Digital Inclusive Finance, Agricultural Industrial Structure Optimization and Agricultural Green Total Factor Productivity
Abstract
:1. Introduction
2. Theoretical Mechanisms and Research Hypotheses
2.1. The Logic of the Impact of Digital Financial Inclusion on Agricultural Green Total Factor Productivity
2.2. The Logic of Digital Inclusive Finance, Industrial Structure Optimization, and Agricultural Green Total Factor Productivity
3. Models, Variables, and Data
3.1. Construction of Agricultural Green Total Factor Productivity Model and Description of Variables
3.1.1. Super Efficiency SBM-ML Model Based on Unexpected Output
3.1.2. Variable Description
- Input variables
- Output variables
3.2. Econometric Model Construction and Variable Description
3.2.1. Benchmark Model
3.2.2. Mediating Effect Model
3.2.3. Variable Description
- Explained variables
- Core explanatory variables
- Control variables
- Mediating variables
3.3. Data Sources and Descriptive Statistics
4. Analysis of Empirical Results
4.1. Analysis of Benchmark Regression Results
4.2. Analysis of the Results of the Mediation Effect Model
4.3. Robustness Test
5. Heterogeneity Discussion and Analysis
5.1. Discussion on the Heterogeneity of Different Dimensions of Digital Inclusive Finance
5.2. Discussion on Regional Heterogeneity
6. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable Name | Variable Characteristics | Unit |
---|---|---|
Land input | The sown area of crops. | Thousand hectares |
Labor input | Multiplying the employment of the primary industry by the proportion of agricultural added value in the added value of agriculture, forestry, animal husbandry and fishery [57]. | Ten thousand people |
Mechanical power input | The total power of agricultural machinery. | 10,000 kilowatts |
Draft animals’ input | The number of large livestock | Million head |
Irrigation input | Effective irrigated area | Thousand hectares |
Pesticide input | Pesticide usage. | Tons |
Agricultural film input | The amount of agricultural film used | 10,000 tons |
Fertilizer input | The pure amount of chemical fertilizer application. | 10,000 tons |
Variable Name | Variable Characteristics |
---|---|
Expected output | In order to eliminate the price factor, this paper selects the total agricultural output value of each provincial unit obtained by deflating 2011 as the base period, the unit is 100 million yuan. |
Unexpected output | Agricultural undesired output refers to agricultural carbon emissions. Agricultural carbon emission mainly refers to the greenhouse gases directly or indirectly produced in the process of agricultural production due to chemical fertilizer, pesticides, energy consumption and land tillage. Based on the research of Li [58], taking agricultural carbon emission as the unexpected output, this paper selects six carbon sources of agricultural carbon emission in the process of agricultural production: chemical fertilizer, pesticide, agricultural film, diesel, tillage, and agricultural irrigation to calculate the total amount of agricultural carbon emission. |
Variable Name | Variable Characteristics | Expected Direction |
---|---|---|
Agricultural structure (STR) | The proportion of the added value of agriculture in the added value of agriculture, forestry, animal husbandry and fishery. | Positive |
