The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China
Abstract
:1. Introduction
1.1. Connotation and Necessity of the Rule of Law of the Chinese Model
1.2. Sustainable Economy and Economic Sustainability
1.3. Transitioning to the Rule of Law in Chinese National Governance
2. Literature Review
2.1. Law and Economic Management
2.2. Rule of Law
2.3. Law and Democracy
2.4. Government Activities
3. Data and Methodology
3.1. Variable Selection and Data
3.2. The Performance of the Rule of Law in China’s Economic Management
3.3. Least Squares Method
3.4. Weighted Least Squares Method
3.5. Stepwise Regression
4. Results
4.1. Least Squares Analysis
4.2. Weighted Least Squares Analysis
4.3. Stepwise Regression Analysis
5. Discussion
5.1. Qualitative Analysis
5.2. Verification of Hypotheses
5.3. Econometric Analysis
6. Conclusions and Implications
6.1. Conclusions
6.2. Research Implications
6.2.1. Practical Implications
6.2.2. Theoretical Implications
6.3. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | GDP (Billion CNY) | CZZC (Billion CNY) | CZSR (Billion CNY) | FYXZ (pc) | JCJZ (pc) | SHZZ (pc) |
---|---|---|---|---|---|---|
2002 | 121,717.4 | 22,053.15 | 18,903.64 | 80,728 | 20,460 | 244,509 |
2003 | 137,422 | 24,649.95 | 21,715.25 | 87,919 | 22,802 | 266,612 |
2004 | 161,840.2 | 28,486.89 | 26,396.47 | 92,613 | 23,012 | 289,432 |
2005 | 187,318.9 | 33,930.28 | 31,649.29 | 96,178 | 25,026 | 319,762 |
2006 | 219,438.5 | 40,422.73 | 38,760.2 | 95,617 | 23,277 | 354,393 |
2007 | 270,092.3 | 49,781.35 | 51,321.78 | 101,510 | 26,594 | 386,916 |
2008 | 319,244.6 | 62,592.66 | 61,330.35 | 108,398 | 27,227 | 413,660 |
2009 | 348,517.7 | 76,299.93 | 68,518.3 | 120,312 | 39,451 | 431,069 |
2010 | 412,119.3 | 89,874.16 | 83,101.51 | 129,133 | 41,909 | 445,631 |
2011 | 487,940.2 | 109,247.79 | 103,874.43 | 136,353 | 43,826 | 461,971 |
2012 | 538,580 | 125,952.97 | 117,253.52 | 129,583 | 41,140 | 499,268 |
2013 | 592,963.2 | 140,212.1 | 129,209.64 | 123,194 | 43,599 | 547,245 |
2014 | 643,563.1 | 151,785.56 | 140,370.03 | 141,880 | 57,463 | 606,048 |
2015 | 688,858.2 | 175,877.77 | 152,269.23 | 220,398 | 97,185 | 662,425 |
2016 | 746,395.1 | 187,755.21 | 159,604.97 | 225,485 | 104,227 | 702,405 |
2017 | 832,035.9 | 203,085.49 | 172,592.77 | 230,432 | 104,288 | 761,539 |
2018 | 919,281.1 | 220,904.13 | 183,359.84 | 256,656 | 140,417 | 817,360 |
2019 | 986,515.2 | 238,858.37 | 190,390.08 | 279,574 | 138,760 | 866,335 |
Project | GDP | CZZC | CZSR | FYXZ | JCJZ | SHZZ |
---|---|---|---|---|---|---|
TDP | 2009–2010 | 2010–2011 | 2010–2011 | 2014–2015 | 2014–2015 | 2013–2014 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
C | 0.204222 | 1.251095 | 0.163235 | 0.8767 |
ln(CZZC) | 0.246239 | 0.263560 | 0.934283 | 0.3930 |
ln(CZSR) | 0.497314 | 0.269763 | 1.843526 | 0.1246 |
ln(FYXZ) | 0.119264 | 0.138831 | 0.859055 | 0.4296 |
ln(JCJZ) | −0.116107 | 0.117700 | −0.986469 | 0.3692 |
ln(SHZZ) | 0.315138 | 0.127141 | 2.478641 | 0.0559 |
R-squared | 0.998662 | – | – | – |
Adjusted R-squared | 0.997324 | – | – | – |
S.E. of regression | 0.019141 | – | – | – |
Sum squared residual | 0.001832 | – | – | – |
Log-likelihood | 32.24346 | – | – | – |
Mean dependent variable | 13.13004 | – | – | – |
S.D. dependent variable | 0.369998 | – | – | – |
Hannan–Quinn criterion | −4.908347 | – | – | – |
Durbin–Watson statistic | 1.644887 | – | – | – |
F-statistic | 746.3235 | – | – | 0.00000 |
Breusch–Pagan–Godfrey Heteroskedasticity Test | |||
---|---|---|---|
F-statistic | 1.076048 | Prob. F(5,5) | 0.4689 |
Obs*R-squared | 5.701470 | Prob. Chi-Square(5) | 0.3364 |
Scaled explained SS | 1.244284 | Prob. Chi-Square(5) | 0.9406 |
Breusch–Godfrey Serial Correlation LM Test | |||
F-statistic | 3.889756 | Prob. F(2,3) | 0.1468 |
Obs*R-squared | 7.938637 | Prob. Chi-Square(2) | 0.0189 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
C | −0.347755 | 0.955082 | −0.364110 | 0.7221 |
ln(CZZC) | 0.401480 | 0.210680 | 1.905645 | 0.0809 |
ln(CZSR) | 0.272338 | 0.198602 | 1.371279 | 0.1954 |
ln(FYXZ) | 0.079300 | 0.135077 | 0.587069 | 0.5680 |
ln(JCJZ) | −0.084709 | 0.103978 | −0.814684 | 0.4311 |
ln(SHZZ) | 0.427872 | 0.120284 | 3.557186 | 0.0039 |
