	Evolutionary life history theory	Demography
Definition	The study of human behaviour within and between societies as understood from an evolutionary framework based on natural selection	The study of populations and the cornerstones of population change: fertility, mortality and migration
Key aims	To generate and test hypotheses to develop understanding of human behaviour and evolutionary theory	To describe and forecast population patterns of fertility, mortality and migration, and improve public health and reduce inequalities
Populations of interest	Typically traditional, small-scale societies, with recent increased interest in high-income contexts	Anywhere data are available, but predominantly populations with censuses, large surveys or registers
Theory	Life history theory (and others) from evolutionary biology	Less reliant on theory, or informed by theory from the social sciences e.g. sociology
Data	Small-scale household surveys conducted by anthropologists, historical records, e.g. parish records, and more recently population registers and census data	Register, census or survey data, sometimes linked to health registers; rarely collected by those who analyze the data
Age groups of interest	Mainly (but not limited to) individuals of reproductive age, or behaviours that can be traced to that age group	Either the whole population, or specific sub-groups that can be defined for interventions, e.g. “the oldest old”
Outcomes of interest	Age at first birth, total fertility, risk-taking behaviours and intrinsic mortality. Multiple demographic behaviours are expected to fall on a continuum of a life history strategy	Measures of fertility, life expectancy, all-cause mortality or specific causes of death, often studied separately, migration
Focal area/contextual predictors	Extrinsic mortality (often proxied using measures of deprivation) and more recently other indicators e.g. morbidity rate	Neighbourhood poverty, deprivation indices

Contextual Effects on Fertility

Much demographic research on tempo and quantum of fertility (when people have children and how many they have) has explored individual determinants, such as women’s labour market participation and education (Hoem and others, 2006), rates of cohabitation, marriage and separation (Kohler and others, 2002) and the impact of various family policies (Neyer and Andersson, 2008). However, it is widely recognized that to predict an individual’s or couple’s childbearing behaviour, considering the context in which people live is important. In early demographic studies that sought to explain the first demographic transition, spatial analyses were integral as fertility decline was propelled by urban dwellers before rural family sizes followed suit (Coale and Watkins, 1986). The decisions of whether or not to have a child, when to start, how to space children and when to stop all impact fertility rates, and are complex decisions with multiple determinants governed by the norms, institutions and policies at country or regional level (for review see Balbo and others, 2013).

Many insights into, between and within country variations in fertility come from studies on the first demographic transition. One example of the latter is Belgium, a small country geographically, but diverse in terms of religion, culture, language and development: it demonstrated variation between two neighbouring provinces that could be comparable to a lag of fifty years of fertility decline (Lesthaeghe, 1977). From the late nineteenth century until 1960, fertility variation at the national level increased in Western Europe, and at the subnational level it decreased with increasing homogenization within national states (Watkins, 1990). Then in the 1960s and 1970s, Europe underwent a second demographic transition, where a gender revolution and a resurgence in female participation in the labour force that was incompatible with childcare led to the postponement of first births and an overall decrease in fertility (Lesthaeghe, 2010). Nevertheless, substantial variation in fertility has persisted between European countries and regions (Billari and Kohler, 2004).

In the debate on whether country differences in fertility will persist or whether convergence is likely to continue (Frejka and Calot, 2001; Wilson 2001), some have argued that it is surprising that there is not a greater body of work on within-country fertility variation (Kulu and Boyle, 2009), and the local residential context. Much of regional fertility variation has been analysed at the national or sub-national level perhaps because variables of interest, such as labour market conditions and level of economic development (Fox and others, 2018), or the impact of family policies on fertility are easily operationalized on the national or sub-national level, but make less sense at the neighbourhood level. Moreover, it is possible that the objective to avoid very low fertility at the population level contributes. That is to say, from a policy standpoint, very low levels of fertility are problematic at a national level, but less alarming in smaller areas of resolution, both because of how nation states are organized, and because people move between areas.

