سرمایه اجتماعی : تجزیه و تحلیل عوامل مؤثر بر سرمایه گذاری

This paper investigates how individual and community characteristics affect individual social capital investment behavior. We assume a representative individual maximizes her net benefits from social capital by choosing the amount of social capital investment in each period of her lifecycle. The model parameters are estimated by fitting the model to observed data using computational techniques. Simulations determine how perturbations to individual and community characteristics affect individual social capital behavior. The results suggest that social capital investment occurs irrespective of future benefits, personal characteristics affect the level and variance of investment, and institutions matter in determining social capital investment behavior.

Recent research on social capital provides new understanding into private and corporate human behavior. Economists have used the concept of social capital to explain phenomena ranging from technology adoption to the formation of informal institutions. At the micro-level, studies of the effect of social capital have focused on agribusiness, international trade, household income, financial transactions, and the knowledge exchange between venture capitalists and new firms. Fafchamps and Minten (2002) show that agricultural traders with a larger network of contacts have higher sales and value added. Wilson (2000) and Rauch (2001) show that social capital enhances the competitiveness of firms by reducing contract enforcement costs, reducing uncertainty, and increasing information sharing. Narayan and Pritchett (1999) find that village social capital stocks contribute more to household income than does own-household social capital stock. Schmid and Robison (1995) demonstrate how social capital affects the probability of receiving a loan, the sale price of assets, and the willingness to share risk. Maula et al. (2003) find that social capital between a corporate venture capitalist and a new firm increases the knowledge exchange between the two organizations, especially when they are in complementary industries. Lyons and Snoxell (2005) examine the roles of inhereted and self-created social capital in the survival strategies of traders in two Nairobi markets. Cainelli et al. (2007) examine firm level research and development decisions and find evidence that social capital inclines firms to engage in innovative activity, though it is not a sufficient condition. Social capital has been used to explain phenomena at aggregated levels, too, such as regions or nations. Kraybill and Weber (1995), Castle (1998), Barkley (1998) and Rainey et al. (2003) emphasize the importance of social capital in the growth of rural places in the US. Castle argues that social capital substitutes for formal institutions that would otherwise sustain a region and make it prosperous. Kraybill and Weber and Barkley use social capital to explain the success of endogenous development strategies in rural places, while Rainey et al. argue that social capital encourages economies of scale and other efficiencies that make rural places more competitive in a global economy. Rupasingha et al., 2000 and Rupasingha et al., 2002 find that positive social capital increases the growth rate of US counties. Knack and Keefer (1997) conclude that social capital manifested as trust and civic cooperation significantly influences a nation's economic performance. In an attempt to elucidate a definition of social capital, Chamlee-Wright (2008) uses the concept of social capital to explain the process of economic develoment stemming from entrepreneurial activity. Rupasingha and Goetz (2007) show that social capital decreases county-level poverty rates. Despite the empirical and theoretical evidence that suggests social capital is a powerful concept to explain economic phenomena, few studies have examined how social capital is formed. The studies that exist use regression analysis in an attempt to identify factors that influence social capital formation. Glaeser et al. (2002), in addition to presenting a theoretical foundation for social capital, provide empirical evidence of factors influencing social capital. Their main conclusion was that more education led to higher social capital stocks; other factors that they analyzed include: age, race, gender, income, peer group effects and homeowndership. Rupasingha et al. (2006) largely validate the results of Glaeser, Laibson and Sacerdote using county level data and an index of social capital consisting of voter participation rates, Census participation, and the numbers of membership and tax-exempt organizations. Bellemare and Kroger (2007) identify factors that relate to Dutchmen's social capital behavior by regressing experimental game outcomes on individual characteristics. Iyer et al. (2005) in the US and Fidrmuc and Gërxhani (2008) in Europe both demonstrate the important role of national, economic and institutional factors which influence individual social capital behavior. The present study differs from the above in its approach; rather than utilize regression techniques, which face well documented endogeneity and heteroskedasticity problems, we use computational techniques to model individual behavior over time. In addition to avoiding the stochastic problems associated with regression techniques, the method affords us two distinct benefits. First, intertemporal decisions are explicitly modeled, so that investment and stock levels are deterministically linked. Second, using computational techniques enables the researchers to perform counterfactual simulations; such simulations provide insights into how individuals would change their social capital investments when faced with various scenarios. This paper provides a theoretical model of individual social capital investment and provides empirical estimates of how individuals’ decisions to invest are affected by various factors. Following a discussion of social capital and the investment model, we discuss the computational methodology employed to estimate the parameters of a social capital investment model. The fifth section of the paper discusses the survey and secondary data used in our analysis. The sixth section presents our results, and we conclude with a discussion of limitations and policy implications.

This paper investigates how individual and community characteristics affect individual social capital investment behavior. We present a model of social capital investment, utilizing the definition of social capital given by Collier (1998) and modifying the neoclassical social capital investment model developed by Glaeser et al. (2002). We assume a representative individual maximizes his or her net benefits from social capital by choosing the amount of social capital investment in each period of his/her lifecycle. The modified version of the social capital investment model incorporates immediate returns to social interactions as well as the transformation of social interactions into social capital. The model parameters are estimated by fitting the model to observed data using computational techniques. We then perform simulations to determine how perturbations to individual and community characteristics affect individual social capital behavior. The results can be summarized by three themes: social capital investment occurs irrespective of future benefits, personal characteristics affect the level and variance of investment over the lifecycle, and institutions matter in determining social capital investment behavior. To parallel our results, there are three main conclusions we draw from our results: 1. Our results raise the question as to whether the neoclassical capital investment model, which relies heavily on the ability to anticipate future returns, is an appropriate model for analyzing social capital investment. 2. More research is warranted to understand the volatility of investment and social capital's role as a risk-mitigating feature of communities. 3. Communities seeking to increase social capital should encourage the development of formal sectors of the economy and not concentrate on the not-for-profit sectors. Given that our data stems from a localized survey of homeowners, we acknowledge that our findings may be influenced by survey design. Additional research in other locales is necessary to determine if these results are representative of homeowners in other locations. For example, our findings related to community social capital may reflect the urban lifestyle, in which urban dwellers often worship, work and recreate in places outside of their residential community. Furthermore, zip codes may be too large of a region to proxy for neighborhoods; they also limit the ability to examine other community characteristics, such as crime, because they do not conform to Census boundaries. Caution must be used, then, in attempting to generalize these results, though they continue serve as a case study of social capital investment behavior in Franklin County, Ohio and demonstrate a methodology for estimating the parameters of the social capital investment model. Given the inability to generalize our results, we can only illustrate the policy implications of our results. The positive correlation between educational attainment and social capital investment suggests that communities would be able to increase social capital investment by increasing educational attainment. However, our results also suggest that higher wages reduce social capital investment, so that gains in social capital driven by educational attainment might be offset by social capital losses from higher wages. Our results also suggest that formal institutions facilitate social capital investment, so communities could increase social capital investment by instituting policies that support existing businesses (e.g., subsidized employee training or marketing assistance) and encourage small business ownership (such as streamlined building permitting and business licensing procedures, ensuring access to technical assistance and capital or loan funds). Another potential policy implication is that communities should not focus on cultivating community social capital (e.g., encouraging participation in non-profit organizations), since community social capital had no effect on individual's social capital investment behavior.