.50
46.42
50.65
mean
1
12
3.07
75
1
1
1
74
1
max
.50
2.48
1.02
26.67
.43
.41
.50
13.24
.50
SE
0 192,120 3,512.20
0 192,720 4,070.30
0
1
-2.78
0
0
0
0
20
0
min
Population sample
Table 1: Descriptive statistics among twins and non-twins
197.52
858.99
59.85
6.80
-.03
27.36
16.87
20.15
59.55
44.16
53.43
mean
MZ
231.52
869.42
59.31
6.38
.11
29.90
14.31
22.41
59.66
45.60
62.59
mean
DZ
.485
.955
.033
.087
.740
.180
.000
.588
.000
.000
.186
p
.677
.994
.077
.024
.008
.001
.000
.467
.000
.160
.000
p
2
.367
.915
.846
.002
.016
.118
.216
.329
.971
.036
.001
p
MZpop DZpop MZDZ
3,487 1,042.11
3,034
Amount financial assistance ($)
Gave practical and emotional support to
3,004
2,928
2,709
Sharing person (1-7)
Household income (x $ 1,000)
Assets (x $ 1,000)
2,896
Social responsibility (factor score)
friends
Hours spent supporting family and
3,034
3,487
Gave financial assistance (0-1)
family or friends (0-1)
3,487
Hours volunteered other than religion
111.21
66.85
6.09
-.01
984.70
93.21
43.88
30.97
58.97
3,487
Hours volunteered
37.34
3,487
Volunteers (0-1)
1
2880
4992
1
.50
117.68
173.36
.48
0
0
1
-3.30
0
0
.25
1,000
300
7
2.53
205.38
59.49
1.12
1.01
50.17
35.21
60.85
40.17
93.34
124.91
77.15
6.15
-.04
93.05
70.20
6.12
.11
979.28
93.30
986.11 1,072.80
47.31
31.40
58.05
41.04
43,200 2,512.23 1001.64
1
0 420,072 3,154.16
0
0
0
0
.148
.000
.272
.475
.872
.903
.663
.101
.928
.897
.070
.065
.237
.565
.016
.963
.942
.829
.005
.426
.805
.192
3
.007
.054
.716
.014
.852
.974
.590
.314
.546
.735
.754
1. Is the MIDUS twin sample representative of the US population sample?
Although the twins were identified using the same sampling strategy as the population
sample, the twin sample differs in a number of ways from the population sample. MZ twins were
younger, more likely to be Protestants, less likely to be of an ‘other’ religion (i.e., not Catholic or
Protestant), were more likely to donate money to charity, reported higher levels of social
integration and trust, and a higher household income than the US population sample. DZ twins
were more likely to be female, were more likely to be Protestants but less likely to have an
‘other’ religion, were more religious, attended church more often, had a lower level of education,
were more likely to give financial assistance to friends and family, felt more social
responsibility, felt more social integrated and had more trust in others than the US population
sample respondents. Also the MZ and DZ twin samples differed in a number of characteristics.
DZ twins were more likely to be female than MZ twins, were somewhat older, had a lower level
of education, felt more social responsibility, and reported having assets worth less than MZ
twins.
4
2. Is there enough variance between MZ twins?
MZ twins are certainly not identical in terms of their prosocial behavior and its correlates.
Among MZ twins, 45% reports exactly the same level of education. Stated otherwise: 55% of
MZ twins report a different level of education and are discordant with respect to this
characteristic. Among DZ twins the concordance level is 36%. The difference suggests the
presence of genetic effects on educational achievement. Exactly half of MZ twins report the
same religious affiliation; among DZ twins concordance reaches almost the same level (48%).
While MZ twins are more alike than DZ twins in all of the characteristics investigated here, the
minimal difference in religious affiliation suggests that genetic effects on this characteristic are
small.
