International R&D Spillovers and Innovation Efficiency
Abstract
:1. Introduction
2. Literature Review
2.1. International R&D Spillovers
2.2. Innovation Efficiency
3. Econometric Strategy
3.1. Knowledge Production Function (KPF)
3.2. Stochastic Frontier Analysis (SFA)
4. Variable Selection and Data Sources
4.1. Variable Selection
4.1.1. Input Variables
- Domestic R&D capital stock (RDD). Country-level annual R&D expenditure data can be obtained from Science, Technology and Innovation of UNESCO’s database and Research and Development Statistics of OECD database, by the name of gross domestic expenditure on R&D (GERD). Data in current American dollars from these two official publications are rendered comparable by being converted into data at 2005 constant price based on the Purchasing Power Parities (PPP) method. Moreover, instead of expenditure, R&D capital stock should be used in the context of the knowledge production function. Accordingly, the data on annual R&D expenditure are transformed into R&D capital stock applying the perpetual inventory method as suggested by OECD [62], which can be specified as:
- Foreign R&D capital stock (RDF). The pioneering proposition about the measurement of foreign R&D capital stock appears in CH which constructs the foreign R&D capital stock as a weighted sum of the other countries’ domestic R&D capital stocks ,
- R&D personnel (L). Two major norms are used to measure R&D personnel, researchers (L1) and total R&D personnel (L2) [47,50]. Researchers are those who are both engaged in R&D activities and equipped with intermediate or above titles or a doctor’s degree, while total R&D personnel covers all those who are involved in the concept formation or creation of new knowledge, new product, new processes, new methods or new systems, and those related professionals in project management, even Ph.D. candidates in R&D field (ISCED97, level 6). It is evident that the number of researchers is less than that of total R&D personnel. However, the data of the latter are missing for some countries, such as the USA and Columbia, so that the former is taken as a substitute.
4.1.2. Output Variables
- Patents (PAT). Patents are probably the most typical and important R&D fruit. The Patents Statistics of OECD database and the United States Patent Office offer the access to four types of patent indicators: domestic patents (DPAT), PCT patents (PCTPAT), triadic patents (TPAT) and US Patents (USPAT). In terms of quantity, DPAT generally far outweighs PCTPAT and TPAT, and it seems that TPAT is the least. Without doubt, TPAT is an efficacious indicator of advanced technologies, but it is not applicable to a cross-country research because many countries only achieve a few TPAT, or even have no record of this item, especially for underdeveloped countries. As a consequence, TPAT is excluded while the other three are selected.
- Scientific papers (PAP). Scientific papers, as a kind of academic publication, are also the most common outcome of R&D activities, which play an exceptional role in delivering and sharing new ideas and laying a solid theoretical foundation for R&D practices. The source of this indicator is the S&E (Science & Engineering) Article from Science and Engineering Indicators, which collects and sorts global papers, books and conference publications, primarily including the papers published on the journals on the lists of Science Citation Index (SCI) or Social Sciences Citation Index (SSCI).
4.1.3. Environmental Variables
- Internet coverage (IT). The Internet, as a vital vehicle of information spreading and knowledge sharing, is essential to the function of international R&D spillovers. The ratio of Internet users in the last 12 months per 100 residents, an indicator released by WDI (World Development Indicators), is used to reflect the Internet coverage of each country. It is hypothesized that the broader the Internet coverage is, the easier it is to approach and share foreign R&D fruits and the higher the innovation efficiency may be.
- Human capital (HK). The average educational level (years) of employees is the most popular measurement of human capital [65]. However, the average value is not necessarily accurate in evaluating R&D activities which, to a great extent, are the game of those intellectuals. Hence, the enrollment rate of tertiary education serves as the proxy, which is supposed to benefit the absorption of international R&D spillovers and the improvement of innovation efficiency.
- Service industry development level (Srv). The ratio of value added by the service industry to GDP is included to identify the industrial structure of each country. Then, the service industry consists of several totally different sub-industries, like information technology and tourism. In practice, the former is beneficial to innovation and R&D spillovers, such as the case of Japan, while the latter is not, as is the case for Thailand.
- High-tech industry development level (Hightec). On one hand, the level of high-tech industry, indicated by the ratio of value added by this industry to GDP, does rest with the R&D capacity. On the other hand, it is believed that the well-installed R&D infrastructure is accompanied by enhanced absorptive capability and innovation efficiency.
- Intensity of R&D expenditure (Gerd). In terms of the ratio of total R&D expenditure to GDP, this indicator comes directly from WDI, letting us know the importance attached to R&D by both the government and firms of each country. It seems that this factor should strengthen innovation enthusiasm and efficiency.