The recent surge in spatial analyses of fertility was long overdue and may be related to more refined measures and methods available from geo-coded data and multilevel models (Voss, 2007; Matthews and Parker, 2013). Echoing the early studies on urban/rural differentials, one strand of current within-country research has posed the question of whether fertility differs according to residence type. On balance, these studies tend to show that there is higher fertility in rural and suburban areas than in urban areas, which persists even when the SES composition of such areas is controlled (Kulu and Boyle, 2009). However, because some areas are seen as more suitable for bringing up a child, the “migrant selection” effect is a likely confounder (Courgeau, 1989). It is also vital that studies comparing different geographical areas (e.g. regions, census tracts, wards or some other spatial unit) separate the contextual from the compositional, the latter referring to the fact that fertility rates of a given region may be due to the composition of individuals who live there, rather some other property of the area. Evidence that fulfils these criteria is mixed; some studies suggest that context does not matter for fertility once differences at the individual level are accounted for (Hank, 2002), while others demonstrate evidence of clear regional differences that are robust even after adjustment for individual characteristics such as women’s employment and civil status are made (Kertzer and others, 2009). There might also be differences with the type of outcome examined. For example, in the UK the influence of the residential area level has been found to be relevant for the transition to first birth, but second- and third-birth progressions are correlated to the characteristics of the couple, and not the area where they reside (Fiori and others, 2014).

Some contextual studies examine fertility behaviours of individuals with different language identities, or who have a different ancestry or ethnic background. Recently revisiting the subject of Belgian fertility variation, Klüsener and colleagues (2013) have demonstrated that individuals living in the German-speaking regions of Belgium, which bear the influence of German family norms but enjoy Belgian family policies, have fertility profiles that are more similar to the Belgian than the German. The authors argue that institutional context is more influential than cultural norms, although how these might be fully disentangled remains a sticky point. Another perspective on local influence comes from studies of the fertility of immigrants and their descendants, where residential segregation is used as a proxy for exposure to norms of the destination country (Kulu and González-Ferrer, 2014). For instance, child migrants to England and Wales who grow up in areas with lower levels of residential segregation have fertility levels that are more similar to those of natives, as compared with peers who grow up in more segregated areas. This points to the fact that residential context during childhood is important for future fertility behaviour (Wilson and Kuha, 2018).

Further efforts to focus on the role of context include research that goes beyond the arbitrary residential areas and investigates the influence of nearby neighbours and colleagues on fertility behaviour. Recent evidence from Norway suggests that neighbours influence couples’ transition to a third birth, even after couple confounders were adjusted for (Bergsvik and others, 2016). The results held when varying the area sizes between the five hundred and the twelve nearest neighbours. In the same vein, but examining the social influence of colleagues in the workplace, data from Germany suggest that women are influenced by female colleagues; odds of progression to first birth doubled the year after a peer gives birth, after which the odds decreased and were diminished after two years (Pink and others, 2014). A recognition that meso-level factors (such as social network and family-level factors) are important, sandwiched in between micro-level (individual) and macro-level factors (institutions and norms), is growing, and provides a link between small areas and the larger contexts in which they are embedded (Balbo and others, 2013).

Teenage Childbearing and Neighbourhood Deprivation

Teenage childbearing is one aspect of fertility that has been studied extensively from a contextual perspective; these studies show that neighbourhood deprivation is associated with earlier onset of childbearing (Harding, 2003; Imamura and others, 2007). Teenage childbearing has been seen as an undesirable behaviour by policy makers, often linked to adverse birth outcomes and sexual risk-taking and, as such, subject to many policy interventions (Dickins and others, 2012; Allen and others, 2007). Nevertheless, neighbourhood deprivation studies have not generated conclusive evidence on what it is about deprivation that is associated with earlier childbearing. Some evidence suggests that the risk of teenage pregnancy is higher if a high-poverty neighbourhood is adjacent to a more prosperous neighbourhood, hinting at an effect of inequality (see also Gold and others, 2004; Wilkinson and Pickett, 2007). Contextual effects on early fatherhood have not received much attention, echoing the general trend of more emphasis on female than male fertility. Where female and male childbearing at young ages have been compared, there is some indication that different contextual effects matter for early motherhood and early fatherhood (Uggla and Mace, 2016a), however this area remains underexplored.

Covariation in Fertility and Mortality

While demographic work has established that fertility and mortality often change in concert, demographers have primarily been concerned with trying to describe shifts in fertility and mortality at the population level, rather than examining variation at the local and individual level. Even less work has attempted to ascertain the effect that population mortality rates have on individual reproductive decision-making. In contrast, the opposite relationship, i.e. the impact of an individual’s childbearing on longevity, has generated interest in both demography- and evolutionary-informed work (Chereji and others, 2013; Doblhammer and Oeppen, 2003). That is not to say that the idea that mortality rates can impact fertility is a foreign one in demography. Conversely, it is well known that a decline in child mortality on the population level, together with economic development, predated the decline in fertility of the first demographic transition (Kirk, 1996). To consider children as part of an economic quality-quantity trade-off, where child quality is favoured over quantity when mortality rates decline (Becker, 1981), is an example of how economic theory has been integrated into life history models, and is central to the evolutionary study of human fertility (Hill and Kaplan, 1999; Kaplan and others, 2002).

