The Global Innovation Index 2012

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78 THE GLOBAL INNOVATION INDEX 2012 1: Statistical Tests on the GII Figure 3a: Sensitivity analysis: Impact of modelling choices (Imputation) Figure 3b: Sensitivity analysis: Impact of modelling choices (Geometric average) Global Innovation Index 2012 Global Innovation Index 2012 Rank based on imputation Rank based on geometric average 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 rs = 0.989 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 rs = 0.993 Innovation Input Sub-Index 2012 Innovation Input Sub-Index 2012 Rank based on imputation 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 Rank based on imputation 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 Source: Saisana and Philippas, European Commission Joint Research Centre, 2012. Note: Rs = Spearman rank correlation; imputation based on expectation-maximization algorithm. 1 11 21 31 41 51 61 71 81 91 101 111 121 131 rs = 0.993 141 rs = 0.998 1 11 21 31 41 51 61 71 81 91 101 111 121 GII rank GII rank GII rank Rank based on geometric average 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 Innovation Output Sub-Index 2012 Innovation Output Sub-Index 2012 Rank based on geometric average 141 131 121 111 101 91 81 71 61 51 41 31 21 11 1 131 rs = 0.962 141 rs = 0.993 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 GII rank GII rank GII rank
could improve as data become available, as suggested by theory. In fact, between 2011 and 2012 the association between these two output pillars increased from 0.51 to 0.65.The currently observed moderate correlation might be caused by (1) the fact that missing values are particularly distorting; (2) the use of count and not value variables; (3) the use of proxies due to the lack of statistics, particularly on 7.2 (expenditure on recreation and culture, exports of creative goods and services as proxies for creative outputs). For an in depth discussion of these results, the reader is referred to Saisana and Philippas, 2012. For transparency, Table 2 reports the original country ranks and the 90% interval for the simulated rank for the GII, the Input Sub-Index, and the Output Sub-Index. Our intention is to be explicit about on which countries the simulated interval either does not include the reference rank or is too wide to allow for a reasonable inference. Overall, all country ranks in the GII or any of the Innovation Sub-Indices lay within the simulated intervals. Simulated intervals are narrow enough for most countries (less than 10 positions) to allow for meaningful inferences to be drawn. Sensitivity analysis results Complementary to the uncertainty analysis, sensitivity analysis has been used to identify which of the modelling assumptions have the highest impact on certain country ranks. Detailed results are available in the main JRC assessment report, but the main conclusion is that the impact of the imputation alone is noteworthy for some countries, although it may be moderated when considering a geometric aggregation and a variation in the weights for the pillars. Figure 3 plots the reference GII ranks (and the two sub-indices) versus one-at-a-time changes of either the imputation method or the aggregation formula. These plots show that the most influential assumption is the choice of no imputation versus EM imputation in particular for the Output Sub-Index, then for the GII and least for the Input Sub-index. For example, in one case a country does not shift position if a geometric aggregation is applied, although it is found to lose 24 positions in the Output ranking if EM imputation is applied. If both assumptions are changed (and weights remain at the reference values), the impact of the imputation would be moderated. This sensitivity is the result of data availability. Although all countries have data coverage above 70% in the Input variables, 21 countries have data coverage below 65% in the Output variables, which explains the impact of imputation on these countries ranks. Sensitivity analysis, by assessing the impact of the modelling choices, has given more transparency in the entire process and can help to appreciate the GII results with respect to the assumptions made during the development phase. Sensitive ranks usually concern countries with poor data coverage on the Innovation Output Sub-Index, and to a more limited extent on the Innovation Input Sub-Index—an impact that propagates to the estimation of the overall GII. For an in depth discussion of these results, the reader is referred to Saisana and Philippas, 2012. The recommendation for the future would be to apply the 63% criterion for data availability within each of the two sub-indices so as to avoid drawing a better picture for countries with poor data quality on one of the two sub-indices, in particular on the Innovation Output Sub-Index. For this year, drawing upon the analysis made by the JRC, the recommendation is to consider country ranks in the GII 2012 and in the Input and Output Sub-Indices not only at face value but also within the ranges simulated by uncertainty analysis in order to better appreciate to what degree a country rank depends on the methodological choices made during the development of the GII 2012. Conclusion The JRC analysis suggests that the conceptualized multi-level structure of the GII 2012 is statistically coherent and balanced (i.e., not dominated by any pillar or sub-pillar). Furthermore, the analysis has offered statistical justification for the weights and the use of arithmetic averaging at the various levels of aggregation. Together with other fine-tuning suggestions made in the sections above, a key recommendation for future years is to apply the data coverage criterion for countries’ inclusion not at the overall GII level, as is currently done, but within each of the two Innovation Sub-Indices. Furthermore, the ’no imputation’ choice for not treating missing values, common in relevant contexts and justified on grounds of transparency and replicability, can at times have an undesirable impact on aggregate scores, with the additional negative side-effect that it may discourage countries from reporting low data values. Finally, the GII team’s choice this year to use weights as scaling coefficients during the development of the index constitutes a significant departure from the traditional vision of weights as a reflection of indicators’ importance in a weighted average. Such a consideration will hopefully be made also by other developers of composite indicators. 79 THE GLOBAL INNOVATION INDEX 2012 1: Statistical Tests on the GII
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The Global Innovation Index 2012 St
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The Global Innovation Index 2012: S
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iv THE GLOBAL INNOVATION INDEX 2012
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vi THE GLOBAL INNOVATION INDEX 2012
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Foreword The Coherence Premium in I
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cONTrIBuTOrS Contributors to the Re
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Rankings
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Global Innovation Index rankings (c
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cHApTEr 1 The Global Innovation Ind
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The GII helps to create an environm
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4. The Innovation Efficiency Index
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Table 1: Global Innovation Index ra
