科学学研究 ›› 2026, Vol. 44 ›› Issue (6): 1129-1143.
收稿日期:2025-04-30
修回日期:2026-01-08
出版日期:2026-06-15
发布日期:2026-06-15
通讯作者:
杨云鹏(1991-),男,助理研究员,博士, E-mail:作者简介:杨维新(1976-),男,副教授、博士生导师。
基金资助:
YANG Wei-xin1, YANG Yun-peng2(
)
Received:2025-04-30
Revised:2026-01-08
Online:2026-06-15
Published:2026-06-15
摘要:
在大国博弈日益激烈的背景下,关税政策作为国际经济争端中的关键调控工具,其引发的政策不确定性对企业创新韧性产生深远影响。基于中国制造业A股上市公司数据,运用文本分析法构建核心解释变量,采用双重差分模型识别关税冲击对企业创新韧性的因果效应。实证结果表明:关税冲击显著增强中国制造业上市企业的创新韧性,印证“逆境激发创新”的理论观点。机制分析显示,这种影响通过宏观政策不确定性与微观公司治理的双重路径实现。研究进一步发现,连锁董事网络与管理层过度自信会强化创新韧性,而高管团队断裂带则会削弱该效应。本研究为理解关税冲击与创新韧性的内在关联提供新经验证据,对政策制定者应对贸易摩擦、营造创新友好型环境提供理论支持。
中图分类号:
杨维新, 杨云鹏. 外部关税冲击、制造企业治理与创新韧性[J]. 科学学研究, 2026, 44(6): 1129-1143.
YANG Wei-xin, YANG Yun-peng. External tariff shocks, manufacturing firm governance, and innovation resilience[J]. Studies in Science of Science, 2026, 44(6): 1129-1143.
| 变量类型 | 变量名称 | 变量代码 | 变量定义 | 数据来源 |
|---|---|---|---|---|
| 被解释变量1 | 研发支出水平 | RD | ln(1+RD) | Wind数据库 |
| 被解释变量2 | 研发支出的投入强度 | Inv | 研发支出合计/营业收入 | Wind数据库 |
| 核心解释变量1 | 中国制造业上市公司是否 受到了关税冲击的影响 | Treat | 若Treat=1,则该上市公司受关税冲击的影响; 若Treat=0,则表明不受影响 | 巨潮资讯网、中国证券报官网 |
| 核心解释变量2 | 中国制造业上市公司受到 关税冲击影响的强度 | Strength1 | ln(1+Strength) | 巨潮资讯网、中国证券报官网等 |
| 控制变量 | 公司规模 | Size | 对年度总资产进行自然对数处理 | Wind数据库 |
| 控制变量 | 资产负债率 | Lev | 年末总负债/年末总资产 | Wind数据库 |
| 控制变量 | 总资产净利润率 | Roa | 净利润/总资产平均余额 | Wind数据库 |
| 控制变量 | 独立董事比例 | Indep | 独立董事/董事人数 | Wind数据库 |
| 控制变量 | 托宾Q值 | TobinQ | (流通股市值+非流通股股份数× 每股净资产+负债账面值)/总资产 | Wind数据库 |
| 控制变量 | 公司成立年限 | Firmage | ln(当年年份-公司成立年份+1) | Wind数据库 |
| 控制变量 | 第一大股东持股比例 | Top1 | 第一大股东持股数量/总股数 | Wind数据库 |
表1 变量定义及数据来源汇总
Table 1 Summary of variable definitions and data sources
| 变量类型 | 变量名称 | 变量代码 | 变量定义 | 数据来源 |
|---|---|---|---|---|
| 被解释变量1 | 研发支出水平 | RD | ln(1+RD) | Wind数据库 |
| 被解释变量2 | 研发支出的投入强度 | Inv | 研发支出合计/营业收入 | Wind数据库 |
| 核心解释变量1 | 中国制造业上市公司是否 受到了关税冲击的影响 | Treat | 若Treat=1,则该上市公司受关税冲击的影响; 若Treat=0,则表明不受影响 | 巨潮资讯网、中国证券报官网 |
| 核心解释变量2 | 中国制造业上市公司受到 关税冲击影响的强度 | Strength1 | ln(1+Strength) | 巨潮资讯网、中国证券报官网等 |
| 控制变量 | 公司规模 | Size | 对年度总资产进行自然对数处理 | Wind数据库 |
| 控制变量 | 资产负债率 | Lev | 年末总负债/年末总资产 | Wind数据库 |
| 控制变量 | 总资产净利润率 | Roa | 净利润/总资产平均余额 | Wind数据库 |
| 控制变量 | 独立董事比例 | Indep | 独立董事/董事人数 | Wind数据库 |
| 控制变量 | 托宾Q值 | TobinQ | (流通股市值+非流通股股份数× 每股净资产+负债账面值)/总资产 | Wind数据库 |
| 控制变量 | 公司成立年限 | Firmage | ln(当年年份-公司成立年份+1) | Wind数据库 |
| 控制变量 | 第一大股东持股比例 | Top1 | 第一大股东持股数量/总股数 | Wind数据库 |
| 变量 | 观测值 | 均值 | 标准差 | 最小值 | 最大值 |
|---|---|---|---|---|---|
| RD | 8829 | 18.224 | 1.394 | 14.378 | 23.491 |
| treat | 8829 | 0.408 | 0.491 | 0 | 1 |
| inv | 8829 | 0.023 | 0.017 | 0 | 0.161 |
| size | 8829 | 22.298 | 1.155 | 20.136 | 27.547 |
| lev | 8829 | 0.398 | 0.182 | 0.06 | 1.352 |
| roa | 8829 | 0.042 | 0.063 | -0.25 | 0.669 |
| indep | 8829 | 0.376 | 0.056 | 0.333 | 0.8 |
| tobinq | 8829 | 2.204 | 1.521 | 0.852 | 27.338 |
| firmage | 8829 | 2.906 | 0.298 | 2.079 | 3.989 |
| top1 | 8829 | 0.324 | 0.138 | 0.081 | 0.85 |
表2 主要变量的描述性统计
Table 2 Descriptive statistics of main variables
| 变量 | 观测值 | 均值 | 标准差 | 最小值 | 最大值 |
|---|---|---|---|---|---|
| RD | 8829 | 18.224 | 1.394 | 14.378 | 23.491 |
| treat | 8829 | 0.408 | 0.491 | 0 | 1 |
| inv | 8829 | 0.023 | 0.017 | 0 | 0.161 |
| size | 8829 | 22.298 | 1.155 | 20.136 | 27.547 |
| lev | 8829 | 0.398 | 0.182 | 0.06 | 1.352 |
| roa | 8829 | 0.042 | 0.063 | -0.25 | 0.669 |
