A composite indicator like the one proposed by IMAGINE can be very useful for analysing the effects of local development strategies and therefore it can be inspiring for LEADER evaluations. This composite indicator represents a preliminary attempt to aggregate data from several variables in order to capture the structure of local economic performance at a very refined scale.
The analysis of the composite indicator must be considered as explorative within the sphere of local economic development as it is based on data from the IMAJINE dataset, but also opens the possibility for further development of local datasets. Therefore, in order for the indicator to be used in other contexts/Member States, the dataset may need to be adapted, while methodologies for collecting local level data should also be in place. Considerations include time and resources for applying it as well as knowledge of Principal Component Analysis, which is the IMAJINE recommended methodology for defining weights and synthesising the composite indicator.
This would be worthwhile as the indicator helps to reduce complexity in analysing multi-dimensional economic phenomena such as local economic performance and development, relevant in the context of LEADER for example. Evidence from the countries analysed by the IMAJINE project show that the methodological approach for developing the composite indicator helps produce accurate results.
In addition, the analysis stresses the need to pursue multidimensionality at a local level to prevent policy makers from setting ad hoc policies and interventions. Local composite indicators allow us to better consider differences at low geographical scale, target disadvantaged areas within certain regions and develop very accurate policies that may help ensure policy effectiveness, which again stresses the relevance for local development under LEADER. The performance of such indicators becomes then the subject of evaluation and feeds back into further policy improvement/development.
The transferability of the tool depends on the availability of data at the local level. Another IMAJINE output (the local level database of socio-economic indicators) offers an approach for acquiring local level data using official statistics, censuses and applying the Generalised Cross Entropy (GCE) method.