5
Table 2: Concordance, correlation and sources of variance in education, religion and prosocial
behaviors among monozygotic and same-sex dizygotic twins
Level of education
Religious affiliation
a2
c2
e2
Concordance
Correlation
MZ: 45.2%
MZ: .667
334 (.256) (.426) (.318)
DZ: 35.8%
DZ: .555
287
MZ: 50.0%
N/A
295
DZ: 46.8%
n
.298
.386
.315
238
MZ: 48.6%b
MZ: .619
228 (.392) (.227) (.381)
DZ: 39.5%
DZ: .423
292
Frequency of church
MZ: 41.2%b
MZ: .483
300 (.072) (.411) (.517)
attendance
DZ: 31.5%
DZ: .447
241
Dollar amount spent on
MZ: 46.3%b
MZ: .342
335 (.335) (.000) (.668)
donations
DZ: 39.9%
DZ: .137
289
Amount donated other
MZ: 49.4%b
MZ: .118
335 (.094) (.024) (.882)
than religion
DZ: 47.8%
DZ: .071
289
Hours volunteered
MZ: 49.9%b
MZ: .167
335 (.137) (.000) (.863)
DZ: 49.6%
DZ: -.005
289
Hours volunteered other
MZ: 67.2%b
MZ: .187
335 (.154) (.000) (.847)
than religion
DZ: 60.3%
DZ: -.005
289
Dollar amount spent on
MZ: 41.3%b
MZ: .135
335 (.099) (.000) (.901)
financial assistance
DZ: 39.0%
DZ: -.037
289
Hours spent on practical
MZ: 62.3%b
MZ: .277
305 (.234) (.000) (.767)
and emotional support
DZ: 54.1%
DZ: -.003
250
Strength of religiosity
d2
.228
.327
.467
.337
.122
.393
.533
.663
.878
.842
.823
.879
.735
.158
.177
.121
.266
6
a
Entries in brackets in the first row are results of Falconer’s formula, a2 = 2 (rMZ - rDZ); c2 = 2 rDZ - rMZ; and e2 = 1 –
rMZ. Entries in the second row are results of biometric models with the best fit to the covariance matrix data
(evaluated at p = .05). Estimates are obtained using a model fitting tool provided online by Shaun Purcell at Harvard
University, available at http://pngu.mgh.harvard.edu/~purcell/bgim/sim/sim1.html.
b
To compute the proportion with identical scores, this variable was recoded into 4 groups.
The estimates of genetic effects on donations are similar to previous variance decompositions of
donations in dictator games (Cesarini et al., 2009), but lower than the estimates for helping
behavior reported by Rushton et al. (1984, 1986).
7
3. Are higher educated and more religious citizens more prosocial?
In these analyses we use all the MIDUS respondents; patterns among twins are similar.
Figure 1 presents histograms of the dollar amounts spent on donations to organizations (panel
A), financial assistance to family and friends (panel C), and the number of hours spent
volunteering (panel B) and giving emotional and practical support to family and friends (panel
D). positive relationship emerges between the level of education and three of the four behaviors.
The strongest relationship we find between the level of education and the total amount donated
to organizations. Respondents holding a PhD donate $1,750 per year; almost ten times the
amount donated by respondents who received no schooling or some years of grade school
($180). The number of hours volunteered per year also increases with the level of education,
with those holding a master’s degree spending 118 hours, almost five times the number of hours
spent by respondents with no schooling or some years of grade school (25). Interestingly, the
number of volunteer hours declines at the very top of the education distribution, with
respondents holding a PhD spending 88 hours. The amount spent on financial assistance also
increases with the level of education, by a factor of 8.5 from junior high school ($360) to the top
of the education distribution ($3,010). The number of hours spent giving emotional and practical
support to family and friends is clearly anomalous to the hypothesis that prosocial behavior
increases with the level of education: the higher the level of education, the lower the number of
hours spent on support. Respondents who received no schooling or some years of grade school
spent an average 1,140 hours per year, more than twice the number of hours that respondents
holding a PhD spent (560).