- Structure of R&D expenditure sources (Govrd). Defined as the part of the government in the total R&D expenditure, this variable will give us the idea about the heterogeneity of public and private R&D inputs.
- Language distance (Lang). Referring to the data of West and Graham [66], language distance measures the degree of difficulty of learning English for different countries. As the global language, English fills the gap in international communication and is used for paper writing and information diffusion, globally speaking. It is assumed that a closer language distance leads to a higher level of international R&D spillovers.
- Country dummy variable (G8). Following CH and its followers, a country dummy variable is introduced into our model so as to distinguish those most powerful and R&D-intensive countries, namely G8 nations.
4.2. Data Sources
5. Empirical Results
5.1. Scientific Papers as Output
5.2. PCT Patents as Output
5.3. US Patents as Output
5.4. Domestic Patents as Output
5.5. Robustness Checks
- Adoption of alternative lag periods. It is believed that there exists a time lag from R&D input to output. A two-year lag is proved to be appropriate by conducting a correlation and regression analysis and used extensively in precedent researches [72,73], although no attention is paid to time lag in numerous papers [47,74]. Our one-year and two-year lag models both conclude results similar to those reported above.
- Re-examination using different depreciation rates of R&D capital. In spite of the popularity of 15% depreciation rate, 20% [9,70], and 5% [19] are employed as well in practice. Depreciation which levels directly influences the R&D capital stock, further our regressions. Our re-examinations with those two alternative depreciation rates do not change the interpretation of primary conclusions.
- Variable substitution. Given the inherent complexity, foreign R&D capital stocks have no widely accepted measure [18]. Returning to CH, we operate, once again, the regressions following their way of measurement of foreign R&D capital stock. Moreover, L1 is substituted with L2, ceteris paribus. Then, the key points remain unchanged, although some variables turn out to be less statistically robust.
- Considering that knowledge is a basket of heterogeneous and sequential layers and referring to the two-factor knowledge production function put forward by Jaffe [75] and Acs et al. [64], which relates knowledge output to two parts, namely R&D performed by industry and research conducted by universities, we use the number of scientific papers as a proxy of research by universities and then add it into the KPFs of PCT patents, US patents, and domestic patents respectively as a complementary input. The results are presented in Table A2, Table A3 and Table A4 located in Appendix B. We find our empirical results and conclusions are very robust to different forms of KPF.
6. Conclusions and Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
R&D Output (USPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.639 *** (5.417) | 1.993 *** (22.227) | 2.168 *** (30.582) | 1.937 *** (17.185) | 2.056 *** (28.396) | 1.281 *** (27.669) | 1.575 *** (14.810) | 1.979 *** (24.885) | 1.321 *** (12.855) |
