The Reproductive Ecology of Industrial Societies, Part I
The Reproductive Ecology of Industrial Societies, Part I
Why Measuring Fertility Matters 0 1 2
Gert Stulp 0 1 2
Rebecca Sear 0 1 2
Louise Barrett 0 1 2
0 Department of Psychology, University of Lethbridge , Lethbridge, AB T1K 3M4 , Canada
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
Is fertility relevant to evolutionary analyses conducted in modern industrial societies? This question has been the subject of a highly contentious debate, beginning in the late 1980s and continuing to this day. Researchers in both evolutionary and social sciences have argued that the measurement of fitness-related traits (e.g., fertility) offers little insight into evolutionary processes, on the grounds that modern industrial environments differ so greatly from those of our ancestral past that our behavior can no longer be expected to be adaptive. In contrast, we argue that fertility measurements in industrial society are essential for a complete evolutionary analysis: in particular, such data can provide evidence for any putative adaptive mismatch between ancestral environments and those of the present day, and they can provide insight into the selection pressures currently operating on contemporary populations. Having made this positive case, we then go on to discuss some challenges of fertility-related analyses among industrialized populations, particularly those that involve large-scale databases. These include “researcher degrees of freedom” (i.e., the choices made about which variables to analyze and how) and the different biases that may exist in such data. Despite these concerns, large datasets from multiple populations represent an excellent opportunity to test evolutionary hypotheses in great detail, enriching the evolutionary understanding of human behavior.
Fertility; (mal)adaptive behavior; Industrial population; Secondary database; Researcher degrees of freedom; Family
Today’s industrialized world, with its selfie-sticks, cat cafés, and pilotless planes, differs
quite remarkably from the world we occupied only a few decades ago. The contrast is,
of course, even more stark when we reflect on the environments we occupied a few
hundred thousand years ago, as hunter-gatherers on the African plains. From an
evolutionary perspective, a selfie-stick is, arguably, a trivial addition to modern life,
but other changes undoubtedly have influenced evolutionary processes: we have vastly
reduced rates of child mortality, substantially increased our lifespans, and many
countries now display below-replacement levels of fertility.1
Given this state of affairs, it is not surprising that humans are often considered to
have transcended their biological heritage, resulting in the view that evolutionary
processes are irrelevant to an understanding of human behavior. Many social scientists
who study fertility rarely, if ever, view their work through an evolutionary lens (see van
den Berghe 1990; Cochran and Harpending 2009; Kaplan 1996; Morgan and King
2001; Sear 2015a; Turke 1989; Udry 1996; Wilson 1999). Whether an evolutionary
approach is essential to the study of fertility is not our focus here (for a review, see Sear
2015b). Rather, we address the claim that measures of fitness in contemporary
populations (or their proxies, such as fertility) cannot offer any insight into evolutionary
processes. In particular, many evolutionary psychologists argue that an evolutionary
approach should concentrate on the outcomes of past selection, identifying the
adaptations that suited us to a hunter-gatherer niche (e.g., Barkow et al. 1992; Cosmides and
Tooby 2013; Symons 1990; Tooby and Cosmides 2005). Not all evolutionarily minded
researchers agree, of course, and there is long-standing debate on this issue: during the
late 1980s and early 1990s in particular, the value, or lack thereof, of studying fertility
differentials was fiercely contested, with human behavioral ecologists on one side and
evolutionary psychologists on the other (Alexander 1990; Barkow 1990; Betzig 1989;
Caro and Borgerhoff Mulder 1987; Crawford 1993; Smuts 1991; Symons 1989, 1990;
Tooby and Cosmides 1990; Turke 1990a, b; see also Smith et al. 2001).
Here, we revisit this debate, arguing that the measurement of fertility in
contemporary populations is integral, and not irrelevant, to evolutionary psychology. This is
because the study of fertility can provide empirical evidence to support the (usually
untested) assumption of adaptive mismatch that is central to much evolutionary
psychological theorizing (Barkow et al. 1992; Confer et al. 2010; Deaner and
Winegard 2013; Geher 2013; Tooby and Cosmides 2015), by highlighting how and
why people fail to maximize fitness. In other words, the study of present-day fertility
can and does enrich our understanding of evolutionary processes, despite the fact that,
or even precisely because, fertility behavior in industrial societies may be maladaptive.
We also emphasize that understanding fertility among industrial societies will enrich
the study of human behavioral ecology as well: it is clear that the blanket term
“industrial society” masks a considerable amount of variation, thus offering the
opportunity to conduct comparative research that complements the array of studies conducted
on small-scale societies. We should not, therefore, treat industrial society as a monolith,
but recognize that industrial settings consist of a number of distinct “ecologies” that
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.
offer a rich source of insight into how and why reproductive decision-making varies
with environmental circumstances. In the second half of our paper, we discuss some of
the benefits and issues associated with analyzing large-scale databases that are
commonly used to study fertility—concerns that may not be recognized by those not well
acquainted with these kinds of data sources. The companion paper (Stulp et al. 2016,
this issue) provides an empirical example of how to handle the challenges of using large
databases, via an analysis of the relationship between wealth and fertility in the
contemporary United States.
In what follows, we define reproductive ecology as the evolutionary study of
reproductive strategies and decision-making that is responsive to ecological context
(in line with Ellison 1994; Jasienska 2013; Voland 1998). Life history trade-offs and
(adaptive) physiological mechanisms are considered essential to an understanding of
how reproduction is regulated (Voland 1998), but we do not consider physiological
mechanisms explicitly here (see Ellison 1994). In addition, although our focus is
exclusively on fertility among industrialized populations, many of our points hold
equally well (sometimes even more so) for nonindustrial populations, ranging from
small-scale societies to “transitioning” populations (e.g., Alvergne et al. 2013; Bolund
et al. 2015; Colleran et al. 2014; Gibson and Lawson 2014; Moorad 2013; Ross et al.
