The Impact of Blockchain on the Administrative Efficiency of Provincial Governments Based on the Data Envelopment Analysis–Tobit Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Envelopment Analysis (DEA)
2.2. Stochastic Frontier Analysis (SFA)
2.3. Tobit Model
3. Empirical Analysis and Results
3.1. Dependent Variable: Administrative Efficiency
3.1.1. Indicator Selection
3.1.2. The First Stage Measurement
3.1.3. The Second Stage Measurement
3.1.4. The Third Stage Measurement
3.2. Independent Variable: Blockchain-Related Variables
3.2.1. Variable Selection
3.2.2. Hypothesis and Model Construction
3.3. Multicollinearity Test
3.4. Empirical Results and Analysis
3.5. Robustness Test
4. Conclusions
5. Discussion
5.1. Academic Implications
5.2. Managerial Implications
5.3. Limitations
5.4. Opportunities for Further Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number of Times | Ca | Cs | Cr |
---|---|---|---|
1 | 0.92 | 0.78 | 0.85 |
2 | 0.88 | 0.8 | 0.84 |
Primary Indicators | Secondary Indicators | Unit | Data Sources |
---|---|---|---|
Input indicators | The number of administrative practitioners | ten thousand people | China National Bureau of Statistics |
General public budgeting expenditures | billion | ||
Output indicators | Total annual power supply | TWh | |
Three types of patent authorizations | piece | ||
Pension insurance coverage rate | % | ||
The number of beds per thousand people in hospitals and health institutions | piece | ||
Total afforestation area | hectares | ||
Actual road length at the end of the year | ten thousand kilometers | ||
Environment variable | The year-end resident population of each region | ten thousand people | |
The Gross Regional Product | billion |
Province/ Municipality | TE | PTE | SE | RTS |
---|---|---|---|---|
Beijing | 0.821 | 1 | 0.821 | DRS |
Tianjin | 1 | 1 | 1 | - |
Hebei | 0.739 | 0.753 | 0.982 | DRS |
Shanxi | 0.875 | 1 | 0.875 | DRS |
Inner Mongolia | 1 | 1 | 1 | - |
Liaoning | 0.72 | 0.996 | 0.723 | DRS |
Jilin | 0.694 | 1 | 0.694 | DRS |
Heilongjiang | 0.721 | 1 | 0.721 | DRS |
Shanghai | 1 | 1 | 1 | - |
Jiangsu | 1 | 1 | 1 | - |
Zhejiang | 0.998 | 0.998 | 1 | - |
Anhui | 0.929 | 1 | 0.929 | DRS |
Fujian | 0.964 | 0.968 | 0.995 | DRS |
Jiangxi | 0.813 | 0.977 | 0.832 | DRS |
Shandong | 1 | 1 | 1 | - |
Henan | 0.803 | 0.939 | 0.856 | DRS |
Hubei | 0.986 | 1 | 0.986 | DRS |
Hunan | 0.728 | 1 | 0.728 | DRS |
Guangdong | 1 | 1 | 1 | - |
Guangxi | 0.704 | 0.734 | 0.958 | DRS |
Hainan | 0.842 | 0.853 | 0.988 | DRS |
Chongqing | 0.947 | 1 | 0.947 | DRS |
Sichuan | 0.86 | 1 | 0.86 | DRS |
Guizhou | 0.812 | 0.962 | 0.844 | DRS |
Yunnan | 0.941 | 1 | 0.941 | DRS |
Tibet | 1 | 1 | 1 | - |
Shanxi | 0.842 | 1 | 0.842 | DRS |
Gansu | 0.862 | 1 | 0.862 | DRS |
Qinghai | 1 | 1 | 1 | - |
Ningxia | 1 | 1 | 1 | - |
Xinjiang | 0.948 | 1 | 0.948 | DRS |
Mean | 0.889 | 0.974 | 0.914 |