| indep | 8829 | 0.376 | 0.056 | 0.333 | 0.8 |
| tobinq | 8829 | 2.204 | 1.521 | 0.852 | 27.338 |
| firmage | 8829 | 2.906 | 0.298 | 2.079 | 3.989 |
| top1 | 8829 | 0.324 | 0.138 | 0.081 | 0.85 |
| 因变量 | (1) RD | (2) RD | (3) RD | (4) RD | (5) RD | (6) RD | (7) RD |
|---|---|---|---|---|---|---|---|
| treat×post | 0.474*** | 0.061*** | 0.198*** | 0.623*** | 0.052*** | 0.026** | 0.091*** |
| (13.607) | (4.171) | (5.071) | (36.043) | (3.227) | (2.289) | (5.255) | |
| inv | 45.330*** | 45.306*** | 38.609*** | ||||
| (121.888) | (120.960) | (87.440) | |||||
| size | 0.938*** | 0.932*** | 0.967*** | 0.811*** | |||
| (139.054) | (136.138) | (82.043) | (47.673) | ||||
| lev | -0.158*** | -0.146*** | -0.253*** | -0.204*** | |||
| (-3.715) | (-3.443) | (-5.999) | (-3.371) | ||||
| roa | 0.293*** | 0.347*** | 0.174** | 0.198** | |||
| (2.690) | (3.163) | (2.505) | (2.002) | ||||
| indep | -0.150 | -0.164 | 0.033 | -0.177 | |||
| (-1.381) | (-1.506) | (0.325) | (-1.236) | ||||
| tobinq | -0.036*** | -0.040*** | -0.007** | 0.013*** | |||
| (-8.001) | (-8.485) | (-2.114) | (2.768) | ||||
| firmage | -0.093*** | -0.115*** | 0.135*** | -0.159 | |||
| (-4.486) | (-5.218) | (3.490) | (-1.176) | ||||
| top1 | 0.018 | 0.038 | -0.040 | 0.035 | |||
| (0.393) | (0.838) | (-0.583) | (0.358) | ||||
| 18.115*** | -3.295*** | 18.179*** | 18.081*** | -3.098*** | -4.521*** | 0.694 | |
| (1081.660) | (-21.484) | (1067.252) | (2680.780) | (-19.349) | (-19.601) | (1.308) | |
| FirmFE | 否 | 是 | 否 | 是 | 否 | 是 | 是 |
| YearFE | 否 | 是 | 是 | 否 | 是 | 否 | 是 |
| Controls | 否 | 否 | 否 | 否 | 是 | 是 | 是 |
| N | 8829 | 8607 | 8829 | 8814 | 8607 | 8587 | 8587 |
| R2 | 0.020 | 0.839 | 0.053 | 0.866 | 0.840 | 0.962 | 0.924 |
表3 双重差分模型的基准回归结果
Table 3 Baseline regression results of the difference-in-differences model
| 因变量 | (1) RD | (2) RD | (3) RD | (4) RD | (5) RD | (6) RD | (7) RD |
|---|---|---|---|---|---|---|---|
| treat×post | 0.474*** | 0.061*** | 0.198*** | 0.623*** | 0.052*** | 0.026** | 0.091*** |
| (13.607) | (4.171) | (5.071) | (36.043) | (3.227) | (2.289) | (5.255) | |
| inv | 45.330*** | 45.306*** | 38.609*** | ||||
| (121.888) | (120.960) | (87.440) | |||||
| size | 0.938*** | 0.932*** | 0.967*** | 0.811*** | |||
| (139.054) | (136.138) | (82.043) | (47.673) | ||||
| lev | -0.158*** | -0.146*** | -0.253*** | -0.204*** | |||
| (-3.715) | (-3.443) | (-5.999) | (-3.371) | ||||
| roa | 0.293*** | 0.347*** | 0.174** | 0.198** | |||
| (2.690) | (3.163) | (2.505) | (2.002) | ||||
| indep | -0.150 | -0.164 | 0.033 | -0.177 | |||
| (-1.381) | (-1.506) | (0.325) | (-1.236) | ||||
| tobinq | -0.036*** | -0.040*** | -0.007** | 0.013*** | |||
| (-8.001) | (-8.485) | (-2.114) | (2.768) | ||||
| firmage | -0.093*** | -0.115*** | 0.135*** | -0.159 | |||
| (-4.486) | (-5.218) | (3.490) | (-1.176) | ||||
| top1 | 0.018 | 0.038 | -0.040 | 0.035 | |||
| (0.393) | (0.838) | (-0.583) | (0.358) | ||||
| 18.115*** | -3.295*** | 18.179*** | 18.081*** | -3.098*** | -4.521*** | 0.694 | |
| (1081.660) | (-21.484) | (1067.252) | (2680.780) | (-19.349) | (-19.601) | (1.308) | |
| FirmFE | 否 | 是 | 否 | 是 | 否 | 是 | 是 |
| YearFE | 否 | 是 | 是 | 否 | 是 | 否 | 是 |
| Controls | 否 | 否 | 否 | 否 | 是 | 是 | 是 |
| N | 8829 | 8607 | 8829 | 8814 | 8607 | 8587 | 8587 |
| R2 | 0.020 | 0.839 | 0.053 | 0.866 | 0.840 | 0.962 | 0.924 |