2016; Shenk et al. 2013). A number of the other articles in this special issue also attest
to this. Industrial societies, however, are considered particularly peculiar from an
evolutionary perspective, so we begin by outlining the reasons why it is important to
characterize fertility profiles and reproductive differentials in such populations, before
going on to discuss the specific challenges of studying fertility in industrial settings.
On Why We Should Study Fertility within Industrial Societies
Because Natural Selection Continues to Operate
One frequent claim made for why natural selection is no longer relevant to industrial
society is because of advances in modern medicine, and a consequent reduction in
mortality rates (see discussions in Bolund et al. 2015; Stearns et al. 2010; Tait 1869;
Zampieri 2009). These ideas have entered popular culture and influence how the
subject is presented to the public at large: the renowned naturalist and television
presenter Sir David Attenborough said recently, “We stopped natural selection as soon
as we started being able to rear 90–95% of our babies that are born. We are the only
species to have put a halt to natural selection” (Meikle 2013). Assertions such as this
rest on the assumption that drastically reduced variation in health and mortality renders
natural selection no longer “effective”—in other words, it fails to eliminate those
individuals who would not have been expected to survive and reproduce in earlier
eras. Similar but weaker claims about the power of natural selection are made by
adherents of the “Santa Barbara” school of evolutionary psychology (e.g., Barkow et al.
1992; Tooby and Cosmides 2015), and it is this stance in particular that we counter
here. Specifically, this evolutionary psychological theorizing suggests that natural
selection has reduced in importance since the dawn of agriculture because subsequent
rates of cultural change have been too rapid for genetic evolution to keep pace (with the
technologies that lead to reduced mortality being a prime example). Although both of
these claims may contain a kernel of truth, the point here is that they should be tested
empirically, not taken as axiomatic.
It is also vital to remember that natural selection acts on differential reproduction, and
that differential survival is just one of the forces that reduces opportunities to propagate
genes. That is, although low rates of (child) mortality may result in reduced variation for
this component of fitness, variation in fitness because of fertility may still be significant.
Importantly, it is relative fitness that matters most to evolutionary processes (i.e., an
individual’s fitness scaled to the fitness of the rest of the population: e.g., Orr 2009). Put
differently, it is variation in (relative) fitness that is relevant to an assessment of natural
selection, rather than levels of mortality in a population or the mean number of surviving
children per individual. Moreover, it is important to realize that, in order for a genetic
response to occur, the fitness-related trait of interest must be (genetically) inherited, and
crucially, there must be a genetic association between the trait and relative fitness
(Bolund et al. 2015; Mills and Tropf 2015; Morrissey et al. 2010; Orr 2009; Tropf
et al. 2015b). When heritability (referring to the amount of phenotypic variation in a trait
that can be explained by genetic variation) is high, the genetic response to natural
selection on a trait is likely to be stronger than when heritability is low. Thus, far from
natural selection being weakened in industrial settings, it may, in fact, act more strongly
on particular traits (see also Bolund et al. 2015; Tropf et al. 2015a, b; Udry 1996). This is
because the heritabilities of certain traits are likely to be much higher now than in the
past since the environment has become more homogenous (owing to universal health
care, vaccination programs, etc.; see Bolund et al. 2015; Bras et al. 2013; Kohler et al.
2002; Tropf et al. 2015a; Udry 1996).
Indeed, a number of researchers have argued that natural selection has been stronger
since the advent of agriculture, precisely because of large cultural shifts (Cochran and
Harpending 2009; Hawks et al. 2007), and there is strong evidence for selective sweeps
(Hawks et al. 2007; Mathieson et al. 2015; Pritchard et al. 2010; Turchin et al. 2012; see
also Bolhuis et al. 2011; Laland and Brown 2011). Others have taken up the challenge
of assessing the strength of natural selection in contemporary populations and, using
phenotypic, genetic, or pedigree data, have found evidence for selection on various
traits, including age at first birth and height (e.g., Byars et al. 2010; Stearns et al. 2010;
Stulp et al. 2012a, b, c, 2015; Tropf et al. 2015b). Note that although such studies are
highly suggestive, they are not conclusive since, typically, lifetime reproductive success
is used. Such proxies do not reflect fitness in any direct sense because (a) this is not a
measure on the molecular genetic level, and thus changes in allele frequencies cannot
be established; (b) lifetime reproductive success may be a poor proxy for a more
longterm measure of fitness because of life history trade-offs: high levels of fertility in the
current generation may come at a cost to future fitness (Stearns 1992; see Lynch 2016
for a recent example in an industrialized population); and (c) using lifetime
reproductive success as a measure of fitness is inappropriate when the population is not
stationary but either growing or shrinking, or when population growth is
densitydependent (e.g., Baldini 2015; Jones and Bird 2014; Jones 2015; Low et al. 2002).
For example, a recent analysis has shown that a well-timed birth in a growing
population may have a greater influence on fitness than the production of additional
numbers of children (Jones and Bird 2014).
These caveats aside, it is clear that empirical assessments are needed to determine
whether natural selection is operating in contemporary populations or whether the
strength of, or response to, such selection has changed over time (see also Scranton
et al. 2016); measuring fitness-related traits such as fertility and lifetime reproductive
success is an obvious place to start such an investigation. Thus, the idea that natural
selection has been of limited importance in more recent times seems to have little
empirical support given the highly suggestive nature of the work cited above. On-going
developments in genomics will be able to contribute to these topics in the not too
distant future (see also Chen et al. 2016; Fieder and Huber 2016), allowing us to look at
past selection by examining a large set of current genomes, or by comparing genomes
sampled historically across generations.