8
Figure 1. Levels of prosocial behavior by the level of education
A. Total amount donated ($)
12 PH.D, ED.D, MD, DDS, LLB, LLD, JD, OR…
11 MASTERS DEGREE
10 SOME GRADUATE SCHOOL
9 GRAD 4 OR 5 YEAR COLLEGE OR…
8 GRAD 2 YEAR COLLEGE OR VOC SCHOOL,…
7 3 OR MORE YEARS OF COLLEGE, NO…
6 1 TO 2 YEARS OF COLLEGE, NO DEGREE…
5 GRADUATED FROM HIGH SCHOOL
4 GENERAL EDUCATION
3 SOME HIGH SCHOOL
2 EIGHTH GRADE/ JUNIOR HIGH SCHOOL
1 NO SCHOOL/SOME GRADE SCHOOL
0
200 400 600 800 1000 1200 1400 1600 1800 2000
B. Hours volunteered
12 PH.D, ED.D, MD, DDS, LLB, LLD, JD, OR…
11 MASTERS DEGREE
10 SOME GRADUATE SCHOOL
9 GRAD 4 OR 5 YEAR COLLEGE OR…
8 GRAD 2 YEAR COLLEGE OR VOC SCHOOL,…
7 3 OR MORE YEARS OF COLLEGE, NO…
6 1 TO 2 YEARS OF COLLEGE, NO DEGREE YET
5 GRADUATED FROM HIGH SCHOOL
4 GENERAL EDUCATION
3 SOME HIGH SCHOOL
2 EIGHTH GRADE/ JUNIOR HIGH SCHOOL
1 NO SCHOOL/SOME GRADE SCHOOL
0
20
40
60
80
100
120
140
9
C. Financial assistance ($)
12 PH.D, ED.D, MD, DDS, LLB, LLD, JD, OR…
11 MASTERS DEGREE
10 SOME GRADUATE SCHOOL
9 GRAD 4 OR 5 YEAR COLLEGE OR…
8 GRAD 2 YEAR COLLEGE OR VOC SCHOOL,…
7 3 OR MORE YEARS OF COLLEGE, NO…
6 1 TO 2 YEARS OF COLLEGE, NO DEGREE…
5 GRADUATED FROM HIGH SCHOOL
4 GENERAL EDUCATION
3 SOME HIGH SCHOOL
2 EIGHTH GRADE/ JUNIOR HIGH SCHOOL
1 NO SCHOOL/SOME GRADE SCHOOL
0
500
1000
1500
2000
2500
3000
3500
D. Hours giving support
12 PH.D, ED.D, MD, DDS, LLB, LLD, JD, OR…
11 MASTERS DEGREE
10 SOME GRADUATE SCHOOL
9 GRAD 4 OR 5 YEAR COLLEGE OR…
8 GRAD 2 YEAR COLLEGE OR VOC SCHOOL,…
7 3 OR MORE YEARS OF COLLEGE, NO…
6 1 TO 2 YEARS OF COLLEGE, NO DEGREE…
5 GRADUATED FROM HIGH SCHOOL
4 GENERAL EDUCATION
3 SOME HIGH SCHOOL
2 EIGHTH GRADE/ JUNIOR HIGH SCHOOL
1 NO SCHOOL/SOME GRADE SCHOOL
0
200 400 600 800 1000 1200 1400 1600 1800 2000
Figures 2A to 2D plot the average amount donated to organizations, hours volunteered, amount
spent on financial assistance and hours spent giving support by the frequency of church
attendance. The two formal prosocial behaviors (donating money and volunteering time) are
positively related to the frequency of church attendance, but the two informal prosocial behaviors
(financial assistance and providing support) are not. The amount donated increases more than
eightfold from no church attendance ($300) to more than weekly attendance ($2,485). The
10
number of hours volunteered increases almost threefold (from 49 to 136). The amount spent on
financial assistance, however, does not show a linear relationship with respondents attending
church less than once a month spending the highest amount ($1,600) and respondents never
attending church spending the lowest amount ($1,015).
Figure 2. Levels of prosocial behavior by the frequency of church attendance
A. Total amount donated ($)
MORE THAN ONCE A WEEK
ABOUT ONCE A WEEK
ONE TO THREE TIMES A MONTH
LESS THAN ONCE A MONTH
NEVER
0
500
1000
1500
2000
2500
3000
B. Hours volunteered
MORE THAN ONCE A WEEK
ABOUT ONCE A WEEK
ONE TO THREE TIMES A MONTH
LESS THAN ONCE A MONTH
NEVER
0
20
40
60
80
100
120
140
160
11
C. Financial assistance ($)
MORE THAN ONCE A WEEK
ABOUT ONCE A WEEK
ONE TO THREE TIMES A MONTH
LESS THAN ONCE A MONTH
NEVER
0
200
400
600
800
1000
1200
1400
1600
1800
D. Hours giving support
MORE THAN ONCE A WEEK
ABOUT ONCE A WEEK
ONE TO THREE TIMES A MONTH
LESS THAN ONCE A MONTH
NEVER
750
800
850
900
950
1000
1050
1100
The number of hours spent giving emotional and practical support to friends and family shows a
weakly negative relationship with the frequency of church attendance, with respondents never
attending church or less than once a month spending the highest number of hours (1,040) and
respondents attending church more than once a week spending the lowest number of hours (880).
12
4. Are within-twin differences in religion and education correlated with prosocial
behavior?
A scatter plot of within-pair differences in the total amount donated and the frequency of church
attendance among MZ twins (Figure 1) shows a positive association (r = .346, (n = 613), p <
.000), despite a large number of observations at the horizontal and vertical axes representing twin
pairs with the same frequency of church attendance and amount donated.
