Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital
Abstract
:1. Introduction
2. Literature Review and Research Hypothesis
3. Variables and Data
3.1. Measurement of Total Factor Productivity: A measure of Economic Sustainability
3.2. Measurement of Variables
4. Does the Threshold Effect Exist?
5. The Impact of Social Security on Productivity
5.1. Measurement Model Setting
5.2. Estimation Results and Analysis
5.3. Robustness Test
6. Conclusions and Policy Recommendations
Author Contributions
Funding
Conflicts of Interest
References
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Mean | Changes from 2007 to 2016 | Mean | Changes from 2007 to 2016 | Mean | Changes from 2007 to 2016 | |
---|---|---|---|---|---|---|
Beijing | 0.0227 | 0.0097 | 11.5894 | 1.2187 | 2.4693 | 3.7363 |
Tianjin | 0.0164 | 0.0060 | 10.2753 | 0.9657 | 1.3344 | 2.0430 |
Hebei | 0.0194 | 0.0100 | 8.6279 | 0.8078 | 0.2109 | 0.3489 |
Shanxi | 0.0332 | 0.0112 | 9.1641 | 0.9193 | 0.1703 | 0.2146 |
Inner Mongolia | 0.0281 | 0.0118 | 8.9235 | 1.3263 | 0.1235 | 0.1779 |
Liaoning | 0.0343 | 0.0154 | 9.5547 | 0.9848 | 0.4150 | 0.3497 |
Jilin | 0.0300 | 0.0045 | 9.1443 | 0.7367 | 0.2044 | 0.2611 |
Heilongjiang | 0.0365 | 0.0170 | 9.0803 | 0.6760 | 0.3280 | 0.3625 |
Shanghai | 0.0232 | 0.0131 | 10.6278 | 0.5895 | 1.9471 | 1.4680 |
Jiangsu | 0.0098 | 0.0034 | 9.0217 | 1.0771 | 2.1356 | 2.4769 |
Zhejiang | 0.0093 | 0.0076 | 8.7448 | 1.0102 | 2.6584 | 3.1456 |
Anhui | 0.0281 | 0.0032 | 8.1148 | 1.3206 | 0.5357 | 0.9284 |
Fujian | 0.0109 | 0.0023 | 8.4528 | 0.9789 | 0.7958 | 1.5183 |
Jiangxi | 0.0264 | 0.0097 | 8.6141 | 0.5043 | 0.2303 | 0.6380 |
Shandong | 0.0117 | 0.0048 | 8.6407 | 0.8030 | 0.6336 | 0.7425 |
Henan | 0.0217 | 0.0076 | 8.5574 | 0.6315 | 0.2644 | 0.4408 |
Hubei | 0.0252 | 0.0073 | 8.9187 | 0.8739 | 0.3874 | 0.5946 |
Hunan | 0.0256 | 0.0043 | 8.7694 | 0.9405 | 0.2881 | 0.4096 |
Guangdong | 0.0113 | 0.0053 | 9.1226 | 0.9326 | 1.3709 | 1.7707 |
Guangxi | 0.0235 | 0.0104 | 8.3603 | 0.7295 | 0.1402 | 0.2671 |
Hainan | 0.0389 | 0.0168 | 8.7864 | 0.7969 | 0.1204 | 0.1764 |
Chongqing | 0.0336 | 0.0064 | 8.4470 | 1.3492 | 0.6592 | 1.2248 |
Sichuan | 0.0324 | 0.0143 | 8.0518 | 0.8696 | 0.4505 | 0.6336 |
Guizhou | 0.0321 | 0.0066 | 7.4636 | 0.9238 | 0.1722 | 0.2457 |
Yunnan | 0.0435 | 0.0111 | 7.4800 | 1.2066 | 0.1271 | 0.2048 |
Tibet | 0.0913 | 0.1304 | 4.8223 | 0.4675 | 0.0505 | 0.0505 |
Shaanxi | 0.0316 | 0.0062 | 8.9425 | 0.8726 | 0.4660 | 1.1777 |
Gansu | 0.0543 | 0.0250 | 7.8999 | 1.3820 | 0.1391 | 0.2653 |
Qinghai | 0.0844 | 0.0121 | 7.5619 | 0.6157 | 0.1010 | 0.1886 |
Ningxia | 0.0371 | 0.0241 | 8.3998 | 1.3329 | 0.1765 | 0.3481 |
Xinjiang | 0.0341 | 0.0265 | 8.8457 | 0.5870 | 0.1737 | 0.2235 |