Province/ Municipality | The Number of Administrative Practitioners before the Correction (Ten Thousand People) | General Public Budgeting Expenditure before the Correction (Billion) | The Number of Administrative Practitioners after the Correction (Ten Thousand People) | General Public Budgeting Expenditure after the Correction (Billion) |
---|---|---|---|---|
Beijing | 43.6 | 7205.12 | 49.44 | 7248.98 |
Tianjin | 20 | 3152.55 | 25.24 | 3196.47 |
Hebei | 97.4 | 8848.21 | 98.61 | 8862.53 |
Shanxi | 63.3 | 5046.62 | 67.07 | 5080.03 |
Inner Mongolia | 56.5 | 5239.57 | 61.13 | 5278.72 |
Liaoning | 64.1 | 5879.21 | 67.46 | 5909.26 |
Jilin | 39.5 | 3696.84 | 43.74 | 3734.71 |
Heilongjiang | 49.6 | 5104.81 | 53.27 | 5138.78 |
Shanghai | 19.3 | 8430.86 | 25.01 | 8473.51 |
Jiangsu | 93.2 | 14,585.26 | 97.47 | 14,607.34 |
Zhejiang | 80.9 | 11,014.59 | 84.64 | 11,039.97 |
Anhui | 60.2 | 7591.05 | 62.69 | 7613.22 |
Fujian | 51.8 | 5204.72 | 56.33 | 5238.91 |
Jiangxi | 63.1 | 6778.87 | 66.33 | 6807.71 |
Shandong | 134.9 | 11,713.16 | 135.93 | 11,719.79 |
Henan | 123.1 | 9784.29 | 123.10 | 9788.12 |
Hubei | 76.4 | 7933.67 | 79.53 | 7958.76 |
Hunan | 89 | 8325.5 | 91.21 | 8345.39 |
Guangdong | 154.2 | 18,247.01 | 155.20 | 18,247.01 |
Guangxi | 59.3 | 5806.54 | 61.83 | 5831.65 |
Hainan | 15 | 1971.37 | 20.08 | 2015.64 |
Chongqing | 38 | 4835.06 | 42.28 | 4870.89 |
Sichuan | 110.4 | 11,215.69 | 111.51 | 11,227.27 |
Guizhou | 72.4 | 5590.01 | 75.67 | 5620.73 |
Yunnan | 70.7 | 6634.36 | 73.64 | 6661.76 |
Tibet | 15.4 | 2027.01 | 20.82 | 2074.15 |
Shanxi | 60.7 | 6069.22 | 64.43 | 6101.26 |
Gansu | 50.5 | 4032.56 | 54.48 | 4069.26 |
Qinghai | 15.3 | 1854.52 | 20.59 | 1900.62 |
Ningxia | 11.9 | 1427.89 | 17.14 | 1473.47 |
Xinjiang | 86.1 | 5376.91 | 90.31 | 5414.14 |
Province/ Municipality | TE | PTE | SE | RTS |
---|---|---|---|---|
Beijing | 0.852 | 1 | 0.852 | DRS |
Tianjin | 1 | 1 | 1 | - |
Hebei | 0.743 | 0.768 | 0.968 | DRS |
Shanxi | 0.892 | 1 | 0.892 | DRS |
Inner Mongolia | 1 | 1 | 1 | - |
Liaoning | 0.724 | 0.996 | 0.727 | DRS |
Jilin | 0.7 | 1 | 0.7 | DRS |
Heilongjiang | 0.729 | 1 | 0.729 | DRS |
Shanghai | 1 | 1 | 1 | - |
Jiangsu | 1 | 1 | 1 | - |
Zhejiang | 0.998 | 0.998 | 1 | - |
Anhui | 1 | 1 | 1 | - |
Fujian | 0.964 | 0.969 | 0.995 | DRS |
Jiangxi | 0.827 | 0.985 | 0.84 | DRS |
Shandong | 1 | 1 | 1 | - |
Henan | 0.809 | 0.941 | 0.86 | DRS |
Hubei | 0.995 | 1 | 0.995 | DRS |
Hunan | 0.735 | 1 | 0.735 | DRS |
Guangdong | 1 | 1 | 1 | - |
Guangxi | 0.71 | 0.74 | 0.96 | DRS |
Hainan | 0.891 | 0.899 | 0.992 | IRS |
Chongqing | 1 | 1 | 1 | - |
Sichuan | 0.876 | 1 | 0.876 | DRS |
Guizhou | 0.818 | 0.963 | 0.85 | DRS |
Yunnan | 0.951 | 1 | 0.951 | DRS |
Tibet | 1 | 1 | 1 | - |
Shanxi | 0.852 | 1 | 0.852 | DRS |
Gansu | 0.868 | 1 | 0.868 | DRS |
Qinghai | 1 | 1 | 1 | - |
Ningxia | 1 | 1 | 1 | - |
Xinjiang | 0.951 | 1 | 0.951 | DRS |
Mean | 0.9 | 0.976 | 0.922 |
Factor | Frequency | Percent |
---|---|---|
Technology and technology adoption | 29 | 31.52% |
Blockchain technology structure, models, etc. | 19 | 20.65% |
Government management and policy | 18 | 19.57% |
User acceptance | 11 | 11.96% |
Information transparency | 8 | 8.70% |
