Table 5.

The estimated coefficients and marginal effects of logit regressions for the job loss (The dependent variable of job loss takes unity when a respondent suffers income loss, otherwise 0).

	Model 1		Model 2
	Coefficient	ME	Coefficient	ME
Gender (base group = Female)	0.330***	0.006***	0.250***	0.004***
	(0.029)	(0.001)	(0.037)	(0.001)
Education (base group$=$less than high school)
High school	−0.019	−0.001	−0.043	−0.001**
	(0.030)	(0.001)	(0.032)	(0.001)
Diploma I/II/III	−0.559***	−0.009***	−0.487***	−0.008***
	(0.098)	(0.001)	(0.100)	(0.001)
Bachelor/Diploma IV	−0.800***	−0.012***	−0.586***	−0.010***
	(0.060)	(0.001)	(0.066)	(0.001)
Employment (base group$=$Regular)
Self-employed	0.112*	0.001	0.080	0.001
	(0.044)	(0.001)	(0.045)	(0.001)
Temporary	0.458***	0.012***	0.435***	0.011***
	(0.047)	(0.001)	(0.048)	(0.001)
Urban area (base group$=$Rural area)	−0.143***	−0.001	−0.090*	0.000
	(0.042)	(0.001)	(0.042)	(0.001)
Employment × residency (base group$=$“Regular × Urban”)
Self-employed × Urban	0.341***	–	0.337***	–
	(0.059)		(0.059)
Temporary × Urban	0.369***	–	0.375***	–
	(0.068)		(0.068)
Age			−0.024***	−0.001***
			(0.001)	(0.001)
Internet usage			−0.149***	−0.001*
			(0.035)	(0.001)
Work from home			−0.178**	−0.003**
			(0.066)	(0.001)
Household head			0.251***	0.004***
			(0.039)	(0.001)
Married			0.189***	0.003***
			(0.044)	(0.001)
Income			−0.152***	−0.003***
			(0.005)	(0.000)
Intercept	−3.696***		−0.810***
	(0.108)		(0.136)
Regional effects	NO		YES
AIC	58 897.301	59 366.858	57 849.194	58 290.979
Log likelihood	−29 405.650	−29 673.429	−28 875.597	−29 129.490
Num. obs.	291 919	291 919	291 919	291 919

***$p<0.001$; **$p<0.01$; *$p<0.05$.