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The European Commission’s science and knowledge service · 2017-12-11 · The Innovation Output...
Transcript of The European Commission’s science and knowledge service · 2017-12-11 · The Innovation Output...
The European Commission’s science and knowledge service
Joint Research Centre
The Innovation Output Indicator
Dániel Vértesy
COIN CoP 2017 – Composite Indicators & Scoreboards Community of Practice: 2nd Annual Meeting 09-10/11/2017, Ispra (IT)
What does the IOI measure?
Innovation output: “the extent to which innovative ideas can reach the market”
• A composite index of 4 components (5 variables): • Published by DG RTD since 2013 • Methodology developed with JRC/COIN support
Innovation Output Indicator (IOI)
PCT KIABI COMP
GOOD SERV
DYN
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JP IL SE FI CH DE
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PCT PCT patent applications per billion GDP (in PPS) KIABI Share of employment in knowledge-intensive activities in business industries (in %)
Components
GOOD Exports of medium- and high-technology products as a share of total product exports (in %) SERV Knowledge-intensive services exports as percentage of total services exports (in %)
01020304050607080
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DYN Employment in fast-growing enterprises in the top 50% most innovative sectors as a percentage of total employment (in %)
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Why measure innovation output? • Innovation key to Europe 2020 strategy:
make the EU a “dynamic, sustainable, knowledge-based (‘smarter’) economy”
• The political mandate from the European Council:
Complement the Lisbon agenda’s 3% R&D [input] target with output-oriented indicator(s)
• Aim to complement the Innovation Union Scoreboard, and its Summary Innovation Index (SII), which measures overall performance, by zooming in on a reduced set of dimensions
“ Each of them, alone, yields only a partial view of the innovation phenomenon, but together they provide if not for a perfect, at least for a clearer assessment
”
High-level panel on the measurement of innovation proposed a short list of indicators:
EC (2010)
Why multiple indicators?
• Single indicator: hourly labor productivity: indirect measure • Short list of indicators offering a clearer picture of the innovation phenomenon
• No ideal world:
• Existing data: not good enough • Proxies based on data produced by official statistics are available
• Policy demand for a concise picture
How: a composite index or a list of indicators?
• Disagreement: use a composite index or not?
First solution: a complex formula of 2 indices (sectoral innovation performance, sectoral innovation dynamics)
• Make use of innovation coefficients computed by OECD and Eurostat’s new employment in high-growth enterprises data
Against: HLP’s reservations: not easy to interpret
or... if there is a composite, HLP advised to: • keep the number of indicators limited to ensure transparency; • Ensure a relative independence of components; • Make possible to monitor the policies addressing the various components
Stakeholder reservation: Too complicated; insufficient coverage!
How: a composite index or a list of indicators?
• Political decision to go for a composite index
• Inter-service Task force (DG RTD, ECFIN, ESTAT, GROW, JRC, TRADE, etc.) established to develop an index from a list of 4 components
For: Summarize an admittedly multi-dimensional phenomenon Useful for delivering policy messages (factors hampering R&I system performance) Easy to interpret
JRC team’s role in developing the index
• Building indicator • Improve robustness
• Statistical validation: • checking conceptual and statistical soundness; • Perform global sensitivity analyses (MC simulations,
alternative weights, DEA, etc.) • Computing scaling factors:
• Decision to address statistical imbalances across dimensions using effective equal weights
Updates and refinements
• Communication challenge: align with EIS • Similar measures: avoid different message • Frequency of publication: align or not?
• Refinements: “if you want to measure change, better not change
the measurement” • Increase country coverage (given data availability) • Indicators refined
Results
The EU in international comparison
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JRC - COIN Statistical Support
Balancing components (Computing scaling coefficients) for annual updates
JRC - COIN Statistical Support
Balancing components (Computing scaling coefficients) for annual updates
EU28
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Statistical coherence
PCT KIABI COMPEU DYNimp N.Obs. 234 234 234 234 Min 3.5 2.1 2.5 1.7 Max 9.4 10.1 7.5 9.0 Mean 5.0 5.0 5.0 5.0 Std. Dev. 1.5 1.5 1.1 1.5 Skewness 1.2 0.9 -0.1 0.3 Kurtosis 0.5 1.4 -0.7 -0.4
Correlation PCT 1.000 KIABI 0.583 1.000 COMPEUR 0.524 0.414 1.000 DYNimp. 0.112 0.123 0.286 1.000
JRC - COIN Statistical Support
Developing alternatives Alternative ways of measuring components – i.e., DYN
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Year: '2016' (2014); R-squared = 0.961
JRC - COIN Statistical Support
Robustness analyses when revising components Sensitivity analyses to reveal impact of changing modelling assumptions
• Moderate, positive association:
• SII: relatively stronger influence of science and R&D-based innovation activities;
• IOI: relatively stronger influence of firm dynamics
Comparing scores: IOI vs SII
EU
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Summary Innovation Index
• Strong positive correlation:
• SII: high-growth, innovative firms component receives stronger relative weight
• Still some differences: consider Sweden, Germany
Comparing scores: IOI vs ‘Employment Impacts’ of EIS
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Summary Innovation Index: Impacts Employment (4.1)
• Moderate, positive association • Gives a different picture • Caveat:
• Not all innovation is R&D based; • No linear relationship, • considerable time lag
• Yet, high spenders feel “not rewarded” (i.e. AT)
Output vs Input: IOI vs R&D expenditure
AT BE BG
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Gross R&D expenditures per GDP (2015)
Results
Limitations, debates
• Structural differences across countries: • i.e., what do exports measure? • Indicators affected by geography, natural resource endowments
(<> smart specialization?)
• Economic impact of innovation output: where is it realized? • transnational companies vs. national borders
• DYN: conceptually as well as statistically different from the rest of the indicators • International comparison limited: different benchmarks
• Is there a validator: a single measure of innovation? • HLP: hourly labour productivity
Challenge for innovation indicators
Advocacy tool
• 15+ years of experience (think of SII; GII!)
• Innovation policy (more than technology!) now high on the agenda “innovate out of the crisis”
• Advocacy for data improvements
Analytical tool
• Contents of “the rest of the reports” • Data challenges:
• Established official statistics (i.e., R&D); • Improving official statistics:
(i.e., CIS surveys, 20 years); • Emerging data sources: experimental mainstream /admin, balance-sheet data, ‘big data’ (i.e., introduction and diffusion of new products, their consumption)/ => make it useful for policy monitoring
Welcome to email us at: [email protected] [email protected]
THANK YOU COIN in the EU Science Hub https://ec.europa.eu/jrc/en/coin COIN tools are available at: https://composite-indicators.jrc.ec.europa.eu/
The European Commission’s Competence Centre on Composite Indicators and Scoreboards
References
EC (2010) Elements for the setting-up of headline indicators for innovation in support of the Europe 2020 strategy: Report of the High Level Panel on the measurement of Innovation 2010.