4.3.1 Difference-in-differences Estimator (DID Estimator)
Table 3 presents the DID estimator of innovation for intra-industry analysis. In order to consider the time span effect of the Biopharmaceutical Act, we consider the time interval of the pre-event and post-event year from (t-1, t+1) to (t-3, t+3) where t is the event year, i.e. the approval year, when the biopharmaceutical firm is approved by the Biopharmaceutical Act. Panel A and B present the DID estimator of the R&D investment and the DID estimator of the patent adjusted citations, respectively.
Table 3 DID Estimator: Intra-industry Analysis
Panel A: DID Estimator of R&D Investment: Intra-industry Analysis
t-1 t+1 Differences t-2 t+2 Differences t-3 t+3 Differences
Treated 14.3224 14.7030 0.3806 13.5214 14.6223 1.1010 13.0075 13.4002 0.3927
(0.8686) (0.5928) (0.8183)
Control 1 15.1741 10.3605 -4.8135** 13.6871 10.7748 -2.9123 11.8360 10.5049 -1.3311
(0.0222) (0.1002) (0.3658)
Control 2 14.3681 10.5547 -3.8134** 12.4338 10.1346 -2.2992* 10.9985 9.7941 -1.2043
(0.0216) (0.0685) (0.2863)
Control 3 13.9605 11.2267 -2.7338** 11.6300 10.8426 -0.7875 10.3005 10.3891 0.0886
(0.0392) (0.4661) (0.9234)
Control 4 13.1208 10.3647 -2.7561** 11.2082 10.1346 -1.0736 10.2415 9.7688 -0.4726
(0.0231) (0.2412) (0.5553)
Diff.1 -0.8517 4.3425 5.1942** -0.1657 3.8476 4.0133* 1.1715 2.8953 1.7238
(0.0394) (0.0956) (0.3540)
Diff.2 -0.0458 4.1483 4.1941* 1.0876 4.4877 3.4002 2.0091 3.6061 1.5970
(0.0646) (0.1148) (0.3541)
Diff.3 0.3619 3.4763 3.1144 1.8914 3.7798 1.8884 2.7070 3.0111 0.3041
(0.1319) (0.3653) (0.8500)
Diff.4 1.2016 4.3383 3.1367 2.3132 4.4877 2.1745 2.7661 3.6314 0.8653
(0.1240) (0.2695) (0.5662)
Panel B: DID Estimator of Adjusted Patent Citation: Intra-industry Analysis
t-1 t+1 Differences t-2 t+2 Differences t-3 t+3 Differences
Treated 0.0253 0.0648 0.0395 0.0348 0.0477 0.0129 0.0689 0.0796 0.0107
(0.2171) (0.5468) (0.7261)
Panel B: DID Estimator of Adjusted Patent Citation: Intra-industry Analysis
t-1 t+1 Differences t-2 t+2 Differences t-3 t+3 Differences
Control 1 0.1224 0.0219 -0.1004 0.0617 0.0150 -0.0467 0.0099 0.0158 0.0060
Note: This table presents the DID estimator of innovation for intra-industry analysis. Panels A and B present the DID estimator of the R&D investment and the DID estimator of adjusted patent citations, respectively. t is the event year, i.e. the year in which the Biopharmaceutical firm is approved by the Biopharmaceutical Act. Treated represents the treated firms, i.e. approved biopharmaceutical firms. Control 1, Control 2, Control 3, and Control 4 respectively represent one, two, three, and four control firms matched to each treated firm. The control firms in the intra-industry analysis are unapproved biopharmaceutical firms. Diff.1, Diff.2, Diff.3, and Diff.4 represent the mean difference of variables between Treated and Control 1, Control 2, Control 3, and Control 4, respectively. Numbers in parentheses are p-values. ***,**, and * denote significance at the 1%, 5%, and 10% levels, respectively.
