Table 3

Impact of Fintech and traditional determinants on unemployment

Model 1Model 2Model 3
UnemploymentCoefficientp>|z|[95% conf. Interval]Coefficientp>|z|[95% conf. Interval]Coefficientp>|z|[95% conf. Interval]
GDPA−0.50030.3240.5003−1.0213**−1.0213**0.048−2.0344−0.0082    
GDPC0.56600.2480.56651.0338**1.0338**0.0380.056692.0109    
HCI0.26220.9510.2622−8.1736**−8.1736**0.036−15.8002−0.5471    
FDI−0.01680.915−0.0168−0.09233−0.09230.546−0.39180.20721    
GFCF0.03500.5070.03520.018830.018830.761−0.10260.14033    
BA−5.512***0.001−5.5127     −5.9872***0.000−8.3045−3.6698
IU1.60170.6551.6012     6.9136*0.10−1.486715.3138
DP−7.3234**0.046−7.3237     −7.0696**0.019−12.9752−1.1603
MDP8.8117**0.0488.81178.8117**    9.11249.112949.112949.1129
MMA        −1.0811−1.0811−1.0811−1.0811
_cons7.3245***0.0097.32457.3245***10.9697***0.0005.67872616.26074.62520.02547.65848.6258
R-squared0.53890.27570.5191
χ2 (p-value)30.69 (0.000)9.28*** (0.08)34.22(0.000)

Note(s): *p< 0.1, ** p < 0.05, *** p < 0.01

Source(s): Authors’ calculations

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