Income distribution (IND) | The ratio of the per capita net income of rural households to the per capita disposable income of urban households. | Positive |
Degree of disaster (NAT) | The proportion of the affected area to the total sown area of crops. | Negative |
Degree of agricultural mechanization (MEC) | The total power of agricultural machinery per unit sown area. | Uncertain |
Education level (EDU) | Drawing on existing research results, it is measured by the average years of education [61]. | Positive |
Level of financial support to agriculture (FIN) | The proportion of agricultural fiscal expenditure to total fiscal expenditure. | Positive |
Variable | Mean | Standard Deviation | Mean | Standard Deviation | Mean | Standard Deviation | Mean | Standard Deviation | Mean | Standard Deviation |
---|---|---|---|---|---|---|---|---|---|---|
National | Eastern | Central | Western | Northeast | ||||||
AGTFP | 1.0666 | 0.1704 | 1.0729 | 0.2597 | 1.0342 | 0.1151 | 1.0657 | 0.1771 | 1.1134 | 0.2640 |
Index | 5.0730 | 0.6700 | 5.2350 | 0.5531 | 5.0309 | 0.6859 | 4.9638 | 0.7339 | 5.0171 | 0.6858 |
Coverage | 4.9037 | 0.8322 | 5.1432 | 0.5837 | 4.8146 | 0.8295 | 4.7445 | 1.0010 | 4.8675 | 0.7180 |
Usage | 5.0578 | 0.6442 | 5.2650 | 0.5108 | 5.0757 | 0.5928 | 4.8789 | 0.7259 | 4.9872 | 0.6532 |
Digital | 5.3923 | 0.7335 | 5.3501 | 0.8194 | 5.3982 | 0.7209 | 5.4276 | 0.6610 | 5.3913 | 0.7479 |
STR | 0.5765 | 0.0900 | 0.5387 | 0.0639 | 0.5695 | 0.0701 | 0.6191 | 0.0969 | 0.5607 | 0.1107 |
IND | 2.6706 | 0.4690 | 2.3933 | 0.2516 | 2.5948 | 0.2461 | 3.0881 | 0.3892 | 2.2161 | 0.4570 |
NAT | 0.1607 | 0.1185 | 0.1219 | 0.1222 | 0.1639 | 0.0937 | 0.1878 | 0.1232 | 0.1836 | 0.1039 |
MEC | 0.6327 | 0.2356 | 0.8046 | 0.2869 | 0.6362 | 0.1518 | 0.5091 | 0.1265 | 0.5051 | 0.0899 |
EDU | 6.9147 | 0.0968 | 6.9940 | 0.0997 | 6.8965 | 0.0513 | 6.8532 | 0.0722 | 6.9127 | 0.0336 |
FIN | 0.1134 | 0.0308 | 0.0887 | 0.0294 | 0.1134 | 0.0081 | 0.1325 | 0.0231 | 0.1257 | 0.0319 |
YH | 0.2678 | 0.0813 | 0.2587 | 0.0599 | 0.2592 | 0.1073 | 0.2838 | 0.0883 | 0.2564 | 0.0429 |
Fixed Effect (1) | Fixed Effect (2) | Random Effect (3) | |
---|---|---|---|
Index | 0.2745 *** | 0.2509 *** | 0.2463 *** |
(3.79) | (4.17) | (5.54) | |
STR | 6.1386 ** | 3.0952 ** | |
(2.62) | (2.05) | ||
IND | 0.5089 ** | 0.2722 ** | |
(2.29) | (2.37) | ||
NAT | −0.4556 * | −0.3072 | |
(−1.7) | (−1.02) | ||
MEC | 0.3379 | 0.2765 | |
(1.06) | (1.21) | ||
EDU | 2.4151 | 1.1590 | |
(1.56) | (0.82) | ||
FIN | 2.7348 | 1.7398 | |
(0.57) | (0.35) | ||
-Cons | −21.9347 * | −10.6924 | |
(−1.77) | (−0.98) | ||
N | 231 | 210 | 210 |
0.1854 | 0.3455 | 0.3189 |
Variable | Benchmark Regression | Mediation Effect | |
---|---|---|---|
AGTFP (4) | YH (5) | AGTFP (6) | |
Index | 0.2509 *** | 0.0085 * | 0.2385 *** |
(4.17) | (1.95) | (4.22) | |
YH | 2.4852 * | ||
(1.78) | |||
STR | 6.1386 ** | 0.4056 *** | 5.3222 ** |
(2.62) | (2.95) | (2.53) | |
IND | 0.5089 ** | −0.0076 | 0.5528 ** |
(2.29) | (−0.51) | (2.43) | |
NAT | −0.4556 * | −0.0074 | −0.4215 |
(−1.7) | (−0.37) | (−1.53) | |
MEC | 0.3379 | −0.0272 | 0.4079 |
(1.06) | (−0.87) | (1.26) | |
EDU | 2.4151 | 0.0506 | 2.6195 * |
(1.56) | (0.34) | (1.71) | |