| 第一阶段 | 第二阶段 | |||
|---|---|---|---|---|
| 因变量 | treat×post | RD | inv | |
| 工具变量 | 0.985*** (0.051) | |||
| treat×post | 0.218*** (0.083) | 0.004** (0.001) | ||
| size | 0.050*** (0.011) | 0.837*** (0.019) | -0.004*** (0.000) | |
| lev | -0.027 (0.040) | -0.245*** (0.065) | 0.000 (0.001) | |
| roa | -0.029 (0.059) | 0.087 (0.095) | -0.001 (0.002) | |
| indep | 0.137 (0.093) | -0.038 (0.150) | -0.005* (0.003) | |
| tobinq | 0.007** (0.003) | 0.010* (0.005) | 0.001*** (0.000) | |
| firmage | 0.098 (0.084) | -0.191 (0.135) | 0.003 (0.002) | |
| top1 | -0.068 (0.065) | 0.063 (0.105) | 0.003 (0.002) | |
| FirmFE | 是 | 是 | 是 | |
| YearFE | 是 | 是 | 是 | |
| LMstatistic | 360.144 | |||
| WaldF | 378.108 | |||
| N | 8582 | 8582 | 8582 | |
| R2 | 0.440 | 0.484 | 0.080 | |
表4 工具变量回归结果
Table 4 Instrumental variable regression results
| 第一阶段 | 第二阶段 | |||
|---|---|---|---|---|
| 因变量 | treat×post | RD | inv | |
| 工具变量 | 0.985*** (0.051) | |||
| treat×post | 0.218*** (0.083) | 0.004** (0.001) | ||
| size | 0.050*** (0.011) | 0.837*** (0.019) | -0.004*** (0.000) | |
| lev | -0.027 (0.040) | -0.245*** (0.065) | 0.000 (0.001) | |
| roa | -0.029 (0.059) | 0.087 (0.095) | -0.001 (0.002) | |
| indep | 0.137 (0.093) | -0.038 (0.150) | -0.005* (0.003) | |
| tobinq | 0.007** (0.003) | 0.010* (0.005) | 0.001*** (0.000) | |
| firmage | 0.098 (0.084) | -0.191 (0.135) | 0.003 (0.002) | |
| top1 | -0.068 (0.065) | 0.063 (0.105) | 0.003 (0.002) | |
| FirmFE | 是 | 是 | 是 | |
| YearFE | 是 | 是 | 是 | |
| LMstatistic | 360.144 | |||
| WaldF | 378.108 | |||
| N | 8582 | 8582 | 8582 | |
| R2 | 0.440 | 0.484 | 0.080 | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| RD | inv | RD (PSM-DID) | inv (PSM-DID) | 剔除2020 后的RD | 剔除2020 后的inv | |
| strength1 | 0.055*** | |||||
| (4.731) | ||||||
| size | 0.812*** | -0.004*** | 0.811*** | -0.004*** | 0.805*** | -0.004*** |
| (47.728) | (-11.901) | (47.655) | (-11.890) | (41.776) | (-11.066) | |
| lev | -0.206*** | 0.000 | -0.205*** | 0.001 | -0.126* | 0.001 |
| (-3.396) | (0.429) | (-3.375) | (0.483) | (-1.885) | (0.653) | |
| roa | 0.192* | 0.000 | 0.195** | 0.000 | 0.208* | 0.002 |
| (1.935) | (0.047) | (1.970) | (0.022) | (1.875) | (1.127) | |
| indep | -0.182 | -0.005* | -0.177 | -0.005* | -0.276* | -0.004 |
| (-1.272) | (-1.788) | (-1.239) | (-1.755) | (-1.749) | (-1.516) | |
| tobinq | 0.013*** | 0.001*** | 0.014*** | 0.001*** | 0.018*** | 0.001*** |
| (2.759) | (6.279) | (2.891) | (6.376) | (3.163) | (5.341) | |
| firmage | -0.152 | 0.005* | -0.161 | 0.004* | -0.117 | 0.005* |
| (-1.126) | (1.907) | (-1.193) | (1.774) | (-0.739) | (1.804) | |
| top1 | 0.032 | 0.002 | 0.037 | 0.002 | 0.004 | 0.002 |
| (0.325) | (1.166) | (0.374) | (1.253) | (0.037) | (0.804) | |
| treat×post | 0.002*** | 0.091*** | 0.002*** | 0.081*** | 0.002*** | |
| (5.576) | (5.259) | (5.633) | (4.512) | (4.838) | ||
| 0.662 | 0.092*** | 0.683 | 0.093*** | 0.670 | 0.093*** | |
| (1.248) | (9.402) | (1.287) | (9.484) | (1.107) | (8.495) | |
| Year | 是 | 是 | 是 | 是 | 是 | 是 |
| Firm | 是 | 是 | 是 | 是 | 是 | 是 |
| Controls | 是 | 是 | 是 | 是 | 是 | 是 |
| N | 8587 | 8587 | 8584 | 8580 | 7357 | 7357 |
| R2 | 0.924 | 0.817 | 0.924 | 0.817 | 0.926 | 0.827 |
表5 稳健性检验结果
Table 5 Robustness test results
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| RD | inv | RD (PSM-DID) | inv (PSM-DID) | 剔除2020 后的RD | 剔除2020 后的inv | |
