The Reproductive Ecology of Industrial Societies, Part II
The Reproductive Ecology of Industrial Societies, Part II
Gert Stulp 0 1 2 3
Rebecca Sear 0 1 2 3
Susan B. Schaffnit 0 1 2 3
Melinda C. Mills 0 1 2 3
Louise Barrett 0 1 2 3
0 Department of Sociology and Nuffield College, University of Oxford , Manor Road, Oxford OX1 3UQ , UK
1 Department of Sociology, University of Groningen / Inter-university Center for Social Science Theory and Methodology (ICS) , Grote Rozenstraat 31, 9712 TG Groningen , The Netherlands
2 Department of Population Health, London School of Hygiene & Tropical Medicine , Keppel Street, London WC1E 7HT , UK
3 Department of Psychology, University of Lethbridge , Lethbridge, AB T1K 3M4 , Canada
Studies of the association between wealth and fertility in industrial populations have a rich history in the evolutionary literature, and they have been used to argue both for and against a behavioral ecological approach to explaining human variability. We consider that there are strong arguments in favor of measuring fertility (and proxies thereof) in industrial populations, not least because of the wide availability of largescale secondary databases. Such data sources bring challenges as well as advantages, however. The purpose of this article is to illustrate these by examining the association between wealth and reproductive success in the United States, using the National Longitudinal Study of Youth 1979. We conduct a broad-based exploratory analysis of the relationship between wealth and fertility, employing both cross-sectional and longitudinal approaches, and multiple measures of both wealth (income and net worth) and fertility (lifetime reproductive success and transitions to first, second and third births). We highlight the kinds of decisions that have to be made regarding sample selection, along with the selection and construction of explanatory variables and control measures. Based on our analyses, we find a positive effect of both income and net
worth on fertility for men, which is more pronounced for white men and for transitions
to first and second births. Income tends to have a negative effect on fertility for women,
while net worth is more likely to positively predict fertility. Different reproductive
strategies among different groups within the same population highlight the complexity
of the reproductive ecology of industrial societies. These results differ in a number of
respects from other analyses using the same database. We suggest this reflects the
impossibility of producing a definitive analysis, rather than a failure to identify the
“correct” analytical strategy. Finally, we discuss how these findings inform us about
Evolutionary analyses of fertility 1 behavior among industrialized populations have
increased in both frequency and prominence in recent years. This reflects both an
ongoing interest in trying to understand patterns of fertility decline within an evolutionary
framework (e.g., Sear et al. 2016), as well as greater recognition of the potential that
large-scale population surveys hold for testing evolutionarily relevant questions (Nettle
et al. 2013). Some advocates of an evolutionary approach, however, argue that the
study of modern fertility behavior is rather uninformative and should be foregone in
favor of studying psychological processes, under the assumption that cultural change
has been too rapid for genetic evolution to keep pace and has resulted in a mismatch
between our evolved adaptations and modern-day industrial environments. In our
companion paper (Stulp et al. 2016), we take issue with this assessment, arguing that
the measurement of fertility continues to matter because (1) we have to test the
assumption that natural selection has been negligible in recent times, not simply regard
it as axiomatic; (2) only measurements of fitness components, such as fertility, can
provide evidence for a maladaptive mismatch between ancestral environments and the
present day; and (3) if our current behavior does turn out to be maladaptive, patterns of
fertility within and between industrial populations can provide insight into the
psychological mechanisms and environmental conditions that result in a failure to maximize
fitness. In addition, in that paper we considered both the advantages and limitations of
large-scale secondary databases in tackling the question of the (mal)adaptiveness of
modern-day fertility behavior.
Here, we expand on the central points of our companion paper by presenting an
example that walks through the methodological and analytical challenges presented by
the National Longitudinal Survey of Youth 1979 (NLSY79), a large US database, as we
explore the relationship between fertility behavior and wealth (in the form of income
and assets). Wealth, as a measure of access to resources, is an evolutionarily relevant
variable, and studies of the association between wealth and fertility have a long history
in economics (e.g., Becker 1960; Easterlin 1975) and in the evolutionary social
sciences (e.g., Pérusse 1993; Turke 1989; Vining 1986; see Hopcroft 2006; Nettle
and Pollet 2008; Stulp and Barrett 2016a for reviews). In particular, findings of a
1 Although our use of the term “fertility” conforms to the definition used in the social sciences (i.e., the
number of children born) rather than the biological definition, which considers fertility to be the ability to
conceive, we extend beyond this strict definition to encompass all aspects of reproductive behavior in our
discussion of why measuring fertility is important.
negative association between wealth and fertility have been used as evidence to suggest
humans are maladapted to the conditions of industrialized societies (e.g., Tooby and
Cosmides 2015; Vining 1986), but other studies suggest the relationship is more
complex than these analyses suggest, and initial findings may reflect the kinds of data
used and the manner in which they are analyzed (e.g., Stulp and Barrett 2016a). Thus,
our use of the wealth-fertility relationship as a case study is well suited to exploring the
challenges of secondary databases. In addition, we can use the results of our analysis to
address another point made in our companion paper—namely, that the study of fertility
behavior in industrial societies can shed light on the mechanisms that produce these
patterns and help explain when and why people fail to behave in a fitness-maximizing
fashion. However, our analyses are not intended to be a comprehensive exploration of
the association between wealth and fertility, nor do we aim to provide a full explanation
of (mal)adaptive behavior. Rather we focus on those elements that best illustrate some
of the conceptual points made in our companion paper.
The National Longitudinal Survey of Youth 1979
The precise research question we ask here is: Are different forms of material wealth—
income (or earnings) and net worth (or assets)—associated with fertility outcomes? To
answer this, we first provide some descriptive analyses of fertility (adjusted for mortality)
and wealth patterns in the NSLY79 and then present cross-sectional analyses that test for an
association between these forms of wealth and lifetime reproductive success (the number of
surviving children). We then use a longitudinal analysis—which is more appropriate for
establishing the direction of causality—to examine whether wealth influences the initial
transition to parenthood and to the production of a second and third child. This strategy is
less commonly pursued in the evolutionary literature, possibly because of a focus on lifetime
reproductive success as a proxy for fitness. As we proceed, we discuss the methodological
and analytical issues that researchers face when attempting such analyses, including sample
selection, the existence of confounding variables, the heterogeneity of large samples,
assessing data quality, and identifying various forms of bias.
Although we consider a detailed methods section to be the most important part of any
paper, given that our main aim is to highlight the difficulties that arise when conducting
secondary analyses, we provide only the most relevant aspects of our analyses here. We fully
recognize that this seems to be in conflict with our plea for clarity in terms of sample
selection and the decision to include and exclude variables, but in order to keep the paper to a
bearable length, we decided to place the full details of the sample, variables, analyses, and
imputations in the ESM. See ESM Tables 1 and 2 for descriptive statistics. All analyses were
performed in R (R Development Core Team 2008), including the use of the lme4 package.
Graphics were produced using ggplot2 (Wickham 2009). We use p values to indicate
whether there is strong or weak evidence for an association, but we refrain from assessing
relationships as “significant” to avoid assessments in terms of arbitrary cutoffs.
Sample Selection and Researcher Degrees of Freedom
Large, complex datasets contain an enormous array of data and therefore present
researchers with an equivalently large number of “degrees of freedom” (i.e., the choices
made about which variables to analyze, and how). Consequently, sample selection and
the choice of (confounding) variables to include in the analyses are the first decisions
that have to be made, and they are some of the most important; such decisions can have
a profound influence on the outcome of an analysis (Silberzahn and Uhlmann 2015). It
is crucial, therefore, to explain how and why such decisions are made. As such, we
begin by accounting for our decision-making process with respect to sample selection.
We also note where these choices differ from the decisions made by other studies using
the same database and tackling similar research questions. We then deal with the issue
of heterogeneity that is often found in large-scale databases, using the ethnicity of
respondents as our example, and whether there are differences in reproductive measures
and partnership formation that need to be accounted for.
Our study uses the National Longitudinal Survey of Youth 1979 (NLSY79), which
follows the lives of 12,686 individuals (6283 females) born between 1957 and 1964.
Respondents were first interviewed in 1979, when their ages varied between 14 and 22.
Respondents have been interviewed subsequently every year up until 1994, and every
two years after that. The last round of interviews took place in 2012, when the
respondents were between 47 and 56 years old.
The NLSY79 divides ethnicity into three large categories: non-black/non-Hispanic
(N = 7150), black (N = 3174), and Hispanic or Latino (N = 2002). Moreover, the study
consists of three different subsamples: (i) a cross-sectional sample of respondents
(N = 6111) designed to represent the noninstitutionalized civilian segment of the
population; (ii) a supplementary sample of civilian Hispanic or Latino, black, and
economically disadvantaged nonblack/non-Hispanic respondents (N = 5925), and (iii) a
sample designed to represent the population serving in the US military (N = 1280). We
decided to drop the military sample because funding constraints severely limited the
sample size from 1984 onwards (after which only 201 respondents remained). We also
excluded the economically disadvantaged nonblack/non-Hispanic respondents, because
these individuals were no longer followed after 1990, and also because their inclusion
would have biased our sample toward poorer individuals.
The sampling design of the survey involved selecting every eligible person of a
certain age within the household. Thus, family members and spouses of the main
respondents living in the household were also included in the sample. To avoid issues
of non-independence and pseudo-replication that can arise from such sampling, we
decided to include data only from the first selected respondent. We also excluded
respondents who had served time in prison because incarceration obviously hinders
reproductive decision-making and income is not generated.
These decisions differ from those of a recent study by (Hopcroft 2014), using the
same data to answer a similar question. She selected a different subsample (dropping,
for instance, all of the supplementary samples), plus multiple individuals from the same
household were included in the analyses, along with individuals who had served time in
prison. Another recent study chose similar subsamples to us but again included multiple
individuals per household (Breen and Chung 2015). Differences in study outcomes can
therefore arise from decisions made at the very beginning of a study, before any
analytical procedure has even been attempted (see the studies of Sear et al. 2004 and
Courtiol et al. 2013 for a striking example in which the selection of a different
subsample and analytical strategy led to exactly opposite conclusions; see Stulp and
Barrett 2016b for possible explanation).
Descriptive Analysis of Reproductive Patterns
The first step in our analysis was to describe overall patterns in (proxies of) fertility.
These patterns can, by themselves, begin to offer an insight into reproductive
decisionmaking. We start by showing, in Fig. 1, the frequency distribution of the number of
surviving children, or lifetime reproductive success (LRS; by sex and ethnic group).
For these analyses, we chose to use the lifetime number of surviving children because
this is a better proxy for fitness than the number of children ever born (i.e., fertility);
this is particularly relevant because increased wealth has been shown to have a
protective effect on child survival even in low-mortality populations (Remes et al.
Retention and Response Bias
When measuring LRS, we restricted our sample to those individuals aged 45
and over since probabilities of birth are low beyond this age (only 0.3% of
women and 3% of men in our sample had their last birth over the age of 45).
This restriction means that our sample of respondents contained only those
people who had participated in follow-up interviews for at least twenty years.
This is likely to introduce a bias because the likelihood of dropout over time is
not random (e.g., Rendall et al. 1999; Watson and Wooden 2006)—the people
retained by the sample are likely to share certain characteristics that are not
shared by those who dropped out. Having said this, the NLSY79 has remarkably
high rates of retention (Zagorsky 1999) and is seen as the “gold standard” for
longitudinal studies (Randall 2005). This kind of retention bias may therefore be
a relatively minor concern. In addition, our longitudinal analyses will account
Fig. 1 Frequency distribution of lifetime reproductive success (number of surviving children) for white,
black, and Hispanic men and women. Dot and error bars reflect median and 95% range of 1000 random
Poisson simulations of similar sample size and mean
for right-censoring (i.e., the fact that younger individuals will not have
completed their reproductive life spans or that people may exit the study before the final
wave of data collection).
These descriptive analyses also raise a further methodological point and highlight a
wellknown limitation of secondary databases. It is apparent that women report more surviving
children than men (Fig. 1). Controlling for ethnicity, women report 8% more ever-born
children and 7% more surviving children than men (a similar magnitude for this sex
difference is observed in a different sample from the US; Stulp et al. 2012). Given that
every child must have both a mother and a father, this indicates a bias. One possibility is that,
because of their longer (potential) reproductive life spans, men continue to produce offspring
at much older ages, but those children are not captured by our sample because, by necessity,
we are only able to follow the men into their mid-fifties (i.e., some of these men may
continue to have more children). Although, of course, this question can be answered
following future waves of data collection, it remains speculative at present. In addition,
some of the women in the sample may be reproducing with much older men, but the men
themselves do not form part of the sample population. The inclusion of these “missing”
births and “missing” men could narrow the sex difference in this measure of fertility.
Having said this, given that births at older ages occur only sporadically (Stulp et al.
2015), the differences in the number of children reported by men and women are more
reasonably explained by reporting biases and nonrandom dropout (Rendall et al. 1999):
men are more likely to underreport births from previous marriages and, in particular,
extramarital births, and previously married men are likely to be underrepresented in the
sample. Men may also underreport the number of children they have fathered through
simple ignorance of paternity, whereas this is unlikely to affect female respondents.
Given these problems, not all of which can be solved satisfactorily, it is not surprising
that demographers have typically ignored male fertility (Becker 1996; Greene and
Biddlecom 2000; Watkins 1993). In terms of our subsequent analyses, it is evident that
the sexes must be treated separately (something likely to be true in any case, given that
different processes may guide fertility decisions in men and women), and we should be
particularly cautious when interpreting the results for men.
Simulation Choices and Population Heterogeneity in Reproductive Strategies
To get some feel for whether observed patterns of LRS show evidence for variation in
reproductive rates across individuals, we compared the observed distribution of the
number of surviving children to that expected on the basis of a Poisson distribution
with a similar mean (and with variance equal to the mean), where all individuals have a
similar reproductive rate. We chose the Poisson model as our null model for two
reasons: (1) it is a rather parsimonious distribution in which only one parameter has
to be specified (the mean of the distribution), and (2) previous studies have used exactly
this modeling strategy, and we wished to ensure comparability with these findings
(Hruschka and Burger 2016; Morita et al. 2012, 2015). Whether the Poisson model
really is a good null model is debatable (given the large discrepancies of the observed
and actual distributions, one could argue that it is not), and obviously, we could have
chosen different distributions (e.g., negative binomial, zero-inflated, or hurdle models).
Nevertheless, the use of the Poisson is justified by our desire for a parsimonious
simulation strategy that would enable a comparison with previous work. We
also decided to present separate analyses for each ethnicity in case there are
differences in reproductive rates and strategies that map onto this form of
heterogeneity in the database.
We therefore generated 1000 simulations of Poisson distributions and determined
the frequency of specific fertility outcomes (from no births to a maximum of nine)
across all simulations. We then calculated a median value and 95% range across all
1000 simulations. So, for example, a median value of 20%, with a 95% range of 15–
25%, for a fertility of 2 indicates that about 20% of families should have two births if
the distribution is the outcome of a Poisson process, and that in 95% of all cases this
value lies between 15% and 25%. If the observed values fall outside the calculated 95%
range, this suggests that fertility outcomes in this sample have deviated from a situation
in which all individuals reproduce at a similar rate. Our simulations revealed clear
evidence that this was the case: in particular, the likelihood of remaining childless
or having only one child were both much lower than expected, whereas families
with two and, to a lesser extent, three children occurred at much higher
frequencies than expected.
These patterns hold across all ethnicities, although they are particularly striking
among white men and women, among whom there are approximately twice as many
two-child families as expected (Fig. 1; only the first 9 births are plotted). White men
and, in particular, white women also display very low variation in fertility (i.e., in the
case of women the variation is lower than the mean, indicating underdispersion),
showing that people behave more similarly than expected if everyone were to possess
a similar reproductive rate.
This similarity in behavior (in particular the low number of one-child families and
the high number of two-child families) is highly suggestive of a fertility norm.
Preferences for two-child families (Carey and Lopreato 1995; Morita et al. 2015;
Sobotka and Beaujouan 2014) and a disinclination to produce an only child are well
established (Blake 1974) and fit well with these findings. In addition, these patterns
show that more people reproduce and have at least one child than would be expected on
the basis of a Poisson process with such a low mean. These findings therefore help
frame and contextualize our subsequent analyses concerning wealth: given such
low variation in fertility, particularly among whites, resources are unlikely to have
the same magnitude of effect as that observed among nonindustrial populations
(Nettle and Pollet 2008).
In addition, the differences between white and the other ethnic groups are sufficient
to warrant the interpretation that these groups have different reproductive strategies,
and hence that wealth may have a differential influence across these groups. Such
intuitions are borne out by patterns in age at marriage and age at the first three births
(Fig. 2): Hispanic individuals marry and give birth at young ages, and they reach the
highest overall LRS of all three groups. White individuals marry similarly young (and
have highest rates of getting married; see ESM Tables 1 and 2) yet give birth at older
ages and achieve lower overall LRS. Black individuals marry latest and also have the
lowest rates of marriage, yet they have their children at ages similar to Hispanics and
reach similarly high levels of fertility (Fig. 2). These results, as well as previous
research, suggest that different ethnic groups may indeed follow different reproductive
strategies, with variation seen in total fertility/LRS (although these differences are
decreasing), the timing of childbearing, the relational context of having children (e.g.,
10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50
Fig. 2 Box- and violin-plots of age at first marriage and age at the first three births for white, black, and
Hispanic men and women
married or not), and in the frequencies of intended and unintended births (related to
contraceptive use; see Sweeney and Raley 2014 for review). At the very minimum,
then, these patterns suggest that it is important to account for ethnicity in this sample
when examining fertility outcomes. Thus, as with sex, we chose to analyze the different
ethnicities separately in all our analyses.
Descriptive Analysis of Wealth
The Many Measures of Wealth: Income versus Net Worth
Having described patterns of fertility, we then turned our attention to how we would
measure wealth in our analyses. Wealth has many meanings, and different measures of
wealth could influence fertility outcomes in different ways. This, then, is another reason
why we selected the NLSY79 cohort for our analyses: it represents a rich source of
information on economic resources (Zagorsky 1999). Each wave includes detailed
questions on various sources of income, as well as information on assets and debt.
More specifically, assets minus debt is calculated to construct a variable that represents
an individual’s “net worth.” This “net worth” is what economists have in mind when
they talk about “wealth” (e.g., Emmons and Noeth 2015). In what follows, we refer to
this as “net worth” because the term “wealth” has a broader meaning in human
behavioral ecology (e.g., Borgerhoff Mulder and Beheim 2011), and we do not wish
to confuse the two. Although income feeds into net worth (Semyonov and
LewinEpstein 2013), they are not necessarily strongly related: for a given income level, there
can be stark variations in net worth (Braveman et al. 2013; Jez 2014; Keister 2000,
2003; ESM Figure 3). Indeed, inequality in net worth is much more pronounced than
inequality in earnings (Semyonov and Lewin-Epstein 2013).
A high net worth is considered more beneficial to individuals than receiving a
certain level of income because, among other things, it increases financial stability by
providing a buffer for emergencies, it can be passed on to subsequent generations, and
it can be invested in ways that generate further wealth (Jez 2014; Keister 2003).
Importantly for our purposes here, income and net worth (sometimes also referred to
as the flow and stock of resources, respectively) can have different effects on particular
kinds of outcome measures, such as health and education (Braveman et al. 2013; Jez
2014). It therefore seems reasonable to test whether the same is true of reproductive
outcomes, especially as net worth is not often measured or included in studies of health,
education, or fertility (Braveman et al. 2013; Jez 2014; Stulp and Barrett 2016a). This is
also true of most studies in the human evolutionary sciences. Here, we examine the
effects of both income and net worth on fertility decisions using both cross-sectional
and longitudinal analyses.
