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. 2022 Aug 31;2022:3487014. doi: 10.1155/2022/3487014

Table 2.

Benchmark regression model.

Variable Pure technical efficiency of agricultural production
Agricultural land confirmation 0.082∗∗∗ (0.011) 0.083∗∗∗ (0.010)
Average age −0.002∗∗∗ (0.000) −0.001 (0.000)
Proportion of highly educated population −0.016 (0.022) −0.001 (0.020)
The proportion of women 0.004 (0.028) 0.029 (0.026)
Population −0.014∗∗∗ (0.003) −0.008∗∗∗ (0.003)
Intergenerational work −0.003 (0.007) 0.004 (0.006)
Per capita income 0.006 (0.005) 0.006 (0.004)
Number of plots 0.017∗∗∗ (0.002) 0.001 (0.002)
Willingness to transfer land −0.001 (0.002) 0.001 (0.002)
Grain sowing proportion −0.268∗∗∗ (0.021) 0.013 (0.025)
Proportion of agricultural training 0.039 (0.024) 0.043∗∗ (0.021)
Agricultural machinery usage −0.030 (0.030) 0.017 (0.028)
Irrigation conditions −0.027∗∗∗ (0.005) 0.010∗∗ (0.005)
Soil fertility −0.019∗∗∗ (0.006) −0.005 (0.005)
Village terrain 0.021 (0.027) 0.006 (0.015)
Distance 0.007∗∗∗ (0.002) 0.002 (0.001)
Traffic conditions 0.003 (0.005) 0.006 (0.005)
Intercept term 0.392∗∗∗ (0.101) 0.191∗∗∗ (0.071)
County dummy variable No Yes
Observation 2081 2081
R 2 0.249 0.395

Note. , ∗∗, and ∗∗∗ represent significant statistical levels at 10%, 5%, and 1%, respectively; robust standard errors are in brackets.