L1 | −0.401 *** (−3.691) | −0.739 *** (−7.605) | −1.017 *** (−16.067) | −0.726 *** (−5.376) | −0.719 *** (−9.921) | −0.495 *** (−8.662) | −0.358 *** (−3.136) | −0.863 *** (−11.941) | 0.011 (0.103) |
RDF | 0.289 *** (4.209) | −0.345 *** (−5.935) | −0.245 *** (−4.158) | −0.237 *** (−3.903) | −0.494 *** (−9.384) | 0.163 *** (4.236) | −0.200 *** (−3.686) | −0.122 * (−1.923) | −0.306 *** (−6.322) |
Constant (1) | −3.296 *** (−3.065) | −13.996 *** (−21.483) | −15.840 *** (−32.400) | −14.988 *** (−19.172) | −12.824 *** (−24.564) | −11.567 *** (−23.643) | −13.019 *** (−20.631) | −16.214 *** (−30.896) | −10.800 *** (−22.801) |
IT | −0.389 *** (−7.412) | −0.039 (−1.045) | |||||||
HK | −1.004 *** (−5.294) | −0.104 (−0.973) | |||||||
Srv | −3.744 *** (−4.808) | −1.721 *** (−3.462) | |||||||
Hightec | −0.801 *** (−10.982) | −0.466 *** (−8.901) | |||||||
Gerd | −1.057 *** (−20.406) | −0.411 *** (−4.430) | |||||||
Govrd | 2.218 *** (6.901) | 0.754 *** (3.845) | |||||||
Lang | 0.303 *** (4.323) | 0.208 *** (6.461) | |||||||
G8 | 1.421 *** (5.784) | −1.752 *** (−4.050) | −2.551 *** (−3.152) | −1.775 ** (−2.276) | −1.391 *** (−5.786) | −0.691 *** (−7.696) | −1.172 *** (−4.510) | −2.704 *** (−2.851) | −0.470 *** (−2.848) |
Constant (2) | 2.120 *** (9.016) | 4.304 *** (8.667) | 16.157 *** (5.153) | −2.095 *** (−5.017) | 2.557 *** (59.744) | −7.000 *** (−5.452) | −0.369 (−0.599) | 4.034 ** (2.261) | |
σ2 | 10.607 *** (4.144) | 1.682 *** (7.422) | 1.965 *** (5.687) | 2.046 *** (5.373) | 1.239 *** (10.611) | 0.705 *** (25.248) | 1.611 *** (8.997) | 2.203 *** (4.820) | 0.925 *** (12.603) |
γ | 0.977 *** (168.445) | 0.918 *** (36.197) | 0.879 *** (31.869) | 0.913 *** (34.884) | 0.919 *** (44.258) | 1.000 *** (29.135) | 0.957 *** (60.498) | 0.888 *** (33.710) | 0.966 *** (85.136) |
Log-likelihood | −683.313 | −1040.753 | −1047.858 | −1064.306 | −964.183 | −946.042 | −1040.582 | −1063.644 | −874.109 |
LR test value | 873.995 | 189.752 | 175.544 | 142.646 | 342.893 | 379.175 | 190.095 | 143.970 | 523.041 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
R&D Output (PCTPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.622 *** (7.968) | 1.544 *** (30.968) | 1.621 *** (34.687) | 1.548 *** (31.279) | 1.595 *** (36.410) | 0.761 *** (17.648) | 1.144 *** (15.340) | 1.532 *** (29.535) | 0.875 *** (14.298) |
L1 | −0.182 *** (−2.738) | −0.710 *** (−12.997) | −0.916 *** (−22.710) | −0.872 *** (−19.371) | −0.912 *** (−23.515) | −0.458 *** (−12.355) | −0.690 *** (−12.258) | −0.898 *** (−20.637) | −0.235 *** (−3.874) |
RDF | 0.112 ** (2.363) | −0.265 *** (−6.251) | −0.264 *** (−5.790) | −0.281 *** (−6.112) | −0.447 *** (−11.210) | 0.189 *** (5.139) | −0.179 *** (−4.059) | −0.233 *** (−4.643) | −0.105 ** (−2.198) |
PAP | 0.576 *** (14.354) | 0.289 *** (6.120) | 0.414 *** (9.929) | 0.468 *** (12.321) | 0.566 *** (16.920) | 0.415 *** (12.913) | 0.600 *** (16.829) | 0.473 *** (11.897) | 0.390 *** (10.291) |
Constant (1) | −8.430 *** (−10.975) | −10.448 *** (−28.675) | −10.821 *** (−28.797) | −10.297 *** (−26.869) | −8.850 *** (−23.748) | −7.014 *** (−18.784) | −7.957 *** (−15.282) | −10.578 *** (−26.163) | −7.056 *** (−15.715) |
IT | −0.474 *** (−8.722) | −0.120 *** (−4.997) | |||||||
HK | −1.519 *** (−4.370) | −0.081 (−1.417) | |||||||
Srv | −6.975 ** (−2.277) | −0.044 (−0.168) | |||||||
