Table 8 Sensitivity analysis based on Cinelli–Hazlett method.
From: Big data products and income inequality of e-commerce farmers: evidence from China
Core explanatory variable | Coefficient | S.E. | t (H0) | \({R}_{Y \sim D|X}^{2}\) | RV | RV0.05 | |
|---|---|---|---|---|---|---|---|
Whether or not to use big data products | −0.053 | 0.015 | −3.507 | 0.031 | 0.164 | 0.076 | |
Reduced variable | Bound | \({R}_{D \sim W|X}^{2}\) | \({R}_{Y \sim W|D,X}^{2}\) | Coefficient | S.E. | t (H0) | CI |
Gender | \({k}_{D}={k}_{Y}=1\) | 0.040 | 0.0004 | −0.052 | 0.016 | −3.353 | [−0.083,−0.022] |
\({k}_{D}={k}_{Y}=2\) | 0.079 | 0.0008 | −0.051 | 0.016 | −3.202 | [−0.082,−0.020] | |
\({k}_{D}={k}_{Y}=3\) | 0.119 | 0.0013 | −0.050 | 0.016 | −3.049 | [−0.082,−0.018] | |
Age | \({k}_{D}={k}_{Y}=1\) | 0.027 | 0.0000 | −0.053 | 0.016 | −3.442 | [−0.084,−0.023] |
\({k}_{D}={k}_{Y}=2\) | 0.054 | 0.0000 | −0.053 | 0.016 | −3.381 | [−0.084,−0.022] | |
\({k}_{D}={k}_{Y}=3\) | 0.080 | 0.0001 | −0.053 | 0.016 | −3.320 | [−0.084,−0.022] | |
Education | \({k}_{D}={k}_{Y}=1\) | 0.013 | 0.0011 | −0.052 | 0.015 | −3.411 | [−0.083,−0.022] |
\({k}_{D}={k}_{Y}=2\) | 0.025 | 0.0021 | −0.051 | 0.016 | −3.318 | [−0.082,−0.021] | |
\({k}_{D}={k}_{Y}=3\) | 0.038 | 0.0032 | −0.050 | 0.016 | −3.225 | [−0.081,−0.020] | |
