Impacts of Aging Agricultural Labor Force on Land Transfer: An Empirical Analysis Based on the China Family Panel Studies
Abstract
:1. Introduction
2. Theoretical Analysis and Research Hypothesis
3. Methodology
3.1. Data Source
3.2. Variable Selection
3.2.1. Dependent Variables
3.2.2. Independent Variable
3.2.3. Moderating Variables
3.2.4. Control Variables
3.3. Model Selection
4. Analysis of Empirical Results
4.1. The Impact of Aging Agricultural Labor Force on Farmers’ Land Transfer
4.2. The Effect of Aging Agricultural Labor Force on the Mechanism of Land Transfer
4.2.1. The Interaction between Aging Agricultural Labor Force and the Health Level
4.2.2. The Interaction between Aging Agricultural Labor Force and Pension Insurance
4.3. Robustness Test
4.3.1. Substitution of Independent Variables
4.3.2. Test of Bivariate Probit Estimation Method
5. Discussion
6. Conclusions and Implications
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
1 | Among the 26,909 samples used in this study, 7313 households were included, of which 2845 were tracked five times, 1441 were tracked four times, and 1561 were tracked three times. Therefore, sample households with a tracking rate of three or more accounted for 79.95% of the total sample. |
2 | The agricultural labor force was categorized as very healthy, relatively healthy, and healthy in terms of self-rated health levels as high health levels, and fair and unhealthy in terms of self-rated health levels as low health levels. |
3 | In the land rent-in and land rent-out models, the Hausman test results in p-values equal to 0.718 and 0.475, respectively, both of which are much greater than 0.05; hence, the random effects model is used for the regression. |
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Variables | 2010 | 2012 | 2014 | 2016 | 2018 | Total |
---|---|---|---|---|---|---|
Number of households | 6980 | 5738 | 5864 | 4681 | 3646 | 26,909 |
Average age of agricultural labor force | 47.58 | 47.84 | 51.19 | 53.16 | 54.97 | 50.44 |
At least one elderly person in the household (%) | 35.00 | 39.32 | 48.47 | 52.36 | 51.81 | 44.15 |
The labor force in the household is all elderly (%) | 11.78 | 13.99 | 15.16 | 18.01 | 17.96 | 15.01 |
Per household contracted land area (mu a) | 10.96 | 10.60 | 11.56 | 12.03 | 12.40 | 11.40 |
Land rent-in rate (%) | 16.00 | 15.33 | 15.76 | 16.04 | 12.37 | 15.32 |
Land rent-out rate (%) | 4.23 | 10.98 | 11.19 | 13.74 | 16.10 | 10.45 |
Variables | Definition | Mean | Std.Err. |
---|---|---|---|
Inrent | Whether farmers rent-in land (1 = yes; 0 = no) | 0.153 | 0.360 |
Outrent | Whether farmers rent-out land (1 = yes; 0 = no) | 0.105 | 0.306 |
Aging | Average age of the agricultural labor force (years) | 50.434 | 11.066 |
Aging * Aging | The square term of the average age of the agricultural labor force | 2666.037 | 1150.654 |
Health | Whether the average health of the agricultural labor force is high (high = 1; low = 0) | 0.628 | 0.483 |
Pension | Whether family members have pension insurance (yes = 1; no = 0) | 0.310 | 0.463 |
Eduy | Average years of education of the agricultural labor force | 5.502 | 3.370 |
Net_agri | Net agricultural income (10,000 CNY) | 0.651 | 1.745 |
Hire | Whether to hire workers (1 = yes; 0 = no) | 0.146 | 0.353 |
Labor | Total labor force in the family (person) | 2.782 | 1.274 |
Land_per | Per capita land area of family (mu/person) | 3.241 | 7.921 |
Landscape | The landform of the village is plain = 1, otherwise = 0 | 0.395 | 0.489 |
Road | Whether the village has access to roads (yes = 1; no = 0) | 0.881 | 0.362 |
Dum_area | Regional dummy variable (east, middle, west, northeast) | NA | NA |
Dum_year | Time dummy variable (2010/2012/2014/2016/2018) | NA | NA |
Variables | Land Rent-In | Land Rent-Out | ||
---|---|---|---|---|
Model (1) | Model (2) | Model (3) | Model (4) | |
Aging | 0.143 *** | 0.127 *** | −0.063 *** | −0.053 *** |
(0.019) | (0.019) | (0.016) | (0.017) | |
Aging * Aging | −0.002 *** | −0.002 *** | 0.001 *** | 0.001 *** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Health | −0.106 * | −0.176 *** | ||