Government innovation | 7 | 7.61% |
Variable Type | Variable | Variable Code | Concrete Content | Data Sources |
---|---|---|---|---|
Dependent variable | Administrative efficiency | y | Comprehensive efficiency value | Based on the results obtained from the three-stage DEA |
Independent variable | Blockchain research investment | x1 | Ln (R&D investment in the province and municipality) | Statistical bureaus of various provinces and municipalities |
Blockchain research output | x2 | The number of blockchain patent applications in urban areas of the province | Patent Search and Analysis System of China National Intellectual Property Administration | |
The number of blockchain policies | x3 | The number of blockchain-related policies in the province’s urban areas | Peking University Magic Treasure Database | |
The number of procurements for blockchain government projects | x4 | The number of procurements for blockchain government projects by province and municipality | China Government Procurement Network | |
Population size | x5 | Ln (The year-end resident population) | China National Bureau of Statistics |
Name of Variable | Sample Size | Mean | Standard Deviation | Minimum Value | Maximum Value |
---|---|---|---|---|---|
y | 310 | 0.85 | 0.17 | 0.20 | 1.00 |
x1 | 310 | 5.53 | 1.53 | 0.59 | 8.29 |
x2 | 310 | 41.94 | 138.33 | 0.00 | 1097.00 |
x3 | 310 | 0.20 | 0.66 | 0.00 | 6.00 |
x4 | 310 | 10.07 | 56.39 | 0.00 | 641.00 |
x5 | 310 | 8.13 | 0.84 | 5.75 | 9.45 |
Variable | VIF | ONE/VIF |
---|---|---|
x1 | 3.82 | 0.261834 |
x2 | 3.13 | 0.319205 |
x3 | 1.99 | 0.501756 |
x4 | 1.42 | 0.705103 |
X5 | 1.41 | 0.709365 |
Mean VIF | 2.35 |
Variable | Value |
---|---|
x1 | 0.0577398 *** (2.69) |
x2 | 0.0003761 *** (2.63) |
x3 | 0.0104702 ** (2.55) |
x4 | 0.0002871 (0.08) |
x5 | 0.3145285 ** (2.35) |
_cons | 1.830676 *** (4.99) |
N | 155 |
Waldchi2(5) | 23.85 |
Prob > chi2 | 0.0001 |
Log-likelihood | 111.54339 |
Variable | Value |
---|---|
x1 | 0.0190841 *** (2.84) |
x2 | 0.000353 *** (2.67) |
x3 | 0.0149654 ** (2.53) |
x4 | 0.0009253 (0.17) |
x5 | 0.0882341 ** (2.09) |
_cons | 1.56373 *** (5.82) |
N | 155 |
Waldchi2(5) | 14.16 |
Prob > chi2 | 0.0146 |
Log-likelihood | 78.371058 |
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Fan, J.; Wang, Q.; Wang, Y. The Impact of Blockchain on the Administrative Efficiency of Provincial Governments Based on the Data Envelopment Analysis–Tobit Model. Sustainability 2024, 16, 2909. https://doi.org/10.3390/su16072909
Fan J, Wang Q, Wang Y. The Impact of Blockchain on the Administrative Efficiency of Provincial Governments Based on the Data Envelopment Analysis–Tobit Model. Sustainability. 2024; 16(7):2909. https://doi.org/10.3390/su16072909
Chicago/Turabian StyleFan, Jiongan, Qingnian Wang, and Yunpei Wang. 2024. "The Impact of Blockchain on the Administrative Efficiency of Provincial Governments Based on the Data Envelopment Analysis–Tobit Model" Sustainability 16, no. 7: 2909. https://doi.org/10.3390/su16072909