One of the more successful approaches to the measurement of social capital across US counties relies on a two-step algorithm procedure. In the first step, ten variables accounting for the per capita number of various types of voluntary organizations are averaged to generate an Aggregate Index. In the second step, the Aggregate Index and three other factors are used to extract an overall Social Capital Index. Here, we propose several methodological improvements to this already solid methodology. We replace the Aggregate Index calculated as a simple average with a measure generated with principal component analysis, and subsequently with a formative partial least squares dimension-reduction procedure. We explore variations of these procedures, according to the rent-seeking nature of the organizations that make up our groupings. We illustrate our methodology by using US county data. We find that, even when holding the normative concept and the data constant we generate alternative metrics with different characteristics. This result has far-reaching implications for both the theory of social capital and the public policies that rely on the evidence surrounding social capital. There appears to be an inherent arbitrariness to measuring complex social phenomena using a reductionist analytical framework. At the same time, there are limits to evidence-based policy interventions. These limits need to be mitigated with a balanced approach relying on both analytical tools and qualitative evaluations.
Keywords: Complexity; Partial Least Squares; Principal Component Analysis; Public policy; Social capital.
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