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Table 14 Robustness: alternative parametric survival models

From: Entrepreneurial, institutional and financial strategies for FinTech profitability

Variables

Weibull

Exponential

Entrepreneurship

− 0.575*

− 0.391**

 

(0.307)

(0.176)

Single entrepreneur

0.608***

0.333***

 

(0.178)

(0.108)

Born mobile app

0.453

0.217

 

(0.417)

(0.252)

Born tech cluster

− 0.537**

− 0.341***

 

(0.214)

(0.127)

Born accelerator

0.977***

0.254

 

(0.299)

(0.265)

Initial seed capital

0.695***

0.261

 

(0.253)

(0.178)

Initial venture capital

− 0.132

− 0.0933

 

(0.316)

(0.168)

Equity finance

0.815

0.304

 

(0.657)

(0.450)

Investment

0.665

0.295

 

(0.715)

(0.492)

Personal finance

− 0.393

− 0.400

 

(0.872)

(0.654)

Lending

0.543

0.236

 

(0.659)

(0.454)

Neobank

2.004***

0.953**

 

(0.676)

(0.451)

Payments

0.558

0.274

 

(0.673)

(0.462)

Currencies

0.369

0.168

 

(0.871)

(0.579)

Financial product distribution

− 0.292

− 0.268

 

(0.705)

(0.482)

Business consultancy

0.744

0.362

 

(0.674)

(0.463)

Financial infrastructure

1.031

0.530

 

(0.673)

(0.461)

Constant

− 2.166***

− 0.915*

 

(0.777)

(0.505)

Observations

170

170

Clustered standard errors

FinTech

FinTech

  1. This table presents the results of the survival analysis on the time (years) to reach profitability. In Column 1, we estimate a parametric model assuming that the data follow a Weibull distribution. In Column 2, we estimate a parametric model assuming that the data follow an exponential distribution. All variables are described in Table 2. Standard errors are clustered at the FinTech-level. A constant term (reported) is included. *, **, *** Coefficients are statistically significant different than zero at least at 10%, 5%, and 1% levels