Can China’s Aging Population Sustain Its Entrepreneurship? Evidence of Nonlinear Effects
Abstract
:1. Introduction
2. Literature Review
2.1. Nonlinear Effects of Age on Entrepreneurship at the Micro Level
2.2. Nonlinear Effects of Aging on Entrepreneurship at the Macro Level
3. Model
3.1. Data Collection
3.2. Variables
3.3. Empirical Model
4. Results
4.1. Linear and Nonlinear Effects Analysis
4.2. Robustness Check
5. Discussion, Conclusion and Implications
5.1. Discussion and Conclusion
5.2. Managerial Implications
6. Limitations and Future Research
Author Contributions
Funding
Conflicts of Interest
References
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Variables | Definition | Mean | SD | Min | Max |
---|---|---|---|---|---|
OADR | Old Age Dependency Ratio, population ages 65+ to working age | 12.78 | 2.630 | 7.440 | 21.88 |
OADR2 | OADR squared | 170.3 | 72.04 | 55.35 | 478.7 |
IE | Innovation in Entrepreneurship, log of patent license number | 8.980 | 1.650 | 4.250 | 12.51 |
BE | Business for Entrepreneurs, self-employed/employed in private enterprises of total employment | 41.88 | 10.36 | 16.53 | 69.41 |
HC | Human Capital, log of average years of education | 2.170 | 0.110 | 1.830 | 2.550 |
FD | Financial Development, year-end loan balance to GDP | 1.170 | 0.410 | 0.540 | 2.580 |
FO | Financial Openness, Foreign Direct Investment in CNY to GDP, inverse indicator | 0.020 | 0.020 | 0.000 | 0.080 |
MKT | Marketization, added value of state-owned enterprises to that of the second industry, inverse indicator | 0.400 | 0.190 | 0.070 | 0.840 |
ST | Science and Technology, Government Science and Technology of total government expenditure | 1.570 | 1.320 | 0.220 | 7.200 |
GI | Government Intervention, government spending to GDP | 0.210 | 0.090 | 0.080 | 0.630 |
OPR | Older Population Rate, population ages 65+ of total population | 9.360 | 1.920 | 5.430 | 16.38 |
OPR2 | OPR squared | 91.34 | 38.31 | 29.48 | 268.3 |
Variables | Linear Effects | Nonlinear Effects | ||||
---|---|---|---|---|---|---|
OLS-IE | OLS-BE | FE-IE | FE-BE | FE-IE | FE-BE | |
OADR | 0.188 *** | 0.702 *** | 0.0701 *** | 0.852 *** | 0.162 ** | 3.195 *** |
(10.63) | (4.58) | (4.74) | (4.74) | (2.44) | (3.26) | |
OADR2 | −0.00388 * | −0.0837 ** | ||||
(−1.68) | (−2.46) | |||||
HC | 7.457 *** | 65.87 *** | ||||
(17.82) | (11.26) | |||||
FD | −0.236 * | 0.847 | 1.414 *** | 6.745 *** | 0.615 *** | 2.874 * |
(−1.89) | (0.83) | (11.52) | (3.82) | (5.99) | (1.81) | |
FO | −14.64 *** | −0.232 | −4.213 * | 11.05 | −0.842 | 44.56 * |
(−5.69) | (−0.01) | (−1.83) | (0.39) | (−0.48) | (1.78) | |
MKT | −3.529 *** | −32.96 *** | −4.230 *** | −39.26 *** | −2.109 *** | −24.81 *** |
(−13.01) | (−14.80) | (−15.88) | (−9.80) | (−8.91) | (−6.63) | |
ST | 0.661 *** | 0.397 *** | 0.238 *** | |||
(15.95) | (15.33) | (11.08) | ||||
GI | 52.62 *** | 23.74 ** | −13.72 | |||
(10.78) | (2.49) | (−1.50) | ||||