| strength1 | 0.055*** | |||||
| (4.731) | ||||||
| size | 0.812*** | -0.004*** | 0.811*** | -0.004*** | 0.805*** | -0.004*** |
| (47.728) | (-11.901) | (47.655) | (-11.890) | (41.776) | (-11.066) | |
| lev | -0.206*** | 0.000 | -0.205*** | 0.001 | -0.126* | 0.001 |
| (-3.396) | (0.429) | (-3.375) | (0.483) | (-1.885) | (0.653) | |
| roa | 0.192* | 0.000 | 0.195** | 0.000 | 0.208* | 0.002 |
| (1.935) | (0.047) | (1.970) | (0.022) | (1.875) | (1.127) | |
| indep | -0.182 | -0.005* | -0.177 | -0.005* | -0.276* | -0.004 |
| (-1.272) | (-1.788) | (-1.239) | (-1.755) | (-1.749) | (-1.516) | |
| tobinq | 0.013*** | 0.001*** | 0.014*** | 0.001*** | 0.018*** | 0.001*** |
| (2.759) | (6.279) | (2.891) | (6.376) | (3.163) | (5.341) | |
| firmage | -0.152 | 0.005* | -0.161 | 0.004* | -0.117 | 0.005* |
| (-1.126) | (1.907) | (-1.193) | (1.774) | (-0.739) | (1.804) | |
| top1 | 0.032 | 0.002 | 0.037 | 0.002 | 0.004 | 0.002 |
| (0.325) | (1.166) | (0.374) | (1.253) | (0.037) | (0.804) | |
| treat×post | 0.002*** | 0.091*** | 0.002*** | 0.081*** | 0.002*** | |
| (5.576) | (5.259) | (5.633) | (4.512) | (4.838) | ||
| 0.662 | 0.092*** | 0.683 | 0.093*** | 0.670 | 0.093*** | |
| (1.248) | (9.402) | (1.287) | (9.484) | (1.107) | (8.495) | |
| Year | 是 | 是 | 是 | 是 | 是 | 是 |
| Firm | 是 | 是 | 是 | 是 | 是 | 是 |
| Controls | 是 | 是 | 是 | 是 | 是 | 是 |
| N | 8587 | 8587 | 8584 | 8580 | 7357 | 7357 |
| R2 | 0.924 | 0.817 | 0.924 | 0.817 | 0.926 | 0.827 |
| (1) | (2) | (3) | |
|---|---|---|---|
| 小型企业 | 中型企业 | 大型企业 | |
| did2018 | 0.058 | 0.087* | 0.066 |
| (1.317) | (1.907) | (1.131) | |
| did2019 | 0.099** | 0.114** | 0.068 |
| (1.990) | (2.213) | (1.051) | |
| did2020 | 0.169*** | 0.182*** | -0.007 |
| (2.930) | (3.311) | (-0.093) | |
| lev | 0.793*** | 0.174 | 0.080 |
| (4.558) | (1.136) | (0.287) | |
| roa | 0.551** | 0.755*** | 1.193*** |
| (2.571) | (2.948) | (3.121) | |
| indep | -0.533* | -0.169 | -0.062 |
| (-1.764) | (-0.580) | (-0.154) | |
| tobinq | -0.007 | 0.007 | -0.021 |
| (-0.540) | (0.434) | (-1.181) | |
| firmage | -0.345 | -0.013 | 0.292 |
| (-0.917) | (-0.035) | (0.635) | |
| top1 | -0.115 | -0.103 | 0.244 |
| (-0.340) | (-0.323) | (0.535) | |
| _cons | 18.239*** | 18.124*** | 18.326*** |
| (16.637) | (16.620) | (13.300) | |
| Firm FE | YES | YES | YES |
| Year FE | YES | YES | YES |
| Controls | YES | YES | YES |
| Scale | 小型企业 | 中型企业 | 大型企业 |
| N | 2751 | 2715 | 2878 |
| Adjusted R2 | 0.838 | 0.873 | 0.895 |
表6 企业规模异质性回归结果
Table 6 Heterogeneity regression results by firm size
| (1) | (2) | (3) | |
|---|---|---|---|
| 小型企业 | 中型企业 | 大型企业 | |
| did2018 | 0.058 | 0.087* | 0.066 |
| (1.317) | (1.907) | (1.131) | |
| did2019 | 0.099** | 0.114** | 0.068 |
| (1.990) | (2.213) | (1.051) | |
| did2020 | 0.169*** | 0.182*** | -0.007 |
| (2.930) | (3.311) | (-0.093) | |
| lev | 0.793*** | 0.174 | 0.080 |
| (4.558) | (1.136) | (0.287) | |
| roa | 0.551** | 0.755*** | 1.193*** |
| (2.571) | (2.948) | (3.121) | |
| indep | -0.533* | -0.169 | -0.062 |
| (-1.764) | (-0.580) | (-0.154) | |
| tobinq | -0.007 | 0.007 | -0.021 |
| (-0.540) | (0.434) | (-1.181) | |
| firmage | -0.345 | -0.013 | 0.292 |
| (-0.917) | (-0.035) | (0.635) | |
| top1 | -0.115 | -0.103 | 0.244 |
| (-0.340) | (-0.323) | (0.535) | |