In the debates of the 1980s and 1990s, evolutionary psychologists argued that
organisms do not, and could not, possess any kind of generalized fitness-maximizing
mechanism, and therefore, they considered the contemporary study of fitness outcomes
irrelevant to the study of evolutionary adaptation (Barkow et al. 1992; Cosmides and
Tooby 1997; Symons 1989, 1990; Tooby and Cosmides 1990). Instead, these
evolutionary psychologists argued that evolutionary analyses should focus on the
psychological mechanisms that underpin behavior, along with the environmental cues to
which they are responsive. This in turn raises the possibility that environmental change,
if it occurs rapidly and is drastic enough, will result in maladaptive behavior. Indeed,
evolutionary psychologists argue that differences between human psychological
mechanisms and the cues available in the post-Pleistocene industrial environments are
sufficiently large to generate a mismatch between our evolved psychology and our
behavior. Consequently, it is deemed unlikely that we continue to act in
fitnessenhancing ways. This is why the study of fitness differentials in contemporary
populations (“counting babies”; Crawford 1993, 2000) is deemed uninformative: such
studies can neither identify underlying mechanisms nor are they likely to identify the
optimal fitness-enhancing strategy (since such strategies can no longer result in
adaptive behavior). Although the outright dismissal of the “counting babies” approach is
much less common today, the idea that modern human behavior is necessarily
mismatched to the environment is a regular feature of much work in evolutionary
psychology (e.g., Confer et al. 2010; Deaner and Winegard 2013; Geher 2013; Tooby and
Cosmides 2015; Van Vugt et al. 2008), and consequently, people’s fertility behavior is
rarely measured. There are also more recent (and on-going) discussions about whether
psychometric measures are more informative than biometric measures, which tend to
mirror these earlier debates (e.g., Copping et al. 2014; Dunkel et al. 2015; Figueredo
et al. 2015). Claims therefore continue to be made that measuring fertility should be
foregone in favor of studying other, allegedly more informative traits (for instance: “In
modern times, mating success must be used as a proxy for [reproductive success]”
[Camargo et al. 2013:138], a statement that contains the implicit, and wholly untested,
assumption that current mating success is a better proxy for ancestral fitness than
Counters to these evolutionary psychological arguments have been made repeatedly
(Alexander 1990; Betzig 1989; Caro and Borgerhoff Mulder 1987; Irons 1990; Smuts
1991; Turke 1990a, b). Turke (1990a, b), for example, argues convincingly that
documenting the environments in which adaptive behavior does or does not occur
provides a powerful means of identifying the potential cues or mechanisms that result
in (non-)fitness maximizing behaviors. Such data can therefore provide the empirical
grounding for evolutionary psychologists’ claims that shifts in the nature of
environmental cues can and will result in maladaptive behavior. After all, if we do not collect
measures of fitness, the idea that behavior in industrial settings is maladaptive is simply
an unsupported assumption (see also Stulp and Barrett 2016b). Moreover,
investigations into psychological mechanisms alone are insufficient (Alexander 1990): if novel
cues feed into psychological mechanisms but have no adverse effects on fitness
outcomes, then, by definition, the behavior is not maladaptive. Strong claims regarding
maladaptation and mismatch should thus reflect the findings of relevant behavioral
measures and fitness outcomes and should not be based on mere speculation or simply
by highlighting differences between the supposed ancestral world and contemporary
environments (see also Smith et al. 2001; Zuk 2013). Studying fertility behavior in
contemporary populations is therefore worthwhile because it allows us to get a better
understanding of the environmental cues that feed into reproductive decision-making,
regardless of whether that decision-making turns out to be adaptive or maladaptive in
the long run.
In contrast to the evolutionary psychological view, human behavioral ecologists are
interested in behavioral strategies and their functional outcomes—in other words, the
extent to which they contribute to survival and reproductive success. As such, their
approach requires the measurement of fitness-related traits, including fertility. Although
many human behavioral ecologists recognize the potential for an evolutionary
mismatch, central to the behavioral ecological approach is the application of the phenotypic
gambit and, more recently, the behavioral gambit (Borgerhoff Mulder and Schacht
2012; Fawcett et al. 2013; Grafen 1984; Rittschof and Robinson 2014): the assumption
that there are no (or very few) constraints (genetic or otherwise) on humans’ ability to
arrive at a fitness-maximizing solution. That is, humans are argued to be sufficiently
plastic to track environmental changes in fitness-enhancing ways (Borgerhoff Mulder
and Schacht 2012), especially given that we have engineered those environments for
ourselves via a process of niche construction (Laland and Brown 2006). Flexible
learning mechanisms allow individuals to identify benefits, costs, and trade-offs in a
given environment and, in doing so, to behave adaptively and maximize their fitness.
Thus, behavioral ecologists consider the adaptiveness of behavior in post-Pleistocene
and industrial environments to be an empirical issue (e.g., Borgerhoff Mulder 1998). Of
course, there may be limits to humans’ ability to respond adaptively to change, but
these limits cannot be predicted a priori.
Indeed, it seems fair to say that behavioral ecologists have assumed that the behavior
of humans in industrialized populations is unlikely to be fitness-maximizing, based on
low fertility and high rates of childlessness. This, in turn, may partly explain why such
populations have been under-studied from a behavioral ecological perspective, relative
to small-scale societies (Sear et al. 2007). Indeed, many early studies that aimed to
explain low fertility were theoretical accounts examining whether this could be
adaptive in the long-run (e.g., Boone and Kessler 1999; Hill and Reeve 2005; Mace 1998;
Rogers 1990), rather than empirical studies of people’s actual behavior. Having said
this, there has been a noticeable upturn in the number of empirical studies conducted in
industrial populations over the past decade or so (Nettle et al. 2013). This may be
ascribed to several developments: (a) major increases in computing power, which has
made sophisticated statistical modelling of the kind needed for such analyses more
tractable and within the reach of almost all researchers; (b) the increased availability of
longitudinal databases that contain sufficient data to allow for evolutionarily based
analyses: many now contain a sufficient sample of individuals who have completed
reproduction, and some are even multigenerational; (c) the expansion of human
behavioral ecologists into disciplines beyond anthropology, which formed their original
home, and where the study of cultural diversity naturally included a greater focus on
small-scale societies; and (d) increased awareness that such large datasets from
industrial populations provide an excellent means for testing evolutionary ideas. Goodman
et al. (2012), for example, used a unique multigenerational dataset from Sweden to test
whether limiting fertility was an adaptive strategy (i.e., increased the number of [great-]
grandchildren). Their results suggest that fertility limitation resulted in reduced fitness
over the long term and hence was maladaptive, although there was also evidence to
suggest that the descendants of larger families suffered present-day costs in terms of
lower social status and educational achievement.