For our analyses, we constructed a value of respondent income on the basis of income
generated from the labor market—in other words, income from wages, salaries, and
tips, from business and farm work, and from military income (sometimes referred to as
“earnings”; see Breen and Chung 2015 for a similar strategy). Note that this excludes
income from other sources, such as alimony, child support, unemployment
compensation, or food stamps, because (a) they were not consistently measured across years and
(b) the amount of money received from some of these sources depends on household
characteristics, including the number of dependent children. Our measure therefore
contrasts with a previous study that used income from wages only (Hopcroft 2014). In
addition, we used income data from 1983 onward only, as this was the year in which
questionnaires were standardized and identical across all respondents.
For the years 1985–1990, 1992–2000, 2004, 2008, and 2012, respondents were
asked questions about their assets as well as income. In addition to providing
information on “raw assets,” these data are represented in the database as a constructed variable
of “net worth.” Here, for each survey year, the total value of assets a respondent
possessed (including the worth of their home, cash savings, stock and bond portfolios,
estate, business, and automobile assets, along with retirement and other saving plans)
has been subtracted from their debt (including mortgage debt, property, business and
automobile debt). Net worth can therefore take a negative value for those with more
debts than assets. This newly constructed variable was checked for inconsistencies and
missing data were imputed (details in the ESM). Net worth thus provides us with a
measure of economic “wealth” and illustrates how the rich data characteristic of
largescale databases can be exploited to generate measures that are meaningful and directly
relevant to the target question.
Top-Coding, Response Bias, Retention Bias, and Reliability of Measures
For reasons of confidentiality, all income variables in the NLSY79 are top-coded (i.e.,
data points above an upper bound are censored), and in varying ways (Zagorsky 1999;
e.g., the highest 2% of incomes are given the average value of that 2%). This artificial
truncation of the wealth continuum obviously presents a limitation on any analysis
performed using these data. Since 2% is a very small fraction, however, and since we
are not specifically interested in the very wealthy, but in the entire wealth distribution,
this limitation is unlikely to distort our findings, and we decided to include the
topcoded values in our analyses. Nevertheless, top-coding is one of the reasons why
income variables need to be transformed in order to allow comparability across survey
years and respondent age (see below). Other studies using the same dataset have
variously discarded the top-coded individuals (Breen and Chung 2015; Zagorsky
1999) or retained the top-coded values (Hopcroft 2014) during analyses.
Another problem pertinent to our analyses (and to secondary databases more
generally) is that of response bias. This can be a particular problem when dealing with
sensitive issues, including measures of net worth and income (Ross and Reynolds
1996). Zagorsky’s (1999) detailed investigation of the income and assets data in the
NLSY79, however, revealed that only a very small proportion of individuals either
refused to answer or did not know the specific value of their assets. Moreover, such
refusal was less likely for assets such as mortgages, vehicles, and possessions, and
more likely for items such as cash savings, stocks, and bonds. There were also
associations, albeit small, between net worth and the likelihood of refusing to answer
or not knowing the answer to a question. In contrast to this slight reluctance to report on
their assets, those who possessed more assets were more likely to participate every year
than those with fewer. Thus, although there is clear evidence for some response (and
retention) biases, high retention rates combined with high response rates meant that
little imputation of values was needed (Zagorsky 1999).
It is also important to mention those measures of income we did not include in our
analyses. Most notably, the NLSY79 incorporates a constructed variable of “household
income,” which combines the income of all respondents related by blood or marriage
residing in the household (i.e., income from siblings, parents, and/or children living in
the household, as well as spousal income). It does not, however, include partner income
in the case of unmarried couples, even when the partner resides in the same household:
the criterion for inclusion in the household measure is that people be related by either
“blood or marriage.” For our purposes, this is problematic because partner income is
likely to be more relevant to understanding childbearing decisions than the income of
other relatives in the household. Although there are separate variables for partner and
spousal incomes, they are not measured consistently, particularly for partners.
Moreover, respondents were less certain about their spouse’s income than about their
own income, and even much less certain about their non-spousal partner’s income.
Given this, we focused only on the income and net worth of the main respondent and
did not include information on partners or spouses. This is, of course, a clear limitation,
because childbearing decisions are likely to be dependent on the income and net worth
of both partners in a relationship.
Ethnic Differences in Income and Net Worth
Examining the patterns of income and net worth across the life span, we find stark
differences across ethnicities (Fig. 3). These replicate earlier studies from both the
NLSY79 and other US samples (Emmons and Noeth 2015; Keister 2000). White
respondents of both sexes acquire much greater net worth than black and Hispanic
respondents, although there is substantial variation within whites. These ethnic
differences in net worth are argued to result from differences in inheritance patterns
(Keister 2000), education (Emmons and Noeth 2015), financial decision-making
(Emmons and Noeth 2015; Keister 2000), discrimination, cumulative disadvantage,
and early learning experiences (Emmons and Noeth 2015). White men also have much
higher income compared with the other ethnic groups. For women, differences in
income are much less pronounced across ethnicities, and women in general earn much
less than their male counterparts.
These findings are important to consider in light of our research question. First,
increases in income and net worth differ by sex and ethnicity in complex ways. For
example, we might expect net worth to play a larger role than income in white women’s
Hispanic 20 30 40
reproductive decision-making because (a) there is large variation in net worth and (b)
average levels of net worth are much higher in white women than in women of other
ethnicities, whereas this is much less pronounced in the case of income. Second,
although we should be cautious of committing the ecological fallacy (Pollet et al.
2015), when we examine these aggregate patterns, it is clear that the groups with the
highest income and net worth (white men and women) also report the lowest number of
children (see Figs. 1–3). This suggests that other factors exert an influence on fertility
besides wealth, or at least “material wealth,” which again implies that different ethnic
groups may follow different reproductive strategies.
The Cross-Sectional Analysis of the Association between Wealth and Lifetime
Following this descriptive assessment of the available data, we now turn to our
cross-sectional analysis of wealth in relation to LRS: a question that can only be
dealt with effectively using cross-sectional data, given the nature of the dependent
variable. Another reason for performing such an analysis is because it permits a
direct comparison with another recent analysis of the wealth-fertility relationship
in the NLSY79. Thus, we can provide a neat illustration of how differences in
sample selection, along with variable selection and construction, can affect the
outcomes of analyses.