| _cons | 18.239*** | 18.124*** | 18.326*** |
| (16.637) | (16.620) | (13.300) | |
| Firm FE | YES | YES | YES |
| Year FE | YES | YES | YES |
| Controls | YES | YES | YES |
| Scale | 小型企业 | 中型企业 | 大型企业 |
| N | 2751 | 2715 | 2878 |
| Adjusted R2 | 0.838 | 0.873 | 0.895 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| 化工原材料制造业 | 食品消费品制造业 | 其他制造业 | 医药制造业 | |
| did2018 | 0.088** | 0.055 | -0.028 | 0.068 |
| (2.163) | (1.637) | (-0.248) | (0.552) | |
| did2019 | 0.124** | 0.089** | -0.019 | 0.168 |
| (2.487) | (2.308) | (-0.191) | (1.062) | |
| did2020 | 0.134** | 0.080* | 0.000 | 0.313 |
| (2.531) | (1.961) | (0.001) | (1.401) | |
| size | 0.875*** | 0.800*** | 0.500*** | 0.612*** |
| (11.792) | (18.552) | (4.823) | (3.201) | |
| lev | 0.029 | -0.279** | -0.052 | 0.326 |
| (0.125) | (-2.184) | (-0.186) | (0.529) | |
| roa | -0.115 | 0.216 | 0.803 | 0.401 |
| (-0.545) | (1.161) | (1.353) | (0.514) | |
| indep | 0.244 | -0.288 | -0.294 | -0.045 |
| (0.719) | (-1.107) | (-0.718) | (-0.043) | |
| tobinq | 0.018 | 0.028*** | -0.010 | 0.019* |
| (1.362) | (3.486) | (-0.559) | (1.983) | |
| firmage | 0.042 | -0.470* | 0.092 | 1.798 |
| (0.106) | (-1.820) | (0.132) | (1.335) | |
| top1 | -0.133 | 0.180 | -0.084 | -0.172 |
| (-0.289) | (0.780) | (-0.143) | (-0.101) | |
| _cons | -1.157 | 1.840 | 6.277* | -0.548 |
| (-0.584) | (1.597) | (1.883) | (-0.091) | |
| Firm FE | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES |
| Controls | YES | YES | YES | YES |
| Type of Industry | 化工原材料制造业 | 食品消费品制造业 | 其他制造业 | 医药制造业 |
| N | 2284 | 4997 | 1149 | 142 |
| Adjusted R2 | 0.941 | 0.926 | 0.868 | 0.940 |
表7 行业差异异质性回归结果
Table 7 Heterogeneity regression results by industry
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| 化工原材料制造业 | 食品消费品制造业 | 其他制造业 | 医药制造业 | |
| did2018 | 0.088** | 0.055 | -0.028 | 0.068 |
| (2.163) | (1.637) | (-0.248) | (0.552) | |
| did2019 | 0.124** | 0.089** | -0.019 | 0.168 |
| (2.487) | (2.308) | (-0.191) | (1.062) | |
| did2020 | 0.134** | 0.080* | 0.000 | 0.313 |
| (2.531) | (1.961) | (0.001) | (1.401) | |
| size | 0.875*** | 0.800*** | 0.500*** | 0.612*** |
| (11.792) | (18.552) | (4.823) | (3.201) | |
| lev | 0.029 | -0.279** | -0.052 | 0.326 |
| (0.125) | (-2.184) | (-0.186) | (0.529) | |
| roa | -0.115 | 0.216 | 0.803 | 0.401 |
| (-0.545) | (1.161) | (1.353) | (0.514) | |
| indep | 0.244 | -0.288 | -0.294 | -0.045 |
| (0.719) | (-1.107) | (-0.718) | (-0.043) | |
| tobinq | 0.018 | 0.028*** | -0.010 | 0.019* |
| (1.362) | (3.486) | (-0.559) | (1.983) | |
| firmage | 0.042 | -0.470* | 0.092 | 1.798 |
| (0.106) | (-1.820) | (0.132) | (1.335) | |
| top1 | -0.133 | 0.180 | -0.084 | -0.172 |
| (-0.289) | (0.780) | (-0.143) | (-0.101) | |
| _cons | -1.157 | 1.840 | 6.277* | -0.548 |
| (-0.584) | (1.597) | (1.883) | (-0.091) | |
| Firm FE | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES |
| Controls | YES | YES | YES | YES |
| Type of Industry | 化工原材料制造业 | 食品消费品制造业 | 其他制造业 | 医药制造业 |
| N | 2284 | 4997 | 1149 | 142 |
| Adjusted R2 | 0.941 | 0.926 | 0.868 | 0.940 |
| (1) SOE=0 | (2) SOE=1 | (3) FC_0 | (4) FC_1 | |
|---|---|---|---|---|
| did | 0.190*** | 0.288*** | 0.102*** | 0.079*** |
| (3.471) | (11.029) | (4.300) | (2.696) | |
| size | 0.907*** | 0.915*** | 0.776*** | 0.795*** |
| (42.516) | (66.655) | (35.015) | (26.922) | |
| lev | -0.029 | -0.044 | -0.087 | -0.451*** |
| (-0.205) | (-0.547) | (-1.055) | (-4.420) | |
| roa | 1.867*** | 1.277*** | 0.230* | 0.084 |
| (3.899) | (6.848) | (1.717) | (0.555) | |
| indep | -0.849** | 0.771*** | -0.144 | 0.180 |
| (-2.289) | (3.821) | (-0.751) | (0.795) | |
| tobinq | -0.039** | 0.057*** | 0.013** | 0.013* |
| (-2.137) | (7.349) | (2.002) | (1.785) | |
| firmage | -0.331*** | -0.106*** | -0.194 | -1.298** |
| (-3.651) | (-2.901) | (-0.930) | (-2.283) | |
| top1 | 0.003 | -0.220*** | 0.082 | 0.041 |
| (0.018) | (-2.640) | (0.602) | (0.257) | |
| _cons | -0.783 | -2.308*** | 1.499** | 4.557** |
| (-1.471) | (-7.328) | (2.046) | (2.385) | |
| Year | Yes | Yes | Yes | Yes |
| Firm | Yes | Yes | Yes | Yes |
| Controls | Yes | Yes | Yes | Yes |
| N | 2514 | 6093 | 4108 | 4318 |
| r2_a | 0.555 | 0.539 | 0.949 | 0.906 |
表8 公司性质与融资能力异质性回归结果
Table 8 Heterogeneity regression results by ownership type and financing capacity
| (1) SOE=0 | (2) SOE=1 | (3) FC_0 | (4) FC_1 | |
|---|---|---|---|---|
| did | 0.190*** | 0.288*** | 0.102*** | 0.079*** |
| (3.471) | (11.029) | (4.300) | (2.696) | |
| size | 0.907*** | 0.915*** | 0.776*** | 0.795*** |
| (42.516) | (66.655) | (35.015) | (26.922) | |
| lev | -0.029 | -0.044 | -0.087 | -0.451*** |
| (-0.205) | (-0.547) | (-1.055) | (-4.420) | |
| roa | 1.867*** | 1.277*** | 0.230* | 0.084 |
| (3.899) | (6.848) | (1.717) | (0.555) | |
| indep | -0.849** | 0.771*** | -0.144 | 0.180 |
| (-2.289) | (3.821) | (-0.751) | (0.795) | |
| tobinq | -0.039** | 0.057*** | 0.013** | 0.013* |
| (-2.137) | (7.349) | (2.002) | (1.785) | |
| firmage | -0.331*** | -0.106*** | -0.194 | -1.298** |
| (-3.651) | (-2.901) | (-0.930) | (-2.283) | |
| top1 | 0.003 | -0.220*** | 0.082 | 0.041 |
| (0.018) | (-2.640) | (0.602) | (0.257) | |
| _cons | -0.783 | -2.308*** | 1.499** | 4.557** |
| (-1.471) | (-7.328) | (2.046) | (2.385) | |
| Year | Yes | Yes | Yes | Yes |
| Firm | Yes | Yes | Yes | Yes |
| Controls | Yes | Yes | Yes | Yes |
| N | 2514 | 6093 | 4108 | 4318 |
| r2_a | 0.555 | 0.539 | 0.949 | 0.906 |
| 变量 | R&D | R&D |
|---|---|---|
| did | -0.011 | -0.067** |
| (-0.514) | (-2.337) | |
| tpu | 0.094*** | |
| (8.037) | ||
| 0.170*** | ||
| (6.038) | ||
| epu | 0.244*** | |
| (9.939) | ||
| 0.201*** | ||
| (5.654) | ||
| size | 0.801*** | 0.800*** |
| (50.427) | (50.163) | |
| lev | -0.181*** | -0.184*** |
| (-3.093) | (-3.133) | |
| roa | 0.158 | 0.170* |
| (1.627) | (1.749) | |
| indep | -0.130 | -0.120 |
| (-0.927) | (-0.853) | |
| tobinq | 0.013*** | 0.012*** |
| (2.890) | (2.603) | |
| firmage | -0.093 | 0.360*** |
| (-0.840) | (4.072) | |
| top1 | 0.058 | 0.080 |
| (0.613) | (0.841) | |
| -0.598* | -1.114*** | |
| (-1.671) | (-3.099) | |
| Firm | 是 | 是 |
| Year | 否 | 否 |
| N | 8829 | 8829 |
| R2 | 0.518 | 0.516 |
表9 政策不确定性的调节效应回归结果
Table 9 Regression results of the moderating effects of policy uncertainty
| 变量 | R&D | R&D |
|---|---|---|
| did | -0.011 | -0.067** |
| (-0.514) | (-2.337) | |
| tpu | 0.094*** | |
| (8.037) | ||
| 0.170*** | ||
| (6.038) | ||
| epu | 0.244*** | |
| (9.939) | ||
| 0.201*** | ||
| (5.654) | ||
| size | 0.801*** | 0.800*** |
| (50.427) | (50.163) | |
| lev | -0.181*** | -0.184*** |
| (-3.093) | (-3.133) | |
| roa | 0.158 | 0.170* |
| (1.627) | (1.749) | |
| indep | -0.130 | -0.120 |
