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Explanations oﬀered for fertility decline in both developed and developing countries underwent fundamental changes in the last decades of the twentieth century. In addition to more reﬁned economic reasoning and the introduction of cultural aspects, theories of the fertility transition now routinely reserve a place for diﬀusion eﬀects. Such eﬀects arise because individuals are themselves members of larger groups. The information that is known to group members, the choices they make, and the norms and values they adhere to, can all exert a powerful inﬂuence on individual incentives to innovate. In settings in which fertility has been high, for example, innovation might take the form of adopting modern contraception and fertility limitation. Social interaction with group members represents one important avenue along which such innovative demographic behavior can diﬀuse: peers, community members or relatives may provide relevant information to potential adopters of family planning, and they also represent the institutional and social structures that may facilitate or constrain individual fertility or family planning decisions.
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Theories of diﬀusion usually encompass two central elements. First, they emphasize a particular set of social interactions that are associated with the adoption of innovations, such as social networks, opinion leaders, public media, etc. Second, diﬀusion models have an explicit dynamic dimension in which the adoption of an innovation is often associated with ‘contagion’ or ‘tipping point’ phenomena, which can accelerate social change. Because fertility transitions are often rapid, irreversible, and only weakly associated with the speciﬁc socioeconomic conditions in a population, such diﬀusionist explanations have appealed to demographers ever since the European Fertility Project was carried out (Coale and Watkins 1986)
At the same time, diﬀusionist approaches have been criticized for several key reasons, such as the unclear deﬁnition of what diﬀuses, the unclear connection with economic models of behavior, the mostly indirect empirical evidence, and the insuﬃcient knowledge about the preconditions for the onset of the diﬀusion process. Several of these potential problems can be overcome by investigating the diﬀusion process with the tools of social network analysis.
1. Why Social Networks Matter For Understanding Fertility
The theoretical perspective of social networks rests on one central insight: human action is not carried out by individuals who make their decisions in isolation and independence, but by socialized actors who are deeply rooted in their social environments. These environments consist of other individuals with whom a focal actor stays in various relationships such as kinship, love, friendship, competition, or business transactions. Within these relationships, norms are negotiated and enforced, and information and goods are exchanged (Mitchell 1973). On the one hand this implies that a network member has access to resources owned by other network partners. In addition, a network enables its members to use the resources of community members to whom they are tied only indirectly. Social networks therefore constitute a pool of resources which form opportunity structures for action (Lin 1999). The kind and quality of these resources are decisive for the goals that are attainable for the network members. On the other hand, social relationships create, maintain, and modify attitudes (Erickson 1988). People feel uncomfortable if the attitudes of their network partners are too diﬀerent from their own. Social networks therefore bring the attitudes of their members in accordance with each other. Alternatively, if the network fails to bring attitudes into conformity, the relationships become less intensive or are abandoned.
The resources that are available in a network, and the tendencies towards similar attitudes, depend on the characteristics of individuals’ direct relationships with one another as well as on the structure of the entire network. One basic characteristic of network relations is their strength (Granovetter 1973). Strong relationships, i.e., relationships that are characterized by a high frequency of contact or by a high level of emotional intensity, can be inﬂuential because they tend to transfer more valuable goods, more binding norms and more trustworthy—but also more redundant—information. On the other hand, a fundamental dimension of the network structure is its density. A dense network, i.e., a network in which a high proportion of the individuals are in direct contact with one another, can lead to a high degree of normative pressure on its members and it can provide intensive support for members in situations when help is needed. Less dense networks are characterized by a greater degree of heterogeneity. Such networks provide more opportunities for individual development, and they are the source of more diverse and heterogeneous information.
The mechanisms by which social networks aﬀect the diﬀusion process can be summarized under the headings ‘social learning,’ ‘joint evaluation,’ and ‘social inﬂuence’ (Bongaarts and Watkins 1996, Montgomery and Casterline 1996). At early stages, potential adopters need to be informed about the existence of new methods of fertility control, their availability, costs and beneﬁts, and possible side eﬀects. ‘Social learning’ is deﬁned as the process by which individuals (a) learn about the existence and technical details of new contraceptive techniques, and (b) reduce the uncertainties associated with the adoption of these methods by drawing on the experience of network partners.