FIN | 2.7348 | −0.2852 * | 3.0672 |
(0.57) | (−1.90) | (0.65) | |
-Cons | −21.9347 * | −0.2874 | −23.6824 * |
(−1.77) | (−0.27) | (−1.93) | |
N | 210 | 209 | 206 |
0.3455 | 0.2210 | 0.3738 |
Excluding Municipalities Directly under the Central Government (7) | The Core Explanatory Variable with a Lag Period (8) | Distance to Hangzhou City (9) | |
---|---|---|---|
Index | 0.1818 *** | 0.2038 ** | 0.3840 *** |
(4.22) | (2.16) | (3.91) | |
STR | 6.2091 *** | 5.7614 *** | 5.0170 *** |
(3.75) | (4.98) | (4.64) | |
IND | 0.3122 ** | 0.2325 | 0.6452 *** |
(2.21) | (1.21) | (2.69) | |
NAT | −0.2444 | −0.3831 | −0.4683 |
(−1.19) | (−1.35) | (−1.63) | |
MEC | 0.2878 | 0.2218 | 0.2732 |
(1.41) | (0.88) | (1.06) | |
EDU | 0.9855 | 1.7387 | 0.8367 |
(0.79) | (1.24) | (0.64) | |
FIN | 0.5319 | 0.7281 | 1.8371 |
(0.12) | (0.26) | (0.47) | |
-Cons | −11.0024 | −15.5710 | −10.9896 |
(−1.14) | (−1.47) | (−1.15) | |
N | 178 | 184 | 188 |
0.2963 | 0.8134 | 0.7455 |
Coverage (10) | Usage (11) | Digital (12) | |
---|---|---|---|
Coverage | 0.1684 *** | ||
(2.79) | |||
Usage | 0.21516 *** | ||
(3.13) | |||
Digital | 0.1817 *** | ||
(4.71) | |||
STR | 4.9417 * | 5.4019 * | 4.9686 * |
(1.81) | (2.01) | (1.84) | |
IND | 0.4415 * | 0.3801 * | 0.3892 ** |
(1.83) | (1.70) | (2.18) | |
NAT | −0.3864 | −0.4080 | −0.4901 * |
(−1.27) | (−1.35) | (−1.78) | |
MEC | 0.5385 | 0.5320 | 0.4455 |
(1.41) | (1.39) | (1.25) | |
EDU | 3.3934 * | 3.1668 * | 3.0471 |
(1.88) | (1.81) | (1.63) | |
FIN | 2.6013 | 2.3706 | 3.8853 |
(0.57) | (0.25) | (0.74) | |
-Cons | −27.4989 * | −26.2652 * | −25.2074 * |
(−1.98) | (−1.92) | (−1.72) | |
N | 214 | 214 | 214 |
0.2918 | 0.3021 | 0.3052 |
Eastern (13) | Central (14) | Western (15) | Northeast (16) | |
---|---|---|---|---|
Index | 0.3033 *** | 0.1563 * | 0.2249 * | −0.0069 |
(3.92) | (2.14) | (2.09) | (−0.02) | |
STR | −2.5268 | 3.7334 | 0.7163 | 23.0129 |
(−1.55) | (1.77) | (0.18) | (1.39) | |
IND | 0.0212 | 0.1629 | −0.5122 | −0.2295 |
(0.07) | (0.99) | (−0.79) | (−0.67) | |
NAT | −0.1660 | −0.0480 | −2.3790 * | −0.0900 |
(−0.6) | (−0.21) | (−1.84) | (−0.16) | |
MEC | 0.6337 | −0.2306 | −1.0019 | 1.1287 |
(1.05) | (−1.44) | (−0.79) | (0.47) | |
EDU | 0.2924 | −1.4865 | −8.2680 | 0.8953 |
(0.12) | (−1.84) | (−1.18) | (0.06) | |
FIN | 3.2511 | −5.7032 | −3.9086 | 20.7254 |
(0.63) | (−1.24) | (−0.77) | (0.74) | |
-Cons | −1.6942 | 8.9253 | 59.6202 | −19.7178 |
(−0.11) | (1.44) | (1.14) | (−0.22) | |
N | 80 | 48 | 88 | 24 |
0.3509 | 0.4728 | 0.4028 | 0.5905 |
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Hong, M.; Tian, M.; Wang, J. Digital Inclusive Finance, Agricultural Industrial Structure Optimization and Agricultural Green Total Factor Productivity. Sustainability 2022, 14, 11450. https://doi.org/10.3390/su141811450
Hong M, Tian M, Wang J. Digital Inclusive Finance, Agricultural Industrial Structure Optimization and Agricultural Green Total Factor Productivity. Sustainability. 2022; 14(18):11450. https://doi.org/10.3390/su141811450
Chicago/Turabian StyleHong, Mingyong, Mengjie Tian, and Ji Wang. 2022. "Digital Inclusive Finance, Agricultural Industrial Structure Optimization and Agricultural Green Total Factor Productivity" Sustainability 14, no. 18: 11450. https://doi.org/10.3390/su141811450