Work on so called “mortality shocks” and their impact on fertility are analogous to evolutionary work outlined in previous sections of this chapter. For example, Nobles and colleagues used data from regions affected by the 2004 Indian Ocean tsunami, whose impact was considered to hit communities randomly, to study the impact of mortality rates on fertility (Nobles and others, 2015). They found evidence of replacement fertility (women who lost children were more likely to have another birth) and that women without children before the tsunami commenced childbearing earlier in regions affected by the tsunami (Nobles and others, 2015).

While evidence linking local mortality risk and reproductive behaviour remains rare, other factors such as economic insecurity and uncertainty have been scrutinized in detail by demographers. Economic downturns and increases in regional unemployment rates tend to be negatively associated with fertility (Sobotka and others 2011, for review). A recent study showed that, following the recent recession in 2008 in the US and Europe, both unemployment rates and overall uncertainty (measured as drop in consumer confidence and sovereign debt risk) were negatively associated with childbearing (Comolli, 2017). While the type of data and operationalizations of uncertainty differ, this type of work has much in common with evolutionary studies that have also explored the associations between uncertainty and reproductive behaviours (Nolin and Ziker, 2016; Davis and Werre, 2008). Thus, central research questions about the role of extrinsic mortality risk and uncertainty for reproduction exist in parallel, and greater cross-disciplinary integration would be beneficial.

Contextual Effects on Mortality

Recent years have seen an explosion of studies on how neighbourhood factors are associated with health and mortality. Several converging trends are likely to be responsible for this influx, including better statistical methods that allow isolating individual and area effects, developments within geo-referencing technologies, a renewed interest in health inequalities, and the idea that individual characteristics are insufficient to explain health outcomes (Diez Roux and Mair, 2010). As with fertility, the processes leading to mortality are complex. Mortality is determined by a combination of diet, physical activity, health behaviours, genetic predisposition, social support, access to health care and physical barriers such as pollutants or toxins that may vary between areas.

A key reason mortality risk is examined at the local level is because it is thought that many health interventions can be implemented at this level. However, with indications that the magnitude of the area effects are sometimes negligible, there has been debate about the usefulness of health policy implementations at the neighbourhood level (Lupton, 2003). While some studies map contextual effects on life span or all-cause mortality, others are conducted by medical experts who have an interest in a particular outcome, e.g. ischemic heart disease or suicides. When a particular disease or cause of death is of interest, it may be easier to hypothesize about the potential impact of the local context. However, rarely are different causes of mortality that could be considered under individual control categorized together. In part, this might be due to the fact that the types of death that are preventable varies over time and space (Page and others, 2006).

Most contextual studies examine the association between some form of aggregated SES measure or deprivation and mortality. These studies generally find that higher deprivation is linked to higher all-cause mortality risk after controlling for individual SES and other factors. An early example from the US reported that, after controlling for age, sex, race and health status, individuals in deprived areas had a 50% higher risk of death (Haan and others, 1987). This association is remarkably consistent across countries (in European and US datasets) (van Lenthe and others, 2005). A different set of studies examine variation or inequalities without having a priori predictions of why there is variation between areas. Area variances — and not just the means — are important in order to accurately understand differences between areas (Merlo and others, 2009) and provide an indication of the magnitude and change over time of health inequalities.

Heterogeneity of effects is central to understanding the plethora of results of contextual effects of mortality. There is evidence that the association between area SES and mortality is stronger for men, and for older individuals (Meijer and others, 2012). Winkleby and colleagues (2006) report that in the US, the benefits associated with residence in a more affluent area do not extend to men and women with a lower SES. Furthermore, the effect of the local area on mortality may vary depending on the individual’s life stage. For instance, multiple waves of census data from Norway suggest that, for young individuals, only the most recent area of residence was linked to mortality from violence and mental health issues, whereas for older individuals, areas from previous stages of life had additional effects (Naess and others, 2008). However, because different outcomes might have different relationships with different neighbourhood characteristics, caution should be exercised when generalizing from one dependent variable (Roos and others, 2010).

