Abstract
Social capital is the bond that links societies together and without which there is little opportunity for economic growth or individual well-being. Thus, this paper aims to contribute to the literature by providing an analytically reliable concept of social capital and a methodological tool for empirically testing a theoretical model of how social capital is built. Based upon a decomposition of the concept of social capital characterising three main dimensions (i.e., structural, relational and cognitive), for each specific group of individuals under study the structural equation model allows us: (1) to confirm the multidimensional construct of social capital; (2) to measure the interrelation between its different attributes and; (3) to set a solid basis for additional research on the effects of social capital. This approach has been empirically applied to Andalusian (southern Spain) farmers as case study. We believe this research to be a fundamental starting point for informing social capital policymakers and helping them implement the necessary tools to facilitate sustainable development processes at different moments in time as it takes into account the multidimensional, contextual and dynamic nature of the concept.
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Notes
It is important to remember that while a long list of benefits (such as facilitating coordinated actions, a reduction in the cost of transactions, and so on) is attached to the concept of social capital (Coleman 1988, 1990; Putnam et al. 1993; Onyx and Bullen 2000; Sobels et al. 2001), it is widely recognised in the literature that social capital may also have a ‘dark side’ which could generate negative effects (Woolcock 1998; Fine 1999; Sobel 2002; Moseley and Phal 2007). This also applies to the farming sector.
Sabatini (2009a) highlights that networks and their relational contents could be used in order to gain narrow and sectarian interests against the well-being of the wider community.
As indicated by Woodhouse (2006, p. 85), it should be taken into account that the concepts of bonding and bridging social capital contain elements of both the relational and structural dimensions in that they indicate both a tendency for people to act in a certain manner (the norm of tending towards bonding or bridging links) and the capacity to do so (the fact of having friends or contacts either locally—bonding links, or externally—bridging links). This unobserved characteristic shall not be forgotten.
Not all the variables enumerated in Table 6 were finally used in the analysis. Before estimating the structural equation models, scale validation tests were performed to ensure that the variables met the required psychometric properties. Those variables that fail to do so were removed from the analysis (further details provided in Sect. 6).
Psychometric properties are the requirements that a measuring scale must meet in order to fulfil its purpose in a rigorous and scientifically valid manner. Satisfying these properties is essential if a measuring scale is to be efficient in collecting data related to the measurable construct, while also representing reality as accurately and reliably as possible (Nunnally 1978).
Desirable item characteristics are high correlation (to increase the internal consistency of the scale), high variance (making it easier to differentiate between respondents with different levels of the trait being measured), and a mean close to the middle of the range (to minimise outliers). The full list of variables used in the analysis is reported in Appendix 1.
According to Hair et al. (1998), composite reliability (CR) is a measure of the internal consistency of the indicators of a construct showing the degree to which they indicate the common latent construct. Average variance extracted (AV) is another reliability measure showing the amount of total variance in the indicators that is captured by the latent construct.
Meaning of statistic considered. χ2: Chi square; CFI: comparative fit index; GFI: goodness-of-fit index; AGFI: adjusted goodness-of-fit index; RMSEA: root mean square error of approximation. The CFI, GFI and AGFI indices should be close to 0.9 or 1.0 and the error measure should not exceed 0.1 and ideally lie between 0.05 and 0.08 as noted by Hair et al. (1998).
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Acknowledgments
The authors would like to express their gratitude to the anonymous reviewers for their constructive comments. This research was made possible by the support provided by the Spanish Ministry of Economy and Competitiveness and FEDER through the research projects AGRIGOBERSOS (AGL2010-17560-C02-01) and CAPSOC (CSO2011-27465).
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Appendices
Appendix 1. Variables Used in the Measurement of Social Capital Among Farmers
See Table 6.
Appendix 2. Missing Data Imputation
We first identified the variables with the greatest number of missing values. These are Trstpubl4, Trstpubl5 and Trstpubl6 with around 10 % of missing values in each one. In a second step, we examined whether the missing values in each of the variables in the model tended to appear next to the missing values of other variables. We found that precisely the variables Trstpubl4, Trstpubl5 and Trstpubl6 are those that tend to submit missing values jointly.
To see if the pattern of missing values was related to the values of other variables, we analysed the correlation between the variables with missing values and all the questionnaire variables, focusing on the variables included in the model and especially on the variables with the greatest number of missing values.
In light of the results of the analysis, it can be assumed that the missing data behave as missing at random and are therefore likely to be imputed without loss of representativeness.
Two imputation techniques were used:
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Multiple imputation using the algorithm “multivariate imputation by chained equations” implemented in the R package mice (van Buuren and Groothuis-Oudshoorn 2011). Basically, this algorithm predicts the missing values of a variable by using a predictive model that takes into account the values in other variables. These predictions are used to predict the missing values of other variables, including those that acted as predictive variables. The process is repeated until the missing values in all the variables are stabilised or the degree of change is negligible.
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k-nearest neighbour imputation. This is a non-parametric method that defines the distance between individuals in a p-dimensional space where p is the number of variables considered. Thus, an individual that has a lost value in variable X will be assigned the most frequent value of X between the k individuals that are most similar to him in the rest of the variables.
In the process of multiple imputation, 5 sets of imputed data were obtained. We calculated the structural equation model for each of the 5 sets of data by comparing it with the adjusted model to the data without imputed values. No significant differences were found. Results are available from the authors upon request.
Another set of imputed data was obtained by the method of k-neighbours. In this case we chose to consider 5 neighbours. It was noted that, for this data set, the matrix of correlations and the parameters of the model were very similar to those obtained both in the non-imputed data set and the data set obtained using multiple imputation. Therefore, we chose to use the set of data obtained by the k-neighbours method, even though we could have chosen any of the previously obtained data sets (Fig. 4).
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Vera-Toscano, E., Garrido-Fernández, F.E., Gómez-Limón, J.A. et al. Are Theories About Social Capital Empirically Supported? Evidence from the Farming Sector. Soc Indic Res 114, 1331–1359 (2013). https://doi.org/10.1007/s11205-012-0205-7
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DOI: https://doi.org/10.1007/s11205-012-0205-7