Can Digital Inclusive Finance Promote Food Security? Evidence from China
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Analysis
3.1. Direct Effects: The Direct Effects of Digital Inclusive Finance on Food Security
3.2. The Farmland Scale Effect of Digital Inclusive Finance
3.3. Moderating Effect of Agricultural Machinery Progress
4. Empirical Methods and Data Description
4.1. Baseline Regress
4.2. Intermediary Model Construction
4.3. Moderating Effect Model Construction
4.4. Variable Selection
4.5. Data Source
5. The Empirical Results
5.1. Baseline Regression Analysis Results
5.2. Robustness Tests
5.3. Endogenous Discussion
5.4. Heterogeneity Analysis
5.4.1. Perspectives of Main Grain Producing Areas, Main Marketing Areas and Balanced Production and Marketing Areas
5.4.2. Eastern, Central and Western Perspectives
5.5. Intermediation Mechanism and Moderating Effect Test
5.5.1. Test of Farmland Scale Effect
5.5.2. Further Discussion: A Test of the Moderating Effect of Progress in Agricultural Machinery
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Stats | PFE | DFI | INCO | FERT | DISA | INDUS | PGDP | FINAN | |
---|---|---|---|---|---|---|---|---|---|
2011 | mean | 425.90 | 40.80 | 7566.00 | 359.30 | 0.0767 | 0.89 | 40,087.00 | 0.11 |
S.D. | 306.20 | 18.39 | 3050.00 | 113.10 | 0.0663 | 0.05 | 18,765.00 | 0.03 | |
min | 52.50 | 18.33 | 3909.00 | 151.50 | 0.0006 | 0.74 | 16,413.00 | 0.04 | |
max | 1454.00 | 80.19 | 16,054.00 | 569.00 | 0.2720 | 0.99 | 85,213.00 | 0.16 | |
2012 | mean | 449.70 | 100.70 | 7771.00 | 366.10 | 0.0803 | 0.90 | 44,336.00 | 0.11 |
S.D. | 341.70 | 21.95 | 3134.00 | 117.20 | 0.0481 | 0.05 | 20,423.00 | 0.03 | |
min | 45.90 | 61.47 | 4015.00 | 167.30 | 0.0212 | 0.75 | 18,645.00 | 0.05 | |
max | 1566.00 | 150.80 | 16,508.00 | 564.90 | 0.1940 | 0.99 | 96,972.00 | 0.16 | |
2013 | mean | 449.70 | 156.70 | 7985.00 | 596.60 | 0.0883 | 0.90 | 48,615.00 | 0.11 |
S.D. | 341.70 | 25.52 | 3211.00 | 233.90 | 0.0557 | 0.05 | 22,258.00 | 0.03 | |
min | 45.90 | 118.00 | 4141.00 | 211.90 | 0.0042 | 0.76 | 20,976.00 | 0.04 | |
max | 1566.00 | 222.10 | 16,888.00 | 1128.00 | 0.2540 | 0.99 | 109,094.00 | 0.16 | |
2014 | mean | 450.40 | 180.90 | 8144.00 | 609.30 | 0.0914 | 0.90 | 52,649.00 | 0.11 |
S.D. | 348.20 | 22.88 | 3281.00 | 255.70 | 0.0742 | 0.05 | 24,054.00 | 0.03 | |
min | 30.00 | 145.90 | 4230.00 | 215.40 | 0.0000 | 0.77 | 23,241.00 | 0.04 | |
max | 1628.00 | 239.50 | 17,335.00 | 1256.00 | 0.3170 | 1.00 | 120,003.00 | 0.16 | |
2015 | mean | 458.10 | 221.10 | 8266.00 | 611.90 | 0.0814 | 0.90 | 56,749.00 | 0.12 |