(0.060) | (0.063) | |||
Pension | −0.011 | 0.143 ** | ||
(0.062) | (0.063) | |||
Eduy | −0.042 *** | 0.064 *** | ||
(0.011) | (0.011) | |||
Net_agri | 0.175 *** | −0.177 *** | ||
(0.015) | (0.033) | |||
Hire | 0.848 *** | −0.564 *** | ||
(0.067) | (0.084) | |||
Labor | 0.017 | −0.061 ** | ||
(0.025) | (0.028) | |||
Land_per | −0.006 | 0.010 *** | ||
(0.005) | (0.004) | |||
Landscape | −0.038 | 0.207 ** | ||
(0.086) | (0.083) | |||
Road | −0.270 *** | −0.081 | ||
(0.090) | (0.088) | |||
Dum_area | control | control | control | control |
Dum_year | control | control | control | control |
Constant | −5.538 *** | −4.599 *** | −3.453 *** | −3.728 *** |
(0.453) | (0.486) | (0.429) | (0.469) | |
Wald | 260.62 | 573.33 | 665.98 | 788.82 |
Variables | Land Rent-In | Land Rent-Out | ||
---|---|---|---|---|
Model (5) | Model (6) | Model (7) | Model (8) | |
Aging | −0.048 *** | −0.050 *** | 0.270 *** | 0.029 *** |
(0.004) | (0.005) | (0.004) | (0.004) | |
Health | −1.298 *** | −1.296 *** | 0.440 * | 0.371 |
(0.229) | (0.232) | (0.236) | (0.235) | |
Aging * Health | 0.025 *** | 0.025 *** | −0.012 ** | −0.011 ** |
(0.005) | (0.005) | (0.005) | (0.005) | |
Control variables | uncontrol | control | uncontrol | control |
Constant | −0.677 *** | −0.244 | −5.685 *** | −5.860 *** |
(0.228) | (0.295) | (0.237) | (0.307) | |
Wald | 228.61 | 651.08 | 653.51 | 988.88 |
Variables | Land Rent-In | Land Rent-Out | ||
---|---|---|---|---|
Model (9) | Model (10) | Model (11) | Model (12) | |
Aging | −0.033 *** | −0.034 *** | 0.021 *** | 0.022 *** |
(0.003) | (0.003) | (0.003) | (0.003) | |
Pension | −0.104 | −0.088 | 0.026 | 0.033 |
(0.249) | (0.252) | (0.263) | (0.264) | |
Aging * Pension | 0.003 | 0.003 | 0.001 | 0.001 |
(0.005) | (0.005) | (0.005) | (0.005) | |
Control variables | uncontrol | control | uncontrol | control |
Constant | −1.443 *** | −1.046 *** | −5.332 *** | −5.519 *** |
(0.185) | (0.255) | (0.207) | (0.278) | |
Wald | 202.60 | 632.71 | 642.42 | 986.57 |
Variables | Land Rent-In | Land Rent-Out | ||||
---|---|---|---|---|---|---|
Model (13) | Model (14) | Model (15) | Model (16) | Model (17) | Model (18) | |
P_aging | −0.014 *** | 0.009 *** | ||||
(0.001) | (0.001) | |||||
Elderly households | −0.792 *** | 0.541 *** | ||||
(0.097) | (0.089) | |||||
Young households | 0.333 *** | −0.125 * | ||||
(0.064) | (0.072) | |||||
Male_aging | −0.011 *** | 0.006 * | ||||
(0.003) | (0.003) | |||||
Female_aging | −0.022 *** | 0.015 *** | ||||
(0.003) | (0.003) | |||||
Control variables | control | control | control | control | control | control |
Wald | 638.64 | 635.31 | 495.81 | 989.65 | 977.63 | 675.71 |
Variables | Land Rent-In | Land Rent-Out |
---|---|---|
Aging | 0.052 *** | −0.024 *** |
(0.007) | (0.006) | |
Aging * Aging | −0.001 *** | 0.000 *** |
(0.000) | (0.000) | |
Health | −0.032 | −0.103 *** |
(0.022) | (0.023) | |
Pension | −0.035 | 0.069 *** |
(0.024) | (0.026) | |
Control variables | control | control |
Constant | −1.730 *** | −1.362 *** |
(0.163) | (0.160) | |
/athrho | −0.127 *** (0.018) | |
Wald | 1926.11 |
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Li, C.; Li, X.; Wang, J.; Feng, T. Impacts of Aging Agricultural Labor Force on Land Transfer: An Empirical Analysis Based on the China Family Panel Studies. Land 2023, 12, 295. https://doi.org/10.3390/land12020295
Li C, Li X, Wang J, Feng T. Impacts of Aging Agricultural Labor Force on Land Transfer: An Empirical Analysis Based on the China Family Panel Studies. Land. 2023; 12(2):295. https://doi.org/10.3390/land12020295
Chicago/Turabian StyleLi, Chaozhu, Xiaoliang Li, Jiaxu Wang, and Tianchu Feng. 2023. "Impacts of Aging Agricultural Labor Force on Land Transfer: An Empirical Analysis Based on the China Family Panel Studies" Land 12, no. 2: 295. https://doi.org/10.3390/land12020295
APA StyleLi, C., Li, X., Wang, J., & Feng, T. (2023). Impacts of Aging Agricultural Labor Force on Land Transfer: An Empirical Analysis Based on the China Family Panel Studies. Land, 12(2), 295. https://doi.org/10.3390/land12020295