Figure 1. Scatterplot of within-twin pair differences differences in church attendance and the
total amount donated to organizations among MZ twins
13
The plot in Figure 1 shows that the more actively religious twin from an MZ pair donates higher
amounts to charity than the less actively religious co-twin with equal genetic endowments. The
scatter plot of within-pair differences in the total amount donated and the level of education
(Figure 2) shows a much less clear pattern, and a weaker association (r = .067, (n = 683), p =
.081). From this plot it can be inferred that controlling for genetic endowments the level of
education is hardly associated with the amount donated.
Figure 2. Scatterplot of within-twin pair differences in church attendance and the total amount
donated to organizations among MZ twins
14
Table 3: Regressions of total amount donated among monozygotic twins (between effects models)
Total amount
Excluding
Total hours
Excluding
donated
donations to
volunteered
religious
religion
Level of education
volunteering
168.269
53.139
8.978
5.998
34.052***
11.474***
2.912***
2.018***
571.044
115.911
-3.295
-6.854
178.896***
60.280*
15.298
10.600
Other religion
351.184
173.222
-32.809
-12.346
(SE)
442.638
149.150
37.852
26.227
Strength of religiosity
34.187
-76.487
20.290
14.803
(SE)
117.857
39.713*
10.079**
6.983**
Church attendance
19.887
2.362
-0.136
-0.343
(SE)
4.527***
1.525
0.387
0.268
Constant
-1,161.981
-297.823
7.499
7.415
(SE)
294.149***
99.116***
25.154
17.429
ni
617
617
617
617
nj
329
329
329
329
(SE)
Protestant
(SE)
* significant at 10%; ** significant at 5%; *** significant at 1%
15
Table 4: Regressions of amounts spent on financial assistance and hours spent on support to
family and friends among monozygotic twins
Financial assistance
Emotional and practical support
Between Effects
Fixed Effects
Between Effects
Fixed Effects
Level of education
98.857
100.855
-106.936
68.171
(SE)
53.320*
93.805
38.413***
74.683
Protestant
497.888
-370.157
242.193
-276.018
(SE)
280.124*
441.243
201.807
351.297
Other religion
-18.523
-624.093
127.170
170.063
(SE)
693.103
887.981
499.325
706.968
Strength of religiosity
-21.526
145.774
114.214
-95.577
(SE)
184.547
217.060
132.951
172.813
Church attendance
-5.461
11.004
-1.159
7.915
(SE)
7.088
7.080
5.107
5.637
Constant
201.771
326.645
1,613.509
481.604
(SE)
460.592
725.693
331.819***
577.762
ni
617
617
617
617
nj
329
329
329
329
* significant at 10%; ** significant at 5%; *** significant at 1%
16
5.729
Household income ($k)
92.230
(SE)
4.488**
-50.041
4.562**
(SE)
19.870
Sharing person
19.728
Church attendance
117.058
85.612
116.088
(SE)
183.113
(SE)
114.006
Strength of religiosity
35.884
38.204
34.316**
(SE)
68.713
Social responsibility
160.643
Donations
Level of education
mediators (Model 2)
1.523
2.226
38.759
-57.820
11.457**
52.153
31.526
1.347
26.101
-33.933
24.228
39.952
1.270
0.533
33.128
-31.511
10.155**
to religion
Excluding donations
0.385
-0.103
9.786
19.085
2.893**
8.853
0.091
7.867**
20.502
7.302*
17.342
0.383
0.120
9.984
6.831
3.061*
6.583
Volunteer hours
0.266
-0.333
6.778*
13.648
2.004**
6.045
17
0.169
5.458*
11.766
5.066**
13.577
0.266
-0.040
6.927
4.070
2.123
3.881
volunteering
Excluding religious
Table 5: Between effects reduced form regression coefficients of donations and volunteering (Model 1) among MZ twins including
-390.432
625.113
561
-720.864
262.395**
617
329
Constant
(SE)
ni
nj
329
617
87.607*
-205.161
315
561
176.909
35.173
0.123**
0.378
0.430**
329
617
22.118
4.011
315
561
53.318*
-123.047
0.037
0.027
0.130
329
617
15.320
1.871
315
561
36.990*
-74.449
0.026
-0.017
0.090
variables and to genetic effects on other variables not measured here.