Cons | 7.572 *** | 34.16 *** | 7.612 *** | 33.68 *** | −8.835 *** | −119.2 *** |
(25.68) | (13.04) | (24.34) | (8.18) | (−8.20) | (−7.74) | |
Obs | 450 | 450 | 450 | 450 | 450 | 450 |
N | 30 | 30 | 30 | 30 | ||
R2 | 0.720 | 0.471 | 0.790 | 0.570 | 0.881 | 0.670 |
F | 232.2 | 80.94 | 345.5 | 125.9 | 480.6 | 135.1 |
F-Statistic | 50.41 (p = 0) | 17.75 (p = 0) | 86.45 (p = 0) | 25.49 (p = 0) |
Variables | Linear Effects | Nonlinear Effects | ||||
---|---|---|---|---|---|---|
OLS-IE | OLS-BE | FE-IE | FE-BE | FE-IE | FE-BE | |
OPR | 0.303 *** | 1.163 *** | 0.125 *** | 1.486 *** | 0.360 *** | 6.106 *** |
(12.46) | (5.33) | (5.70) | (5.52) | (3.71) | (4.21) | |
OPR2 | −0.0134 *** | −0.228 *** | ||||
(−2.96) | (−3.38) | |||||
HC | 7.304 *** | 63.10 *** | ||||
(17.55) | (11.01) | |||||
FD | −0.277 ** | 0.216 | 1.394 *** | 6.792 *** | 0.621 *** | 3.418 ** |
(−2.29) | (0.21) | (11.48) | (3.90) | (6.10) | (2.17) | |
FO | −17.39 *** | −9.450 | −4.392 * | 9.117 | −1.313 | 37.84 |
(−6.92) | (−0.40) | (−1.93) | (0.33) | (−0.75) | (1.51) | |
MKT | −3.513 *** | −32.62 *** | −4.040 *** | −37.77 *** | −2.015 *** | −24.33 *** |
(−13.60) | (−14.84) | (−14.94) | (−9.43) | (−8.47) | (−6.54) | |
ST | 0.618 *** | 0.395 *** | 0.232 *** | |||
(15.49) | (15.48) | (10.81) | ||||
GI | 54.57 *** | 21.02 ** | −18.91 ** | |||
(11.18) | (2.22) | (−2.05) | ||||
Cons | 7.316 *** | 32.65 *** | 7.293 *** | 30.61 *** | −9.268 *** | −122.6 *** |
(26.21) | (12.57) | (22.43) | (7.25) | (−8.80) | (−8.20) | |
Obs | 450 | 450 | 450 | 450 | 450 | 450 |
N | 30 | 30 | 30 | 30 | ||
R2 | 0.740 | 0.479 | 0.795 | 0.578 | 0.882 | 0.674 |
F | 256.5 | 83.64 | 355.3 | 129.8 | 486.7 | 137.9 |
F-Statistic | 47.23 (p = 0) | 17.83 (p = 0) | 82.57 (p = 0) | 26.35 (p = 0) |
SYSGMM-IE | SYSGMM-BE | |||
---|---|---|---|---|
Coefficient | Standard Errors | Coefficient | Standard Errors | |
OADR | 0.1165 ** | 0.0546 | 0.1817 | 0.7310 |
OADR2 | −0.0043 *** | 0.0016 | −0.0049 | 0.0266 |
IEt-1 | 0.9454 *** | 0.0710 | ||
BEt-1 | 0.9597 *** | 0.0880 | ||
OPR(1)-p | 0.002 | 0.001 | ||
OPR(2)-p | 0.749 | 0.586 | ||
Wald test | 75,354.70 (p = 0) | 4118.43 (p = 0) | ||
Hansen-p | 0.00 | 0.06 |
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Yang, H.; Yu, J.; Su, C.-H.; Chen, M.-H.; Zhou, D. Can China’s Aging Population Sustain Its Entrepreneurship? Evidence of Nonlinear Effects. Sustainability 2020, 12, 3434. https://doi.org/10.3390/su12083434
Yang H, Yu J, Su C-H, Chen M-H, Zhou D. Can China’s Aging Population Sustain Its Entrepreneurship? Evidence of Nonlinear Effects. Sustainability. 2020; 12(8):3434. https://doi.org/10.3390/su12083434
Chicago/Turabian StyleYang, Hongying, Jin Yu, Ching-Hui (Joan) Su, Ming-Hsiang Chen, and Dahui Zhou. 2020. "Can China’s Aging Population Sustain Its Entrepreneurship? Evidence of Nonlinear Effects" Sustainability 12, no. 8: 3434. https://doi.org/10.3390/su12083434