**Figure 6. Actual and benchmarked value of innovation output index: developing East Asian countries and top performers, 2017**

<!-- Start of picture text --> 80 Top 5 countries<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Observed value<br>predicted value(linear)<br>predicted value(quadratic)<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Bars show the observed innovation output index value. Dots show the benchmark predicted by a (linear) regression with (the log of) GDP per capita adjusted for (2011) purchasing power parity as the explanatory variable. The regression used all countries available in the GII and WDI dataset.

**Figure 7. Relative importance of enabling factors to explain innovation output index across all countries**

<!-- Start of picture text --> (absolute contributions to R-squared; 90percent percentile<br>confidence intervals with median)<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Median CI lower CI upper<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Bars show the share of total explained variance of innovation output index by individual regressor variables (GDP per capita, and all sub components grouped under each enabling factor: Institutions, Human capital and research, Infrastructure, Market sophistication, and Business sophistication). The percentage contributions are calculated as Owen values for each group of enabling factor. While the goodness of fit (R2) decomposition does not have standard errors, bootstrapping is applied to help attach greater reliability to comparisons of importance. The analysis used all countries available in GII and WDI dataset Which enabling factors are more important to explain innovation output performance across all countries? To answer this question, a cross-country model is estimated where the innovation output index is regressed against GDP per capita and all innovation input sub components (as displayed in Figure 4) grouped under each enabling factor/pillar: _Institutions_ ; _Human capital and research; Infrastructure; Market sophistication; and Business sophistication_ .<sup>6</sup> Then, in order to identify which group of covariates presents higher importance to explain the variance of the innovation output index across countries, the estimated model’s goodness of fit (R2)<sup>7</sup> is decomposed according to the Owen values of each (exogenous) group of explanatory variables. Figure 7 displays the 90 percent bootstrap confidence intervals for the absolute Owen values for each set of variables, grouped under each enabling factor.<sup>8</sup> Not surprisingly, the “usual suspects” _Human capital and research_ and _Business sophistication_ stand out as key factors explaining the variation of innovation output performance across countries; results suggest that the supply of

> **6** Precisely, the following variables, besides GDP per capita,  are included in the regression analysis: political environment, regulatory environment and business environment (under Institutions); education, tertiary education and research and development (under Human Capital); ICTs, general infrastructure and ecological sustainability (under Infrastructure); credit, investment and trade, competition and market scale (under Market sophistication); and knowledge workers, innovation linkages and knowledge absorption (under Business sophistication).

> 7 Which turned out to be around 85 percent.

> 8 For a brief discussion about the Owen and Shapley decompositions, see Huettner and Sunder (2012).

8