The spread of information about modern contraceptives is always accompanied by the spread of new ideas about the social role of women, the status of the family, and the desired number of children. ‘Joint evaluation’ refers to the fact that social networks (a) facilitate the transformation of these new ideas into terms that are meaningful in the local context, and (b) assist in the evaluation of the respective social changes. A woman’s decision to adopt a modern form of contraception, however, rests not only on information, conviction, and discursive evaluation, but also on the normative pressures of dominant groups or actors. ‘Social inﬂuence’ describes the process by which an individual’s preferences and attitudes are inﬂuenced by the prevailing norms and values of the social environment. The direction of this social inﬂuence can diﬀer in diﬀerent stages of fertility decline. In the early stages, social inﬂuence may constrain the adoption of contraception, which demographers may interpret as being in a woman’s best interest, because deliberate control of fertility is still considered deviant. Once a noticeable fertility decline has set in, however, the direction of social inﬂuence can shift. As low fertility becomes commonplace, the interaction among women or couples is likely to encourage a decline in the demand for children and behaviors that lead to a limiting of fertility.
2. Empirical Evidence
The earliest systematic evidence for the inﬂuence of diﬀusion processes and social networks on fertility decisions is found in two seminal analyses of demographic change. First, the European Fertility Project (Coale and Watkins 1986) showed that areas which are culturally similar, in language or ethnicity for example, often exhibit similar fertility patterns despite substantially diﬀerent socioeconomic conditions and development levels. Thus, new ideas about fertility as well as new contraceptive methods seem to have diﬀused within cultural and linguistic boundaries. More sophisticated approaches employing similar methodological ideas have used the correlation pattern in pooled time series to show support for the role of social interactions in the adoption of family planning over time (Montgomery and Casterline 1993). Second, the Taichung family planning experiment in Taiwan (Freedman and Takeshita 1969) used a randomized experiment and longitudinal follow-up to show how direct interventions of a family planning program targeted to some individuals spread to others who were not directly contacted by the program. The study thus suggests that family planning interventions extend beyond the direct eﬀect on the contacted individuals and that interaction in social networks constitutes an important aspect in the diﬀusion of new contraceptive methods and ideas.
The above evidence, although intriguing, is far from conclusive. In all of these data, the evidence is indirect and based on correlated fertility developments in geographically and/or culturally close populations. Support for the relevance of social networks in fertility transitions requires more individual-level data, gathered either in structured surveys or ethnographic studies. The work of Watkins and collaborators in South Nyanza, Kenya, for example, represents an important step in this direction. Qualitative group interviews show that casual conversations about family planning occur frequently as women go about their daily activities. The network partners in these conversations are usually geographically and socially close to one another and are of a similar age or education. Frequently they also know each other and share multiple relations, e.g., stemming from money lending, help in the household, or family ties.
These qualitative impressions are further supported by survey data that include individuals and their social networks. Until recently, only one eﬀort had been made to assemble such data, and this was by E. Rogers and colleagues in their pioneering work in rural Korea (Rogers and Kincaid 1981). Currently there are several related research projects being conducted in Kenya and Malawi (Watkins and colleagues), Thailand (Entwisle and colleagues), and Ghana (Casterline and colleagues). In all of these projects socioeconomic panel information on women and couples is being collected along with data on social networks that may have inﬂuenced contraceptive decisions. Research using these data has consistently shown that the prevalence of contraceptive use in social networks is positively associated with the probability of women using family planning, and that the use of family planning by network partners tends to increase both the knowledge about modern methods and the willingness to use them (Entwisle and Godley 1998, Kohler et al. 2001, Montgomery and Chung 1998). Although this association is robust across diﬀerent socioeconomic settings and studies, a causal interpretation is not necessarily warranted. In particular, it has been challenging for researchers to distinguish between true network eﬀects on fertility decisions on one side, and other unobserved factors which render the behavior of network members similar on the other. Nevertheless, additional work that fully exploits the panel nature of the above studies and improves the temporal measures of interactions and the adoption of family planning has the potential to overcome this limitation.
A further criticism of existing empirical work is that the link between the theoretical arguments of why social interaction matters and the respective empirical implementation is weak in many analyses. One possible way to overcome this limitation is to use additional information about the structure of the social network, e.g., the density. Dense networks impose more behavioral constraints on the adoption or nonadoption of family planning than do sparse networks, which are more open to innovative information and behavior. Thus, density can be used as an indicator for the mechanisms of ‘social inﬂuence’ and ‘social learning.’ If the prevalence of contraceptive use in dense networks is more relevant for the decision to adopt contraception than it is in sparse networks, then social inﬂuence is the most relevant aspect of social networks. If it is the other way around, however, then it is social learning that is the dominant component. In the South Nyanza district of Kenya, for instance, evidence for both pathways of inﬂuence can be found. Moreover, the relative importance of these two mechanisms depends on the socioeconomic context of the regions. Social learning seems to be relatively more important in areas with higher levels of market activity (Kohler et al. 2001).