In Panel A of Table 3, the approved biopharmaceutical firms do not change R&D investment significantly after the approval year. However, when we consider the time interval (t-1, t+1), the unapproved biopharmaceutical firms exhibit significantly lower R&D investment after the approval year. The DID estimators of the R&D investment for one and two control matched firms are significant. This result implies that compared with unapproved biopharmaceutical firms, approved biopharmaceutical firms have a significantly higher proportion of R&D expenditures to total assets after the
Table 3 DID Estimator: Intra-industry Analysis (cont.)
Biopharmaceutical Act.
Panel B of Table 3 shows that approved biopharmaceutical firms do not significantly change their adjusted patent citations, while unapproved biopharmaceutical firms experience significantly reduced adjusted patent citations for the time interval (t-1, t+1).
In this short time interval, the DID estimators of adjusted patent citations are significantly positive, implying that the approved biopharmaceutical firms have significantly higher innovation output than unapproved biopharmaceutical firms after the Biopharmaceutical Act.
Accordingly, the results of the DID estimators show that compared to unapproved biopharmaceutical firms, the Biopharmaceutical Act encourages approved biopharmaceutical firms to increase their input into innovation activities, leading to higher innovation quality. In addition, both panels of Table 3 show that there are no significant DID estimators for the time intervals (t-2, t+2) and (t-3, t+3), implying that the influence of the Biopharmaceutical Act on innovation input and output has only a short-term effect.
This result is consistent with David et al. (2000), who find that the recipients of tax credits tend to concentrate on projects with short-term prospects.
4.3.2 Difference-in-differences Regression (DID Regression)
To obtain more accurate results, we conduct the DID regression by additionally considering the heterogeneous dynamics of other variables for innovation measures. Table 4 shows the DID regression results for the intra-industry analysis. Panel A of Table 4 shows the DID regression results for R&D investment. For matched firms the significantly negative coefficients of After show that all biopharmaceutical firms reduce their R&D investments after the Biopharmaceutical Act. In addition, the significantly negative coefficients of Treatment indicate that the approved biopharmaceutical firms on average have lower R&D investments than the unapproved biopharmaceutical firms. Further, the significantly positive coefficients of the interaction term, After×Treatment, show that compared with unapproved biopharmaceutical firms, approved biopharmaceutical firms have significantly higher R&D investments after the approval. By combining the coefficient results of Treatment and After×Treatment, we find that relative to control firms, the Biopharmaceutical Act encourages the group of treated firms, which have lower R&D intensity, to improve their R&D input. Accordingly, unlike the unapproved biopharmaceutical firms, the approved biopharmaceutical firms, which respond to the exogenous shock of the Biopharmaceutical Act and receive its benefits, are induced to improve their input into innovation activities.
Table 4 DID Regression Result: Intra-industry Analysis
Panel A: DID Regression Results for R&D Investment: Intra-industry Analysis
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert -2.0664** -1.9547** -2.0441*** -1.9966*** -2.2199*** -2.1781*** -1.7430*** -1.7228***
(0.0232) (0.0312) (0.0012) (0.0016) (0.0000) (0.0000) (0.0001) (0.0001) Treatmenti -3.4668*** -2.7768*** -2.6599*** -2.1885*** -2.6303*** -2.1488*** -2.0912*** -1.7150***
(0.0000) (0.0009) (0.0001) (0.0013) (0.0000) (0.0007) (0.0003) (0.0032) Aftert × Treatmenti 3.9568*** 3.9036*** 3.5181*** 3.5151*** 3.1617*** 3.1731*** 2.8590*** 2.8811***
