The Impact of Crop Insurance on Fertilizer Use: Evidence from Grain Producers in China
Abstract
:1. Introduction
2. Theoretical Mechanism and Policy Background
2.1. Theoretical Mechanism
2.2. Crop Insurance in China
2.3. Heterogeneity in Farm Size
2.4. Chemical Fertilizer Use in China
3. Model and Data
3.1. Model and Variables
3.2. Endogeneity and Estimation Strategy
3.2.1. Endogenity of Insurance Decision
3.2.2. Instrumental Variable Estimation
3.3. Data Source and Description
4. Estimation Results
4.1. The Impact of Crop Insurance on Fertilizer Use
4.2. The Impact of Crop Insurance on Fertilizer Use among Farms of Different Scales
4.3. Robustness Checks
4.3.1. Different Estimation Methods
4.3.2. Fertilizer Quantity as an Independent Variable
5. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Unit | Mean | SD | Min | Max | |
---|---|---|---|---|---|---|
Dependent Variables | Log fertilizer expenditure | RMB/mu | 4.855 | 0.622 | 0 | 6.89 |
Log fertilizer quantity | kg/mu | 1.73 | 0.598 | 0 | 2.53 | |
Independent Variables | Insured or not | 1 = yes 0 = no | 0.364 | 0.481 | 0 | 1 |
Plot quality | 1 = good 2 = Medium 3 = Bad | 1.624 | 0.642 | 1 | 3 | |
Rented in | 1 = yes 0 = no | 0.425 | 0.494 | 0 | 1 | |
Manure | 1 = yes 0 = no | 0.320 | 0.467 | 0 | 1 | |
Plot size | Mu | 18.49 | 75.39 | 0.1 | 1750 | |
Farming years | Years | 31.87 | 13.70 | 0 | 67 | |
Crop type | 1 = Corn 0 = rice | 0.533 | 0.499 | 0 | 1 | |
Gender | 1 = Male 0 = Female | 0.965 | 0.183 | 0 | 1 | |
Education | years | 6.791 | 3.071 | 0 | 16 | |
Rate of non-farm labor | % | 0.411 | 0.337 | 0 | 1 | |
Large farmer or not | 1 = year 0 = no | 0.439 | 0.497 | 0 | 1 | |
Instrumental Variables | Village participation rate | % | 48.67 | 39.38 | 0 | 100 |
Implemented in town or not | 1 = yes 0 = no | 0.847 | 0.361 | 0 | 1 | |
Premium rate | % | 0.015 | 0.027 | 0 | 0.333 |
OLS | IV | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | ||
Insured or not | 0.0344 | −0.392 *** | −0.141 * | −0.125 | −0.193 | −0.172 |
(0.0316) | (0.0799) | (0.0799) | (0.0786) | (0.138) | (0.117) | |
Plot quality | ||||||
Medium | 0.0540 * | 0.0538 * | 0.0688 ** | 0.0434 | 0.0514 | |
(0.0319) | (0.0308) | (0.0318) | (0.0311) | (0.0321) | ||
Bad | 0.0858 * | 0.0896 * | 0.0986 * | 0.0783 | 0.0823 | |
(0.0507) | (0.0511) | (0.0512) | (0.0507) | (0.0510) | ||
Property right | −0.00392 | −0.0132 | −0.0120 | −0.00524 | −0.00727 | |
(0.0307) | (0.0306) | (0.0318) | (0.0293) | (0.0309) | ||
Manure | 0.0347 | −0.0111 | −0.00395 | 0.0264 | 0.0274 | |
(0.0345) | (0.0332) | (0.0330) | (0.0353) | (0.0352) | ||
Plot size | −0.0004 | 0.0006 | 0.0003 | 0.00003 | 0.00004 | |
(0.00021) | (0.00021) | (0.0002) | (0.00022) | (0.00021) | ||
Crop type | 0.298 *** | 0.262 *** | 0.282 *** | 0.314 *** | 0.313 *** | |
(0.0549) | (0.0330) | (0.0359) | (0.0558) | (0.0566) | ||
Farming years | 0.000343 | 0.000350 | −0.000113 | |||
(0.00112) | (0.00109) | (0.00116) | ||||
Gender | 0.165 | 0.232 * | 0.144 | |||
(0.114) | (0.123) | (0.116) | ||||
Education | −0.00721 | −0.00244 | −0.00750 * | |||
(0.00448) | (0.00482) | (0.00455) | ||||