Hightec | −0.977 *** (−7.264) | −0.111 *** (−3.795) | |||||||
Gerd | −0.867 *** (−25.301) | −0.424 *** (−7.315) | |||||||
Govrd | 2.172 *** (9.143) | 0.771 *** (7.624) | |||||||
Lang | 0.538 * (1.720) | 0.087 *** (4.807) | |||||||
G8 | −0.335 ** (−2.021) | −1.296 *** (−3.192) | −2.725 ** (−2.331) | −5.702 * (−1.834) | −1.493 *** (−3.827) | −0.368 *** (−5.496) | −1.072 *** (−3.872) | −9.171 (−1.587) | −0.234 ** (−2.463) |
Constant (2) | 1.656 *** (10.896) | 4.796 *** (6.963) | 26.079 ** (2.425) | −4.211 *** (−5.064) | 2.101 *** (27.208) | −7.349 *** (−7.193) | −5.336 (−1.320) | −1.288 (−1.278) | |
σ2 | 2.267 *** (5.057) | 0.884 *** (6.325) | 1.733 *** (3.882) | 3.193 ** (2.264) | 1.007 *** (6.512) | 0.289 *** (18.557) | 0.562 *** (7.612) | 4.120 * (1.836) | 0.296 *** (14.290) |
γ | 0.954 *** (97.616) | 0.899 *** (40.669) | 0.934 *** (51.801) | 0.962 *** (60.885) | 0.910 *** (45.380) | 1.000 *** (7079.120) | 0.799 *** (16.405) | 0.968 *** (61.151) | 0.906 *** (23.382) |
Log-likelihood | −334.059 | −706.451 | −744.031 | −771.882 | −676.359 | −612.709 | −686.694 | −779.513 | −539.632 |
LR test value | 1036.657 | 313.293 | 238.133 | 182.431 | 373.477 | 500.777 | 352.806 | 167.169 | 646.930 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
R&D Output (USPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.427 *** (3.397) | 1.978 *** (23.250) | 2.164 *** (29.538) | 1.962 *** (20.010) | 2.020 *** (29.628) | 1.256 *** (26.190) | 1.531 *** (16.964) | 1.974 *** (24.576) | 1.318 *** (13.253) |
L1 | −0.618 *** (−5.324) | −0.636 *** (−5.918) | −1.026 *** (−14.209) | −0.846 *** (−6.687) | −0.902 *** (−11.742) | −0.524 *** (−8.970) | −0.579 *** (−5.957) | −0.940 *** (−11.099) | −0.048 (−0.445) |
RDF | 0.055 (0.695) | −0.316 *** (−5.383) | −0.252 *** (−3.906) | −0.272 *** (−4.250) | −0.659 *** (−10.551) | 0.107 ** (2.396) | −0.324 *** (−4.757) | −0.174 ** (−2.510) | −0.343 *** (−6.202) |
PAP | 0.370 *** (5.735) | −0.108 (−1.548) | 0.020 (0.291) | 0.112 * (1.761) | 0.326 *** (5.804) | 0.078 *** (2.763) | 0.339 *** (6.142) | 0.119 * (1.849) | 0.086 (1.461) |
Constant (1) | 3.571 ** (2.263) | −14.269 *** (−22.203) | −15.739 *** (−26.312) | −14.650 *** (−20.693) | −10.489 *** (−14.770) | −10.593 *** (−19.079) | −10.900 *** (−15.340) | −15.582 *** (−24.418) | −10.306 *** (−17.633) |
IT | −0.425 *** (−6.865) | −0.018 (−0.444) | |||||||
HK | −0.990 *** (−5.055) | −0.094 (−0.880) | |||||||
Srv | −3.324 *** (−4.107) | −1.730 *** (−3.632) | |||||||
Hightec | −0.725 *** (−12.311) | −0.468 *** (−8.993) | |||||||
Gerd | −1.041 *** (−18.314) | −0.382 *** (−3.097) | |||||||
Govrd | 2.002 *** (8.296) | 0.822 *** (4.086) | |||||||
Lang | 0.275 *** (3.573) | 0.196 *** (5.862) | |||||||
G8 | 1.790 *** (5.740) | −1.693 *** (−4.567) | −2.559 *** (−3.082) | −2.002 ** (−2.336) | −1.232 *** (−6.394) | −0.775 *** (8.549) | −0.954 *** (−4.933) | −2.731 *** (−2.882) | −0.477 *** (−2.937) |
Constant (2) | 2.268 *** (10.257) | 4.249 *** (8.016) | 14.347 *** (4.459) | −1.509 *** (−4.258) | 2.615 *** (46.566) | −5.840 *** (−5.916) | −0.292 (−0.436) | 3.773 ** (2.167) | |
σ2 | 15.415 *** (4.173) | 1.690 *** (7.858) | 1.959 *** (5.616) | 2.036 *** (5.022) | 1.007 *** (11.326) | 0.705 *** (18.260) | 1.222 *** (10.071) | 2.140 *** (4.488) | 0.898 *** (12.569) |
