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. 2022 May 24;8(1):62. doi: 10.1186/s40854-022-00367-0

Table 6.

Heckman’s two-stage model results

Variables The second stage results
PIN
1 2 3 4 5 6
Intercept 0.3965*** 0.4035*** 0.3938*** 0.3930*** 0.3913*** 0.3925***
(5.74) (5.84) (5.70) (5.69) (5.67) (5.68)
Loan size - 0.0028***
(- 3.07)
Tbank - 0.0047***
(- 2.64)
OL 0.0084**
(2.03)
OL rate 0.0261***
(2.63)
OL Tbank 0.0209***
(2.70)
OL Nbank 0.0048*
(1.96)
Lambda - 0.0056 0.0054 0.0055 - 0.0042 - 0.0042 - 0.0041
(- 1.11) (1.36) (1.38) (- 0.84) (- 0.83) (- 0.81)
Controls Yes Yes Yes Yes Yes Yes
Year × industry-fixed effect Yes Yes Yes Yes Yes Yes
Firm-fixed effect Yes Yes Yes Yes Yes Yes
Adjusted R2 0.0805 0.0805 0.0804 0.0758 0.0758 0.0757
Obs. 27025 27025 27025 27025 27025 27025

This table reports the results of the second stage of the Heckman (1979) two-step procedure that considers the potential selection bias. The dependent variable in the first-stage probit regression is a dummy variable that equals 1 if a firm has at least one outstanding loan in a given month and equals 0 otherwise. In the second stage, we estimate Eq. (2) including an additional control variable equal to the inverse Mills ratio obtained from the first stage. The t-statistics reported are based on standard errors clustered by firm. Symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively