The implications of social capital for economic development: new insights using different measures and geographical contexts
- Autores: Jesús Peiró Palomino
- Directores de la Tesis: Emili Tortosa Ausina (dir. tes.)
- Lectura: En la Universitat Jaume I ( España ) en 2014
- Idioma: inglés
- Tribunal Calificador de la Tesis: Aurora García Gallego (presid.), Luis Díaz Serrano (secret.), Christian Bjørnskov (voc.)
- Materias:
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- Resumen
This dissertation attempts to contribute to the literature on social capital and economic development by using different measures of social capital and testing its implications on growth and income convergence profiles in different geographical settings at different levels of spatial disaggregation.
The concept social capital, referred in a broad sense to social attitudes such as trust, cooperation and civic engagement, has become increasingly popular in a wide variety of academic disciplines in the last three decades. Anthropologists, sociologists, geographers and economists have tried to properly define the concept, to disentangle its nature, and to identify its multiple elements, as well as its likely implications in a variety of fields. In the particular field of economics, the scholars¿ interest has been mainly focused on the links between social capital and economic development, as well as the likely transmission mechanisms.
Chapter 1 provides an in-depth discussion on the concept social capital and its relationship with economics in general, and growth and development in particular. Virtually all scholars agree that the effects of social capital are seen in reduced transaction costs in economic operations. Putnam (1993a) put forth that social capital facilitates coordination and cooperation for mutual benefit, as well as it helps solve problems of collective action and reduces the incentives for opportunism and egoism. In the same line, Knack and Keefer (1997) argued that trust reduces the cost of monitoring possible free riding behavior. As Durlauf and Fafchamps (2005) noted, this might occur as a result of an increase in information flows, groups, flexibility and coordinated actions. A similar argument is put forward by Dearmon and Grier (2009), who concluded that trust mitigates information asymmetries between negotiating parties, and it may facilitate contracts and agreements.
A variety of other activities, which at the same time positively impact on economic performance, might be influenced by social capital¿i.e. they would be indirect transmission channels. One of these activities is investment, as put forward by Knack and Keefer (1997), Zak and Knack (2001) and Dearmon and Grier (2011) using a trust index. Human capital is also one of these activities. Bjørnskov (2009; 2012) as well as Dearmon and Grier (2011) concluded that trust influences schooling and the results suggest that in high-trust economies human capital might be more easily transmitted. Another activity positively influenced by trust is technological innovation (Akçomak and Ter Weel, 2009) and Guiso et al. (2004) concluded that trust might also foster participation in the credit market and financial development. Finally, Knack (2002) and Bjørnskov (2012) documented a positive influence from trust to better governance.
Chapter 2 analyzes the influence of social capital on both economic growth and physical capital investment (residential and non residential) in the context of the 50 Spanish provinces for the period 1985¿2005. The measure of social capital used is the one provided by the Ivie-FBBVA (see Pérez, et al. 2005), which has some particularities that makes its use appropriate in the Spanish context, and the study covered the period.
The first part of the chapter focuses on the relationship between social capital and economic growth. In this part, a neoclassical growth model (see Mankiw et al. 1992), augmented with social capital is estimated. In the second part, a model based on Dearmon and Grier (2011), also augmented with social capital, is used to estimate the determinants of investment.
After the construction of a balanced panel data, results are estimated by means of OLS estimations (with provincial fixed effects). In order to control for the likely existence of endogeneity, alternative 2SLS estimations with instrumental variables are also performed. In all cases (for economic growth and for physical capital investment) social capital is positive and highly significant, suggesting that it affects economic growth at the provincial level and that investment is one of the likely transmission mechanisms from social capital to growth. The robustness of these results is tested via bootstrap estimations, which corroborates the results.
Chapter 3 expands the analysis carried out in the Spanish context to the European regional frame, in particular during the period 1995¿2008. Contributions analyzing social capital in the European regions are scant. We only find Schneider et al. (2000) and Beugelsdijk and Van Schaik (2005) and both of them confine the analysis to the late nineties. However, the last decade was a period of intense economic growth for most of the European regions, as well as a period of expansion for the European Union, but the contributions considering the influence of social capital on this context are still yet to come. Therefore, the objective of this chapter is evaluating the implications of social capital for economic growth in that setting, using a sample of 85 European regions (NUTS 1). This chapter considers, following Bjørnskov (2006), three different elements of social capital, namely trust, active participation in associations and the quality of social norms taken from the European Values Study (EVS).