(0.0003) (0.0004) (0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0002) LN (TA)t -1.4952*** -1.4720*** -1.2323*** -1.2478*** -0.8971*** -0.9764*** -0.6687*** -0.7396***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) RDt-1 0.1601*** 0.1623*** 0.2039*** 0.2060*** 0.2442*** 0.2461*** 0.2701*** 0.2716***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ROAt -0.4388*** -0.4344*** -0.3842*** -0.3813*** -0.3642*** -0.3604*** -0.3367*** -0.3337***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Debt Ratiot 0.0424*** 0.0326*** 0.0318*** 0.0259***
(0.0006) (0.0009) (0.0002) (0.0003)
Observations 1,350 1,350 2,019 2,019 2,689 2,689 3,474 3,474
Adjusted R2 0.6287 0.6317 0.6179 0.6198 0.6141 0.6161 0.5989 0.6003
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)
Aftert -0.0210 -0.0177 -0.0381** -0.0378** -0.0145 -0.0134 0.0016 0.0026 (0.2324) (0.3256) (0.0169) (0.0211) (0.2616) (0.3065) (0.8751) (0.0507)
Treatmenti 0.0102 0.0199 -0.0153 -0.0084 -0.0047 0.0061 0.0026 0.0148
(0.4913) (0.2118) (0.3315) (0.6229) (0.7443) (0.6924) (0.8443) (0.2905) Aftert × Treatmenti -0.0006 -0.0074 0.0169 0.0132 0.0027 -0.0050 -0.0071 -0.0146 (0.9769) (0.7223) (0.4215) (0.5529) (0.8860) (0.8065) (0.6848) (0.4246) LN
(1+NetSalest-1) 0.0026* 0.0036** 0.0004 0.0013 0.0004 0.0020 -0.0001 0.0017 (0.0813) (0.0262) (0.7847) (0.4355) (0.7384) (0.1607) (0.9222) (0.1775)
RDt-1 0.0006 0.0007* 0.0006 0.0007* 0.0005 0.0007* 0.0006* 0.0009***
(0.1040) (0.0713) (0.1440) (0.0977) (0.2244) (0.0895) (0.0671) (0.0098)
Tobin’s Qt-1 -0.0006 -0.0008 -0.0003 -0.0008
(0.6629) (0.5914) (0.8329) (0.5517)
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.)
Accordingly, the DID regression, which incorporates other control variables, may obtain more accurate results than the DID estimator. Thus, the results of this study showing that the Biopharmaceutical Act does not influence the innovation quality of the approved biopharmaceutical firms. In sum, the Biopharmaceutical Act encourages biopharmaceutical firms to expand innovation input but does not improve their innovation quality.
4.3.3 Subsample Analysis of Intra-industry
The subsection considers two intra-industry subsample analyses. By grouping firms with similar characteristics, these analyses may help to further realize which groups may dominate the main results of the sample. First, we consider that the subsamples are divided by different operating items. The different operating items in the biopharmaceutical industry may have different effects on the Biopharmaceutical Act’s encouragement of innovation activities. In Taiwan, the biopharmaceutical industry is usually divided into four groups: pharmaceuticals, medical equipment, applied biopharmaceutical, and others.
The approved biopharmaceutical firms in our data include 66 pharmaceutical firms, 15 medical equipment firms and 2 applied biopharmaceutical firms. We divide the approved biopharmaceutical firms into two subgroups: pharmaceutical and non-pharmaceutical firms, because the sample of medical equipment and applied biopharmaceutical firms was too small for the DID regression.27
Table 5 shows the DID regression results for the intra-industry analysis of pharmaceutical and non-pharmaceutical firms. In Panels A.1 and A.2, the dependent variable is R&D investment. In Panels B.1 and B.2, the dependent variable is LN (1+adjusted patent citation). The coefficients of the interaction term, After×Treatment, for two, three and four matched firms of Panel A.1 are significantly positive but those of Panel A.2 are not significant. These results indicate that relative to unapproved pharmaceutical firms, the Biopharmaceutical Act encourages the approved pharmaceutical firms to increase R&D investment.
27 For the PSM exercise, the control firms are matched using the same operating items as the treated firms. Thus, the control firms for the approved pharmaceutical (non-pharmaceutical) firms are the unapproved pharmaceutical (non-pharmaceutical) firms.
