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

Table 14

Logical regression results of loan description text variables and overall text variables.

Var	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
	Default	Default	Default	Default	Default	Default
C	−2.415	−2.332	−2.607	−2.612	−2.393	−2.557
	(0.007)	(0.708)	(0.712)	(0.707)	(0.705)	(0.726)
Length	−0.007					−0.008
	(−0.001)					(0.001)
Ability			−0.060			0.06
			(0.074)			0 (.077)
Willingness				−0.116		−0.021
				(0.069)		(0.071)
Purp 1		0.179				0.087
		(0.138)				(0.235)
Purp 2		0.472				0.462
		(0.133)				(0.135)
Purp 3		0.278				0.224
		(0.129)				(0.131)
Purp 4		0.569				0.532
		(0.163)				(0.164)
Urgency					0.049	0.037
					(0.300)	(0.235)
Controls	YES	YES	YES	YES	YES	YES
Nagelkerke R^2	0.695	0.693	0.694	0.696	0.693	0.697
N	4073	4073	4073	4073	4073	4073

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