Table 4.

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

	Model 1		Model 2
	Coefficient	ME	Coefficient	ME
Gender (base group$=$Female)	0.252***	0.058***	0.256***	0.061***
	(0.009)	(0.002)	(0.011)	(0.003)
Education (base group$=$less than high school)
High school	−0.141***	−0.038***	−0.128***	−0.038***
	(0.010)	(0.002)	(0.010)	(0.002)
Diploma I/II/III	−0.639***	−0.148***	−0.573***	−0.142***
	(0.025)	(0.005)	(0.026)	(0.005)
Bachelor/Diploma IV	−0.920***	−0.211***	−0.804***	−0.198***
	(0.015)	(0.003)	(0.017)	(0.003)
Employment (base group$=$Regular)
Self-employed	1.106***	0.254***	1.082***	0.248***
	(0.013)	(0.003)	(0.013)	(0.003)
Temporary	0.572***	0.149***	0.541***	0.144***
	(0.016)	(0.004)	(0.016)	(0.004)
Urban area (base group$=$Rural area)	0.217***	0.074***	0.257***	0.084***
	(0.012)	(0.003)	(0.012)	(0.003)
Employment × residency (base group$=$“Regular × Urban”)
Self-employed × Urban	0.453***	–	0.453***	–
	(0.018)		(0.018)
Temporary × Urban	−0.047	–	−0.057*	–
	(0.025)		(0.025)
Age			−0.010***	−0.002***
			(0.001)	(0.001)
Internet usage			−0.039***	0.005
			(0.011)	(0.003)
Work from home			−0.037*	−0.006
			(0.017)	(0.004)
Household head			0.114***	0.023***
			(0.012)	(0.003)
Married			0.167***	0.039***
			(0.014)	(0.003)
Income			−0.138***	−0.032***
			(0.004)	(0.001)
Intercept	−0.845***		1.281***
	(0.039)		(0.066)
Regional effects	NO		YES
AIC	356 723.959	3 602 18.686	354 514.367	358 209.238
Log likelihood	−178 318.980	−180 099.343	−177 208.184	−179 088.619
Num. obs.	291 919	291 919	291 919	291 919

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