Daljit Dhadwal

The main aim of the Handbook is to provide builders of composite indicators with a set of recommendations on how to design, develop and disseminate a composite indicator. In fact, methodological issues need to be addressed transparently prior to the construction and use of composite indicators in order to avoid data manipulation and misrepresentation. In particular, to guide constructors and users by highlighting the technical problems and common pitfalls to be avoided, the first part of the Handbook discusses the following steps in the construction of composite indicators:

• Theoretical framework. A theoretical framework should be developed to provide the basis for the selection and combination of single indicators into a meaningful composite indicator under a fitness-for-purpose principle.

• Data selection. Indicators should be selected on the basis of their analytical soundness, measurability, country coverage, relevance to the phenomenon being measured and relationship to each other. The use of proxy variables should be considered when data are scarce.

• Imputation of missing data. Consideration should be given to different approaches for imputing missing values. Extreme values should be examined as they can become unintended benchmarks.

• Multivariate analysis. An exploratory analysis should investigate the overall structure of the indicators, assess the suitability of the data set and explain the methodological choices, e.g. weighting, aggregation.

• Normalisation. Indicators should be normalised to render them comparable. Attention needs to be paid to extreme values as they may influence subsequent steps in the process of building a composite indicator. Skewed data should also be identified and accounted for.

• Weighting and aggregation. Indicators should be aggregated and weighted according to the underlying theoretical framework. Correlation and compensability issues among indicators need to considered and either be corrected for or treated as features of the phenomenon that need to retained in the analysis.

• Robustness and sensitivity. Analysis should be undertaken to assess the robustness of the composite indicator in terms of, e.g., the mechanism for including or excluding single indicators, the normalisation scheme, the imputation of missing data, the choice of weights and the aggregation method.

• Back to the real data. Composite indicators should be transparent and fit to be decomposed into their underlying indicators or values.

• Links to other variables. Attempts should be made to correlate the composite indicator with other published indicators, as well as to identify linkages through regressions.

• Presentation and Visualisation. Composite indicators can be visualised or presented in a number of different ways, which can influence their interpretation.

The above list is from the Handbook on Constructing Composite Indicators: Methodology and User guide. It is published by the OECD and the Joint Research Centre of the European Commission, and it is available for download from the OECD website.