Threshold Model | F Value | P | 10% Threshold Value | 5% Threshold Value | 1% Threshold Value |
---|---|---|---|---|---|
Single threshold | 10.313 | 0.0020 | 2.9113 | 4.1216 | 6.5654 |
Double threshold | 8.7487 | 0.0020 | 2.6150 | 3.8296 | 6.3304 |
Triple threshold | 0.6102 | 0.4375 | 2.9795 | 4.0144 | 7.0600 |
Threshold | Estimated Value | 95% Confidence Interval |
---|---|---|
First threshold value | 7.5138 | [7.5138, 8.0390] |
Second threshold values | 9.3523 | [7.7525, 9.8776] |
LLC Test | IPS Test | |
---|---|---|
−4.5832(0.0000) | −4.0892(0.0000) | |
−10.0108(0.0000) | −7.2362(0.0000) | |
−6.8059(0.0000) | −4.9425(0.0000) | |
−1.7075(0.0439) | −5.9247(0.0000) |
I | II | III | |
---|---|---|---|
0.4787 (12.79) *** | 0.5087 (8.83) *** | 0.4401 (5.51) *** | |
−0.2070 (−1.17) | −0.0098 (−0.05) | 1.3694 (2.15) ** | |
0.0039 (2.00) * | |||
0.0061 (2.32) ** | 0.0084 (2.81) *** | ||
−0.4438 (−2.50) ** | |||
0.0334 (2.51) ** | |||
0.0001 (0.02) | −0.0047 (−0.64) | −0.0104 (−1.60) | |
−0.0056 (−1.39) | −0.0034 (−0.64) | −0.0088 (−1.51) | |
Constant term | 0.3299 (7.49) *** | 0.3052 (4.20) *** | 0.3956 (4.67) *** |
AR(2) | 0.458 | 0.408 | 0.474 |
Hansen | 1.000 | 1.000 | 1.000 |
Difference-in-Hansen | 1.000 | 1.000 | 1.000 |
Ⅳ Han–Philips Moment Estimator | Ⅴ Space System Moment Estimator | Ⅵ Space Han–Philips Moment Estimator | |
---|---|---|---|
0.3618 (0.96) | 0.3799 (1.01) | 0.3459 (0.91) | |
3.4918 (1.50) | 4.6900 (2.08) ** | 3.4639 (1.50) | |
0.0165 (1.96) * | 0.0156 (1.98) ** | 0.0123 (1.33) | |
−1.1354 (−1.64) * | −1.4666 (−2.20) ** | −1.1054 (−1.65) * | |
0.0767 (1.68) * | 0.1040 (2.21) ** | 0.0733 (1.63) * | |
−0.0217 (−1.42) | −0.0075 (−0.48) | −0.0243 (−1.58) | |
−0.0196 (−1.52) | −0.0165 (−1.36) | −0.0243 (−1.81) * | |
Constant term | 0.6285 (51.72) *** | 0.5819 (33.44) *** | 0.6406 (50.60) *** |
Space item | 0.0067 (2.17) ** | 0.0002 (1.13) | |
Wald-test | 19.49 | 26.95 | 21.01 |
F-test | 2.49 | 3.37 | 2.63 |
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Zhang, M.; Zou, X.; Sha, L. Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital. Sustainability 2019, 11, 662. https://doi.org/10.3390/su11030662
Zhang M, Zou X, Sha L. Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital. Sustainability. 2019; 11(3):662. https://doi.org/10.3390/su11030662
Chicago/Turabian StyleZhang, Ming, Xiaorong Zou, and Long Sha. 2019. "Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital" Sustainability 11, no. 3: 662. https://doi.org/10.3390/su11030662
APA StyleZhang, M., Zou, X., & Sha, L. (2019). Social Security and Sustainable Economic Growth: Based on the Perspective of Human Capital. Sustainability, 11(3), 662. https://doi.org/10.3390/su11030662