[Retracted] How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

Table 15

Overall logistic regression results of control variables and text variables.

Model 7
Var	Coefficient	values	EXP (B)	Var	Coefficient	values	EXP (B)
C	−0.031	0.945	1.031	Grade (1)	0.929	0.000	2.532
Amount	0.205	0.007	1.000	Grade (2)	1.456	0.407	0.233
Month	−0.024	0.000	0.976	Grade (3)	0.612	0.207	0.542
Age	0.015	0.005	1.015	Grade (4)	−1.099	0.000	0.333
Sex	0.222	0.025	1.249	Grade (5)	−0.882	0.000	0.414
Edu (1)	1.497	0.000	4.467	Grade (6)	−0.270	0.002	0.764
Edu (2)	−2.015	0.000	7.504	Length	−0.007	0.000	0.993
Edu (3)	−0.929	0.001	2.533	Purp (1)	0.035	0.807	1.035
Income (1)	1.152	0.285	0.316	Purp (2)	0.350	0.011	1.419
Income (2)	1.603	0.000	0.201	Purp (3)	0.246	0.063	1.279
Income (3)	−1.488	0.000	0.226	Purp (4)	0.530	0.001	1.698
Income (4)	−1.588	0.000	0.204	Urgency	0.236	0.000	1.235
Income (5)	−0.981	0.000	0.375
Nagelkerke R^2	0.721
N	4073

Note: the symbols , , and indicate significance at the 10%, 5%, and 1% levels, respectively.