63 above, we only managed to obtain citation data on USPTO utility patents, from Jaffe and Trajtenberg (2002). While utility patents constitute only a potion of total patents, they are likely to be the largest portion. Therefore, even though the patent count variable includes all types of patents, we include the logarithm of the utility patent citations as a separate regressor in order to account for the differences in the quality of patents. The results of the regressions that account for patent quality are presented in Regs A2.3 and A2a.3. We see that the citations variable returns an estimated coefficient that is negative, which is theoretically inconsistent, and is not statistically significant. The estimated coefficient of the patent count variable remains positive and highly statistically significant. In addition, note that in both regressions, there is a substantial jump in the R- square of more than 13 percentage points when the citation variable is included as an additional regressor. This leads us to believe that the inclusion of the citations variable does account for the quality of innovation, but the variable may be an inaccurate measure of innovation quality. In Reg A2.4 and Reg A2a.4, we account for possible interaction effects between the total patent count and the number of utility patent citations received. The coefficient of this interaction variable is negative, again theoretically inconsistent and not statistically significant. Also, the magnitude of the coefficient is extremely small. Note that the inclusion of the interaction variable has no effect on the qualitative nature of the rest of the variables.

For the next set of regressions, Regs A2.5 through A2.7 and Regs A2a.5 through A2a.7, instead of using the total patent count as the basic patent variable, we used the number of USPTO utility patents granted. The results are largely similar as before. The utility patent variable coefficient consistently bears the positive sign and is statistically significant, while both the citations and interaction variables have coefficients that are of the incorrect negative sign and are not statistically significant. However, as in the case when using total patent counts, we see a large increase in the R-square when the citations variable is added as an additional regressor.

The next innovation indicator we used was the number of published scientific and technical journal articles, the regression results of which are shown in Reg A2.8 and A2a.8. We see that the estimated coefficient of the journals variable is positive and highly statistically significant. In Regs A2.9 through A2.12 and Regs A2a.9 through A2a.12, we turned to using royalty receipts and payments as measures of domestic innovation and technological adaptation, respectively. Note that the royalty payments variable, unlike the rest of the innovation variables, is our only explicit measure of technological adaptation. We see that when the logarithm of the royalty payments is included as the sole innovation variable, it produced a coefficient that is positive and statistically significant. This would suggest that technological adaptation is an important determinant of long-term economic growth. Similarly, when the logarithm of the royalty receipts is included as the sole innovation variable, it produced a coefficient that is positive and highly statistically significant. In Reg A2.11 and Reg A2a.11, we included both royalty payments and receipts as separate explanatory variables in the regression