Although the empirical evidence supporting the argument that social networks play an important role in fertility decisions is accumulating from various sources, it remains inconclusive in important aspects, e.g., as regards the empirical identiﬁcation of causal network eﬀects. Nevertheless, the extensive eﬀorts currently in progress to collect data, combined with both an improved theoretical understanding of social interaction and a more sophisticated empirical methodology, are likely to reveal further insights.
3. Implications Of Social Networks For Fertility Change
Current research on social networks and fertility is in part stimulated by the fact that the dynamic implications of fertility models that include social interactions seem to be more consistent with the empirical evidence on fertility transitions than individualistic models. However, speciﬁc theoretical analyses of social interactions and fertility decisions are required as further support for this conclusion. Early formal models treat the diﬀusion of low fertility in a similar manner to the way the spread of contagious diseases is treated in epidemiological models (Rosero-Bixby and Casterline 1993). More recent developments have used stochastic processes or simulation, and they explicitly consider the interplay between social networks, women’s fertility decisions, and economic incentives on a microeconomic level (Kohler 2000a, 2000b).
The main consequence of interactions in social networks is that fertility decisions of women or couples in a population become interdependent and exert mutual inﬂuences on each other. The implications of this interdependence can be illustrated in a simple nonlinear model of social interaction. In particular, assume that the probability that a woman ever adopts modern contraception is given by
where α and β are positive parameters, γ represents individual characteristics, x is the level of family planning eﬀort, F is the cumulative logistic function, and y is the proportion of women in the population who use modern contraception in the reference period. Hence, the social utility term α(y – 0.5) captures the dependence of a woman’s contraceptive choice on the fertility behavior in her network, where we assume for the sake of simplicity that this network includes all members of the population. The model implies that social eﬀects are absent in a population in which half of the members use modern contraception and the other half do not. If the proportion of contraceptive users exceeds (is below) 0 5, then the social network increases (decreases) the probability of using contraception. This simple model already reveals that the aggregate properties of fertility dynamics in the presence of social interaction are distinct from the fertility dynamics in a purely individualistic model. In particular, the relevance of social interaction arises from three factors: social multiplier eﬀects, multiple equilibria, and dense vs. sparse networks.
3.1 Social Multiplier Eﬀects
Social interaction implies the presence of multiplier eﬀects which change how the fertility level adjusts to changes in family planning programs. An increase in family planning eﬀort x, for instance, increases the probability of choosing modern contraception in two distinct ways. The direct eﬀect reﬂects the increase in the propensity to choose modern contraception while holding the population prevalence y constant. However, because the increased level of x aﬀects all community members, the prevalence of modern contraception in the population tends to increase as well. This change in the community has a feedback or social multiplier eﬀect on individual behavior in future periods via the social utility term in Eqn. (1). This means that the total change in the prevalence of family planning exceeds the direct eﬀect. The left-hand panel of Fig. 1 shows how this total eﬀect is composed of a direct eﬀect and an indirect eﬀect through social interaction. The interaction in social networks thus enhances the changes in contraceptive behavior that follow changes in family planning eﬀorts or other socioeconomic changes.
3.2 Multiple Equilibria
Many formal fertility models assume that there is only one aggregate fertility level, denoted as ‘equilibrium,’ associated with any given level of socioeconomic conditions. When the interdependence of fertility decisions in social networks is suﬃciently strong, however, multiple equilibria can emerge (right-hand panel of Fig. 1). This possibility of there being multiple equilibria is relevant because in this case a population can be ‘stuck’ at a low level of contraceptive use, even though a second equilibrium exists with a higher level of contraceptive use and higher individual utility levels. In this case, increases in the policy level x (or other socioeconomic changes) can induce a transition from the low contraceptive use to the high contraceptive use equilibrium. These transitions between equilibria are often thought to occur at a rapid pace, resulting in signiﬁcant changes in fertility behavior within a relatively short space of time. Moreover, both the high and low contraceptive use equilibria are stable, so that a transitory increase in the family planning eﬀort can yield sustainable long-term changes in fertility levels.
3.3 Populations With Dense Versus Sparse Networks
An important question in diﬀusion models is whether ‘more’ interaction in networks always leads to larger multiplier eﬀects and more rapid diﬀusion. It can be shown that already in simple models such as Eqn. (1) social networks can be status-quo enforcing: the long- term eﬀect associated with changes in the program eﬀort can decrease with an increase in parameter α, which reﬂects the importance of the network inﬂuences in individual fertility decisions. The changes in contraceptive use resulting from changes in the program eﬀort can therefore be smaller for very densely knit societies than for more sparse societies.
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