It may seem odd to suggest, on the one hand, that measuring present-day fertility
enriches our understanding of mechanisms underlying (mal) adaptive behavior and, on
the other, that establishing whether behavior is adaptive can be achieved only via
longterm, multigenerational studies. This apparent contradiction is resolved once we
appreciate that these points deal with different levels of explanation. As natural selection
is an outcome rather than a process (Endler 1986), it is only possible to assess its effects
on a given trait retrospectively. The processes and decisions that lead to particular
outcomes, on the other hand, are based on cues received from the current environment.
Although humans use foresight and planning, they do not use these abilities to behave
in ways that precisely map onto the long-term outcome of selection (i.e., people are not
looking five generations into the future and making their decisions accordingly, nor are
they able to). In this, then, we agree with the evolutionary psychologists’ position that
organisms do not possess any form of general-purpose “fitness maximizing”
mechanism (e.g., Tooby and Cosmides 1990)—and, to be fair, this is also the position of most
other behavioral ecologists. Instead, selection produces organisms possessed of
mechanisms that are sensitive to particular kinds of environmental information, and it is the
operation of such mechanisms that results in fitness-enhancing behavior (ranging from
the rather fixed abilities to see or hear certain frequencies to highly flexible
mechanisms, including individual and social learning and planning abilities). Thus, studying
fertility behavior, and establishing how this is influenced environmentally (in terms of
both conscious decision-making as well as other physiological and unconscious
psychological mechanisms), will lead to a better understanding of the mechanisms
involved. The question of whether such decisions are adaptive in the long run is therefore
a related but separate evolutionary question.
The desire to gain a better understanding of fertility behavior and its drivers, rather
than just measures of fitness, increases the scope of our investigations: not only can we
study the number of (surviving) children born to an individual, we can also examine
birth intervals, parity-specific progression (e.g., the likelihood of becoming a parent, or
having a second or third child), within-couple fertility, and multi-partnered fertility,
because different mechanisms likely feed into these different behaviors (Billari et al.
2009; Namboodiri 1972). Fertility norms, for instance, may explain why many couples
end up having two children in contemporary populations, whereas individual
circumstances (e.g., health, wealth) and previous experiences may affect birth intervals or
parity-specific progression (see Stulp and Barrett 2016b for further discussion). These
measures also provide a basis for comparison with small-scale societies. For instance,
whereas the length of the interbirth interval may be a good indicator of overall
reproductive output in high-fertility populations (where intervals are heavily influenced
by the nutritional status and health of the mother), this may not be true for industrialized
populations where nutritional concerns are less important. In the UK, for instance, an
important determinant of interbirth interval and fertility is the age at first birth: highly
educated women in the UK have later first births and fewer children than less-educated
women but tend to progress more rapidly to subsequent births (Berrington et al. 2015;
Rendall and Smallwood 2003).
Because It Connects an Evolutionary Approach to the Broader Social Sciences
With their increasing focus on industrial populations, behavioral ecologists are catching
up with demographers, economists, and sociologists who have been attempting to
understand patterns of fertility (and particularly, patterns of fertility decline) over the
past two centuries (e.g., Balbo et al. 2013; Becker 1960; Lee 2003; Sear et al. 2016).
Work in all these fields makes it apparent that cultural evolutionary processes need to
be considered as part of any kind of evolutionary approach when examining temporal
patterns of fertility variation. There is a general consensus among demographers
(Bongaarts and Watkins 1996; Bras 2014; National Research Council 2001; Pollak
and Watkins 1993), for example, that explanations for low fertility should be sought in
a combination of economic reasons (including considerations of lower child mortality
and increased costs of rearing children) along with the diffusion of novel ideas through
social interactions (e.g., use of contraception)—ideas that are also heavily associated
with work in behavioral ecology and cultural evolution.
Evidence that the decline in fertility cannot be explained solely by economic factors
is argued to lie in the specific cultural and geographical patterns of fertility decline:
fertility behavior is more similar in geographically connected areas (Goldstein and
Klüsener 2014), and in areas that are linguistically similar (van Bavel 2004), regardless
of economic circumstances. It has also been found that culturally distinct groups, such
as religious groups (McQuillan 2004), possess characteristic pro- and anti-natal
attitudes that can persist over time and temper the influence of economic factors. There is
also more direct evidence of such social influences on fertility: Colleran et al. (2014),
for instance, have shown that the characteristics of people in a woman’s social network
can exert a stronger influence on her fertility behavior than her own characteristics (see
also Colleran 2016). Similarly, Balbo and Barban (2014) have shown that the fertility
decisions of individuals are influenced by the decisions of their friends. Structural
changes in society, changing social influences, and the spread of novel ideas through
networks, may explain some of the quirks in contemporary industrialized populations,
such as an increasing number of individuals who have actively (and happily) decided to
be child-free throughout their entire lives, despite having the economic means to raise
children (Nazarinia Roy et al. 2014)—something that is difficult to reconcile with
classic behavioral ecological principles (see also Stulp and Barrett 2016b). The
increasing number of ethnographic and mixed-method accounts of reproductive
decisionmaking in industrial societies (e.g., Bernardi et al. 2014; Cooper 2014; Edin and
Kefalas 2005) can provide the kind of qualitative detail needed to flesh out quantitative
patterns derived from large-scale surveys. Fertility behavior, then, is a topic that can
provide a bridge from evolutionary approaches to the broader social sciences, with each
field offering a unique perspective on a behavior that cannot be fully understood within
a single framework.