Specifically, Hopcroft (2014) presented a cross-sectional analysis of NLSY79 data
in which she showed that income in 2010 (when respondents were between 45 and
53 years old) was positively associated with the number of children ever born in men,
but negatively associated in women. Here, we extend these analyses and offer our own
cross-sectional analyses of the same data, in which we consider income across the life
span. This is because, although the number of surviving children is appropriately
measured at the end of the reproductive life span, the use of a single income measure
taken at the same point in time is less appropriate. That is, income in 2010 was used as
a predictor of the number of children born many years earlier, when it can have no
causal influence on the decision to bear children. The use of such a measure also makes
the implicit assumption that income in early life is strongly correlated to income in later
life, which is not the case in these data (see ESM Figures 1–2). Given that income and
net worth are measured in many more rounds prior to 2010, the use of only a single
measure to represent these variables means a large amount of available information
goes to waste. Finally, selecting one particular year allows the researcher unwarranted
“degrees of freedom” in the choice of the year selected. As a result, we decided to
investigate the association between wealth across the life span and lifetime reproductive
success in order to provide a more comprehensive analysis. That is, rather than
selecting a single year’s income and net worth as representative of an individual’s
material wealth, we constructed income and net worth measures for each wave of data
collection. Thus, our analysis represents a series of cross-sectional “snapshots” taken
across the life span, as we explain below. Moreover, we simultaneously assess the
effect of income and net worth by including both variables in all statistical models.
This, in turn, required us to address two additional analytical issues: (1) the use of
income and net worth in our statistical analysis and (2) selection of, and controlling for,
Constructing the Income Variable
The top-coding of income (see above) along with the typical skew of income
distributions (see Fig. 3), economic inflation over time, and the increase of income with age
means that the income variable required transformation in order to generate consistency
across years and ages before it could be used as a predictor. To account for these effects,
and to ensure we could include the top-coded 2% in our sample, we converted this
variable into quintiles within ages. A value of 1 for income, for instance, means that this
individual was in the lowest 20% of his or her income group relative to individuals of
the same age, whereas a value 5 indicated that the individual was in the highest 20%
income group. We performed this standardization separately for the sexes because of
the large difference in income between men and women. We performed a similar
standardization for net worth. Quintile measures were included in our statistical models
as continuous variables. Quintiles were used because a larger number of categories
(e.g., deciles) was not feasible because too few cases were available or there was too
little variation in income or net worth to enable them to be grouped into more
categories; see Grundy and Read (2015) for similar analytical strategies. Hart (2015)
also used quintiles but included them as a categorical variable in his analyses. In our
case, sample sizes were too low to follow this strategy.
Choice and Justif ication of Confounding Factors
In order to assess the effect of income and net worth on LRS, it is necessary to control
for other variables that affect either the independent or dependent variable(s). Here, we
included the following factors: country of birth (US or other), religion, whether the
respondent lived in a rural or urban environment at age 14, region of the United States
where the interview was held (Northeast; North Central; South; West), the number of
siblings in 1979, maternal education, and respondent education. For reasons discussed
above, we analyzed different sexes and ethnicities separately.
Religious differences are well-known to influence fertility (e.g., McQuillan 2004), as
are geographical differences (reflected in the rural-urban designation, and location in the
US). Country of birth has also been shown to be particularly important in explaining
variation in fertility within Hispanics (Sweeney and Raley 2014). We included maternal
education as a proxy for respondents’ socioeconomic background during childhood,
which is thought to be predictive of fertility. Paternal education arguably would be a
better proxy, but 14.7% of values on paternal education were missing, whereas only
5.5% were missing for maternal education. Thus, using paternal education would not
only reduce sample size, but also bias the sample toward “nuclear” families. One’s own
education is also a strong determinant of fertility (Skirbekk 2008), particularly in women,
as well as being associated with income and net worth (e.g., Boshara et al. 2015).
Number of siblings is often positively associated with fertility, which can occur
through a number of pathways: (a) having multiple brother and sisters may be
indicative of high fecundity, and there is evidence for heritable variation in fertility
that would support this inference (Kohler et al. 1999; Tropf et al. 2015a, b); (b) having
multiple siblings may shape fertility intentions (whether positively or negatively). Our
selection of control variables is thus substantially different from that of Hopcroft
(2014), who included only sex, education, and “intelligence.” It is important to
emphasize here that neither set of decisions is inherently correct, and that analyzing the
data in a variety of ways furthers our understanding of the relationships that exist, and
the extent to which they are robust. At the same time, it is also true that failing to account
for highly influential factors such as sex and ethnicity may result in biased estimates.
To examine how measures of wealth at each age were associated with the number of
surviving children in later life, we used data from the respondents across multiple
waves of data collection. We thus examined how wealth from all individuals who
reported their income at a given age was associated with the number of surviving
children produced over their entire reproductive life span (see ESM for further details).
Figure 4 shows the effects of income and net worth across the life span on lifetime
reproductive success (for respondents who are aged 45 or older) from our Poisson
regression analysis. So, for example, the effect seen at age 20 indicates the strength and
direction of the effect of income earned when 20 years old on the number of surviving
children at older ages. In women, across all ethnicities, a clear picture emerges: income
is negatively associated with LRS across the life span, although the effects tend to
become slightly less negative at older ages. For net worth, there is a consistent positive
affect on LRS for white women only, between the ages of around 25 to 35.
In white and Hispanic men, the effect of income on LRS is almost always positive.
For black men, in contrast, there is no consistent pattern. When it comes to net worth,
we observe almost the opposite effect: for white and Hispanic men, net worth does not
seem to be associated with LRS, whereas for black men, there is an apparent positive
association between net worth and LRS, particularly between the ages of 25 and 35.
In addition to providing these snapshots of wealth at different ages, we also tried to
create measures that captured overall income and net worth generated across an
individual’s life. We calculated a median income across the life span for those respondents
aged 45 and over who had reported on their incomes at least five times. We rounded this
variable and again generated five wealth categories. We then did the same for net worth.
We found very similar results when using these measures (see Fig. 4 for model
estimates). Across all women, median income across the life span was consistently
negatively related to LRS. Furthermore, in white women, the median value of net worth
was positively associated with LRS. Median income was positively associated with
LRS in both white and Hispanic men, and there was a negative association for black
men. In contrast, in black men, a positive association between net worth and LRS was
observed (see ESM Figure 3 for correlations between income and net worth, that were
positive but not particularly high). These results are very similar when analyses are
conducted without controlling for confounding factors—if anything, the associations
between income and LRS are slightly larger in magnitude (see ESM Figure 4).