Deprivation and Extrinsic Risks

Many insights gained from demographic and epidemiological studies on contextual (or neighbourhood) effects on mortality can be linked to evolutionary life history theory. Arguments related to deprivation and death are congruent with extrinsic mortality risk, because deprivation is in many cases associated with higher extrinsic mortality (Pampel and others, 2010; Uggla and Mace, 2015). Thus, the vast amount of evidence on associations between area SES and behaviours constitute a rich source that underpins arguments of extrinsic mortality risk for health and risk-behaviours. However, conclusions based directly on extrinsic mortality risk and its impact on particular health behaviours are almost non-existent. This may be because it appears circular that population mortality rate — even if it is extrinsic — at the area level would predict risk of death of individuals. One exception comes from work based on Demographic and Health Survey (DHS) data from fourteen African countries (Oster, 2012). Oster found that reductions in HIV risk-taking were higher where life expectancy (excluding HIV deaths) was higher, that is, where individuals’ sexual risk-taking had greater impact on their life span. This is one plausible explanation for why behavioural response to HIV has been much slower in this context than the HIV response in some high-income contexts (Oster, 2012).

Proximate Determinants and Constraints

Demography and other data-driven disciplines are well positioned to measure proximate mortality determinants, such as healthcare access or availability of close kin, on mortality. The emphasis on proximate determinants of health and mortality outcomes is a good complement to the life history approach that has been less concerned with proximate causes. As a useful starting heuristic, evolutionary-minded scholars assume that individuals are able to make cost-benefit analyses about mortality risk because they are able to gain accurate information on the costs and benefits of their decisions (not necessarily consciously), and can respond to contextual factors largely without constraints (Borgerhoff Mulder and Schacht, 2012). This might not always be true and provides reason to think about how constraints to invest in health vary between different sub-groups. In the UK, individuals in more deprived areas report feeling less safe using green spaces for physical activity (Jones and others, 2009) and are exposed to more fast-food advertising than individuals in more affluent neighbourhoods (Adams and others, 2011). Such structural differences, coupled with physiological pathways, e.g. that women in deprived areas are more likely to have a blunted cortisol response, might make it difficult to favour day-to-day health choices that are beneficial in the long term over those that offer short-term gratification (Barrington and others, 2014).

Summary

Within demography, much emphasis is placed on describing variation in fertility patterns, though spatial differences are often examined on national or sub-national level, and few examples exist of how mortality risk influences fertility. Indirect measures such as urban versus rural residence, area deprivation and proxies for uncertainty are nevertheless insightful and often overlap with evolutionary perspectives on fertility variation. With regard to mortality, in recent years the number of studies on contextual effects on mortality has grown rapidly. Individuals in areas with high deprivation generally have higher excess mortality, even when individual characteristics have been controlled (or “accounted for”). However, there are notable differences in how determinants are operationalized, and in heterogeneity based on, for example, sex and life-course stage. An increased understanding of the feedback loops between socioeconomic factors, health and reproduction, along with broad interest in proximate pathways, is promising for the aim to decrease inequalities in mortality.

Summary of contextual effects on fertility and mortality within demography:

Motivated by understanding determinants of fertility and improving health and wellbeing of populations.

Takes a “bottom-up” approach and is not always strongly theoretically motivated and/or draws on theories from other social science disciplines.

Individuals in deprived areas have higher rates of teenage childbearing and higher mortality risk, but these topics are seldom studied in unison.

Contextual effects on mortality are part of a burgeoning literature on health inequalities which has documented differences according to type of mortality and individual characteristics.

Demographic studies on fertility and mortality are often characterized by an emphasis on methodological quality, including selection biases, and methodologies that attempt to isolate influences.

Challenges and Future Routes of Research

In this section, I discuss some challenges to research on contextual effects, including selection biases, how to define areas, and understanding the underlying mechanisms. I offer some suggestions for future research and stress the complementary insights of evolutionary theory and demography for these questions.

Selection Biases

Both studies rooted in demography and evolutionary life history research overwhelmingly rely on observational data. While experimental study designs have been used to test life history predictions in both humans and other species, when it comes to factors influencing actual behaviour rather than preferences for childbearing, the experimental method is neither feasible, nor ethical for human subjects. Physiological experiments, including priming methods, are an exception (for review, see McAllister and others, 2016). Inherent to observational data are issues of selection biases, which are problematic when trying to make inferences about the impact of an area on the behaviour of individuals who live there. Even with longitudinal register data, factors that might be associated with a propensity to move to a certain area often cannot be adjusted for.