Most important in this respect, the large body of literature on fertility in
demography and sociology makes it abundantly clear that the industrial world is not a
monolith. Across industrial populations there is wide variety in cultural background
as well as the nature of social institutions, both of which markedly affect behavior.
For instance, the extent to which female earnings are associated with the probability
of becoming a parent or having further children depends on population-specific
policies, such as the availability of childcare, the length of maternity (and paternity)
leave, and whether or not it is paid leave (see Stulp and Barrett 2016b for review).
We want to make clear, therefore, that although this paper makes broad points about
“industrial societies,” we do not consider such societies to form a single
homogeneous unit. This can be seen as an application to fertility research of Henrich and
colleagues’ warning about the overreliance on WEIRD (Western, Educated,
Industrialized, Rich, and Democratic) populations in psychology (Henrich et al.
2010). In addition to avoiding the assumption that WEIRD populations can be taken
as representative of human populations in general, we should also avoid the
assumption that any single WEIRD population can be taken as representative of
all industrialized, economically prosperous societies as a whole. For example, a
recent meta-analysis concluded that there was a significant association between
father absence in childhood and age at menarche (Webster et al. 2014), a topic of
current interest in evolutionary circles. However, not only was every study included
in the meta-analysis conducted on a WEIRD population, but the majority were also
English-speaking (22 of 29 populations where the location of the study could be
identified). The idea of the industrial world as representing a single entity was also
very apparent in this review, as some of the research cited did not even specify the
study population used (which may also reflect the assumption that human
populations throughout evolutionary and historical time have remained essentially
unchanged, and will mount a single, universal response to similar circumstances). Just
as evolutionary generalizations based only on WEIRD data from the industrial
world are no longer excusable given the well-documented diversity in cognitive
processes and behavior (Henrich et al. 2010), generalizations across “the industrial
world,” and even across different social strata within a population, are similarly on
shaky ground (suggestions also made by Henrich and colleagues).
Measuring fertility within, and acknowledging variability between, industrial
populations is also relevant to comparative work, allowing for rigorous cross-population and
cross-species comparisons that further our understanding of life history evolution. Such
comparisons will obviously be incomplete if they do not incorporate the full range of
human lifeways, which means industrial settings must be considered. Indeed, this may
reveal patterns that are missed when industrial populations are excluded. For example,
analyses by Burger et al. (2011), Burnside et al. (2012), and Moses and Brown (2003)
show that fertility behavior within and between industrialized populations is in line
with macro-ecological patterns observed across nonindustrialized human populations
and other mammalian species, suggesting that industrialized societies are not an
Fertility behavior is thus a key area in which to study the intersection of human
behavioral ecology and evolutionary psychology with other evolutionary research
areas, such as comparative life history and cultural evolution. As noted above,
evolutionary approaches can also add value to the studies performed by demographers,
economists, and sociologists, which can help to further contextualize evolutionary
theorizing. The study of fertility thus encompasses all areas of evolutionary research,
as well as the different disciplines within the social sciences, allowing for a fully
biocultural understanding of a key human behavior.
The Use of Secondary Data in Studying Fertility Behavior
in Industrialized Populations
Primary and Secondary Data Collection in Industrialized Populations
Our species exhibits so much behavioral flexibility, and lives in such a wide range of
ecologies, that testing evolutionary hypotheses is always something of a challenge:
truly powerful tests require data from the full range of human societies. Although
human behavioral ecology, in particular, is built on an exceptionally strong foundation
of detailed, in-depth studies of small-scale societies (see Winterhalder and Smith 2000
for a review), including information from industrial societies will give us greater power
to identify variability in human fertility behavior, and the ecological variables that help
generate this variation. Such societies have the advantage that many rich secondary
datasets (i.e., data collected by someone else for a different primary purpose; Smith
et al. 2011) are often available to test evolutionary hypotheses. Although there has been
a long tradition of secondary data analysis using historical datasets in the human
evolutionary sciences (Voland 2000), only recently has the discipline begun to exploit
the large amount of existing data on contemporary industrialized populations (Nettle
et al. 2013). We argue that such datasets are a valuable but underexploited resource in
Evolutionary analyses of human behavior have traditionally focused largely on
primary data collection, an approach that has many strengths. Most notably, primary
data collection exercises can be designed to produce exactly the data needed to test a
particular hypothesis, including both detailed surveys and experimental approaches
(e.g., Henrich et al. 2005; Lamba and Mace 2011). Targeted studies of this kind also
enable contextual, ethnographic, and qualitative detail to be gathered alongside
quantitative data (e.g., Cooper 2014; Edin and Kefalas 2005). Primary data collection is
therefore second to none in terms of providing a controlled and detailed view on the
topic at hand.
Secondary data, in contrast, often suffer from the problem that it is not collected with
a specific question of interest in mind, making it difficult to conduct an adequate and
comprehensive test of a particular hypothesis. Despite this drawback, major positive
arguments can be made in favor of secondary data (see Doolan and Froelicher 2009;
Hofferth 2005; and Smith et al. 2011 for reviews on the use of secondary databases in
distinct fields of research). First and foremost, secondary demographic, sociological,
and/or epidemiological databases boast very large and often nationally representative
samples and contain a wealth of information, typically including demographic, social,
economic, and health information,2 though some focus in detail on specific topics (such
as the UK’s National Survey of Sexual Attitudes and Lifestyles,3 and the UK’s Biobank,4
which has detailed health—including genetic—data). Many are also longitudinal,
following the same individuals over time, allowing for in-depth investigations of life
histories, including how early life experiences influence subsequent life events. Datasets
also exist which allow comparative analysis, across both time and space. For example,
there are now four UK birth cohort studies (with respondents born between 1946 and
2000), allowing comparisons between cohorts, and the Generations and Gender Survey
is a comparative and longitudinal survey conducted across 17 European countries,
Japan, and Australia, which allows between-country comparisons.5 In addition, since
the process of data collection is “blind” to the hypotheses under study, it is less
susceptible to confirmation bias.
