Suggestions for future research
As our discussion suggests, we believe that social capital studies have very often been unpersuasive. We make the following suggestions as to how one can improve this literature.
First, empirical analyses need to step back from grandiose approaches to social capital and focus on the more mundane but potentially far more fruitful task of analyzing specific social components to individual behavior. This does not require abandonment of social capital as a general organizing idea or metaphor, but rather means that evidence in favor of social capital should be derived from specific claims about social influences on individuals.
A useful contrast may be made between the Helliwell and Putnam (2000) paper, the study of regional differences in growth rates in Italy that we have criticized earlier, and a recent study by Glaeser et al. (2000) that explores the determinants of trust. Rather than run regressions that make incredible assumptions about the exchangeability of regional growth rates, Glaeser et al. employ well crafted experiments to see how attitudes and background characteristics influence the choice of strategies in various economic experiments. In the context of these experiments, notions such as trust are quite well defined since it amounts to expectations about the play of other agents in the game. This well-defined environment provides much more compelling evidence of how trust influences behavior than can be obtained from ad hoc regressions. The use of experiments to understand social capital is further developed in Carter and Castillo (2003, 2004), who consider how variation in roles by players in economic experiments can allow for differentiation between altruism and trust as determinants of behaviors.
The importance of experimental evidence should not be exaggerated. Economic experiments are not a panacea for the limits of inference with observational data.
One problem is generalizability; it is far from clear how behavior in economic experiments maps into behavior in the larger economy and society, although Glaeser et al. make an important advance in this regard by attempting to correlate behavior in experiments with behavior in the “real world” by participants. Further, as discussed by Manski (2002) in an important recent paper, there are identification problems in experiments as it is often difficult to distinguish behavior that is driven by altruistic preferences from behavior driven by selfish preferences but with expectations of trustworthy behavior by others. Nevertheless, Glaeser et al. and Carter and Castillo represent a style of research that is an important advance in the social capital literature.In addition, moving the discussion of social capital away from generalities to specific mechanisms in the way we suggest will allow one to deal with issues of endogeneity and exchangeability more effectively, since it will facilitate more precise and comprehensive modeling of causal mechanisms than one finds in the social capital literature. While the great majority of social capital studies include numerous control variables, the choice of these variables is rarely determined by careful delineation of the determinants of behavior of the agents under study. In addition, there has been little attention to questions of parameter heterogeneity.
A concrete implication of this discussion is that future research on social capital by the World Bank, for example, should be careful about the use of highly aggregated data. It is difficult to make compelling exchangeability arguments for data sets in which the observations are countries or regions. Ad hoc assumptions concerning the legitimacy of instrumental variables have plagued this literature for good reason: theories of social capital formation are underdeveloped so that it is difficult for researchers to sensibly construct aggregate measures of social capital.
Second, we believe that future data collection exercises must explicitly attempt to gather information on group-level influences, rather than on social capital alone.
This should include measures of the quality of leadership. At the core of virtually all microeconomic reasoning is the general idea that decisions are purposeful outcomes based on an individual’s preferences over outcomes, constraints on what actions are feasible, and beliefs over the consequences of those actions. The new social economics [cf. Durlauf and Young (2001)], is based upon the recognition that these three components to decisions are deeply influenced by social factors. A data collection exercise designed to explain a given set of outcomes should therefore be based on the development of a typology of what sorts of social factors affect each of the components and the development of plausible empirical analogs to these social factors.[426]The sorts of detailed data collection we advocate are in fact underway in some cases. In particular, the Project on Human Development in Chicago Neighborhoods and data collection based on the World Bank Social Capital Assessment Tool are exemplary. In each case, the levels of specificity in terms of uncovering how individuals interact in villages, communities and social networks is a great advance over the crude measures often used in social capital studies. The most obvious suggestion in terms of the design of these studies would be the exploration of the extent to which the existing survey questions are adequate in terms of dealing with the specification and identification problems we discuss in Section 3. There is no quick answer to this as it would require integrating some theoretical modeling with the survey design. Nevertheless, the payoffs to such an endeavor could be quite high.
How does our admittedly very general advice differ from the way in which data collection on social capital is typically done? We have already discussed one difference, namely, the effectiveness of data collection is augmented when attention is paid to the uses to which the data will be applied. To repeat, the analysis of potential identification problems should inform data collection and not just define limits to which a data set may be used.
Another important difference is that this approach avoids privileging social factors that can be construed as ‘social capital’ over others. As we have argued, the failure to consider alternative social explanations to social capital is an important source of skepticism with respect to existing studies. More importantly, there is no a priori reason to assume that social capital is a more likely source of important effects than other social factors. Another difference is that our proposed approach, by separating social factors as concepts from empirical measurement, will avoid conflating the two, as often occurs. Finally, the exercise of modeling individual choice in order to determine what is meant by social factors should provide some guidance as to the appropriate levels at whichthese factors should be measured. Does an individual’s or a society’s level of trust matter for individual conduct? The appropriate answer to a question like this should derive from the decision problem at hand. Empirical studies of social capital have largely not addressed this question.Third, there needs to be greater recognition of the limits to statistical analysis in contexts such as the evaluation of social capital. This is partly a restatement of the first suggestion in that there simply do not exist any available data or methodology that can allow an assessment of the broad claims of the sort one finds in the social capital literature. But beyond this, we believe economists need to be more receptive to the sorts of evidence found in other disciplines beyond the quantitative analyses that are standard in economics. For example, sustained descriptive histories can teach us much about the ways that social structures influence individual conduct even if they are not constructed in the form of claims about F-statistics and the like. At the other extreme, there is a wealth of information in the social psychology literature that addresses in precise ways the inchoate ideas about individual behavior that underlie the social capital literature.
This suggestion requires greater openmindedness on the part of economists to nonsta- tistical sources of information. But the payoffs can be high both in terms of substantive understanding as well as in facilitating quantitative analyses. As the discussion of identification argued, social capital effects can only be revealed if one has prior information on what group effects do not directly influence individuals. This is information that nonstatistical studies may be able to provide.[427]In fact, it is reasonable to argue that some aspects of the question of how social capital has facilitated socioeconomic or political development should be treated in the same spirit as questions such as what led to the emergence of democracy in ancient Athens versus a martial culture in ancient Sparta or what were the causes of World War I. These are not meaningless questions; but it is necessary to accept limits as to the quantitative precision with which such questions can be answered and what it means to say the question has been answered. Nor is there any reason to believe that persuasive evidence on social capital cannot be marshaled using narrative methods. Ogilvie (2004a, 2004b) does precisely this in her historical investigations of the role of social capital in early modern Germany for understanding questions concerning both economic development and the status of women respectively.
None of this suggests that statistical analysis should play anything other than a primary role in social capital studies; our argument is that the credibility of the social capital literature will be augmented when nonstatistical evidence is better used to motivate assumptions and suggest appropriate ways for formulating hypotheses.
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