| (-0.927) | (-0.853) | |
| tobinq | 0.013*** | 0.012*** |
| (2.890) | (2.603) | |
| firmage | -0.093 | 0.360*** |
| (-0.840) | (4.072) | |
| top1 | 0.058 | 0.080 |
| (0.613) | (0.841) | |
| -0.598* | -1.114*** | |
| (-1.671) | (-3.099) | |
| Firm | 是 | 是 |
| Year | 否 | 否 |
| N | 8829 | 8829 |
| R2 | 0.518 | 0.516 |
| (1) | (2) | (3) | |
|---|---|---|---|
| 研发支出 | 研发支出(无控制变量) | 研发强度 | |
| did2018 | 0.072*** | 0.109*** | 0.001*** |
| (2.645) | (3.337) | (3.127) | |
| did2019 | 0.105*** | 0.131*** | 0.002*** |
| (3.541) | (3.583) | (3.701) | |
| did2020 | 0.106*** | 0.152*** | 0.002*** |
| (3.286) | (3.774) | (3.479) | |
| size | 0.811*** | -0.004*** | |
| (19.533) | (-4.960) | ||
| lev | -0.205* | 0.000 | |
| (-1.887) | (0.214) | ||
| roa | 0.205 | 0.000 | |
| (1.406) | (0.052) | ||
| indep | -0.179 | -0.005 | |
| (-0.952) | (-1.486) | ||
| tobinq | 0.013* | 0.001*** | |
| (1.838) | (3.181) | ||
| firmage | -0.160 | 0.005 | |
| (-0.753) | (1.075) | ||
| top1 | 0.034 | 0.002 | |
| (0.163) | (0.504) | ||
| _cons | 0.686 | 18.202*** | 0.092*** |
| (0.611) | (3131.442) | (4.504) | |
| Firm FE | YES | YES | YES |
| Year FE | YES | YES | YES |
| Controls | YES | NO | YES |
| N | 8587 | 8814 | 8587 |
| Adjusted R2 | 0.924 | 0.897 | 0.817 |
表10 动态效应分析结果
Table 10 Dynamic effect analysis results
| (1) | (2) | (3) | |
|---|---|---|---|
| 研发支出 | 研发支出(无控制变量) | 研发强度 | |
| did2018 | 0.072*** | 0.109*** | 0.001*** |
| (2.645) | (3.337) | (3.127) | |
| did2019 | 0.105*** | 0.131*** | 0.002*** |
| (3.541) | (3.583) | (3.701) | |
| did2020 | 0.106*** | 0.152*** | 0.002*** |
| (3.286) | (3.774) | (3.479) | |
| size | 0.811*** | -0.004*** | |
| (19.533) | (-4.960) | ||
| lev | -0.205* | 0.000 | |
| (-1.887) | (0.214) | ||
| roa | 0.205 | 0.000 | |
| (1.406) | (0.052) | ||
| indep | -0.179 | -0.005 | |
| (-0.952) | (-1.486) | ||
| tobinq | 0.013* | 0.001*** | |
| (1.838) | (3.181) | ||
| firmage | -0.160 | 0.005 | |
| (-0.753) | (1.075) | ||
| top1 | 0.034 | 0.002 | |
| (0.163) | (0.504) | ||
| _cons | 0.686 | 18.202*** | 0.092*** |
| (0.611) | (3131.442) | (4.504) | |
| Firm FE | YES | YES | YES |
| Year FE | YES | YES | YES |
| Controls | YES | NO | YES |
| N | 8587 | 8814 | 8587 |
| Adjusted R2 | 0.924 | 0.897 | 0.817 |
| 被解释变量 | R&D |
|---|---|
| did | 0.103*** |
| (6.043) | |
| cidn | -0.069*** |
| (-13.082) | |
| 0.162*** | |
| (15.090) | |
| size | 0.787*** |
| (49.145) | |
| lev | -0.192*** |
| (-3.322) | |
| roa | 0.120 |
| (1.256) | |
| indep | -0.173 |
| (-1.260) | |
| tobinq | 0.015*** |
| (3.347) | |
| firmage | -0.148 |
| (-1.132) | |
| top1 | -0.005 |
| (-0.054) | |
| 1.092** | |
| (2.247) | |
| Firm | YES |
| Year | YES |
| N | 8829 |
| R2 | 0.539 |
表11 连锁董事网络的调节效应回归结果
Table 11 Regression results of the moderating effects of interlocking directorate networks
| 被解释变量 | R&D |
|---|---|
| did | 0.103*** |
| (6.043) | |
| cidn | -0.069*** |
| (-13.082) | |
| 0.162*** | |
| (15.090) | |
| size | 0.787*** |
| (49.145) | |
| lev | -0.192*** |
| (-3.322) | |
| roa | 0.120 |
| (1.256) | |
| indep | -0.173 |
| (-1.260) | |
| tobinq | 0.015*** |
| (3.347) | |
| firmage | -0.148 |
| (-1.132) | |
| top1 | -0.005 |
| (-0.054) | |
| 1.092** | |
| (2.247) | |
| Firm | YES |
| Year | YES |
| N | 8829 |
| R2 | 0.539 |
| 被解释变量 | R&D |
|---|---|
| did | 0.113*** |