In other words, although secondary data analysis is far from ideal (and certainly not
the only means by which evolutionary researchers can study industrial economies), the
wealth of large, representative secondary datasets does make it worthwhile for
evolutionary researchers to investigate whether they can use this means to test their
hypotheses before engaging in more expensive, time-consuming primary data collection (see
Smith et al. 2011 for a similar view). This approach, of exploring secondary data first,
may also help improve the power of primary data collection, as the latter can be
targeted to fill the gaps in secondary datasets and produce the kinds of information
that are not typically available in large-scale surveys (e.g., experimental work or
detailed ethnographic data; see Daniel Nettle’s work on socioeconomic inequalities in
the UK, which evolved from secondary data analysis [Nettle 2011] to fieldwork [Nettle
Although the value of conducting studies on secondary data is clear, measuring and
interpreting patterns of fertility behavior using secondary data is far from
straightforward: the very complexity of such datasets means they also present a number of
difficulties which may not be apparent if one lacks experience working with them.
For an anthropologist facing the challenge of meticulously collecting primary data over
months and years in the field, the analysis of secondary datasets, often downloadable at
the click of a mouse from one of many online data repositories, may seem a trivial
matter. Despite the ease with which secondary datasets can be acquired, it is important
not to underestimate the amount of work needed to produce useful and comprehensible
results. Here, we tackle several decisions that the individual researcher faces when
dealing with a secondary database before going on to discuss some of the analytical
problems that often arise but about which researchers can do little (except retain an
awareness of the possibility for certain kinds of bias). Note that the issues we describe
are inherent to all secondary data analysis but are particularly relevant to the analysis of
the very large, complex datasets typical of industrialized populations.
2 See https://www.data.gov/ for open access datasets from the US; http://www.data-archive.ac.uk/ for UK
Challenges of Using Secondary Data: Researcher “Degrees of Freedom”
and Population Heterogeneity
The nature of large, rich datasets, which contain an enormous array of information from
a wide range of individuals, presents researchers with an equivalently large number of
“degrees of freedom” (the choices made about which variables to analyze and how).
This can allow inadvertent and unconscious bias to creep in. Before even embarking on
any form of analyses, then, decisions must be made about (i) how to operationalize the
research question—in other words, how to decide which variables will be used to
measure the predictor and outcome; (ii) selection of the sample; and (iii) which
pertinent (confounding) variables to include. The outcome of these decisions can
(and often does) lead to different answers to the same research question. A case in
point is a recent crowd-sourcing study that supplied an identical dataset and research
question (Does football players’ skin color have an effect on the number of red cards
issued to them by a referee?) to twenty-nine independent research teams and asked
them to come up with an answer. This resulted in marked variability in both the analysis
strategy and the statistical modeling approach used and, hence, to the answers produced
(Silberzahn and Uhlmann 2015). Given that such wide variability is possible in a study
with a limited set of variables and a single, straightforward research question, it is not
surprising to find stark differences in research outcomes when a dataset contains
thousands of variables, and when research questions are less well-defined. From a
scientific viewpoint, then, it is vital that researchers fully explain the decisions they
make during sample selection, and the variables they choose to include (or omit) (see
statistician Andrew Gelman for a similar plea in psychology: Gelman and Loken
(2014) and his blog).6 Even when researchers are fully aware of the possibility for
bias and strive to avoid it, the formulation of particular constructs is often highly
Although large sample size is repeatedly emphasized as one of the major advantages
of large demographic databases, sample sizes need to be large because of the
heterogeneity that is common in industrialized populations. In studies of small-scale societies,
it is reasonable to assume that the population is relatively homogenous, and to justify
generalizations regarding reproductive strategies on this basis. In contrast, industrial
settings are associated with various forms of social stratification, which potentially
leads to heterogeneity in reproductive strategies. This applies both across populations
(such that the United States may be markedly different from the United Kingdom), but
also within populations (such as the distinction between rural and urban areas). A
failure to account for such heterogeneity may lead to false inferences (Mace 2008;
Pollet et al. 2015). For example, a recent study found that fewer children were born in
wealthier urban areas of Mongolia than in poorer rural areas; yet, within both urban and
rural areas, there was a positive association between resources and fertility (Alvergne
and Lummaa 2014).
Heterogeneity in reproductive (or parental) strategies may exist on an even finer
scale (e.g., across educational or socioeconomic strata; Kaplan 1996; Lawson and Mace
2011; Mace 2008; see also Stulp et al. 2016). Differences in partnership and
reproductive strategies can be substantial in industrialized populations (not least because of the
high degree of control afforded by modern contraceptive methods). For example,
according to recent data from the UK, around half of women in the lowest wealth
tercile were without a coresident partner at the time of their first birth, compared with
almost none in the highest wealth tercile (Schaffnit 2015). Age at first birth also shows
a clear, and increasing, educational gradient in the UK (Berrington et al. 2015): women
born between 1960 and 1969 show a 7-year gap in median age at first birth for those in
the highest and lowest educational groups, and a gap of about half a child in completed
fertility between the two groups (highly educated women have later first births and a
higher probability of childlessness). Although these patterns may, to some extent,
simply reflect variation in reproductive patterns owing to variation across a particular
dimension (e.g., access to resources), they may also reflect the existence of subgroups
within populations whose reproductive decisions are influenced by rather different
criteria (e.g., Borgerhoff Mulder 2007; Lawson and Mace 2009). These may include
not just a difference in the salient environmental factors to which different subgroups
are sensitive, but also differences in social influences and cultural norms surrounding
reproduction (see Sweeney and Raley 2014 and Stulp et al. 2016 for the case of ethnic
differences in partnership and reproduction).
