Navigating new gender roles: impacts of cultural origins, context of settlement, and religious beliefs on gender attitudes among immigrant origin youth across five European countries

Figures & data

Table 1. Distribution of immigrant backgrounds in each host country.

	Sample size	Immigrant background	Five largest groups by country-of-origin
Norway	7627	2901 (38%)	Pakistan, Somalia, Sri Lanka, Sweden, Poland
Sweden	4531	2322 (51%)	Iraq, Turkey, Bosnia and Herzegovina, Somalia, Lebanon
UK	3389	1654 (49%)	Pakistan, India, Bangladesh, Sri Lanka, Jamaica
Germany	4256	2337 (55%)	Turkey, Russia, Poland, Serbia, Italy
Netherlands	3614	1356 (38%)	Turkey, Morocco, Suriname, the Dutch Antilles, Iraq
Total	23,417	10,570 (45%)

Figure 1. Share who report that they prefer traditional gender roles on at least one out of four items, according to immigration status, country of residence, and level of gender inequality in parental country of origin.

Table 2. Variables used in regression. Immigrant origin respondents only.

Variable	n	%	valid %
Preference for traditional gender roles
Yes	4930	45.6	48.2
No	5180	47.9	51.2
Missing	698	6.5	–
Gender inequality parents’ country of birth
Low	4135	39.1	41.8
Medium	3504	33.2	35.4
High	2253	21.3	22.8
Missing	678	6.4	–
Country of settlement
Germany	2351	21.7	21.75
Netherland	1415	13.1	13.09
UK	1802	16.7	16.67
Sweden	2461	22.8	22.77
Norway	2779	25.7	25.71
Immigrant Background
Foreign born (1st gen.)	2512	23.2	23.2
Native born with two immigrant parents (2nd gen.)	6244	57.8	57.8
One immigrant and one native parents (Mixed)	2052	19.0	19.0
Parents’ highest level of education
Primary education or lower	1993	18.4	18.5
Secondary education	4109	38.0	38.2
University level education	4659	43.1	43.3
Missing	47	0.4	–
Grades in host country language
Lower 50%	4337	40.1	43.8
Upper 50%	5567	51.5	56.2
Missing	904	8.4
Religious affiliation
No religion	2222	20.6	22.1
Christian	3514	32.5	34.9
Muslim	3340	30.9	33.2
Other religions	987	9.1	9.8
Missing	745	6.9	–
Religious salience
Religion is not at all important	2243	20.7	22.2
Religion is not very important	2249	20.8	22.3
Religion is fairly important	2242	20.7	22.2
Religion is very important	3368	31.2	33.3
Missing	706	6.5	–
Gender
Boy	4851	44.9	49.7
Girl	4911	45.4	50.3
Missing	1046	9.7	–

Table 3. Stepwise logistic regression. Preference for traditional gender roles as dependent variable.

	Model 1	Model 3	Model 4	Model 5
Increase in overall prediction percentage, after applying the model^†	51.3→66.4	50.9→65.9	50.6→67.1	50.4→67.8
Specificity (true negatives) ^‡	68.7%	65.7	68.5	68.6
Sensitivity (true positives) ^††	64.0%	66.1	65.7	67.1
Nagelkerke R^2 ^‡‡	17.8	18.2	21.2	22.5
Coefficients (B)
Gender (ref. Boy)
Girl	−0.91***	−0.92***	−0.95***	−0.98***
Gender inequality parents’ country of birth (ref. Low gender inequality)
Medium gender inequality	0.46***	0.46***	0.27***	0.21***
High gender inequality	0.64***	0.62***	0.31***	0.21**
Country of residence (ref. UK)
Germany	0.63***	0.47***	0.33***	0.44***
Netherland	0.34***	0.32***	0.33***	0.37***
Sweden	−0.45***	−0.55***	−0.56***	−0.45***
Norway	−0.96***	−1.02***	−1.09***	−0.93***
Immigrant Generation (ref. foreign born)
Native born with two immigrant parents	−0.17**	−0.13	−0.18**	−0.17*
One foreign born and one native parent	−0.61***	−0.49*	−0.29***	−0.22*
Parents’ education (ref. primary education)
Secondary education		−0.20**	−0.12	−0.09
University education		−0.33***	−0.22**	−0.19
School grades in host language (ref. Lower 50%)
Upper 50%		−0.32***	−0.28***	−0.26***
Religious affiliation (ref. No religion)
Christian			0.40***	0.10
Muslim			1.06***	0.52***
Other religions			0.48***	0.12
Religiosity (ref. Not at all important)
Not very important				0.30***
Fairly important				0.41***
Very important				0.89***

*** p < 0.01, ** p < 0.05, * p < 0.1.

^† This value shows the improvement of prediction of output variable by adding the model. Model 5 shows the best improvement.

^‡ By specificity we can estimate the probability of correct prediction for ‘no’ in outcome variable.

^†† By sensitivity we can estimate the probability of correct prediction for ‘yes’ in outcome variable. For example, if use Model 5 in the future and predict Yes in outcome variable, there is 67% probability that we predict it correctly.

^‡‡ Nagelkerke R² shows the % of variations in the dependent variable is explained by the model. Model 5 has the highest and therefore the best prediction model (for probability of Yes in outcome variable) of these five models.