Table 2.

Estimated AMEs of the covariates on the number of job-search channels using model (4). Temporal effects (year of the survey) were included in the model but are not reported in the table

	Unemployed without previous job experience		Unemployed with previous job experience
Variables	AME	CIs	AME	CIs
Socio-demographic characteristics
Female	–	–	–	–
Male	$0.{01}^{}$	$[-0.03;0.04]$	$0.{16}^{\ast \ast \ast }$	[0.14; 0.18]
Age range
15–24	–	–	$0.{12}^{\ast \ast \ast }$	[0.09; 0.16]
25–34	$-0.{05}^{\ast \ast \ast }$	$[-0.09;-0.01]$	$0.{13}^{\ast \ast \ast }$	[0.11; 0.16]
35–44	$-0.{25}^{\ast \ast \ast }$	$[-0.32;-0.18]$	–	–
45–54	$-0.{60}^{\ast \ast \ast }$	$[-0.70;-0.49]$	$-0.{18}^{\ast \ast \ast }$	$[-0.21;-0.16]$
55–64	$-0.{82}^{\ast \ast \ast }$	$[-1.02;-0.62]$	$-0.{42}^{\ast \ast \ast }$	$[-0.46;-0.39]$
65–74	$-1.{44}^{\ast \ast \ast }$	$[-2.13;-0.75]$	$-0.{89}^{\ast \ast \ast }$	$[-1.08;-0.71]$
Education
Primary education	$-0.{84}^{\ast \ast \ast }$	$[-0.96;-0.72]$	$-0.{69}^{\ast \ast \ast }$	$[-0.75;-0.64]$
Lower secondary education	$-0.{33}^{\ast \ast \ast }$	$[-0.37;-0.29]$	$-0.{27}^{\ast \ast \ast }$	$[-0.29;-0.25]$
Upper secondary education	–	–	–	–
Tertiary education	$0.{06}^{\ast \ast \ast }$	[0.01; 0.10]	$0.{04}^{\ast \ast \ast }$	[0.01; 0.08]
Citizenship
Italian	–	–	–	–
From European Union	$-0.{19}^{\ast \ast \ast }$	$[-0.30;-0.08]$	$-0.{30}^{\ast \ast \ast }$	$[-0.35;-0.26]$
Extra-European Union	$-0.{29}^{\ast \ast \ast }$	$[-0.35;-0.22]$	$-0.{21}^{\ast \ast \ast }$	$[-0.24;-0.18]$
Marital status
Single	–	–	$0.{03}^{\ast \ast \ast }$	[0.01; 0.05]
Married	$-0.{30}^{\ast \ast \ast }$	$[-0.37;-0.23]$	−	−
Separated or divorced	$-0.{18}^{\ast \ast \ast }$	$[-0.31;-0.05]$	$0.{09}^{\ast \ast \ast }$	[0.06; 0.12]
Widowed	$-0.{12}^{}$	$[-0.41;0.17]$	$-0.{05}^{}$	$[-0.14;0.05]$
Main characteristics of previous job
Sector of employment
Agriculture			$-0.{32}^{\ast \ast \ast }$	$[-0.38;-0.25]$
Manufacturing			$0.{19}^{\ast \ast \ast }$	[0.16; 0.22]
Construction			$-0.{01}^{}$	$[-0.05;0.03]$
Trade			$0.{12}^{\ast \ast \ast }$	[0.09; 0.15]
Service			–	–
Type of employment
Employee			–	–
Collaboration agreement			$-0.{01}^{}$	$[-0.05;0.03]$
Self-employed			$-0.{13}^{\ast \ast \ast }$	$[-0.17;-0.09]$
Occupation
Legislator, entrepreneur, manager			$-0.{15}^{\ast \ast \ast }$	$[-0.26;-0.04]$
Intellectual, scientific, and high-specialisation professions			$-0.{15}^{\ast \ast \ast }$	$[-0.19;-0.11]$
Technical professions			$-0.{02}^{}$	$[-0.06;0.01]$
Qualified professions in commercial activities and services			–	-
Office worker			$0.{09}^{\ast \ast \ast }$	[0.06; 0.13]
Craftsmen, specialised workers, and farmers			$-0.{12}^{\ast \ast \ast }$	$[-0.16;-0.08]$
Fixed and mobile machinery workers and vehicle drivers			$0.{05}^{\ast \ast \ast }$	[0.01; 0.10]
Non-qualified professions			$-0.{11}^{\ast \ast \ast }$	$[-0.14;-0.07]$
Armed forces			$-0.{03}^{\ast \ast \ast }$	$[-0.32;0.27]$
Duration of job search
From 0 to 3 months	$-0.{34}^{\ast \ast \ast }$	$[-0.40;-0.28]$	0.01	$[-0.02;0.04]$
From 4 to 6 months	$-0.{11}^{\ast \ast \ast }$	$[-0.17;-0.05]$	$0.{08}^{\ast \ast \ast }$	[0.05; 0.11]
From 7 to 12 months	$-0.02$	$[-0.06;0.02]$	$0.{05}^{\ast \ast \ast }$	[0.03; 0.08]
More than 12 months	–	–	–	–
Spatial variable
Northwest	$0.{46}^{\ast \ast \ast }$	[0.41; 0.51]	$0.{64}^{\ast \ast \ast }$	[0.62; 0.68]
Northeast	$0.{51}^{\ast \ast \ast }$	[0.45; 0.56]	$0.{76}^{\ast \ast \ast }$	[0.73; 0.79]
Centre	$0.{03}^{}$	$[-0.01;0.08]$	$0.{28}^{\ast \ast \ast }$	[0.25; 0.32]
Southern Italy	–	–	–	–
Main islands	$-0.{23}^{\ast \ast \ast }$	$[-0.28;-0.18]$	$-0.{05}^{\ast \ast \ast }$	$[-0.08;-0.01]$
Observations	18,892		55,120
Pseudo-$R^2$	0.02		0.02
Log pseudo-likelihood	-31,976.43		-93,650.44
Deviance goodness-of-fit	8,759.87 (Prob $>{\chi }^2$(18,866)=1.00)		26,679.43 (Prob $>{\chi }^2$(55,080)=1.00)
Sample period	2014–2018

${}^{\ast }P\text{value}<0.1$; ${}^{\ast \ast }P\text{value}<0.05$; ${}^{\ast \ast \ast }P\text{value}<0.01$