Summarizing the effect of a wide array of amenity measures into simple components

A significant issue existing within the rural economic development literature revolves around the difficulty with sorting out the controversy of the effects of amenity activities on rural economic growth. This problem is due to the different ways amenity attributes are linked to regional economic performance. Numerous researchers utilize principal component analysis to compress groups of variables that describe attributes of natural-based amenity and quality of life into scalar measures. While principal components are good at reducing a collection of variables into single measures, they often lack interpretability because they define some abstract scores which are often not meaningful or not well interpretable in practice. We apply the simple component analysis suggested by Rousson and Gasser (2004, Applied Statistics 53, 539-555) to summarize the information in groups of variables into a limited number of simple components and improve interpretability at a modest loss of optimality. Simple components allow us to identify and interpret the effect of attributes that most influence regional economic performance so as to gain better insight into policies to preserve and advance those attributes. The same methodology is appropriate for any social science discipline when there is a need to replace a larger number of multiple indicator measurements with a smaller set.