Table 4. Estimations of the linear and...

Table 4.

Estimations of the linear and nonlinear corruption models

Dependent variable: Transparency (TRANS)
Independent variables	Linear probability model (OLS)^a	Probit model (ML)	Logit model (LM)
PREV	0.0482(0.573)	0.1376(0.6370)	0.2748(0.5676)
AACOR	0.1772**(0.011)	0.5679***(0.0072)	0.9646***(0.0063)
TE	0.0394(0.467)	0.1368(0.4725)	0.2516(0.448)
Intercept	0.5906***(0.000)	0.2048(0.4967)	0.2700(0.5902)
R²/pseudo R²	0.042	0.0382	0.0387
F/ LR statistic	3.637**(0.0135)	6.55*(0.087)	9.77**(0.02)

Dependent variable: Transparency (TRANS)
Independent variables	Linear probability model (OLS)^a	Probit model (ML)	Logit model (LM)
PREV	0.0482(0.573)	0.1376(0.6370)	0.2748(0.5676)
AACOR	0.1772**(0.011)	0.5679***(0.0072)	0.9646***(0.0063)
TE	0.0394(0.467)	0.1368(0.4725)	0.2516(0.448)
Intercept	0.5906***(0.000)	0.2048(0.4967)	0.2700(0.5902)
R²/pseudo R²	0.042	0.0382	0.0387
F/ LR statistic	3.637**(0.0135)	6.55*(0.087)	9.77**(0.02)

Note(s):

***represents significance at the 1 % level; ** represents significance at the 1 % level; * represents significance at the 1 % level; ^aEstimated using heteroscedasticity-robust standard errors and p-values are given in parentheses. TRANS is the presence of transparency, PREV is the preventive measures, AACOR is the actions against corruption, TE is the training and ethics, GEND is gender, AGE is age group and EXP is experience

Source(s): Authors own work

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