156 The Impact of the Act for the Development of Biotech and New Pharmaceuticals Industry on Firm Innovation in Taiwan Panel B: DID Regression Results for Adjusted Patent Citations: Intra-industry Analysis One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms (1) (2) (1) (2) (1) (2) (1) (2) Observations 903 858 1,364 1,298 1,817 1,725 2,358 2,244 Adjusted R2 0.0043 0.0053 0.0156 0.0142 0.0142 0.0148 0.0134 0.0147 Note: This table presents the panel regression results of the intra-industry analysis, including regression of R&D investment and adjusted citations for one, two, three and four matching control firms. The dependent variable of Panel B is the natural logarithm of 1+adjusted patent citation, i.e. 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 treated group and 0 otherwise. The treated firms are approved biopharmaceutical firms and the control firms are unapproved biopharmaceutical firms. The definitions of variables are presented in Appendix Table A1. Numbers in parentheses are p-values. ***,**, and * denote significance at the 1%, 5%, and 10% levels, respectively. The results of other control variables in Panel A of Table 4 are consistent with economic intuition and findings of previous studies. First, the coefficient of the natural logarithm of total assets is significantly negative, meaning that the R&D investment of firms increases when firm size decreases. This result confirms that small firms are more engaged in innovation activities, which is consistent with Shefer and Frenkel (2005) and Hægeland and Møen (2007). Second, the significantly positive lagged R&D expenditure indicates the accumulative effect of R&D, which is consistent with Chan, Lakonishok, and Sougiannis (2001). Third, R&D investment and ROA are negatively correlated because R&D is spent in the income statement. Panel B of Table 4 shows no significant coefficients of After, Treatment, and After×Treatment, indicating that the established Biopharmaceutical Act does not have any effect on the adjusted patent citations of approved biopharmaceutical firms. This result is not consistent with the result of the DID estimator, which shows the positive effect of the Biopharmaceutical Act. To explain the inconsistent outcomes, Buckley and Shang (2002) argue that the DID estimator may not be sufficient to capture the results of the study because this method neglects the heterogeneous dynamics of other important variables. Table 4 DID Regression Result: Intra-industry Analysis (cont.)
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