SOCIAL INTERACTIONS AND SOCIAL NETWORKS

Hans-Peter Kohler presented the results of research on social networks and fertility decisions using data from the Kenya Diffusion and Ideational Change Project and the Malawi Diffusion and Ideational Change Project (http://www.pop.upenn.edu/networks). This work uses the concepts of social learning and social influence to explain the mechanisms through which social networks influence a woman’s use of family planning methods, and contraceptive decision-making more generally. Network density becomes an important factor in determining which mechanism operates.

While the “diffusion of innovation” has become an important aspect of explaining fertility change during the demographic transition, diffusionist arguments often do not specify the micro understandings of why social interaction processes matter and how diffusion and social influences occurs through social networks. To fill this gap, Kohler and colleagues started a project in Kenya collecting longitudinal social network data about who and how many people respondents talk to about family planning. They found that there is much talk about both HIV and family planning, and that often these conversation networks are large. Most network ties are “close” ones, such as relatives from the same compound, but respondents also had many “weak” ties.

Their model incorporates two mechanisms that explain why interactions matter: social learning and social influence. When a person faces a decision under uncertainty (such as whether or not they should use family planning methods- and if so, which), engagement in a social network allows them to talk to someone who has already made a decision. This interaction is essentially a learning process and thus constitutes social learning. This concept implies that the interaction has no effect on a person’s preference, but rather the individual engaged in the interaction to obtain additional information and reduce uncertainty. In contrast, when social interactions primarily operate by reinforcing norms or influencing a person’s preferences, social influence occurs.

Kohler his colleague’s data provides a way of studying this distinction by measuring network density (with whom ego had conversations and also the relationships of ego’s network partners among themselves) and by studying how networks with different densities affect fertility decisions. They argue that information about the density of networks provides a way of distinguishing between social learning and social influence. In particular, if a person wants to maximize their learning component in the face of uncertainty, they presumably want to select a network where individuals don’t know each other very well and therefore select a relatively sparse network. If social influence dominates in social interaction processes, however, it can be expected that dense networks – that is, networks in which members tend to know each other – are more important than sparse networks. Dense networks are likely to have stronger effects in terms of norms-reinforcement, and they are likely to exert a stronger influence of preferences and attitudes of individuals toward family planning.

Their empirical model is a regression analysis model where family planning use is the dependent variable. The explanatory variables of the model include a measure of density as well as an interaction between percentage using family planning and network density. If the interaction is relevant, then networks with different densities have differential effects on the family planning use of the respondent (see Kohler, Behrman, and Watkins 2001 detailed statement on the model).

The results suggest that there is a striking dual existence of both the dominance of social learning and social influence in the two Kenyan regions: in some areas, social influence is found to be the dominating mechanisms through which social interactions affect fertility decisions, and in another region, social learning is the dominant mechanism. Kohler argues that the main difference between the two regions is related to market integration. The region that is more integrated in market activities has a reduced social influence effect, but the social learning component remains.

Support for this argument is also provided by Susan Watkins’s qualitative research that has been conducted as part of the Kenya Diffusion and Ideational Change Project. In particular, her analyses describe how innovations enter the population through various pathways by demonstrating how perception of children’s values has changed in Kenya in response to socioeconomic and political changes, and family planning programs (see Watkins 2000).

Kohler also described how formal models of social interactions can explain a micro-foundation for “diffusionist explanations” of fertility change, and these models can explain how social interactions can result in (i) path-dependence and persistent heterogeneity in the adoption of innovations (such as low fertility or family planning), (ii) social multiplier effects that reinforce the effect of socioeconomic changes on fertility behaviors, and (iii) multiple equilibria that represent high/low fertility regimes and allow for rapid “fertility transitions” as populations move from the high to the low fertility equilibrium.

Questions following this presentation centered on:

· The distinction between social learning and social influence

Both concepts can be thought of as a continuum. In most contexts it is likely to find both mechanisms operating. It is useful, however, to think about social learning as an exercise to reduce uncertainty. Where social influence is weak, social learning remains.

GROUP IDENTIFICATION AND ECONOMIC “UTILITY FUNCTIONS”

Rachel Kranton presented an alternative model to the classic economic models of coordination game versus punishment stream to explain individual rational choice. Her alternative model incorporates notions of identity and has additional explanations for preferences.

The central tenet of classical economics is the individual rational decision-making actor. This precept raises the question of why people facing the same set of economic incentives make different fertility decisions, and what factors besides economic incentives are affecting these decisions. While the classical economic model embraces the concept of preferences (individual actors make choices to maximize utility given these preferences), these are narrowly defined. Behavioral economists incorporate concepts of self-control and cognitive biases into economic models, but preferences are still treated largely as individualistic- they display no particular pattern within society.

Culture and social norms are treated as equilibria. Economists explain group patterns as “equilibrium” outcomes. Two models explain these outcomes. The first is the coordination game. The choice to drive on the left or the right side of the street, for example, requires multiple equilibria: either everybody drives on the right or on the left. Different societies find different equilibria. Culture and social norms are simply aspects of equilibrium of the coordination game.

The second model is one of repeated interactions and sanctions. As rational actors interact with other people over time, they make choices, some of which can be punished by others in the future. Knowing this prevents actors from making particular choices in the present. The incentive for the punisher to punish the deviator is that failure to do will result in him or her being punished by someone else. Individuals follow norms, then, to avoid being punished. This model relies on repeated streams of punishment, which sustain the equilibrium. While both highly popular, neither one of these two models accounts for change in norms, or differences across groups.

Kranton and colleague George Akerlof formulated an alternative model that argues that preferences themselves can be a way of modeling culture and “social norms”. Preferences may be systematically different in different groups, as people have notions of their identity, which affects preferences. By incorporating identity into economic analysis, the tradeoffs and interplay between norms and economic incentives can be observed (see Akerlof and Kranton 2005).

Their model uses an extended utility function that incorporates identity (defined as a person’s sense of self) as a motivation for behavior. In the function, identity is based on social categories and how people in these categories should behave. Identity affects economic behavior through four channels. First, it changes the payoffs from one’s own actions. Second, it changes the payoffs of others’ actions. Third, the choice of different identities affects an individual’s economic behaviors. Finally, the social categories and behavioral prescriptions and behavioral prescriptions can be changed, affecting identity-based preferences (for a detailed explanation of this utility function and its application, see Akerlof and Kranton 2000).

Modeling identity departs from social difference. Each society has its own set of social categories, and appropriate and inappropriate behaviors (norms) associated with each. Following these norms gives people a sense of being in that particular social category. If an individual is offended by another one’s actions that violate the norms for behavior, he or she will sanction that. The choice of identity may be the most important economic decision an individual makes, as this will dictate their preferences.

Following this presentation, participants asked about:

· Defining “identity” as anything that structures preferences and patterns

Identity is not the only factor structuring these preferences. Without identity, it’s still possible to detect patterning, but it won’t be socially-defined. Identity, then, is what structures social patterning.

Date: 2015-01-12; view: 855