Cross-Sectional Analyses in Context
Overall, these results are in line with other studies which also show that, generally
speaking, income is positively associated with LRS in men, and negatively in women
(e.g., Barthold et al. 2012; Fieder and Huber 2007, 2012; Hopcroft 2006, 2014; Nettle
B=−0.087±0.042; p=0.0399 B= 0.081±0.043; p=0.0626
B=−0.002±0.031; p=0.9429 B= 0.027±0.031; p=0.3904
Fig. 4 Poisson regression estimates (B; dot) and 95% confidence interval (shaded area) for the effect of
income and net worth at a given age and lifetime reproductive success (LRS; note that LRS was only
determined when the last age at interview exceeded the age of 44) for white, black, and Hispanic men and
women. The Poisson estimate (B; plus standard error and p value) of the effect of an individual’s median
income or net worth throughout life on LRS is presented in text at the bottom of each panel. With respect to
effect size: eB represents relative risk, with the interpretation that with one quintile increase, LRS would be
increased by eB
and Pollet 2008; Weeden et al. 2006). However, our analyses extend this earlier work in
several ways. First, we show that the effect of income differs across ethnicities,
suggesting that studies that fail to include or control for ethnicity may generate a
slightly misleading picture. Moreover, we find that net worth is positively associated
with LRS among women and black men, suggesting that, for some groups, the stock of
resources exerts a greater influence on reproductive decisions than the flow. We also
observe that the effects of income and net worth are typically most pronounced during
peak childbearing ages (i.e., between 25 and 35), suggesting that resources are
particularly important at those ages. This indicates that using measures of income earned late
in life could provide a biased account of the association between resources and LRS.
Although Fig. 4 plots the associations between our measures of wealth and LRS
across the life span of our respondents, it consists nevertheless of multiple
crosssectional analyses. To gain a better understanding of the process of reproductive
decision-making, and specifically how wealth might factor into the probability of
producing offspring, longitudinal analyses are needed. We turn to these below.
Longitudinal Analysis of the Association between Wealth
and Reproductive Outcomes
It has long been recognized that a better understanding of reproductive decision-making
can be gained by looking at reproduction as a series of decisions distributed across the
life span, rather than treating LRS (or desired family size) as a one-shot decision made
at a single point in life (Goldberg 1960; Namboodiri 1972, 1975; Werding 2014).
Although individuals may have an initial preference for children and a desired family
size (e.g., a two-child norm), desires and preferences can change (Liefbroer 2009), and
child-rearing experiences can and do shape subsequent fertility decisions (Margolis and
Myrskylä 2015). Moreover, each birth may be influenced by a different set of factors—
something that may very well be true for income and net worth. Treating reproduction
as a process also fits with the life-history framework used by (human) behavioral
ecologists (Alvergne and Lummaa 2014): current circumstances influence how future
trade-offs will be resolved. There is also a statistical benefit to such an approach: the
temporal ordering of the variables (with measures of income and net worth taken prior
to, rather than following, a birth or even all births) allows for an assessment of
associations that are less prone to reverse causality (something that could explain the
negative association between female income and LRS—in other words, women with
more children have lower income because they earn less after having children [see
Stulp and Barrett 2016a for discussion]). Finally, longitudinal analysis can account for
censoring (in this instance, the inclusion of individuals who have not yet completed
their reproductive life span) in the data.
Analytical Strategies and Nonindependence
We used discrete-time event history models (Mills 2011; Steele 2011; logistic mixed
models) to assess the effect of wealth on the probability of birth, which meant that we
transformed our data into person-years, such that each individual had a line of data for
each year from the age they entered the dataset (in 1979) and were minimally 18 years
old until the age they were last interviewed. For simplicity, we refer to this effect on
fertility as “probability of birth,” but it should be borne in mind that this refers to the
probability of a birth within a discrete time period following the income measure, and
not the overall probability of birth. We modeled only the first three births because (1)
there were too few births at higher parities for this type of analysis and (2) the ages of
the first three births are directly available and have been checked for consistency and
accuracy by the data developers. Participants were censored at the year of last
interview, at the age of 45, or after the birth of a third child. We included a random intercept
for the respondent to account for the fact that multiple births are nested within the same
individual. Modeling the first three births simultaneously is of utmost importance;
Kravdal (2001), for instance, showed that, although education was positively associated
with the transition to a second and third birth when modeled separately, the association
became negative when modeling all births simultaneously (this is due to problems of
endogeneity—a particular biased subsample of women is left for analysis when only
second and third births are focused on).
We always included time-varying measures of income and net worth simultaneously,
and we also included interactions with parity to see whether the effect of income and
net worth on probability of birth was different for each parity progression. That is, we
could ask whether these constituted different decisions with different underlying
motivations (Namboodiri 1972; Philipov et al. 2006). Moreover, income and net worth
measures were lagged by one, two, and three years, and these lagged variables were
analyzed separately (note that the lag is from the time of interview and not from the
time of birth). See Fig. 5 for an overview of the results. Also note that we are more
interested in the overall patterns of income and net worth across sex and ethnicity, and
less so in individual significant results. Results are very similar when confounding
variables are not included in our analyses (although relationship status seemed
important; see ESM Figure 5).
Longitudinal Patterns between Wealth and Fertility: Investigating
Among white men, lagged income positively predicted the probability of first and
second births but had a negative influence on the probability of a third birth. Income in
the previous year was typically the strongest predictor. With respect to net worth,
lagged values from the previous two and three years positively predicted the probability
of a first and second birth in white men (independently of income). Again, there was a
negative influence on the probability of a third birth, although the magnitudes of the
effects are low. For black men, we found no evidence for an association between
income and births, although, in contrast to the cross-sectional negative association,
income in previous years seemed to positively rather than negatively predict the
probability of births. A similar picture emerges for net worth. In Hispanic men (where
sample sizes were low), we found no evidence for an effect of our wealth measures,
although income lagged by one and two years was negatively associated with the
probability of parenthood, whereas income lagged by two and three years was
positively associated with the probability of a third child. For net worth, the opposite pattern
emerged: when the effect of income was positive, the effect of net worth was negative.
Among white women, income in the previous year was negatively associated with
the birth of a first, second, and third child, whereas income two and three years
previously was typically not associated with the probability of a birth. One exception
is the positive effect of income received three years prior to having a first birth. In stark
contrast, net worth in the previous three years positively predicted the probability of a
first and second birth and negatively predicted the probability of a third birth. For black
women, we found that income lagged by two (and to a lesser extent three) years
Lag One year Two year Three year
White One year Two year Three year
Hispanic 1 2 3
Fig. 5 Logistic mixed model parameter estimates (B and 95% confidence interval) of income and net worth
measured one, two, or three years before the time of interview (Lag “One year,” “Two year,” and “Three year,”
respectively) on the probability of a first, second, or third birth (Parity 1, 2, and 3, respectively) within a time
period of a year for white, black, and Hispanic men and women. Interactions between parity and income, and
parity and net worth were always included, independent of p-value. Individual was included as a random
intercept. With respect to effect size: eB represents odds-ratio, with the interpretation that for a randomly
chosen individual, the odds of having a birth with a value of wealth of X are eB times the odds when having a
wealth of X−1
positively predicted the probability of becoming a mother, as well as net worth in the
previous year. In contrast, income was not associated with the probability of a second
child, and it was associated negatively with the probability of a third child. Net worth
also appeared to negatively predict the probability of a third child. Finally, among
Hispanic women, income from the previous two and three years positively predicted
becoming a mother, whereas income seemed to be negatively related to the probability
of having a third child. Net worth did not seem to have any effect on the probability of a
birth among Hispanic women.