18
not changing much. This pattern suggests that the mediation by household income, the value of assets is due to genetic effects on these
are strongly reduced but retain significance in the between-effects specification, while in the fixed-effects models the relationships are
education and the amount donated as well as the number of volunteer hours and the amount of financial assistance. These relationships
estimates in Model 2 of Table 7 and the fixed effects estimates in Model 2 of Table 6 appear in the relationship between the level of
among MZ twins, while this was not the case in the fixed effects model. Other noteworthy differences between the between-effects
Table 5 shows that social responsibility norms are strongly predictive of the number of volunteer hours in the between effects model
* significant at 10%; ** significant at 5%; *** significant at 1%
315
0.434**
1.588
1.519**
(SE)
Assets (x $1,000)
(SE)
Table 6: Between effects reduced form regression coefficients of assistance and support (Model
1) among MZ twins including mediators (Model 2)
Donations
Excluding donations to
religion
Level of education
90.418
26.238
-110.290
-124.154
(SE)
53.187
59.545
38.199**
42.914**
Strength of religiosity
40.213
76.621
147.541
76.604
(SE)
179.926
194.243
129.224
139.988
Church attendance
-5.268
-3.337
-1.204
3.185
(SE)
7.071
7.447
5.078
5.367
Social responsibility
-0.828
58.600
(SE)
142.061
102.382
Sharing person
95.747
153.923
(SE)
153.044
110.297
Household income ($k)
3.572
1.211
(SE)
2.521
1.817
Assets (x $1,000)
2.093
-0.708
0.720**
0.519
(SE)
Constant
576.426
-105.890
1,799.929
852.099
(SE)
406.687
1,037.293
292.086**
747.564
ni
617
561
617
561
nj
329
315
329
315
* significant at 10%; ** significant at 5%; *** significant at 1%
19
Table 7: Regressions of donations and volunteer hours among monozygotic twins including nonlinear education variables (fixed effects models)
Total amount
Excluding
Total hours
Excluding
donated
religion
volunteered
religion
High school
-305.560
84.683
-4.579
-17.992
(SE)
513.973
183.284
53.936
33.968
Some college
-195.355
33.017
-24.068
-41.295
(SE)
523.107
186.541
54.894
34.572
College and higher
304.551
378.843
-39.130
-51.929
(SE)
601.605
214.533*
63.132
39.759
Protestant
443.352
59.539
-39.278
-41.621
(SE)
290.414
103.562
30.476
19.193**
Other religion
871.015
35.610
-52.131
-59.788
(SE)
583.832
208.195
61.267
38.585
Strength of religiosity
168.849
-26.129
32.174
34.028
(SE)
142.759
50.908
14.981**
9.435***
Church attendance
23.342
2.281
-0.106
-0.318
4.648***
1.657
0.488
0.307
Constant
36.098
-44.590
115.232
107.730
(SE)
545.537
194.539
57.248**
36.054***
ni
617
617
617
617
nj
329
329
329
329
(SE)
* significant at 10%; ** significant at 5%; *** significant at 1
20
0.349
0.269
0.039
Strength of religiosity
(SE)
Church attendance
0.189
-0.133
0.177
0.526
0.222**
(SE)
Sharing person
(SE)
Household income (ln)
(SE)
0.234
0.009***
0.039
0.284*
0.469
0.119*
0.228
Social responsibility
0.009***
0.118
(SE)
(SE)
0.172
Level of education
Donations
among MZ twins including mediators (Model 2)
0.008
-0.001
0.258
0.021
0.114
0.149
0.674
0.176
-0.142
0.189
0.223
0.009
-0.003
0.283
0.143
0.118
0.140
0.221***
to religion
Excluding donations
0.007***
0.021
0.204
0.109
0.090
0.043
0.172
0.038
0.137*
0.257
0.147**
0.326
0.007***
0.026
0.221
-0.110
0.092
-0.013
Volunteer hours
0.003
0.002
0.084**
0.177
0.037
0.020
21
0.071
0.045
0.056
0.026
0.060**
0.154
0.003
0.003
0.090
0.106
0.038
0.013
volunteering
Excluding religious
Table 8: Fixed effects reduced form regression coefficients of log-transformed amounts donated and hours volunteered (Model 1)
329
nj
315
561
1.522
* significant at 10%; ** significant at 5%; *** significant at 1%
617
ni
0.839**
0.464
Constant
(SE)
0.110
(SE)
1.925
-0.020
Assets (ln)
329
617
0.807*
1.518
315
561
1.517
-0.335
0.110
0.057
329
617
0.637
1.007
315
561
1.183
-0.747
0.086
0.107
329
617
0.262
0.368
315
561
22
0.485
-0.050
0.035
0.033