γ | 0.985 *** (263.329) | 0.929 *** (42.901) | 0.877 *** (30.764) | 0.899 *** (34.568) | 0.903 *** (32.449) | 1.000 *** (18.547) | 0.947 *** (48.389) | 0.879 *** (28.897) | 0.965 *** (74.646) |
Log-likelihood | −668.416 | −1039.598 | −1047.815 | −1062.816 | −948.065 | −944.168 | −1021.913 | −1061.881 | −872.961 |
LR test value | 882.944 | 176.989 | 160.555 | 130.553 | 360.056 | 367.850 | 212.360 | 132.425 | 510.263 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
R&D Output (DPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.744 *** (9.604) | 0.661 *** (9.995) | 0.794 *** (12.325) | 0.738 *** (14.408) | 0.643 *** (10.616) | 0.555 *** (8.118) | 0.384 *** (5.578) | 0.657 *** (11.547) | 0.881 *** (12.322) |
L1 | 0.257 *** (3.388) | 1.005 *** (14.061) | 0.934 *** (16.163) | 0.611 *** (9.522) | 0.944 *** (14.263) | 0.927 *** (14.478) | 1.025 *** (16.854) | 0.815 *** (13.647) | 0.554 *** (7.844) |
RDF | −0.153 *** (−2.939) | −0.325 *** (−5.984) | −0.289 *** (−5.333) | −0.247 *** (−4.779) | −0.527 *** (−8.341) | −0.244 *** (−4.508) | −0.336 *** (−6.456) | −0.321 *** (−5.668) | −0.524 *** (−7.939) |
PAP | 0.033 (0.789) | −0.241 *** (−4.445) | −0.328 *** (−6.509) | −0.058 (−1.195) | −0.057 (−0.634) | −0.156 *** (−3.390) | −0.001 (−0.016) | −0.096 ** (−2.071) | 0.032 (0.581) |
Constant (1) | −3.366 *** (−3.312) | −6.587 *** (−8.219) | −8.142 *** (−11.511) | −6.684 *** (−13.458) | −3.946 *** (−4.181) | −3.917 *** (−6.564) | −4.109 *** (−6.219) | −5.935 *** (−10.575) | −3.978 *** (−6.267) |
IT | −0.139 (−1.418) | 0.041 (1.447) | |||||||
HK | −0.876 *** (−8.277) | −0.660 *** (−11.060) | |||||||
Srv | 1.716 *** (7.044) | 2.354 *** (8.751) | |||||||
Hightec | −0.264 *** (−5.341) | −0.169 *** (−5.540) | |||||||
Gerd | −0.131 *** (−2.613) | 0.303 *** (4.356) | |||||||
Govrd | 0.670 *** (8.683) | 0.696 *** (6.653) | |||||||
Lang | −0.048 * (−1.906) | −0.014 (−0.791) | |||||||
G8 | −0.130 (−0.674) | −0.820 (−0.724) | −0.582 ** (−2.311) | −0.622 *** (−7.840) | −0.510 *** (−3.558) | −0.415 *** (−4.268) | −0.383 *** (−5.061) | −1.724 *** (−13.708) | −0.621 *** (−6.959) |
Constant (2) | 0.739 ** (2.049) | 3.181 *** (9.724) | −6.683 *** (−6.953) | −0.446 (−0.970) | 2.583 *** (5.237) | −2.017 *** (−8.778) | 0.556 *** (4.799) | −9.227 *** (−8.778) | |
σ2 | 6.395 *** (4.286) | 0.633 *** (5.581) | 0.539 *** (6.637) | 0.554 *** (19.916) | 0.529 *** (12.258) | 0.573 *** (19.817) | 0.537 *** (20.236) | 0.583 *** (22.842) | 0.404 *** (19.466) |
γ | 0.984 *** (256.878) | 0.203 (1.629) | 0.054 (0.320) | 0.000 (0.149) | 0.004 (0.064) | 1.000 (1.319) | 0.000 (0.374) | 0.047 *** (35.135) | 0.000 (0.304) |
Log-likelihood | −338.574 | −900.587 | −867.713 | −889.761 | −873.706 | −903.980 | −878.944 | −888.420 | −765.263 |
LR test value | 1140.122 | 31.107 | 96.854 | 52.760 | 84.869 | 24.321 | 74.393 | 55.441 | 301.755 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
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Variable Type | Abbreviation | Source |
---|---|---|
Output variable | DPAT | Domestic patents: Patents Statistics of OECD database |
USPAT | US patents: the United States Patent Office | |
PCTPAT | PCT patents: Patents Statistics of OECD database | |
PAP | Scientific papers: Science and Engineering Indicators | |