R-squared | 0.999188 | – | – | – |
Adjusted R-squared | 0.998849 | – | – | – |
S.E. of regression | 0.022933 | – | – | – |
Sum squared residual | 0.006311 | – | – | – |
Log-likelihood | 46.06111 | – | – | – |
Mean dependent variable | 12.88543 | – | – | – |
S.D. dependent variable | 0.676095 | – | – | – |
Akaike info criterion | −4.451235 | – | – | – |
Schwarz criterion | −4.154444 | – | – | – |
Hannan–Quinn criterion | −4.410311 | – | – | – |
Durbin–Watson statistic | 1.130507 | – | – | – |
F-statistic | 2952.578 | – | – | 0.00000 |
Breusch–Pagan–Godfrey Heteroskedasticity Test | |||
---|---|---|---|
F-statistic | 3.782248 | Prob. F(5,12) | 0.0274 |
Obs*R-squared | 11.01225 | Prob. Chi-Square(5) | 0.0511 |
Scaled explained SS | 5.496688 | Prob. Chi-Square(5) | 0.3583 |
Breusch–Godfrey Serial Correlation LM Test | |||
F-statistic | 3.166255 | Prob. F(2,10) | 0.0860 |
Obs*R-squared | 6.979036 | Prob. Chi-Square(2) | 0.0305 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
C | −0.501144 | 3.086151 | −0.162385 | 0.8774 |
ln(CZZC) | −3.192650 | 1.434093 | −2.226250 | 0.0765 |
ln(CZSR) | 3.510815 | 1.318706 | 2.662318 | 0.0448 |
ln(FYXZ) | −1.764831 | 0.978202 | −1.804158 | 0.1311 |
ln(JCJZ) | 1.038185 | 0.676151 | 1.535433 | 0.1853 |
ln(SHZZ) | 1.521817 | 0.272024 | 5.594421 | 0.0025 |
R-squared | 0.999422 | - | - | - |
Adjusted R-squared | 0.998845 | - | - | - |
S.E. of regression | 0.011073 | - | - | - |
Sum squared residual | 0.000613 | - | - | - |
Log-likelihood | 38.26349 | - | - | - |
Mean dependent variable | 13.36163 | - | - | - |
S.D. dependent variable | 20.55392 | - | - | - |
Akaike info criterion | −5.866089 | - | - | - |
Schwarz criterion | −5.649056 | - | - | - |
Hannan–Quinn criterion | −6.002899 | - | - | - |
Durbin–Watson statistic | 1.512624 | - | - | - |
Weighted mean dep. | 13.44314 | - | - | - |
F-statistic | 1730.092 | - | - | 0.00000 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
C | 0.109314 | 0.187193 | 0.583961 | 0.5701 |
ln(CZZC) | 0.342987 | 0.017701 | 19.37688 | 0.0000 |
ln(CZSR) | 0.346015 | 0.022482 | 15.39069 | 0.0000 |
ln(FYXZ) | 0.072992 | 0.013172 | 5.541260 | 0.0001 |
ln(JCJZ) | −0.059553 | 0.010471 | −5.687345 | 0.0001 |
ln(SHZZ) | 0.365345 | 0.024792 | 14.73638 | 0.0000 |
R-squared | 1.000000 | |||
Adjusted R-squared | 1.000000 | |||
S.E. of regression | 0.000302 | |||
Sum squared residual | 1.10 × 10−6 | |||
Log-likelihood | 123.9768 | |||
Mean dependent variable | 12.94637 | |||
S.D. dependent variable | 41.31503 | |||
Akaike info criterion | −13.10853 | |||
Schwarz criterion | −12.81174 | |||
Hannan–Quinn criterion | −13.06761 | |||
Durbin–Watson statistic | 1.536860 | |||
Weighted mean dep. | 13.18801 | |||
F-statistic | 57,047,724 | 0.00000 |
Non-Standardized Coefficient | Standardization Coefficient | t | Significance | |
---|---|---|---|---|
Constant | 0.858 | 1.656 | 0.136 | |
ln(CZSR) | 0.735 | 0.829 | 19.065 | 0.000 |
ln(SHZZ) | 0.286 | 0.179 | 4.114 | 0.003 |
F-Statistic | 2375.110 | - | - | 0.000 |
Non-Standardized Coefficient | Standardization Coefficient | t | Significance | |
---|---|---|---|---|
Constant | 0.239 | 0.386 | 0.705 | |
ln(CZZC) | 0.266 | 0.318 | 2.283 | 0.039 |
ln(SHZZ) | 0.393 | 0.225 | 5.029 | 0.000 |
ln(CZSR) | 0.400 | 0.461 | 3.984 | 0.001 |
F-Statistic | 5424.146 | 0.000 |
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Xiao, W.; Tang, Y.; Obuobi, B.; Qu, S.; Yuan, M.; Tang, D. The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China. Sustainability 2023, 15, 11690. https://doi.org/10.3390/su151511690
Xiao W, Tang Y, Obuobi B, Qu S, Yuan M, Tang D. The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China. Sustainability. 2023; 15(15):11690. https://doi.org/10.3390/su151511690
Chicago/Turabian StyleXiao, Wensheng, Yu Tang, Bright Obuobi, Shaojian Qu, Minglan Yuan, and Decai Tang. 2023. "The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China" Sustainability 15, no. 15: 11690. https://doi.org/10.3390/su151511690
APA StyleXiao, W., Tang, Y., Obuobi, B., Qu, S., Yuan, M., & Tang, D. (2023). The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China. Sustainability, 15(15), 11690. https://doi.org/10.3390/su151511690