The marked variability within industrial societies also suggests that samples may
require more detailed description—more “ethnographic” detail—in order to uncover
the variety of strategies likely to exist within a given population. This kind of detail is
routine in evolutionary studies of small-scale societies, as well as the mixed methods
and qualitative research typical of the social sciences (Bernardi et al. 2014; Cooper
2014; Edin and Kefalas 2005), and it is likely to substantially advance our
(evolutionary) understanding of reproductive decision-making (see also Geronimus
1996). For example, ethnographic evidence suggests that poorer and less-educated
families make their fertility decisions on the basis of short-term favorable
circumstances, rather than on long-term (financial) prosperity (Cooper 2014; Edin and Kefalas
2005; see also Musick et al. 2009). In contrast, richer, more highly educated families,
who are hypothesized to use their wealth as a buffer for future risk and uncertainty,
postpone childbearing until they feel they have sufficient resources for having a
(nother) child (Cooper 2014; Edin and Kefalas 2005; Musick et al. 2009).
Family Influences on Fertility as an Illustrative Case
To illustrate the points made above, we offer the example of our work on whether
family support is associated with fertility. Evolutionary researchers have shown interest
in this topic because the cooperative breeding hypothesis (Hrdy 2009) makes the
straightforward prediction that women who receive a lot of support from family should
have more successful reproductive outcomes, including higher fertility. A number of
studies have now investigated this issue in industrialized populations, but there is clear
variability in the results, particularly within Europe: family support is observed to be
both pro- and anti-natal, and sometimes support has no effect at all (see Schaffnit 2015
and Sear and Coall 2011 for reviews). Such variability could well be due to differences
in institutional and cultural factors across these populations: industrialized populations
are not a monolith. It is also likely, however, that such differences arise from the way
that family support is measured in different datasets—support from both husband’s and
wife’s family may be included, or different measures of support (survival status of
family, coresidence with family, provision of practical support such as childcare,
financial support, emotional support)—and how fertility is measured (age at first birth,
length of birth intervals, total number of children or childlessness have all been
investigated). The variability in Europe contrasts with studies conducted among
industrialized populations in Asia, which show consistently that family support is associated
with higher fertility. This may be because the Asian studies use similar variables to
measure both family support and fertility: most investigate the association between
coresidence with the husband’s family and length of interbirth intervals (reviewed in
Even within the same population, and with similar research questions, differences
may be observed. For instance, Schaffnit (2015) and Mathews and Sear (2013) have
investigated associations between family support and fertility in two UK datasets: the
Millennium Cohort Study and the British Household Panel Survey, respectively. In the
latter, childcare from family members was associated with a faster progression to
second births; in the former, there was much weaker evidence that grandparental
childcare was correlated with a higher probability of second birth. One possible
explanation for these differences may be differences in the way that childcare data
were collected. In the BHPS, women were asked to report who looked after their
children “while you are at work,” meaning such data was only reported by women
employed outside the home; the variable included in the analysis also incorporated
childcare from any relative, which was likely, but not necessarily, to be care provided
by grandparents. In contrast, the MCS contains data from all women on the receipt of
childcare from both sets of grandparents, collected soon after birth. Without a close
inspection of how data on family-provided childcare was collected in each dataset, it
would appear that an ostensibly identical research question (is family-provided
childcare associated with higher fertility in the UK?) inexplicably leads to different
results in the two studies.
This same MCS study also found that different types of support have contrasting
effects on fertility: while emotional support was associated with a higher probability of
second birth, receiving financial support was associated with a lower probability of
second birth (perhaps because the receipt of financial support from kin is associated
with greater need on the part of recipients, rather than being an indication that these
individuals have plentiful support they can convert into childbearing; e.g., Schaffnit and
Sear 2014). Different measures of support are clearly not interchangeable when the
influence of family on fertility is being investigated. Such results suggest that singling
out particular variables and neglecting others may provide a biased and somewhat
misleading picture of the patterns at hand. Another recent study using the MCS, for
instance, showed that contact frequency with parents-in-law predicted faster
progression to second birth (Tanskanen et al. 2014)—results that were interpreted as potentially
consistent with the cooperative breeding hypothesis. Although they provide detailed
description of their methods and the analyses themselves are exemplary, carefully
distinguishing the influence of parents and parents-in-law and separating the analysis
by parity, it is not immediately apparent why contact frequency was chosen as the sole
measure of family support, and why measures of the provision of actual support by kin
were excluded. Reporting why certain measures are used and not others, and justifying
these choices, would be a valuable addition to such analyses, allowing researchers to
more fully understand why variable patterns emerge from equivalent datasets.
Finally, research on family and fertility also highlights how within-population
heterogeneity should be taken into consideration when analyzing secondary datasets.
Although the MCS analysis showed little evidence for an association between
grandparental childcare and higher fertility overall, when the dataset was stratified by
socioeconomic status there was evidence for a positive relationship between such
childcare and fertility in the lowest socioeconomic tercile, but not in the middle or
In addition to the challenges that arise from “researcher degrees of freedom” and
population heterogeneity, there are also issues of data quality and bias (for recent
examples, see Kreyenfeld et al. 2013; Sauer et al. 2016). Such biases are certainly
not restricted to secondary databases; they hold generally for any study with human
subjects. However, when a researcher has not been involved in the collection of a
particular dataset, the flaws may not be obvious in the way that they are when data are
collected by researchers themselves. It is easy to assume, for example, that data have
been collected both consistently and comprehensively across time in longitudinal
studies. In reality, questions can be reworded to produce slightly different answers or
targeted at a slightly different subset of respondents. Moreover, questions can be
dropped from surveys altogether, or new ones added. Additionally, not all the variables
needed to test a given hypothesis or control for confounds may be collected.
Concerns about bias and data quality are, of course, amplified for those interested in
performing comparative analyses across several secondary datasets at once since
information on the area of interest may have been collected using different questions
in different populations. Some multi-country datasets, however, are explicitly designed
to allow comparative analyses, such as the Gender and Generations Programme
mentioned above. Although the surveys used in each country are designed to be as
similar as possible to allow comparative research, there are still differences between
countries in how the surveys were conducted, the exact questions asked, and how
questions were translated and/or interpreted in different countries. Nevertheless, such
cross-national datasets can be useful in comparative research, provided sufficient care is
taken over the analysis and interpretation of results.