Table 5 DID Regression Result of Intra-industry: Subsample Analysis for Different Operating Items
Panel A.1 DID Regression Results for R&D Investment in the Intra-industry Analysis:
Pharmaceutical Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert 1.0945 1.4489 0.1463 0.1518 0.5109 0.5106 0.3880 0.3965
(0.2847) (0.1552) (0.6327) (0.8323) (0.3776) (0.3779) (0.4134) (0.4032)
Treatmenti 0.9153 2.0482** 0.7319 1.0929 0.9790 0.9375 0.9550 0.7290
(0.3179) (0.0316) (0.3308) (0.1607) (0.1545) (0.1842) (0.1278) (0.2560) Aftert × Treatmenti 1.2582 0.8770 1.9375** 1.8978* 1.5925* 1.5980* 1.7259** 1.7547**
(0.2991) (0.4671) (0.0489) (0.0536) (0.0751) (0.0742) (0.0342) (0.0313) LN (TA)t -1.8671*** -1.9708*** -1.3008*** -1.3691*** -1.2887*** -1.2819*** -1.1806*** -1.1328***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) RDt-1 0.1215*** 0.1265*** 0.1423*** 0.1425*** 0.1733*** 0.1731*** 0.2153*** 0.2139***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ROAt -0.5014*** -0.4894*** -0.4562*** -0.4529*** -0.4259*** -0.4262*** -0.3832*** -0.3855***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Debt Ratiot 0.0603*** 0.0193* -0.0025 -0.0131*
(0.0001) (0.0760) (0.7950) (0.0996)
Observations 1,009 1,009 1,508 1,508 2,004 2,004 2,604 2,604
Adjusted R2 0.6848 0.6896 0.6744 0.6749 0.6797 0.6795 0.6559 0.6561
Panel A.2 DID Regression Results for R&D Investment in the Intra-industry Analysis: Non-pharmaceutical Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert 0.9667 0.9603 -0.4504 -0.4521 -0.5938 -0.5917 -0.4110 -0.4054
(0.4181) (0.4219) (0.6327) (0.6318) (0.4698) (0.4719) (0.5408) (0.5465) Treatmenti -0.7413 -0.7252 -1.0261 -1.0168 -0.7747 -0.7628 -0.3770 -0.3704 (0.4625) (0.4730) (0.2624) (0.2681) (0.3809) (0.3896) (0.6336) (0.6397) Aftert × Treatmenti -0.9789 -0.9027 -0.1675 -0.1932 -0.3556 -0.3896 -0.4983 -0.5708 (0.4734) (0.5110) (0.8940) (0.8788) (0.7711) (0.7521) (0.6496) (0.6051) LN (TA)t -1.7651*** -1.7208*** -1.4264*** -1.4365*** -1.7012*** -1.7108*** -1.7368*** -1.7417***
(0.0012) (0.0018) (0.0026) (0.0026) (0.0001) (0.0001) (0.0000) (0.0000)
Panel A.2 DID Regression Results for R&D Investment in the Intra-industry Analysis: Non-pharmaceutical Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
RDt-1 0.4421*** 0.4450*** 0.5135*** 0.5125*** 0.5375*** 0.5363*** 0.5680*** 0.5652***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ROAt -0.1356*** -0.1351*** -0.1359*** -0.1359*** -0.1104*** -0.1102*** -0.0883*** -0.0885***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Debt Ratiot 0.0101 -0.0029 -0.0031 -0.0072
(0.5505) (0.8553) (0.8300) (0.5431)
Observations 257 257 388 388 519 519 661 661
Adjusted R2 0.563 0.5618 0.5586 0.5574 0.5334 0.5325 0.5319 0.5314
Panel B.1 DID Regression Results for Adjusted Patent Citations in the Intra-industry Analysis: Pharmaceutical Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert 0.0194 0.0219 0.0278** 0.0291** 0.0216*** 0.0225*** 0.0179** 0.0183**
(0.1653) (0.1276) (0.0117) (0.0102) (0.0076) (0.0064) (0.0104) (0.0507) Treatmenti 0.0349*** 0.0415*** 0.0252** 0.0308*** 0.0287*** 0.0345*** 0.0270*** 0.0316***
(0.0035) (0.0012) (0.0209) (0.0089) (0.0014) (0.0004) (0.0020) (0.0009) Aftert × Treatmenti -0.0135 -0.0193 -0.0160 -0.0206 -0.0150 -0.0198 -0.0141 -0.0198 (0.3977) (0.2494) (0.2693) (0.1795) (0.2081) (0.1177) (0.2244) (0.1097) LN (1+NetSalest-1) 0.0007 0.0007 0.0009 0.0008 0.0009 0.0008 0.0013* 0.0012*
(0.5029) (0.5653) (0.3316) (0.4059) (0.2445) (0.3119) (0.0579) (0.0958) RDt-1 0.0000 0.0000 0.0006** 0.0005** 0.0005** 0.0005** 0.0005*** 0.0005**
(0.9018) (0.9934) (0.0298) (0.0489) (0.0116) (0.0209) (0.0072) (0.0197)
Tobin’s Qt-1 -0.0003 -0.0004 -0.0004 0.0004
(0.7849) (0.6862) (0.6309) (0.6149)
Observations 676 651 1,013 980 1,358 1,321 1,777 1,720
Adjusted R2 0.0142 0.0160 0.0212 0.0218 0.0221 0.0234 0.0324 0.0332
Table 5 DID Regression Result of Intra-industry: Subsample Analysis for Different Operating Items (cont.)
