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HANDBOOK ON CONSTRUCTING COMPOSITE INDICATORS: METHODOLOGY AND USER GUIDE – ISBN 978-92-64-04345-9 - © OECD 2008 37 Figure 4. Example of colour decomposition presentation Well below Below average average (under 20) (20-40) score Finland TAI Patents Royalties Internet Tech exports Telephones Electricity Schooling University 74 19 46 86 63 100 100 82 100 Japan TAI Patents Royalties Internet Tech exports Telephones Electricity Schooling University 70 100 24 21 100 100 100 78 36 Average (40-60) Above average (60-80) Well above average (over 80) X X X X X X X X X X X X X X X X X X Note: Technology Achievement Index (TAI).

A change in the indicator does not necessarily lead to a change in the composite indicator and vice versa. Countries with high GDP might invest more in technology or more technology might lead to higher GDP. The causality remains unclear in the correlation analysis. g. the Granger causality test. However, Granger causality tests require time series for all variables, which are often not available. The impact of the weights (or normalisation method, or other) on the degree of correlation between a composite indicator and another variable of interest can be evaluated in a Monte Carlo framework.

Alternatively, participatory methods that incorporate various stakeholders – experts, citizens and politicians – can be used to assign weights. This approach is feasible when there is a well-defined basis for a national policy (Munda, 2005a, 2007). For international comparisons, such references are often not available, or deliver contradictory results. , 1997). The budget allocation is optimal for a maximum of 10-12 indicators. If too many indicators are involved, this method can induce serious cognitive stress in the experts who are asked to allocate the budget.

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