Input variable | RDD | Domestic R&D capital stock: Science, Technology and Innovation of UNESCO’s database |
RDF | Foreign R&D capital stock: Science, Technology and Innovation of UNESCO’s database | |
L1 | Full-time equivalent of researchers: WDI and UNESCO’s database | |
L2 | Full-time equivalent of R&D personnel: WDI and UNESCO’s database | |
Environmental variable | IT | Internet coverage: WDI |
HK | Human capital: UNESCO’s database | |
Srv | Service industry development level: WDI | |
Hightec | High-tech industry development level: WDI | |
Gerd | Intensity of R&D expenditure: WDI | |
Govrd | Structure of R&D expenditure sources: WDI | |
Lang | Language distance: West and Graham (2004) | |
G8 | Country dummy variable: G8 countries (1) or not (0) |
Variable | Unit | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|
DPAT | number | 27,920.05 | 59,352.89 | 17.90 | 414,758.50 |
USPAT | number | 2882.26 | 7823.08 | 1.25 | 57,265.88 |
PCTPAT | number | 3950.69 | 14,832.05 | 0.17 | 104,182.90 |
PAP | number | 22,155.19 | 70,348.78 | 15.00 | 704,936.00 |
RDD | USD at 2005 constant price | 116,000,000 | 304,000,000 | 571,762 | 2,340,000,000 |
RDF | USD at 2005 constant price | 18,100,000 | 26,100,000 | 213,116 | 180,000,000 |
L1 | Full-time equivalent | 124,709.60 | 245,326.40 | 1271.32 | 1,592,420.00 |
L2 | Full-time equivalent | 172,195.70 | 337,957.40 | 2034.10 | 3,532,817.00 |
IT | percent | 39.99 | 29.77 | 0.01 | 96.55 |
HK | percent | 54.42 | 22.31 | 5.00 | 127.24 |
Srv | percent | 65.38 | 8.30 | 33.57 | 87.99 |
Hightec | percent | 0.04 | 0.10 | 0 | 0.84 |
Gerd | percent | 1.43 | 0.89 | 0.11 | 4.15 |
Govrd | percent | 41.55 | 14.12 | 3.20 | 89.37 |
Lang | Non-dimensional | 2.06 | 1.66 | 0 | 6 |
Variable | RDD | RDF | L1 | IT | HK | Srv | Hightec | Gerd | Govrd | Lang | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|
VIF value 1 | 26.70 | 12.25 | 15.67 | 4.22 | 2.72 | 2.46 | 2.19 | 2.00 | 1.86 | 1.34 | 7.14 |
VIF value 2 | 13.60 | 5.38 | 7.90 | 8.97 |
R&D Output (PAP) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.445 *** (6.972) | 0.061 * (1.659) | 0.181 *** (5.267) | 0.097 ** (2.392) | 0.157 *** (4.345) | 0.095 *** (2.687) | 0.232 *** (6.521) | 0.119 *** (3.364) | 0.166 *** (4.281) |
L1 | 0.692 *** (18.644) | 0.287 *** (9.038) | 0.429 *** (15.180) | 0.413 *** (14.864) | 0.464 *** (15.837) | 0.443 *** (14.458) | 0.454 *** (15.490) | 0.444 *** (14.825) | 0.494 *** (14.025) |
RDF | 0.525 *** (9.119) | 0.676 *** (15.631) | 0.403 *** (11.663) | 0.514 *** (10.456) | 0.394 *** (11.020) | 0.485 *** (12.849) | 0.325 *** (9.024) | 0.482 *** (12.790) | 0.389 *** (9.981) |
Constant (1) | −13.251 *** (−28.660) | −3.168 *** (−11.071) | −4.695 *** (−19.690) | −4.127 *** (−14.678) | −4.719 *** (−17.868) | −4.299 *** (−17.075) | −5.146 *** (−21.513) | −4.693 *** (−17.673) | −5.364 *** (−21.631) |
IT | −0.590 *** (−7.385) | −0.372 *** (−9.743) | |||||||
HK | −4.482 *** (−3.144) | −0.137 * (−1.651) | |||||||
Srv | −16.21 *** (−3.658) | 2.397 *** (6.154) | |||||||
Hightec | 0.645 *** (9.981) | 0.313 *** (6.599) | |||||||
Gerd | −3.386 *** (−4.424) | −0.750 *** (−7.261) | |||||||
Govrd | −4.004 *** (−5.761) | −1.263 *** (−11.468) | |||||||
Lang | 1.586 *** (4.911) | 0.129 *** (4.604) | |||||||