Panel B.2 DID Regression Results for Adjusted Patent Citations in the Intra-industry Analysis: Non-pharmaceutical Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert -0.0093 -0.0051 -0.0093 0.0010 -0.0054 0.0024 -0.0136 -0.0081
(0.7946) (0.8923) (0.7770) (0.9759) (0.8183) (0.9233) (0.5111) (0.0507) Treatmenti -0.0046 -0.0089 -0.0129 -0.0163 -0.0050 -0.0124 -0.0133 -0.0175 (0.8687) (0.7724) (0.6657) (0.6228) (0.8387) (0.6486) (0.5768) (0.4986) Afteri × Treatmenti -0.0065 0.0013 -0.0100 -0.0014 -0.0073 0.0048 0.0000 0.0109 (0.8666) (0.9754) (0.8105) (0.9758) (0.8330) (0.8980) (0.9996) (0.7587) LN (1+NetSalest-1) -0.0052 -0.0068* -0.0076*** -0.0107*** -0.0068*** -0.0107*** -0.0063*** -0.0092***
(0.1034) (0.0653) (0.0085) (0.0018) (0.0031) (0.0002) (0.0033) (0.0006)
RDt-1 -0.0006 -0.0005 -0.0016 -0.0020 -0.0007 -0.0013 -0.0004 -0.0006
(0.6938) (0.7309) (0.2574) (0.1968) (0.4952) (0.2618) (0.6872) (0.5561)
Tobin’s Qt-1 -0.0063 -0.0092* -0.0080* -0.0089**
(0.2233) (0.0997) (0.0938) (0.0495)
Observations 176 166 262 243 366 334 477 433
Adjusted R2 0.0675 0.0631 0.1044 0.1106 0.0744 0.0852 0.0407 0.0528 Note: This table presents the panel regression results of the subsamples divided by different operating items, including pharmaceutical firms and non-pharmaceutical firms in the intra-industry. Panels A.1 and A.2 show the regression results that explain the R&D investment for pharmaceutical firms and non-pharmaceutical firms, respectively. Panels B.1 and B.2 show the regression results that explain the adjusted patent citations 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, while 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 coefficients of Treatment in Panel B.1 of Table 5 are positive significantly.
These results imply that in the group of pharmaceutical firms, the approved firms always have higher innovation quality (i.e. patent adjusted citation) than the unapproved firms. In addition, in both Panel B.1 and B.2, the coefficients of the interaction term, Table 5 DID Regression Result of Intra-industry: Subsample Analysis for
Different Operating Items (cont.)
After×Treatment, are not significant. These results show that for both pharmaceutical and non-pharmaceutical firms, the Biopharmaceutical Act does not have any effect on innovation quality.
The second subsample analysis is related to the level of a firm’s R&D intensity.
The level of a firm’s R&D intensity appears to be relevant to the incentive effect of the Biopharmaceutical Act because the results in Table 4 show that the treated firms, which have lower R&D intensity, are more likely to be encouraged by the Biopharmaceutical Act. Therefore, we divide the sample into low and high R&D intensity groups. Table 6 shows the results of the DID regression for low and high R&D intensity firms in the intra-industry analysis.