| (6.561) | |
| oc | -0.422*** |
| (-11.344) | |
| 0.304*** | |
| (3.268) | |
| size | 0.793*** |
| (49.089) | |
| lev | -0.192*** |
| (-3.283) | |
| roa | 0.156 |
| (1.613) | |
| indep | -0.166 |
| (-1.197) | |
| tobinq | 0.014*** |
| (3.003) | |
| firmage | -0.198 |
| (-1.503) | |
| top1 | 0.054 |
| (0.572) | |
| 1.364*** | |
| (2.769) | |
| Firm | YES |
| Year | YES |
| N | 8829 |
| R2 | 0.528 |
表12 管理者过度自信的调节效应回归结果
Table 12 Regression results of the moderating effects of managerial overconfidence
| 被解释变量 | R&D |
|---|---|
| did | 0.113*** |
| (6.561) | |
| oc | -0.422*** |
| (-11.344) | |
| 0.304*** | |
| (3.268) | |
| size | 0.793*** |
| (49.089) | |
| lev | -0.192*** |
| (-3.283) | |
| roa | 0.156 |
| (1.613) | |
| indep | -0.166 |
| (-1.197) | |
| tobinq | 0.014*** |
| (3.003) | |
| firmage | -0.198 |
| (-1.503) | |
| top1 | 0.054 |
| (0.572) | |
| 1.364*** | |
| (2.769) | |
| Firm | YES |
| Year | YES |
| N | 8829 |
| R2 | 0.528 |
| 变量 | R&D | R&D | R&D |
|---|---|---|---|
| did | 0.104*** | 0.089*** | 0.099*** |
| (6.045) | (5.294) | (5.832) | |
| fau | 0.545*** | ||
| (9.442) | |||
| -0.424*** | |||
| (-3.415) | |||
| distance | 0.109*** | ||
| (5.150) | |||
| -0.124*** | |||
| (-2.909) | |||
| 0.232*** | |||
| (9.187) | |||
| -0.206*** | |||
| (-3.937) | |||
| size | 0.797*** | 0.802*** | 0.798*** |
| (49.210) | (49.334) | (49.268) | |
| lev | -0.168*** | -0.170*** | -0.166*** |
| (-2.866) | (-2.885) | (-2.820) | |
| roa | 0.165* | 0.165* | 0.158 |
| (1.701) | (1.698) | (1.636) | |
| indep | -0.155 | -0.153 | -0.163 |
| (-1.111) | (-1.089) | (-1.167) | |
| tobinq | 0.013*** | 0.012** | 0.012*** |
| (2.755) | (2.459) | (2.650) | |
| firmage | -0.225* | -0.165 | -0.201 |
| (-1.696) | (-1.238) | (-1.514) | |
| top1 | 0.038 | 0.051 | 0.049 |
| (0.397) | (0.533) | (0.520) | |
| 0.806 | 0.539 | 0.767 | |
| (1.637) | (1.085) | (1.559) | |
| N | 8829 | 8829 | 8829 |
| Firm | YES | YES | YES |
| Year | YES | YES | YES |
| R2 | 0.525 | 0.521 | 0.524 |
表13 高管团队断裂带的调节效应回归结果
Table 13 Regression results of the moderating effects of executive team faultlines
| 变量 | R&D | R&D | R&D |
|---|---|---|---|
| did | 0.104*** | 0.089*** | 0.099*** |
| (6.045) | (5.294) | (5.832) | |
| fau | 0.545*** | ||
| (9.442) | |||
| -0.424*** | |||
| (-3.415) | |||
| distance | 0.109*** | ||
| (5.150) | |||
| -0.124*** | |||
| (-2.909) | |||
| 0.232*** | |||
| (9.187) | |||
| -0.206*** | |||
| (-3.937) | |||
| size | 0.797*** | 0.802*** | 0.798*** |
| (49.210) | (49.334) | (49.268) | |
| lev | -0.168*** | -0.170*** | -0.166*** |
| (-2.866) | (-2.885) | (-2.820) | |
| roa | 0.165* | 0.165* | 0.158 |
| (1.701) | (1.698) | (1.636) | |
| indep | -0.155 | -0.153 | -0.163 |
| (-1.111) | (-1.089) | (-1.167) | |
| tobinq | 0.013*** | 0.012** | 0.012*** |
| (2.755) | (2.459) | (2.650) | |
| firmage | -0.225* | -0.165 | -0.201 |
| (-1.696) | (-1.238) | (-1.514) | |
| top1 | 0.038 | 0.051 | 0.049 |
| (0.397) | (0.533) | (0.520) | |
| 0.806 | 0.539 | 0.767 | |
| (1.637) | (1.085) | (1.559) | |
| N | 8829 | 8829 | 8829 |
| Firm | YES | YES | YES |
| Year | YES | YES | YES |
| R2 | 0.525 | 0.521 | 0.524 |
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| [1] | 阳镇 凌鸿程 陈劲. 经济政策不确定性、企业社会责任与企业技术创新[J]. , 2021, 39(3): 0-0. |
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