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Table 2: Innovation Input Sub-Index
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Table 3: Innovation Output Sub-Inde
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Box 1: A spotlight on the United St
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Figure 2: Innovation Output Sub-Ind
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Malta is ranked 16th in the GII 201
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Figure 3: Global Innovation Index v
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Table 5a: Innovation Efficiency Ind
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Figure 4: GII scores v. GDP per cap
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Page 59 and 60: PPP$ 3,354.8 Viet Nam has a very go
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Page 63: Freeman, C., and L. Soete. 2007.
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ex-territorial innovation centre wi
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cHApTEr 7 Shaping the National Inno
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Table 1: Idea-to-market curve 1. po
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is around 8%—very low, even compa
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culture, environment, and economy.1
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13 See the Department of Biotechnol
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cHApTEr 8 An Integrated Policy Appr
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institutions in developing countrie
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egan to publish monographs on STI p
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cHApTEr 9 Broadband, Inevitable Inn
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Figure 1: Global ICT developments,
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Figure 3: Global fixed and mobile b
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Figure 4: Evolution of ICT-innovati
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cHApTEr 10 The Internet: An Unprece
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egularly update their pages and sha
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Google’s anti-SOPA page, resultin
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cHApTEr 11 We Are All Content Creat
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Table 1: Models of creative industr
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than any single major recording lab
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Pélissié du Rausas, M., J. Manyik
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Country/Economy Profiles I
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176 THE GLOBAL INNOVATION INDEX 201
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THE GLOBAL INNOVATION INDEX 2012 II
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Index of Data Tables 1 Institutions
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1.1.1 Political stability and absen
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1.1.3 Press freedom Press freedom i
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1.2.2 Rule of law Rule of law index
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1.3.1 Ease of starting a business E
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1.3.3 Ease of paying taxes Ease of
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2.1.2 Public expenditure on educati
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2.1.4 Assessment in reading, mathem
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2.2.1 Tertiary enrolment School enr
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2.2.3 Tertiary inbound mobility Ter
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2.3.1 Researchers Researchers, head
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2.3.3 Quality of scientific researc
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3.1.2 ICT use ICT use index* | 2010
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3.1.4 Online e-participation E-part
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3.2.2 Electricity consumption Elect
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3.2.4 Gross capital formation Gross
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3.3.2 Environmental performance Env
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4.1.1 Ease of getting credit Ease o
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4.1.3 Microfinance institutions’
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4.2.2 Market capitalization Market
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4.2.4 Venture capital deals Venture
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4.3.2 Market access for non-agricul
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4.3.4 Exports of goods and services
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5.1.1 Employment in knowledge-inten
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5.1.3 GERD performed by business en
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5.1.5 GMAT mean score Weighted mean
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5.2.1 University/industry research
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5.2.3 GERD financed by abroad GERD:
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5.2.5 Share of patents with foreign
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5.3.2 High-tech imports High-tech n
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5.3.4 Foreign direct investment net
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6.1.2 Patent Cooperation Treaty app
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6.1.4 Scientific and technical jour
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6.2.2 New business density New busi
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6.2.4 ISO 9001 quality certificates
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6.3.2 High-tech exports High-tech n
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6.3.4 Foreign direct investment net
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7.1.2 Madrid Agreement trademark re
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7.1.4 ICT and organizational models
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7.2.2 National feature films produc
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7.2.4 Creative goods exports Creati
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7.3.1 Generic top-level domains (gT
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7.3.3 Wikipedia monthly edits Wikip
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Sources and Definitions III
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412 1 Institutions THE GLOBAL INNOV
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414 2 Human capital and research TH
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416 3 Infrastructure (continued) TH
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418 4 Market sophistication THE GLO
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420 5 Business sophistication (cont
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422 6 Knowledge and technology outp
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424 7 Creative outputs (continued)
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Technical Notes IV
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About the Authors V
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