S.D. | 357.80 | 22.45 | 3354.00 | 266.20 | 0.0706 | 0.05 | 25,945.00 | 0.03 | |
min | 29.00 | 193.30 | 4296.00 | 217.00 | 0.0000 | 0.77 | 25,728.00 | 0.04 | |
max | 1655.00 | 278.10 | 17,755.00 | 1360.00 | 0.2940 | 1.00 | 131,164.00 | 0.17 | |
2016 | mean | 452.60 | 231.30 | 8421.00 | 615.70 | 0.0863 | 0.90 | 60,930.00 | 0.12 |
S.D. | 351.50 | 21.00 | 3445.00 | 281.30 | 0.0718 | 0.05 | 27,917.00 | 0.04 | |
min | 24.70 | 200.40 | 4351.00 | 214.20 | 0.0068 | 0.77 | 28,429.00 | 0.04 | |
max | 1592.00 | 286.40 | 18,328.00 | 1404.00 | 0.2880 | 1.00 | 143,100.00 | 0.19 | |
2017 | mean | 481.60 | 272.90 | 8558.00 | 642.00 | 0.0570 | 0.91 | 65,119.00 | 0.12 |
S.D. | 426.60 | 23.96 | 3516.00 | 377.20 | 0.0682 | 0.05 | 29,229.00 | 0.03 | |
min | 18.90 | 240.20 | 4412.00 | 177.50 | 0.0000 | 0.78 | 31,329.00 | 0.05 | |
max | 1953.00 | 336.70 | 18,636.00 | 1819.00 | 0.2900 | 1.00 | 148,251.00 | 0.18 | |
2018 | mean | 481.60 | 301.10 | 8736.00 | 622.40 | 0.0582 | 0.91 | 69,404.00 | 0.12 |
S.D. | 436.30 | 29.89 | 3589.00 | 361.20 | 0.0436 | 0.05 | 30,629.00 | 0.04 | |
min | 13.40 | 263.10 | 4502.00 | 172.80 | 0.0106 | 0.79 | 34,102.00 | 0.05 | |
max | 1994.00 | 377.70 | 18,933.00 | 1692.00 | 0.1790 | 1.00 | 153,588.00 | 0.19 | |
2019 | mean | 481.60 | 324.70 | 8979.00 | 356.60 | 0.0397 | 0.91 | 73,620.00 | 0.12 |
S.D. | 436.30 | 33.38 | 3682.00 | 146.80 | 0.0391 | 0.05 | 32,179.00 | 0.04 | |
min | 13.40 | 282.60 | 4605.00 | 112.00 | 0.0000 | 0.79 | 36,080.00 | 0.05 | |
max | 1994.00 | 410.30 | 19,406.00 | 699.80 | 0.1670 | 1.00 | 159,271.00 | 0.19 | |
2020 | mean | 504.80 | 342.20 | 9187.00 | 339.30 | 0.0436 | 0.90 | 75,393.00 | 0.12 |
S.D. | 495.70 | 34.83 | 3742.00 | 134.90 | 0.0468 | 0.06 | 32,578.00 | 0.04 | |
min | 13.90 | 298.20 | 4698.00 | 96.25 | 0.0000 | 0.75 | 37,487.00 | 0.05 | |
max | 2347.00 | 431.90 | 19,736.00 | 629.30 | 0.2110 | 1.00 | 161,660.00 | 0.20 |
Variables | (1) | (2) |
---|---|---|
lnDFI | 0.434 ** (0.197) | 0.489 ** (0.169) |
lnINCO | −3.673 ** (1.377) | |
lnFERT | −0.246 * (0.143) | |
lnDISA | 0.313 (0.243) | |
lnINDUS | −2.158 (1.948) | |
lnPGDP | −0.529 ** (0.203) | |
lnFINAN | −0.995 (1.224) | |
Constants | 4.232 *** (0.696) | 45.046 *** (11.597) |
Fixed Time | yes | yes |
Fixed Individual | yes | yes |
R2 | 0.139 | 0.299 |
Sample Size | 300 | 300 |
(3) Dependent Variable: lnPFE | (4) Dependent Variable: lnPFE | (5) Dependent Variable: lnPFE | |
---|---|---|---|
lnDFI | 0.429 ** (0.165) | ||
L.lnDFI | 0.330 ** (0.145) | ||
L2.lnDFI | 0.357 ** (0.169) | ||
Constants | 37.528 *** (10.689) | 42.531 ** (12.644) | 43.099 ** (15.471) |
Controlled Variable | yes | yes | yes |
Fixed Time | yes | yes | yes |