G8 | −2.164 *** (−15.380) | −0.314 (−1.083) | −5.222 ** (−2.073) | −6.506 *** (−2.581) | −9.365 *** (−15.777) | −4.873 *** (−2.780) | −6.644 *** (−3.474) | −5.943 *** (−3.576) | −0.362 * (−1.835) |
Constant (2) | 0.972 *** (5.508) | 9.161 *** (4.444) | 54.962 *** (3.668) | −5.067 *** (−9.220) | −7.007 *** (−3.316) | 9.423 *** (8.081) | −12.620 *** (−4.026) | −2.739 * (−1.828) | |
σ2 | 2.939 *** (4.267) | 0.925 *** (5.974) | 4.121 *** (2.736) | 7.536 *** (3.743) | 4.998 *** (17.125) | 3.907 *** (4.124) | 3.090 *** (4.428) | 5.173 *** (4.389) | 0.286 *** (7.683) |
γ | 0.975 *** (161.448) | 0.940 *** (76.539) | 0.981 *** (135.351) | 0.991 *** (377.559) | 0.986 *** (697.989) | 0.981 *** (210.443) | 0.972 *** (126.906) | 0.985 *** (256.267) | 0.665 *** (10.664) |
Log-likelihood | −200.129 | −514.501 | −541.130 | −581.974 | −588.151 | −562.817 | −559.468 | −574.134 | −385.824 |
LR test value | 1006.031 | 385.640 | 332.382 | 250.692 | 238.340 | 289.007 | 295.706 | 266.373 | 642.993 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
R&D Output (PCTPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.842 *** (9.957) | 1.537 *** (25.266) | 1.709 *** (34.157) | 1.601 *** (28.145) | 1.695 *** (34.505) | 0.878 *** (15.322) | 1.375 *** (17.881) | 1.579 *** (27.233) | 0.979 *** (14.574) |
L1 | 0.082 (1.059) | −0.487 *** (−8.725) | −0.752 *** (−18.973) | −0.621 *** (−12.431) | −0.638 *** (−14.659) | −0.237 *** (−5.208) | −0.451 *** (−7.369) | −0.664 *** (−14.996) | −0.073 (−1.163) |
RDF | 0.518 *** (10.647) | −0.177 *** (−4.014) | −0.093 ** (−2.143) | −0.080 * (−1.701) | −0.209 *** (−4.917) | 0.313 *** (6.887) | 0.035 (0.668) | 0.000 (−0.003) | 0.036 (0.715) |
Constant (1) | −15.707 *** (−27.726) | −11.336 *** (−29.324) | −12.948 *** (−43.517) | −12.749 *** (−38.090) | −12.063 *** (−35.684) | −10.020 *** (−27.873) | −12.548 *** (−33.406) | −13.218 *** (−37.930) | −9.215 *** (−24.826) |
IT | −0.497 *** (−12.184) | −0.214 *** (−9.077) | |||||||
HK | −1.515 *** (−6.834) | −0.145 ** (−2.360) | |||||||
Srv | −7.494 *** (−3.200) | 0.362 (1.246) | |||||||
Hightec | −1.208 *** (−5.098) | −0.076 ** (−2.441) | |||||||
Gerd | −0.987 *** (−21.446) | −0.548 *** (−8.872) | |||||||
Govrd | 3.448 *** (5.235) | 0.364 *** (3.836) | |||||||
Lang | 0.639 ** (2.227) | 0.113 *** (6.270) | |||||||
G8 | −1.526 *** (−5.232) | −0.919 *** (−3.719) | −1.961 *** (−2.916) | −4.431 ** (−2.204) | −2.460 *** (−3.767) | −0.326 *** (−3.447) | −2.204 *** (−3.116) | −6.988 *** (−1.783) | −0.259 *** (−2.642) |
Constant (2) | 2.080 *** (17.598) | 5.472 *** (10.028) | 28.877 *** (3.414) | −5.918 *** (−3.927) | 1.608 *** (14.993) | −13.290 *** (−4.535) | −4.243 (−1.518) | −0.828 (−0.748) | |
σ2 | 3.373 *** (4.242) | 0.769 *** (8.221) | 1.469 *** (5.430) | 3.130 *** (2.970) | 1.879 *** (5.830) | 0.400 *** (14.437) | 1.440 *** (4.154) | 3.602 ** (2.164) | 0.348 *** (13.645) |
γ | 0.961 *** (99.653) | 0.903 *** (44.775) | 0.928 *** (54.037) | 0.961 *** (69.997) | 0.948 *** (89.669) | 0.927 *** (26.955) | 0.885 *** (24.016) | 0.962 *** (54.494) | 0.953 *** (43.927) |
Log-likelihood | −425.422 | −722.500 | −778.561 | −827.348 | −776.670 | −680.170 | −809.728 | −832.885 | −580.927 |
LR test value | 998.697 | 414.038 | 301.916 | 204.341 | 305.698 | 498.699 | 239.582 | 193.268 | 697.184 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