Analogously to Chapter 2, a neoclassical growth model is constructed, including spatial effects (SAR model). Data is averaged for the whole period, and inference is carried out by using techniques from Bayesian statistics, which permit an alternative interpretation of the results. In particular, the Bayesian inference process, based on MCMC Markov Chains computation, provides the posterior density of the true but unknown coefficients of the parameters included the model, in our case the three mentioned components of social capital (in addition to other control variables). Following these methods, results can be directly drawn from the posterior distributions in terms of probability, which is one of the main differences with the techniques employed in classical statistics (for example, OLS estimations), and also one of the main advantages, since the analyst can evaluate with precision the entire distribution of the estimated parameter.
The results suggest that the effects of social capital differ according to the element considered. In particular, for the case of trust and the quality of social norms, their posterior distributions predict a positive effect with a probability above the 80%, whereas for the case of active (voluntary) participation in associations, the evidence supporting a positive effect is not so high (around 70%). These results, however, substantially support other findings for cross-country studies, but are partially opposite to previous contributions focusing on the European context. The likely origin of these differences, however, might be explained by both the different composition of the sample, larger and more heterogeneous than in previous analysis.
Chapter 4 continues to focus on the European regional context. However, in this case the research question is addressed to analyze the influence of social capital on the regional income convergence patterns in 216 European regions during the period 1995-2009. The problem is approached following the distribution dynamics approach, introduced by Quah, 1993a). This technique is among the set of nonparametric tools, and therefore it avoids the strong assumptions which characterize the regression approach. It allows to study how the entire cross-section distribution of income evolves over time, as well as the intra-distribution dynamics, i.e. whether the relative position of the economies has remained stable over the period studied. Some refinements of the technique (see Quah, 1997; Hyndman et al., 1996) allow the distribution of income to be conditioned by a set of factors, which is precisely the main objective of the chapter.
In particular, it evaluates both regional convergence (unconditioned analysis) and the influence of different elements of social capital for the convergence process (conditioned analysis). The elements of social capital selected are those introduced in Chapter 3, namely trust, active participation in associations and the quality of social norms. The results for the unconditioned scenario show evidence of convergence, especially for the most recent years (period 2002¿2009). Previous contributions focused on earlier periods have suggested that the distribution of income shows a persistent bimodality, implying that regions in Europe have shown divergent tendencies. However, when considering the most recent years, the differences tend to vanish, and the distribution of income becomes unimodal. When the different elements of social capital are introduced as conditioning factors, the results suggest that they played an important role in the convergence process. These effects are noticeable over all the period, but they are especially remarkable at the beginning (1995).
Chapter 5 focuses on the behavior of the most used indicator of social capital, namely trust. One of the questions that has arisen most interest among scholars is whether trust effects are equal for all the economies, or whether they depend on the degree of economic development of the economies. This idea has been proposed by a number of authors such as Knack and Keefer (1997), who found evidence in supporting the idea that trust is more essential for the poorer economies. They suggested that trust might provide an informal frame for guaranteeing transactions in those countries where the formal institutional frame is weaker. These ideas are also supported by Ahlerup et al. (2009), who linked the effects of trust to those by the formal institutional environment and found substitutability between them. However, other authors such as Dearmon and Grier (2009) found no evidence of differences in the trust effects between poor and rich countries. Yet these results come from Ordinary Least Squares (OLS) estimations, a technique that focuses on the average effect, which is not the most appropriate strategy to address this question.
In this chapter the analysis is carried out using both OLS and quantile regression. Quantile regression has the advantage of providing differential estimated effects of the independent variables included in the model (neoclassical model augmented with trust) for the different quantiles of the dependent variable, namely the level of income. Therefore, the main objective of this chapter is disentangling if trust shows heterogeneous effects depending on the level of development of the economies. The sample considered includes 80 countries during the period 1981¿2007, and the data on trust is taken from five different waves from the World Values Survey (WVS).
The results revealed that trust is not relevant for the poorest economies (those countries between the 1 and 15 percentiles in the sample, approximately). It might suggest that there is some sort of poverty trap, in the sense that a country needs to reach certain level of economic development for enjoying the positive effects of trust. However, when this poverty threshold is overcome, the effects of trust peak, and then progressively decrease when the country becomes richer.
Finally, Chapter 6 provides some concluding remarks, as well as some policy implications and further research ideas. In general terms, the thesis aims to contribute to the existent literature by providing evidence on the role of different forms of social capital for economic development, as well as it attempts to provide evidence on the behavioral pattern of trust at different stages of economic development. In doing so, the thesis has also attempted to bridge the literatures of economic growth and social capital, by considering some of the statistical techniques recently introduced in growth studies.
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