The Adoption 
of Electronic Banking: Evidence from Liechtenstein 
  
  
Dependent variable 
(4) 
Electronic Banking Adoption 
(5) 
(6) 
  
  
Distance to closest 0.0019*** 0.0049%** 0.0079*** 
branch (min) (0.0006) (0.0015) (0.0024) 
[0.0019] [0.0019] 
Age -0.0044*** -0.0114*** -0.0183*** 
(0.0001) (0.0004) (0.0006) 
[-0.0044] [-0.0044] 
Sex 0.0735*** 0.1894*** 0.3051*** 
(0.0055) (0.0143) (0.0230) 
[0.0734] [0.0735] 
Constant 0.632 0.335 0.544 
R? 0.040 
Pseudo R? 0.029 0.029 
Observations 31’511 31’511 31’511 
Method OLS Probit Logit 
  
Robust standard errors in parentheses 
Marginal effects for the probit and logit models in brackets 
* p<0.10, ** p<0.05, *** p<0.01 
Table 5.2. Regression models distance to closest branch (min). 
Notes: The dependent variable in this regression is Electronic Banking Adoption. Column (4) shows a 
model in which the distance to the closest branch in minutes operates as the explanatory variable and we 
simultaneously control for a client’s age and sex. Column (5) and (6) replicate the model from column 
(4) with a non-linear probit and logit model. For each variable we report the raw coefficients from the 
regression, together with robust standard errors in parentheses. For the probit and logit regression models 
we add the marginal effects in brackets. To give an indication about the model's goodness of fit, we report 
McFadden's R? for the probit and logit models, as well as the R? known from OLS. Definitions of the 
variables are provided in Appendix C. 
Source: Own table, based on the LLB dataset. 
effect of à unit change in some explanatory variable on the dependent variable is simply the 
associated coefficient on the relevant explanatory variable. However, for logit and probit models 
obtaining measures of the marginal effect is more complicated, due to the fact that these models 
are non-linear. We, therefore, follow the best practices for interpreting these results presented in 
Hoetker (2007). When z; is a continuous variable, such as age in our model, its partial effect on 
Pr (y = 1|x) is obtained from the partial derivative 
OPr(y=1|x)  O9G(zx) 
Oz; c x; 
where 
  
dz 
is the probability density function associated with G and where G is a function taking on values 
strictly between zero and one: 0 « G(z) « 1, for all real numbers z. Notice that 0 < G(x) « 1 
ensures that the estimated response probabilities are strictly between zero and one, which, thus, 
addresses the main worries we had about using LPM. Because the density function is non-negative, 
the partial effect of x; will always have the same sign as £;. Since our coefficients in table 5.2 
University of St.Gallen 25
        

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