164 The Impact of the Act for the Development of Biotech and New Pharmaceuticals Industry on Firm Innovation in Taiwan Panel B.2 DID Regression Results for Adjusted Patent Citations in the Inter-industry Analysis: High R&D Intensity Firms One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms (1) (2) (1) (2) (1) (2) (1) (2) Tobin’s Qt-1 -0.0002 -0.0001 0.0001 -0.0002 (0.8629) (0.9398) (0.9691) (0.8376) Observations 347 337 510 495 693 671 960 924 Adjusted R2 0.0641 0.0589 0.0842 0.0831 0.0559 0.0566 0.0262 0.0268 Note: This table presents the panel regression results of the subsamples divided by different R&D intensity levels, including low R&D intensity firms and high R&D intensity firms. Panels A.1 and A.2 show the regression results that explain the R&D investment of low and high R&D intensity firms, respectively. Panels B.1 and B.2 show the regression results that explain the adjusted patent citation for these two subsamples. The dependent variable of Panels B.1 and B.2 is LN (1+adjusted patent citation). The regression is shown in equation (1) of Section 3.3.4. Aftert = 1 if the firm is in the approval year or after approval year and 0 otherwise; Treatmenti = 1 if the firm is in the treated group and 0 otherwise. The treated firms are approved biopharmaceutical firms and control firms are unapproved biopharmaceutical firms. The definitions of the variables are presented in Appendix Table A1. Numbers in parentheses are p-values. ***,**, and * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A.1 of Table 6 shows that the coefficients of interaction term, After×Treatment are significant and positive. Panel A.2 of this table shows that this interaction term has no significant coefficients. These findings show that the approved biopharmaceutical firms with low R&D intensity are the group that captures the main results: this group is motivated more to increase innovation investment. In addition, the Biopharmaceutical Act does not motivate the biopharmaceutical firms with high R&D intensity to raise their innovation input. The subsample analysis findings for different R&D levels are consistent with Hægeland and Møen (2007), who find that R&D tax credit policy motivates low R&D firms more than high R&D firms because this policy decreases the marginal costs of R&D more for low R&D firms. The coefficients of Treatment in Panel B.1 of Table 6 are positive and significant. These results indicate that in the group of low R&D intensity firms, the approved firms Table 6 DID Regression Result of Intra-industry: Subsample Analysis for Different R&D Intensity Level (cont.)
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