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Financial Innovation

Table 1 Probit model estimating pre-chasm usage of robo-advice

From: Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice

Dependent

(1)

(2)

Variable: robo-advice

IRT OC probit

Marginal effects

IRT overconfidence

0.42***

0.07***

 

(0.06)

(0.01)

Willingness to take risk

0.15***

0.02***

 

(0.03)

(0.00)

Female

− 0.01

− 0.00

 

(0.09)

(0.01)

Age categories

  

25–34

− 0.10

− 0.03

 

(0.20)

(0.06)

35–44

− 0.39*

− 0.10

 

(0.20)

(0.05)

45–54

− 0.83***

− 0.18***

 

(0.20)

(0.05)

55–64

− 0.88***

− 0.19***

 

(0.21)

(0.05)

65 + 

− 1.31***

− 0.24***

 

(0.22)

(0.05)

Bachelor or more

− 0.04

− 0.01

 

(0.09)

(0.02)

Household income

  

$50,000–$100,000

− 0.18

− 0.03

 

(0.12)

(0.02)

$100,000 or more

− 0.52***

− 0.08***

 

(0.14)

(0.02)

Married

0.19*

0.03*

 

(0.10)

(0.02)

Ethnic/racial minority

0.17

0.03

 

(0.10)

(0.02)

Constant

− 1.51***

 
 

(0.28)

 

Observations

1937

1937

McFadden's pseudo R2

0.276

 

BIC

1201

 

AIC

1123

 
  1. Column (2) are marginal effects around the mean of Column (1), hence the absence of a constant term. Robust standard errors in parentheses
  2. OC Overconfidence, BIC Bayesian information criterion, AIC Akaike information criterion
  3. ***p < 0.001; **p < 0.01; *p < 0.05
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