1. Causal Connections Between Scientometric Indicators R. D. Shelton, Tarek Fadel, Patricia Foland Which Ones Best Explain High-Technology Manufacturing Outputs?

2. Outline About WTEC About WTEC Model of National Innovation Systems Model of National Innovation Systems Trends in High Tech Outputs Trends in High Tech Outputs Causality by Granger Tests: An Example Causality by Granger Tests: An Example Correlations for HT Manufacturing Correlations for HT Manufacturing Causality Results for HT Manufacturing Causality Results for HT Manufacturing Conclusions Conclusions

3. About WTEC WTEC leads the U.S. in conducting international research assessments via peer review. WTEC leads the U.S. in conducting international research assessments via peer review. WTEC reports are free at wtec.org WTEC reports are free at wtec.org More than 70 studies are available More than 70 studies are available

4. Input/Output Models of National Innovation Systems Linear, simplified model with indicators. Sales are the main way that investments can be recovered. There is also feedback, e.g. one going back from sales to BERD—business expenditures on R&D

5. Trends in High-Tech (HT) Outputs World share of manufacturing of HT products, from a new value-added dataset, which avoids double counting of components. The rise of China is dramatic.

6. Correlation is Not Causation Cross country correlations of these indicators can find connections, but not prove causality Sometimes, one can argue that there is a logical connection: funding of research clearly causes research outputs like papers and patents In other cases the direction of causality is not so clear. Prior work showed high correlation of research funding with HT manufacturing, but which is the cause? The Granger statistical test can sometimes help. It is performed on the time series for a single country.

7. Example of Granger Testing Prior work sought the cause for the EU passing the US in SCI papers in the mid-1990s (Foland & Shelton, 2010) Correlations showed that government investments in R&D (G- GERD) were particularly effective in encouraging papers, because they mostly go to universities. At the end of the Cold War, the US mix of R&D funding shifted sharply from 1/2 government and 1/2 industry, to 1/3 and 2/3 respectively. The EU did less of this, so this shift caused the US to be less efficient than the EU in producing papers At the time this was argued from correlations and the patterns of the time series. Granger testing can now sharpen this.

8. Patterns are almost identical with a small lag. The Granger test can quantify this association

9. Causality Methods: Granger Tests Let y and x be stationary time series. To test the null hypothesis that x does not Granger-cause y, one first finds the proper lagged values of y to include in an autoregression of y:Let y and x be stationary time series. To test the null hypothesis that x does not Granger-cause y, one first finds the proper lagged values of y to include in an autoregression of y: y t = a 1 y t-1 + a 2 y t-2 + … +a k y t-k + residual ty t = a 1 y t-1 + a 2 y t-2 + … +a k y t-k + residual t Next, the autoregression is augmented by adding lagged values of x:Next, the autoregression is augmented by adding lagged values of x: y t = a 1 y t-1 + a 2 y t-2 + … +a k y t-k + b 1 x t-1 + … + b k x t-k + residual ty t = a 1 y t-1 + a 2 y t-2 + … +a k y t-k + b 1 x t-1 + … + b k x t-k + residual t One retains in this regression all lagged values of x that are individually significant according to their t-statistics, provided that they collectively add explanatory power to the regression according to an F-test. The Granger function in the R package was usedhereOne retains in this regression all lagged values of x that are individually significant according to their t-statistics, provided that they collectively add explanatory power to the regression according to an F-test. The Granger function in the R package was used here.

10. Illustration of Granger Tests Significance probabilities p for Granger tests of Government GERD component (USGGFF) causing papers (USPFF). FF means second differences, necessary for stationarity. The “→” symbol means "Granger-causes.” This supports the conclusion in prior work, which attributed the EU passing of the US in papers in the mid-1990s due to a US shift from government to industrial funding of research.

11. Correlations for the Value- Added HT Manufacturing Indicator Coefficients of determination (R 2 in %) of HT exports and overall HT manufacturing with explanatory variables in 2009 (Log scale). Note that new value-added data has much stronger correlations. From Shelton & Fadel (2014)

12. Regression Line for the Value-Added HT Manufacturing Indicator Scattergram of HT manufacturing vs. BERD in 2009. log NM9 = 0.385 + 0.944 log BN9 (R 2 = 84.1%) PLACE FIGURE 3 HERE

13. Causality Results for Value- Added HT Manufacturing Does BERD Granger-cause HT manufacturing (Mfg), or the reverse? Entries are significance probabilities; p < 0.1 is significant (bold type). Causality can indeed run in either direction!

14. Do Traditional Scientometric Indicators Granger-Cause HT Manufacturing? For some countries, PCT international patent applications Granger-cause HT manufacturing. Entries are significance probabilities; p < 0.1 is significant (bold type).

15. Conclusions Statistical testing for causality can enrich study of the connections between scientometric indicators. Statistical testing for causality can enrich study of the connections between scientometric indicators. However, the Granger test is not a panacea; it often fails even when the data is detrended by taking differences. However, the Granger test is not a panacea; it often fails even when the data is detrended by taking differences. New data on value-added manufacturing outputs provides some evidence that R&D funding, and indicators like patents, do produce national benefits that the public most cares about: jobs and viability of a nation’s high-tech industries. New data on value-added manufacturing outputs provides some evidence that R&D funding, and indicators like patents, do produce national benefits that the public most cares about: jobs and viability of a nation’s high-tech industries.

16. Key References Foland, P. & Shelton R D. (2010). Why is Europe so efficient at producing scientific papers, and does this explain the European Paradox? 11th International Conference on S&T Indicators, Leiden.Foland, P. & Shelton R D. (2010). Why is Europe so efficient at producing scientific papers, and does this explain the European Paradox? 11th International Conference on S&T Indicators, Leiden. Shelton, R D. & Fadel, T R. (2014). Which scientometric indicators best explain national performance of high-tech outputs? 15th Collnet Conference, Ilmenau, Germany, Sept. 3, 2014.Shelton, R D. & Fadel, T R. (2014). Which scientometric indicators best explain national performance of high-tech outputs? 15th Collnet Conference, Ilmenau, Germany, Sept. 3, 2014. These papers and others posted at http://itri2.com/s These papers and others posted at http://itri2.com/s http://itri2.com/s

17. Another Example of Granger Tests Does higher education spending Granger cause scientific papers? (HERD stands for higher education spending on R&D)

18. Second Example of Granger Tests Significance probabilities for Granger tests of GERD (G) causing papers (P) in the Web of Science (or the reverse) for 1983-2012. All used second differences for necessary detrending. Countries are the US, EU28, and Japan. As expected, R&D funding “Granger-causes” papers, but not the reverse.