In conclusion, income and net worth affect childbearing decisions differently:
differences were not only observed across the sexes, but also across parities and
ethnicities. Income in the previous year was typically negatively associated with the
probability of birth in women. One possibility is that this reflects the influence of
maternity leave (both in the period preceding the birth as well as after; at the time of
interview, respondents were asked about their income across the previous year—a
pregnancy/birth could coincide with that period). Unpaid maternity leave is typical for
the United States and would reduce yearly income during the early postnatal period. In
addition, although maternity leave does not begin until shortly before a birth, a loss of
income during pregnancy may also occur if women decrease their working hours in
anticipation of the upcoming birth. It is interesting to see that, for white women only,
income received two or three years earlier was not associated with the probability of
birth. Combined with the previous cross-sectional patterns, which indicated that median
income throughout life was negatively associated with lifetime reproductive success,
this suggests that having children actively impedes the generation of income by such
women, rather than high income simply reducing the probability of births at a later
stage. Support for this also comes from the observation that net worth is positively
associated with becoming a mother and having a second child. In other words, it would
be wrong to conclude that women are not translating their resources into having
children. Among black and Hispanic women, income received two or three years
previously positively predicts the probability of becoming a mother, whereas for
white women only income received three years previously did. This suggests that
women may postpone the decision to become a parent until they have sufficient
income, or have a job of a particular standing (see Scott and Stanfors 2011 for a
similar case in Sweden).
For men, the effects of income were most pronounced among whites, where it
positively predicted the probability of both becoming a father and having a second
child. Net worth was also positively associated with a first and second child, this time
across all ethnic groups, although support for these associations was rather weak
(except perhaps for white men).
Net worth was almost always negatively associated with the probability of a third
child (across both sex and ethnicity), a pattern that was particularly clear among both
white and black women. For white men, there were also contrasting effects of income
and net worth on the probability of different births: The relationship was positive with
respect to the first two births, but negative with respect to the third birth. This suggests
that, when it comes to resources, the transition to a third child reflects a somewhat
different decision-making process (see also above).
Reflections on the Association between Wealth and Fertility
Our analyses, both cross-sectional and longitudinal, revealed a complex picture of how
resource availability influences fertility and transitions to parenthood and higher
parities. Income and net worth had separate, and sometimes contrasting, effects on
lifetime reproductive success and fertility transitions. Cross-sectional patterns revealed
that income and net worth measured during the period in which most births occur
(between 25 and 35 years of age) were more strongly associated with lifetime
reproductive success than wealth measures from later in life, suggesting that people do factor
wealth into their fertility decisions during their childbearing years.
For women, median income across the life span was consistently associated with
lower lifetime reproductive success—a pattern often observed. There are, however, two
lines of evidence to suggest that this is not because richer women fail to translate their
resources into more births, but because women receive less income when children are
born: (1) net worth is positively associated with fertility (at least in white women) and
(2) income received in the year prior to the birth shows the strongest negative
association with the probability of a birth, whereas this effect is not observed for
income for two or three years prior to a birth (indeed, this income measure sometimes
had a positive effect, at least on becoming a parent). This reverse causality, where
women pay a substantial monetary cost for giving birth (sometimes referred to as the
“motherhood wage gap”), is pronounced in the United States, where paid maternity
leave is rare and considered a luxury (Scott and Stanfors 2011). In Sweden, for instance,
maternity and paternity leave are generous, and the amount of governmental pay
mothers receive during maternity leave is strongly determined by their income; it is
not surprising, therefore, to see that income positively predicts motherhood in this
population (Dribe and Stanfors 2010; Scott and Stanfors 2011; Stanfors 2014). This
indicates that fertility decisions should be considered within the institutional context of
the particular population under study, and that we shouldn’t regard industrial societies as
homogenous. Clearly, the United States is not representative of all industrial
populations, and our results are unlikely to generalize across all high-income nations (see also
Stulp and Barrett 2016a; Stulp et al. 2016). This further suggests that general economic
theories of fertility, which are heavily based on the US system, are unlikely to be easily
translated to other populations without considerable revision (see also Balbo et al. 2013).
Our analyses also show the value of considering both cross-sectional and
longitudinal patterns. In particular, our longitudinal analyses suggest an alternative
interpretation of the negative cross-sectional relationship between female earnings and fertility
outcomes (see also Hart 2015). The variable nature of our wealth measures, in
addition to the possible negative causal effects of children on income and net
worth, would argue against simplistic cross-sectional studies. Having said this,
our cross-sectional analyses allowed us to investigate overall LRS in relation to
wealth, so perhaps it is more reasonable to suggest that cross-sectional
measures complement longitudinal analyses.
While it is true that our analyses of the association between wealth and fertility go
beyond those typically published in the evolutionary literature, it is also true that our
analyses have only scratched the surface: clearly there are many other possible avenues
to explore that will provide a richer understanding of the patterns at hand. For example,
we could investigate the extent to which the positive effects of wealth on fertility in
men are driven by partner choice (Barthold et al. 2012; see also ESM Figure 5). We
could also test whether fluctuations in wealth (or unemployment) are more important in
explaining fertility decisions than absolute wealth (Currie and Schwandt 2014; Modena
et al. 2014). In addition, we could also consider the extent to which differences in
fertility preferences explain our results, whether resources allow individuals to (more
easily) achieve their fertility goals, and whether resources actively shape fertility
preferences (Modena et al. 2014). A closer examination of the effects of the confounding
variables would similarly pay dividends. It is here that the value of large secondary databases
becomes apparent, for the NLSY79 dataset allows for the testing of all these questions.
Some Suggestions about Reproductive-Decision-Making Mechanisms
Given that we have only scratched the surface with the present analyses, our ability to
point to specific fertility-mechanisms is inevitably rather limited. Nonetheless, our
analyses, combined with previous literature, do indicate certain decision-making
processes, and the results also give pointers where to look further.
It may seem to be a rather obvious point, but the fact that a large majority of people
produced at least one child, in an environment where fertility can be quite tightly and
consciously controlled, suggests that many people wish to become parents: that is, children
do not seem to be merely a(n undesired) side-effect of searching for opportunities to engage
in sex. The active desire for children may explain why people persist in starting a family in
the face of quite extreme economic costs (Morgan and King 2001; Rotkirch 2007). Indeed,
from an economic perspective, it is often difficult to understand why people have any
children at all (Coleman 2000), and an understanding of evolutionary processes may well
pay dividends here (see also Sear 2015). Of course, the possibility remains that people in
certain socioeconomic strata may be less able to control their fertility as well as they might
wish, resulting in unintended births (e.g., Musick et al. 2009): certain sectors of society may
find it difficult to access the contraceptive devices needed to control fertility, or to access
termination services in the case of unwanted pregnancy, given variation in access to family
planning services in the United States.