Large farmer or not | −0.00616 | 0.0153 | 0.0193 | |||
(0.0287) | (0.0305) | (0.0306) | ||||
Rate of non-farm labor | 0.115 ** | 0.156 *** | 0.0997 ** | |||
(0.0452) | (0.0483) | (0.0447) | ||||
County dummy | Yes | No | No | No | Yes | Yes |
Constant | 4.367 *** | 4.998 *** | 4.744 *** | 4.427 *** | 4.548 *** | 4.401 *** |
(0.156) | (0.0313) | (0.0483) | (0.150) | (0.0969) | (0.159) | |
Observations | 1707 | 1707 | 1707 | 1707 | 1707 | 1707 |
R2 | 0.150 | 0.048 | 0.051 | 0.065 | 0.121 | 0.130 |
Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
F value in first stage | - | 407.45 | 63.81 | 45.93 | 44.77 | 42.12 |
Over-identification test | - | 0.11 | 0.67 | 0.75 | 0.62 | 0.69 |
Dependent Variables | Large-Scale Farmers | Small-Scale Farmers |
---|---|---|
Insured or not | −0.279 * | −0.102 |
(0.147) | (0.283) | |
Plot quality | ||
Medium | 0.0182 | 0.0720 |
(0.0447) | (0.0493) | |
Bad | 0.152 * | 0.0180 |
(0.0826) | (0.0716) | |
Property right | 0.0166 | −0.0185 |
(0.0435) | (0.0450) | |
Manure | 0.00001 | −0.0023 |
(0.00024) | (0.0019) | |
Plot size | 0.184 *** | 0.405 *** |
(0.0699) | (0.0833) | |
Crop type | −0.110 * | 0.132 *** |
(0.0604) | (0.0425) | |
Farming years | −0.00115 | −0.000357 |
(0.00169) | (0.00167) | |
Gender | 0.0829 | 0.283 * |
(0.199) | (0.151) | |
Education | −0.000265 | −0.0195 *** |
(0.00681) | (0.00669) | |
Large farmer or not | −0.00421 | 0.175 *** |
(0.0652) | (0.0625) | |
Rate of non-farm labor | — | — |
County dummy | Yes | Yes |
Constant | 4.553 *** | 4.366 *** |
(0.284) | (0.189) | |
Observations | 751 | 956 |
R2 | 0.133 | 0.173 |
Prob > chi2 | 0.000 | 0.000 |
All | Large-Scale Farmers | Small-Scale Farmers | ||
---|---|---|---|---|
(1) | (2) | (3) | ||
Two-stage estimation | Insured or not | −0.155 | −0.282 * | −0.105 |
(0.124) | (0.151) | (0.292) | ||
Observations | 1707 | 751 | 956 | |
R2 | 0.150 | 0.160 | 0.186 | |
Prob > F | 0.000 | 0.000 | 0.000 | |
Bootstrap-S.E.-adjusted two-stage estimation | Insured or not | −0.155 | −0.282 * | −0.105 |
(0.125) | (0.147) | (0.290) | ||
Observations | 1707 | 751 | 956 | |
R2 | 0.150 | 0.160 | 0.186 | |
Prob > F | 0.000 | 0.000 | 0.000 | |
Fertilizer quantity as the dependent variable | Insured or not | −0.120 | −0.314 ** | 0.0504 |
(0.107) | (0.158) | (0.266) | ||
Observations | 1707 | 751 | 956 | |
R2 | 0.138 | 0.133 | 0.204 | |
Prob > F | 0.000 | 0.000 | 0.000 | |
Controls | Plot | Yes | Yes | Yes |
Farm | Yes | Yes | Yes | |
County dummy | Yes | Yes | Yes |
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Zhang, C.; Lyu, K.; Zhang, C. The Impact of Crop Insurance on Fertilizer Use: Evidence from Grain Producers in China. Agriculture 2024, 14, 420. https://doi.org/10.3390/agriculture14030420
Zhang C, Lyu K, Zhang C. The Impact of Crop Insurance on Fertilizer Use: Evidence from Grain Producers in China. Agriculture. 2024; 14(3):420. https://doi.org/10.3390/agriculture14030420
Chicago/Turabian StyleZhang, Chongshang, Kaiyu Lyu, and Chi Zhang. 2024. "The Impact of Crop Insurance on Fertilizer Use: Evidence from Grain Producers in China" Agriculture 14, no. 3: 420. https://doi.org/10.3390/agriculture14030420