Just released the EU Innovation Scoreboard 2011, which has been prepared by the Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT). The report is based on the performance of 25 innovation indicators which are grouped in three categories: (1) enablers that capture the main drivers of innovation performance external to the firm, (2) firm activities that capture the innovation efforts at the level of the firm, and (3) outputs that capture the effects of firms’ innovation activities on new products, growth, employment , and exports.
The UIS 2011 presents:
- The performance of individual indicators, including two types of convergence commonly used in growth studies (sigma-convergence and beta-convergence) and performance by gender
- Innovation profiles of 27 member states, EU candidate countries, and Iceland, Norway, Switzerland
- International comparisons with a selected group of major global competitors. US, Japan and South Korea have a performance lead over the EU27, while EU27 has EU27 has a performance lead over Australia, Canada and all BRICS countries (Brazil, Russia, India, China and South Africa).
A technical annex explains the methodology used for calculating each country’s composite innovation indicator by a 7-step process:
Step 1: Identifying and replacing outliers
Positive outliers are identified as those relative scores which are higher than the mean plus 2 times the standard deviation8. Negative outliers are identified as those relative scores which are smaller than the mean minus 2 times the standard deviation. These outliers are replaced by the respective maximum and minimum values observed over all the years and all countries.
Step 2: Setting reference years
For each indicator a reference year is identified based on data availability for all countries (for all countries data availability is at least 75%). For most indicators this reference year will be lagging 1 or 2 years behind the year to which the IUS refers. Thus for the IUS 2011 the reference year will be 2009 or 2010 for most indicators.
Step 3: Imputing for missing values
Reference year data are then used for “2010’, etc. If data for a year-in-between is not available we substitute with the value for the previous. If data are not available at the beginning of the time series, we replace missing values with the latest available year.
Step 4: Determining Maximum and Minimum scores
The Maximum score is the highest relative score found for the whole time period within all countries excluding positive outliers. Similarly, the Minimum score is the lowest relative score found for the whole time period within all countries excluding negative outliers.
Step 5: Transforming data if data are highly skewed
Most of the indicators are fractional indicators with values between 0% and 100%. Some indicators are unbound indicators, where values are not limited to an upper threshold. These indicators can be highly volatile and can have skewed data distributions. For the following indicators skewness is above 1 and data have been transformed using a square root transformation: Non-EU doctorate students, Venture capital, PCT patents in societal challenges and License and patent revenues from abroad.
Step 6: Calculating re-scaled scores
Re-scaled scores of the relative scores for all years are calculated by first subtracting the Minimum score and then dividing by the difference between the Maximum and Minimum score. The maximum re-scaled score is thus equal to 1 and the minimum re-scaled score is equal to 0. For positive and negative outliers and small countries where the value of the relative score is above the Maximum score or below the Minimum score, the re-scaled score is thus set equal to 1 respectively 0.
Step 7: Calculating composite innovation indexes
For each year a composite Summary Innovation Index is calculated as the unweighted average of the re-scaled scores for all indicators.
Source: DG Enterprise and Industry