R&D Output (DPAT) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
RDD | 0.762 *** (10.262) | 0.627 *** (11.091) | 0.716 *** (12.106) | 0.647 *** (0.936) | 0.648 *** (11.260) | 0.525 *** (7.927) | 0.386 *** (5.941) | 0.692 *** (13.349) | 0.889 *** (13.371) |
L1 | 0.277 *** (3.973) | 0.875 *** (38.562) | 0.788 *** (16.029) | 0.809 *** (11.790) | 0.901 *** (17.966) | 0.859 *** (16.220) | 1.019 *** (19.303) | 0.697 *** (14.414) | 0.571 *** (7.591) |
RDF | −0.131 *** (−3.010) | −0.373 *** (−11.376) | −0.428 *** (−8.809) | −0.342 *** (−8.814) | −0.573 *** (−11.875) | −0.329 *** (−6.898) | −0.336 *** (−7.066) | −0.385 *** (−7.661) | −0.511 *** (−7.557) |
Constant (1) | −3.962 *** (−5.873) | −6.427 *** (−12.182) | −6.010 *** (−13.001) | −6.595 *** (−6.745) | −3.344 *** (−7.915) | −2.843 *** (−7.300) | −4.246 *** (−10.024) | −5.054 *** (−12.502) | −4.632 *** (−8.237) |
IT | −0.232 *** (−48.695) | 0.035 (1.073) | |||||||
HK | −0.583 *** (−6.968) | −0.661 *** (−11.804) | |||||||
Srv | 0.017 (0.019) | 2.358 *** (4.987) | |||||||
Hightec | −0.275 *** (−9.868) | −0.167 *** (−4.686) | |||||||
Gerd | −0.124 ** (−2.474) | 0.300 *** (4.622) | |||||||
Govrd | 0.657 *** (11.348) | 0.673 *** (7.732) | |||||||
Lang | −0.065 ** (−2.000) | −0.011 (−0.710) | |||||||
G8 | −0.153 (−0.919) | −0.038 (−0.118) | −0.877 *** (−2.767) | −0.178 (−0.184) | −0.553 *** (−4.830) | −0.390 *** (−4.222) | −0.395 *** (−12.542) | −1.800 *** (−14.541) | −0.613 *** (−4.714) |
Constant (2) | 0.080 (0.822) | 2.223 *** (8.438) | −0.017 (−0.033) | −0.481 ** (−2.426) | 2.495 *** (7.131) | −2.106 *** (−11.394) | 0.710 *** (6.016) | −9.555 *** (−5.335) | |
σ2 | 6.130 *** (4.265) | 0.606 *** (26.198) | 0.577 *** (14.043) | 0.613 (1.253) | 0.519 *** (18.616) | 0.591 *** (38.256) | 0.539 *** (23.373) | 0.550 *** (25.369) | 0.405 *** (18.456) |
γ | 0.984 *** (245.386) | 0.024 (1.424) | 0.045 (0.527) | 0.000 (0.066) | 0.005 (0.151) | 1.000 *** (12.250) | 0.000 (1.089) | 0.049 *** (15.980) | 0.000 (0.022) |
Log-likelihood | −338.866 | −915.952 | −889.779 | −915.701 | −874.625 | −908.679 | −879.048 | −890.409 | −765.449 |
LR test value | 1147.901 | 11.094 | 63.440 | 11.596 | 93.746 | 25.640 | 84.901 | 62.180 | 312.100 |
Observations | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 | 792 |
Output Variable | Scientific Papers | PCT Patents | US Patents | Domestic Patents | |
---|---|---|---|---|---|
Input variable | RDD | + | + | + | + |
L1 | + | / | / | + | |
RDF | + | / | / | — | |
Environmental variable | IT | + | + | + | + |
HK | + | + | + | + | |
Srv | / | / | + | — | |
Hightec | — | + | + | + | |
Gerd | + | + | + | + | |
Govrd | + | — | — | — | |
Lang | — | — | — | + | |
G8 | + | + | + | + |
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Liu, J.; Lu, K.; Cheng, S. International R&D Spillovers and Innovation Efficiency. Sustainability 2018, 10, 3974. https://doi.org/10.3390/su10113974
Liu J, Lu K, Cheng S. International R&D Spillovers and Innovation Efficiency. Sustainability. 2018; 10(11):3974. https://doi.org/10.3390/su10113974
Chicago/Turabian StyleLiu, Jianping, Kai Lu, and Shixiong Cheng. 2018. "International R&D Spillovers and Innovation Efficiency" Sustainability 10, no. 11: 3974. https://doi.org/10.3390/su10113974