One way to address issues of self-selection is through randomized controlled trials. These are commonly used in medicine and for public health interventions, but are more complicated when applied to questions related to contextual effects. A rare example of experimental data on this topic is the Moving to Opportunity (MTO) project, which was implemented in five large US cities in the 1990s to test the effect of areas on individuals (Leventhal and Dupéré, 2011). In this project, randomly selected participants in high-poverty neighbourhoods were offered vouchers and support to move to less impoverished areas. There have been many studies on the MTO project, one of which reported that young girls who moved to a less deprived area had fewer mental health problems and more benefits in terms of education than non-movers (Leventhal and Dupéré, 2011). However, even if it is considered ethical, moving people to a new area is difficult, expensive and impractical and does not necessarily eliminate doubts about causation (Oakes, 2004). Thus, we are often left attempting to infer causality from observational data. Some have argued that the statistical issues such as endogeneity — the difficulty of defining appropriate geographical borders and extrapolation in multilevel analyses — mean that contextual effects are better investigated through qualitative approaches (Cummins and others, 2007). While they cannot alleviate the problem of selection biases, mixed-methods studies that draw on both qualitative and quantitative data could help to understand the experiences that produce behavioural variation between areas.

How Should Spatial Units Be Defined?

A common assumption of contextual studies is that the area where an individual lives functions as a cue to what life history strategy he or she should adopt, and is where childbearing intentions are formed. Neighbourhoods are often relatively small areas in which individuals may be familiar with the local conditions. But when areas are larger, e.g. census tracts or regions, the area might poorly capture what an individual encounters in his or her daily life. A study on area effects on all-cause mortality in Finland found an effect of residential area SES when the area was 250 x 250 meters, but this effect was attenuated, or completely absent when larger areas were used (Halonen and others, 2013). Another complication is that, in high-income countries, many individuals move between the home and the workplace and encounter multiple areas on a daily basis. Multilevel models with multiple memberships that allow simultaneous incorporation of family and work environments alongside wider contextual effects (Fielding and Goldstein, 2006), may go at least some way towards addressing this bias statistically. Where possible, areas based on a given number of nearest neighbours, applied, for example, in geography (Malmberg and Andersson, 2019), will help to fine-tune research designs so that they are congruent with the research question at hand.

Heterogeneity of Effects

From a population health perspective, heterogeneity in contextual effects is important to identify vulnerable groups, and to understand better the pathways to good health. From an evolutionary perspective, heterogeneity in contextual effects may help test detailed predictions for how costs and benefits of the local environment vary with age, sex or sociocultural context. For example, we may predict that the relationship between extrinsic mortality risk and health behaviour should be stronger among young individuals if local mortality rate has stronger fitness implications for younger than older individuals. However, the evolutionary perspective can generate predictions in different directions depending on the particular assumptions and the outcome in question. For this purpose, broad population data are required to compare different groups within populations, especially when the outcome of interest is premature mortality or other rare events. Anthropologists have often lacked data to test such effects, because, in small-scale societies, entire populations might yield sample sizes that are too small to test interactions. Thus, existing evidence from demography on contextual effect heterogeneity is useful, as it can both help to test the assumptions and to think clearly about the theoretical reasons for why effects may vary.

What Are the Underlying Mechanisms of Contextual Effects?

A major challenge for future research is to understand the pathways by which context is associated with variation in fertility and mortality. One development is the application of biomarkers that can capture how physiological characteristics differ between deprived and affluent neighbourhoods. Allostatic load, a measure of the “wear and tear” of the body has been linked to cumulative neighbourhood disadvantage (i.e. the longer the exposure to a deprived neighbourhood, the worse the condition) (Gustafsson and others, 2014). But even with such new knowledge of biomarkers, we may not be able to assert how various indicators are related to behaviours within the same areas.

Other types of data, e.g. from surveys, can help us to understand people’s perceptions of local conditions, which might be as important as the observable local characteristics. James Gilbert, Ruth Mace and I tested individual perceptions in eight neighbourhoods of Belfast, Northern Ireland, and compared these to the census data of these neighbourhoods (Gilbert et al., 2016). The data suggested that while individuals had an accurate perception of neighbourhood age at death and morbidity, the discrepancies between actual and perceived levels of crime were high. Most individuals across the eight neighbourhoods reported high perceived personal safety, even though these neighbourhoods had been chosen to include both high and low ends of the crime rate distribution. One interpretation is that crime may affect only some individuals in an area and most individuals have reason to feel safe. There are clear parallels to the difficulty of perceiving local conditions accurately, which has been discussed with reference to mortality decline in low-income countries. It has been argued that perceiving mortality decline is difficult due to our tendency to acknowledge events, such as child deaths, more than non-events (child survival) (Montgomery, 2000). Future research will have to deal with the semantics, i.e. that individuals might respond in a manner that fits with the narrative of whether their area is “good” or “bad”, and whether perceptions extracted verbally are meaningful.