Table 6 DID Regression Result of Intra-industry: Subsample Analysis for Different R&D Intensity Level
Panel A.1 DID Regression Result for R&D Investment in Inter-industry Analysis: Low R&D Intensity Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert 1.3733 2.2328** 0.8936 1.3061* 0.9967* 1.3142** 0.9299** 1.1481**
(0.1935) (0.0310) (0.6327) (0.0596) (0.0898) (0.0236) (0.0499) (0.0150) Treatmenti -2.2599** -0.4240 -1.5121** 0.1130 -1.2300* 0.0025 -0.7428 0.1777 (0.0140) (0.6495) (0.0325) (0.8764) (0.0597) (0.9970) (0.2006) (0.7659) Aftert × Treatmenti 2.9642** 2.2799* 2.8348*** 2.5107*** 2.4333*** 2.2252*** 2.3672*** 2.1840***
(0.0164) (0.0581) (0.0027) (0.0065) (0.0052) (0.0095) (0.0023) (0.0045) LN (TA)t -1.6298*** -1.8108*** -1.1485*** -1.5388*** -1.1644*** -1.4693*** -0.9882*** -1.2245***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) RDt-1 0.1710*** 0.1849*** 0.2128*** 0.2242*** 0.2617*** 0.2746*** 0.2878*** 0.2975***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ROAt -0.4561*** -0.4169*** -0.3771*** -0.3451*** -0.3382*** -0.3183*** -0.3015*** -0.2859***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Debt Ratiot 0.0977*** 0.0808*** 0.0689*** 0.0481***
(0.0000) (0.0000) (0.0000) (0.0000)
Observations 746 746 1,121 1,121 1,474 1,474 1,859 1,859
Adjusted R2 0.5761 0.5999 0.5265 0.5475 0.5174 0.5322 0.4932 0.5021
Panel A.2 DID Regression Result for R&D Investment in 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)
Aftert -1.2504 -1.3138 -2.6875** -2.5527** -2.1872** -2.0000** -1.7754** -1.4404*
(0.4433) (0.4196) (0.6327) (0.0301) (0.0214) (0.0327) (0.0222) (0.0601) Treatmenti 2.8124* 2.4456* 2.1704* 1.7589 2.4529** 2.0608* 2.6533** 2.2893**
(0.0561) (0.0990) (0.0911) (0.1677) (0.0366) (0.0751) (0.0135) (0.0307) Aftert × Treatmenti -1.2792 -1.4119 0.3047 -0.2847 -0.1899 -0.8479 -0.5431 -1.2447 (0.5014) (0.4574) (0.8514) (0.8602) (0.8980) (0.5625) (0.6877) (0.3515) LN (TA)t -1.2524** -1.4187** -1.1075*** -1.2407*** -1.2395*** -1.4262*** -1.0844*** -1.1568***
(0.0286) (0.0142) (0.0069) (0.0023) (0.0002) (0.0000) (0.0001) (0.0000) RDt-1 0.1344*** 0.1330*** 0.1671*** 0.1727*** 0.2057*** 0.2070*** 0.2513*** 0.2480***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ROAt -0.4505*** -0.4484*** -0.4149*** -0.4124*** -0.3762*** -0.3733*** -0.3241*** -0.3276***
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Debt Ratiot -0.0427* -0.0674*** -0.0798*** -0.0764***
(0.0723) (0.0001) (0.0000) (0.0000)
Observations 520 520 775 775 1,049 1,049 1,406 1,406
Adjusted R2 0.6598 0.6613 0.6445 0.6518 0.6456 0.656 0.6185 0.6295
Panel B.1 DID Regression Results for Adjusted Patent Citations in the Inter-industry Analysis: Low R&D Intensity Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert 0.0218 0.0261 0.0282** 0.0340** 0.0199* 0.0234** 0.0119 0.0150
(0.2603) (0.2000) (0.0423) (0.0185) (0.0542) (0.0280) (0.2128) (0.0507) Treatmenti 0.0299* 0.0363** 0.0282** 0.0363*** 0.0299*** 0.0372*** 0.0232** 0.0271**
(0.0521) (0.0293) (0.0248) (0.0081) (0.0038) (0.0010) (0.0264) (0.0188)
Table 6 DID Regression Result of Intra-industry: Subsample Analysis for Different R&D Intensity Level (cont.)