Fixed Individual | yes | yes | yes |
R2 | 0.273 | 0.239 | 0.269 |
Sample Size | 300 | 270 | 240 |
Variables | (6) Tool Variables: L.lnDFI | (7) Tool Variables: L.lnDFI | (8) Tool Variables: lnTEL |
---|---|---|---|
lnDFI | 0.509 ** (0.200) | 0.754 *** (0.231) | 0.126 *** (0.033) |
Constants | 31.739 ** (12.965) | 31.710 ** (13.209) | 21.670 *** (5.170) |
Controlled Variable | yes | yes | yes |
Fixed Time | yes | yes | yes |
Fixed Individual | yes | yes | yes |
R2 | 0.199 | 0.188 | |
Whether the recognition is insufficient | NO | ||
Whether weak instrumental variables | NO | ||
Whether the tool variable is exogenous | NO | ||
Sample Size | 300 | 300 | 300 |
Variables | Eastern | Central | Western | Main Grain Producing Areas | Main Marketing Areas | Balanced Production and Marketing Areas |
---|---|---|---|---|---|---|
lnDFI | 0.519 ** (0.204) | −0.002 (0.298) | 0.337 (0.283) | 0.450 (0.501) | 0.455 ** (0.151) | 0.528 * (0.260) |
Constants | 52.786 * (27.191) | 34.948 * (18.539) | 71.887 *** (20.419) | 4.101 (18.036) | 114.358 ** (42.609) | 72.476 *** (19.931) |
Controlled Variable | yes | yes | yes | yes | yes | yes |
Fixed Time | yes | yes | yes | yes | yes | yes |
Fixed Individual | yes | yes | yes | yes | yes | yes |
R2 | 0.352 | 0.636 | 0.439 | 0.314 | 0.596 | 0.446 |
Sample Size | 300 | 300 | 300 | 300 | 300 | 300 |
Variables | Dependent Variable: lnscale | Dependent Variable: lnPFE |
---|---|---|
lnDFI | 0.289 ** (0.129) | 0.259 ** (0.079) |
lnscale | 0.797 *** (0.209) | |
Constants | 26.250 * (14.235) | 24.133 ** (10.698) |
Controlled Variable | yes | yes |
Fixed Time | yes | yes |
Fixed Individual | yes | yes |
R2 | 0.569 | 0.640 |
Sample Size | 300 | 300 |
Variables | Dependent Variable: Ln PFE |
---|---|
lnDFI | 0.511 ** (0.154) |
lnmachine | 0.551 (0.436) |
lnDFI×lnmachine | 0.367 ** (0.173) |
Constants | 36.945 *** (10.233) |
Controlled Variable | yes |
Fixed Time | yes |
Fixed Individual | yes |
R2 | 0.344 |
Sample Size | 300 |
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Lin, Q.; Dai, X.; Cheng, Q.; Lin, W. Can Digital Inclusive Finance Promote Food Security? Evidence from China. Sustainability 2022, 14, 13160. https://doi.org/10.3390/su142013160
Lin Q, Dai X, Cheng Q, Lin W. Can Digital Inclusive Finance Promote Food Security? Evidence from China. Sustainability. 2022; 14(20):13160. https://doi.org/10.3390/su142013160
Chicago/Turabian StyleLin, Qiaohua, Xinyi Dai, Qiuwang Cheng, and Wenhe Lin. 2022. "Can Digital Inclusive Finance Promote Food Security? Evidence from China" Sustainability 14, no. 20: 13160. https://doi.org/10.3390/su142013160