Another worry is that of response bias and selective sampling and, in cases of
longitudinal follow-up, the attrition of respondents through time. Although
demographic surveys aim to be nationally representative, some people, such as those with low
income, tend to be harder to capture and more likely to drop out of surveys (e.g.,
Goyder et al. 2002; Groves 2006; Strandhagen et al. 2010). Such biases may have
serious implications for fertility research, in particular, and for the conclusions that can
be drawn in an evolutionary framework. For example, studies have shown that men
under-report pregnancies from previous relationships, and that previously married and
unmarried men are less likely to be included in demographic samples, leading to sex
differences in the number of reported children (Rendall et al. 1999; Stulp et al. 2012a;
see Stulp et al. 2016 for more examples). Another potential bias may arise through
extra-pair paternity: men may not know they are the father of certain children, whereas
other men may erroneously assign paternity to children who are not, in fact, their own.
Although this problem needs to be acknowledged, it seems unlikely that such errors
will substantially affect outcomes because extra-pair paternity rates are very low
In a different vein, when sampling at older ages, biases resulting from death or
nonrandom dropout should be taken into account: when collecting a sample of older
individuals only, or when asking about certain variables only at older ages, there will
be a bias toward people who have survived and thus remain in a position to respond to
the question. Such (potential) biases should not be neglected, as they pose a real
problem for studies of reproductive decision-making. Research on height provides an
informative case. Height is associated with reproductive success in many populations,
but it is also associated with mortality, the likelihood of getting married, and income
(see Sear 2010; Stulp and Barrett 2016a; Stulp et al. 2012b, c for reviews), all of which
may bias the estimates we observe for the association between height and reproductive
success. For instance, in the Wisconsin longitudinal study, height was only recorded
once the participants had reached the age of 52 (Stulp et al. 2012a, b, c). Given that
unmarried and deceased people are, respectively, less likely and certain not to be
included in follow-up studies, and that both of these factors are also associated with
height, our sample at later ages may be biased with respect to this trait. Moreover, the
respondents in this study had all graduated from high school, which, given the positive
association between height and education, potentially biases the sample toward taller
heights (Stulp et al. 2012b). Similarly, in another study, we found a high incidence of
individuals unwilling to report on their income (Stulp et al. 2015). Again, given the
association between height and income, it is likely that our sample may be somewhat
biased in this respect.
Another concern relates to the accuracy of the data collected. Large-scale surveys
typically make substantial demands on respondents because of the large number of
questions, and the sensitivity of some of those questions. Some respondents may
simply refuse to answer anything that makes them uncomfortable, which leads to a
straightforward response bias (e.g., with respect to wealth questions: Ross and
Reynolds 1996; Zagorsky 1999). A more difficult issue is raised by respondents who
answer these sensitive questions, but do so inaccurately. One means of reducing such
biases is to incorporate data collection techniques such as self-completion
questionnaires (which allow respondents to answer questions via computer screen, rather than
respond directly to an interviewer). Problems of accuracy still persist, however, in cases
where people simply do not know the correct answer to a given question (whether
sensitive or not). There is a marked tendency for people to underestimate their wealth
(Zagorsky 2000), for example, and they are also not particularly confident about their
partner’s income (see Stulp et al. 2016).
Finally, some problems inherent to large questionnaire surveys are almost
impossible to avoid or ameliorate. Survey methodologists are aware, for example, that “context
effects” can cause problems: preceding questions may influence the interpretation of, or
responses to, subsequent questions (Todorov 2000; Tourangeau et al. 2000, 2003). This
may have relevance for fertility research, particularly if researchers are interested in
analyzing data on fertility preferences, which are widely collected in surveys but have
been shown to be influenced by priming effects (Mathews and Sear 2008; Wisman and
Goldenberg 2005). In the first wave of the UK Millennium Cohort Survey, for example,
women were asked questions about their future fertility intentions immediately after
questions about their previous birth, including whether pain relief had been used. Such
ordering is potentially problematic; priming women to think about the pain of childbirth
may lead to (temporarily) reduced future fertility intentions (Mathews 2012).
Experimental studies show that the ordering of questions matters in fertility research
None of the above should be taken to mean that secondary data analyses raise any
more problems that those encountered when analyzing data that researchers have
collected themselves. Problems of response bias, selective sampling, and
questionordering effects are likely in all datasets. The point is that such problems may be less
obvious if researchers have not encountered them at first-hand during data collection,
and efforts should be made to be aware of these issues and consider their potential
impacts on the design and outcome of our analyses.
By highlighting the value of studying fertility in industrial populations, while also
recognizing the challenges of doing so, we have aimed to provide evolutionary
anthropologists with a well-informed incentive to embark on a more comprehensive examination
of the reproductive ecology of industrial societies. Although differences in data collection
between datasets may complicate the interpretation of relationships of interest, they also
open up the possibility of testing alternative hypotheses, allowing us to gain a more
sophisticated understanding of fertility behavior and how it might vary across groups.
We hope, therefore, to have persuaded the reader that measuring fertility does matter,
that there is such a thing as the reproductive ecology of industrial societies, and that
industrial populations, in all their variety, need to be incorporated into an evolutionary
framework. Industrial populations should be seen as a necessary addition to a fully
comprehensive understanding of human (mal) adaptive behavior, and not as an optional
Acknowledgments We thank Cristina Moya, Susie Schaffnit, Paula Sheppard, Kristin Snopkowski, David
Lawson, Felix Tropf, Mirre Simons, the Evolutionary Demography group at the London School of Hygiene
and Tropical Medicine, and the Oxford Sociology group for helpful discussions. GS is supported by an NWO
Rubicon fellowship, RS by a European Research Council Starting Grant (No. 263760), 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.
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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