The distributions shown in Fig. 1 seemingly point to the existence of additional
mechanisms. The fact is that the number of people with either no children at all or an
only child was much lower than expected, whereas a family size of two children was
much more frequent than expected by chance. This suggests that US families prefer to
have two children and are not inclined to have only one child. This, then, requires an
explanation of why this should be the case, and the decision-making process that
underlies such reproductive choices (or indeed the lack of them). Both behavioral
ecological as well as cultural evolutionary ideas have been put forward to explain
patterns of this kind. For example, one reason people apparently avoid having only
one child (Blake 1974; Morita et al. 2015) is argued to be because parents believe that an
only child will not be properly socialized without siblings (Blake 1974). Such an
explanation cannot, however, account for why so many people produce only two
children and not have even more (after all, more siblings means more socialization:
Lawson and Mace 2010a). Behavioral ecological and economic explanations stress the
importance of costs of raising children to explain this phenomenon: a third child may
come at a substantially higher cost than a second child. With increasing family sizes,
there are significant costs to offspring quality, measured in terms of health and
wellbeing, and levels of education and wealth accrued in life (Keister 2003; Lawson and
Mace 2011). Thus, one could interpret our finding showing that net worth
positively predicts the probability of a first and second child, but is unrelated or even
negatively related to the probability of third, as evidence that the decision to have
a third child is indeed different (see also Namboodiri 1972; Philipov et al. 2006),
and that they are perhaps more costly (and perhaps particularly so for wealthy
people; Lawson and Mace 2010b).
An alternative explanation is that resources play a role in initially becoming a parent as
well as permitting the production of a second (“obligatory”) child, but no longer exert an
influence (or at least not a strong one) on the production of a third child. It seems possible
that the transition to parities above two is much less dependent on resources and that other
influences, such as decisions made by those in an individual’s social network or other
idiosyncratic factors gain prominence (e.g., Angrist and Evans 1998; Balbo and Barban
2014). In other words, economic factors may influence the desired goal of two children
(Carey and Lopreato 1995; Morita et al. 2012; Sobotka and Beaujouan 2014), whereas the
desire for higher parities may be more strongly influenced by noneconomic factors. As an
example, families are more likely to have a third child when the first two children are of the
same sex (Angrist and Evans 1998). The decision to have more than two children in this case
seems to satisfy the desire of parents to at least have one child of each sex—something that is
potentially difficult to reconcile in a standard life-history approach. Having said this, if the
sexes pay different economic, social, and (potentially) fitness dividends, it may be possible
to situate such findings within behavioral ecological theorizing. In either case, cultural
evolutionary ideas may also be informative for explaining such patterns (see Colleran
2016; Stulp and Barrett 2016a for further discussion).
A further indication of how non-resource-based influences may be important comes from
the substantial variability that is observed in reproductive strategies in our sample. It is clear
that white, black, and Hispanic men and women have different fertility schedules, and it is
well known that childbearing occurs in different contexts (Hartnett 2014; Sweeney and
Raley 2014): for example, differences in relational context, whether births are planned,
patterns of sexual activity, contraceptive use (see Musick et al. 2009 for a similar point with
respect to educational strata). This is also apparent when examining the different
relationships between measures of wealth and fertility (for example, in black men income is
negatively, and net worth is positively, related to LRS, whereas for white and Hispanic
men, only income is positively associated with LRS). Furthermore, the finding that white
men and women have much higher incomes and net worth than other ethnicities, yet have
the lowest fertility, suggests that economic resources are not the only factor influencing
reproductive decisions. For example, ethnic differences in the composition of networks may
provide a context in which higher childbearing can be supported despite limited resources
(Bereczkei 1998; Chan and Ermisch 2015; Geronimus 1996; Turke 1989). Such ethnic
differences may also arise from differences in perceived costs of raising children, or from
different values and ideas considering having (many) children (Hartnett 2014). Suffice it to
say, variation in reproductive strategies observed across different ethnicities suggest that
different decision-making mechanisms are at play.
Clearly, the factors influencing fertility decisions in industrial populations represent a
fascinating, but highly complex, issue. The possible evolved desires highlighted by
evolutionary psychologists, the life-history trade-offs as studied by human behavioral
ecologists, and the norms and social learning strategies studies by gene-culture
coevolutionary theorists are all needed to explain this behavior. Our analysis of the
relationship between wealth and fertility confirms that the evolutionary story is anything but
simple. Although it is clear that arguments against measuring fertility in industrial
populations are unfounded, it is also true that we need a much deeper understanding of
the mechanisms at play in order to understand low fertility in contemporary
populations. Our main point, however, is that the analyses of large-scale databases offers a
fruitful means of enquiry into evolutionary questions, as long as the limitations and
challenges of such databases are recognized, and care is taken in the selection of
relevant samples, the control of confounding variables, and the construction of an
appropriate statistical modeling strategy.
Acknowledgments The NLSY79 survey is sponsored and directed by the US Bureau of Labor Statistics
and onducted by the Center for Human Resource Research at The Ohio State University. Interviews are
conducted by the National Opinion Research Center at the University of Chicago. We thank the respondents
and the researchers involved in this study who make these data widely available to all researchers. GS is
supported by an NWO Rubicon fellowship, RS by a European Research Council Starting Grant (No. 263760),
MCM is funded by European Research Council (ERC) Consolidator Grant SOCIOGENOME (615603, www.
sociogenome.com) and Economic & Social Research Council (ESRC) UK, National Centre for Research
Methods (NCRM) SOCGEN grant (www.ncrm.ac.uk/research/SoCGEN/), and LB by the NSERC Discovery
Grant and Canada Research Chair Programs.
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
reproduction 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.
Gert Stulp is a postdoctoral research fellow at the Department of Sociology, University of Groningen. He
studies variation in human fertility and reproductive decision-making, with an emphasis on social networks.
Rebecca Sear is an evolutionary demographer and human behavioral ecologist at the London School of
Hygiene and Tropical Medicine. Her research is interdisciplinary, combining evolutionary biology,
anthropology and demography.
Susan Schaffnit is a postdoctoral research fellow in population health at the London School of Hygiene and
Tropical Medicine and member of the Evolutionary Demography Group. Her research focuses primarily on
reproductive decision making in high-income countries.
Melinda Mills is Nuffield Professor of Sociology and head of the Department of Sociology at the University
of Oxford. She leads the Sociogenome group, which studies the interplay between molecular genetic and
social science influences on fertility and assortative mating.
Louise Barrett is a professor of psychology and Canada Research Chair in Cognition, Evolution and
Behaviour at the University of Lethbridge. Her research focuses on socioecology and life history of human
and non-human primates in relation to social network structure.
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