Incorporating Life Course Factors into Contextual Effects

Both demographers and evolutionary scholars recognize the importance of the life course in shaping individual health (Ben-Shlomo and Kuh, 2002; Stulp and Sear, 2019), and it will likely continue to be integrated into new work on contextual effects. One question of interest is whether some periods during early development and childhood have more bearing on adult reproductive decision-making and health outcomes than do later periods in the life course. Whether developmental trajectories are mostly determined by the cumulative exposure to poverty, or whether certain periods, e.g. early childhood or adolescence, constitute a “critical window” is debated in both developmental biology and psychology, and within the health sciences (e.g. Braveman and Barclay, 2009; Murray and others, 2011). Another life-course question is to what extent individual reproductive, socioeconomic and health outcomes will correlate. Despite the strong theoretical motivation of life history scholars to predict multiple outcomes over the life course, they lag behind demographers who have long dealt with the issue of anticipatory analyses, i.e. that individuals may schedule life events (such as having a child) with the anticipation of other future events (such as completing higher education) (see e.g. Hoem and Kreyenfeld, 2006). This is a clear example of where theory and methodological insights from respective discipline could be successfully integrated.

Conclusion

Demographers and evolutionary life history scholars alike are interested in understanding why fertility and mortality vary between contexts. In so doing, scholars from these two fields have different aims, motivations and disciplinary origins that explain why their respective approaches to contextual effects differ — and where they overlap. While demography takes a bottom-up approach, driven by data, evolutionary sciences are top-down where data is a necessary means to answer questions that develop theory (Sear, 2015). Evolutionary anthropology has been characterized by the application of survey data from small-scale populations to understand fertility variation, rather than samples with a large number of data points that lend themselves to complex statistical techniques. The application of demographic data and methodology is now seen, for example, within anthropology, where studies have used a multi-level framework with Demographic and Health Survey data, or teamed up with local NGOs to collect rich data in different spatial areas (Lawson and others, 2015; Howard and Gibson, 2019; Uggla and Mace, 2016b). This development is relevant to area effects because it denotes a shift from the anthropological tradition of comparing populations in different contexts and with distinct cultural attributes, to comparing individuals residing in different settings while holding constant various characteristics that have been collected in a uniform manner.

Despite the differences between the disciplines, there are many shared elements and areas where integration is occurring or where continued interdisciplinary exchange seems promising. Demographers and evolutionary scholars interested in topics related to reproduction and mortality share the quest to understand the pathways that can lead to improved population health. In so doing, a better grasp of the role of life course factors, proximate physiological mechanisms and the role of structural constraints at the local level are key challenges. Identifying the determinants that link health, wellbeing and reproduction is a tall order and an endeavour that necessitates integration of different approaches and theories. The wealth of data from different populations is among the contributions of evolutionary theory to the understanding of human fertility, and it provides an extra layer of explanation that can unify existing frameworks (Sear, 2015). With the continued exchange of novel methodologies and the increased sharing of data from different contexts, further integration between these fields has great promise to enable us to better understand how the local context influences fertility and mortality.

Acknowledgements

I thank Rebecca Sear, Laura Brown, Lee Copping and Ben Wilson for valuable feedback on earlier versions of the chapter. Financial support was received from the Swedish Foundation for Humanities and Social Sciences (Riksbankens Jubileumsfond, RJ), grant registration number M18-0214:1.

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1 Note this chapter has been posted on the Open Science Framework website since 08/04/2020, after it was accepted for publication, so the references will reflect when the chapter was written and not the OBP publication date.

Life History Variation in High-Income Contexts and Cross-Culturally

Summary

Overview

Contextual Effects on Fertility

Deprivation and Extrinsic Risks

Summary

Selection Biases

How Should Spatial Units Be Defined?

Heterogeneity of Effects

What Are the Underlying Mechanisms of Contextual Effects?

Incorporating Life Course Factors into Contextual Effects