Panel B.1 DID Regression Results for Adjusted Patent Citations in the Inter-industry Analysis: Low R&D Intensity Firms
One Matched Firm Two Matched Firms Three Matched Firms Four Matched Firms
(1) (2) (1) (2) (1) (2) (1) (2)
Aftert × Treatmenti -0.0038 -0.0089 -0.0110 -0.0183 -0.0086 -0.0146 -0.0034 -0.0118 (0.8529) (0.6838) (0.5168) (0.3114) (0.5372) (0.3275) (0.8113) (0.4365) LN (1+NetSalest-1) 0.0006 0.0004 0.0013 0.0012 0.0010 0.0010 0.0016* 0.0017*
(0.6859) (0.8091) (0.2786) (0.3223) (0.2640) (0.3203) (0.0628) (0.0707)
RDt-1 0.0000 -0.0001 0.0001 0.0000 0.0002 0.0001 0.0003 0.0003
(0.9282) (0.8281) (0.8892) (0.9675) (0.6504) (0.7343) (0.3944) (0.5308)
Tobin’s Qt-1 -0.0006 -0.0002 -0.0005 0.0022
(0.7524) (0.8916) (0.7378) (0.1561)
Observations 505 480 765 728 1,031 984 1,294 1,229
Adjusted R2 0.0102 0.0110 0.0162 0.0187 0.0186 0.0196 0.0318 0.0340 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)
Aftert -0.0003 -0.0002 0.0115 0.0118 0.0100 0.0110 0.0105 0.0101
(0.9894) (0.9916) (0.5588) (0.5568) (0.4893) (0.4572) (0.3768) (0.0507) Treatmenti 0.0084 0.0092 -0.0185 -0.0218 -0.0051 -0.0072 -0.0033 -0.0013 (0.6106) (0.6049) (0.3451) (0.2996) (0.7577) (0.6831) (0.8270) (0.9348) Aftert × Treatmenti -0.0131 -0.0134 0.0006 0.0036 -0.0036 -0.0022 -0.0068 -0.0082 (0.5537) (0.5672) (0.9824) (0.8965) (0.8708) (0.9237) (0.7398) (0.6976) LN (1+NetSalest-1) -0.0019 -0.0019 -0.0043*** -0.0049*** -0.0034*** -0.0043*** -0.0035*** -0.0035***
(0.2042) (0.2381) (0.0077) (0.0057) (0.0075) (0.0030) (0.0018) (0.0053)
RDt-1 -0.0001 -0.0001 0.0001 0.0000 0.0001 -0.0001 -0.0001 -0.0001
(0.8032) (0.8450) (0.8172) (0.9608) (0.8829) (0.8145) (0.8257) (0.8592)
Table 6 DID Regression Result of Intra-industry: Subsample Analysis for Different R&D Intensity Level (cont.)
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.)
always have higher innovation quality (i.e. adjusted patent citations) than the unapproved firms. In addition, in both Panel B.1 and B.2, the coefficients of the interaction term, After×Treatment, are not significant. These results show that for both low and high R&D intensity firms, the Biopharmaceutical Act does not have any effect on the innovation quality.
In sum, the results from pharmaceutical and low R&D intensity firms help to explain the influence of the Biopharmaceutical Act on R&D investment. These two subsample findings may have similar economic implications because Yang et al. (2012) find that pharmaceutical firms usually have low R&D intensity.28 The pharmaceutical firms are more likely to have more serious R&D underinvestment than non-pharmaceutical firms because of higher risks and fewer successful cases of new medicine research, long periods required for innovations, and substantial investment necessaries. In addition, low R&D intensity firms tend to have greater R&D underinvestment. Thus, these findings for pharmaceutical and low R&D intensity firms imply that the firms with more serious R&D underinvestment problems receive greater encouragement from the Biopharmaceutical Act.