1 Religion, Immigration, and Fertility in France Julia Andrea Behrman

Religion, immigration, and fertility in France

Julia Andrea Behrman1 Jeylan Erman2

1 Corresponding author. Postdoctoral Prize Research Fellow in Sociology, Nuffield College, University of Oxford. [email protected] 2 Doctoral Student, Department of Sociology and Demography, University of Pennsylvania.

Acknowledgements: We are grateful to Elisabeth Becker, Monica Caudillo, and Abigail Weitzman for providing useful feedback on an earlier version of this manuscript. We are grateful to the Centre Maurice Halbwachs for granting access to the data [Trajectoires et origines (TEO)— version complete—2008: (2008, fichier electronique), INED et INSEE (producteur), Centre Maurice Halbwachs (CMH, diffuseur)].

1 Abstract: In contemporary Europe, the debate over migration and fertility has been polarized given low native-born fertility, high levels of Muslim migration, and contention over European identity. We deepen understandings of the linkages between religion and fertility in France using data from the French Trajectories and Origins (TeO) survey. Conducting a Cox- proportional hazards analysis, we find Muslim women have significantly higher expected hazards of first, second, and third births than Christian women, women with no religion, and women of other religions, net of controls for immigration status and socio-economic characteristics; however, most of these associations become null upon controlling for religiosity and ideal family size. Muslim women on average have higher religiosity and higher ideal family sizes, which suggests that these socio-cultural factors, rather than religion itself are associated with higher fertility.

Keywords: Religion, Religiosity, Fertility, Immigration, France

2 Introduction

The fertility patterns of women migrating from high-fertility countries to low-fertility

countries have garnered considerable research and policy attention. In contemporary Europe, the

debate over migration and fertility has been polarized given low native-born fertility, high levels

of Muslim migration, and contention over European identity. The Pew Research forum has

shown that Muslim women have a higher birth rate than women of Judeo-Christian origin in

Europe, and that Muslims have the largest projected absolute population growth between 2010-

2050 (Hackett, Connor, Stonawski, & Washington, 2015). Nonetheless, limited research

explores what accounts for religious differences in fertility in the context of European

immigration. Immigrant populations typically have higher levels of religiosity and higher levels of socio-economic disadvantage, thus making it difficult to disentangle the influence of religion from socioeconomic status (SES) and religiosity. Furthermore, fertility of immigrant women varies according to time spent in the country and generational status, necessitating a better understanding of what moderates the relationship between religion and fertility.

This paper explores religious differences in fertility in contemporary France, where unique socio-historical conditions make the country a particularly interesting case study. France is Europe’s second-largest country and is home to one of the largest immigrant populations in

Europe, including Europe’s largest Muslim population (Hackett et al., 2015). In 2013, 8.9 percent of the French population was foreign born, with immigrants from Portugal, Morocco and

Algeria being the largest groups (INSEE, 2016). A longstanding history of migration from

Christian-majority and Muslim-majority countries allows for analysis of multiple generations as well as several religious groups. In 2010, the descendants of the foreign-born population

3 composed 10 percent of the population and originated primarily from Southern Europe and

Northern Africa (INSEE, 2012).

France is also an interesting case because questions of religion and assimilation have

become central to French political discourse. High religiosity among immigrant populations,

coupled with a state policy of secularism in the public sphere, has heightened social tensions

about the appropriateness of expressing religious identities in daily life (Alba, 2005). The French

context is an interesting contrast to the United States, where religion is often a stepping-stone

towards immigrants' incorporation into American society. In Europe, secular state policies,

along with migrants' diverging religious and socioeconomic composition, has made religion—

rather than race or ethnicity—arguably the fundamental source of social division (Alba & Foner,

2015). As religion is an important marker separating natives and immigrants in Europe, it

becomes important to better understand religious differences in family behavior.

We use data from the French Trajectories and Origins (TeO) survey, a unique data source

that has information on religion, religiosity, socio-economic status, and family formation among

both first-and second-generation immigrant women and native-born French populations. We draw on and combine two separate but related literatures—one on religion and fertility and another on immigration and fertility—to develop a conceptual framework that explicates why religion might be associated with fertility in France specifically and Europe more broadly, with special attention to understanding fertility differentials among Muslim populations. Next, we run a series of nested models where we first show the hazard of birth for the different religious groups without any additional covariate since there are few to no studies that explicitly explore how fertility varies by religion in France. Next, we explore whether the hazard of first, second, and third birth is moderated upon controlling for the three major pathways of our conceptual

4 framework (i) immigration status; (ii) socio-economic status; and (iii) socio-cultural factors. Our analysis deepens understanding of the linkages between religion and fertility in contemporary

France and contributes to a broader debate about how cultural mechanisms are associated with fertility among immigrant groups.

Religion and fertility: theoretical and empirical perspectives

Questions of religion and fertility are intimately tied with questions of immigration in present-day Europe because until recently non-Christians made up a very small segment of the population (Frejka & Westoff, 2007a). As migrants to Europe have increasingly come from

Muslim-majority countries there has been heightened interest in understanding religious-fertility differentials (Coleman, 2006; Goldstone, 2010). As noted, in the aforementioned Pew study,

Muslim women in Europe have a higher birth rate than those of Judeo-Christian religions, and that Muslims have the largest projected absolute population growth between 2010-2050 (Hackett et al., 2015). Another cross-national comparative study finds immigrant women from Muslim majority countries (e.g. Bosnia) in Europe have slightly higher fertility than non-Muslim women immigrants in Europe (Westoff & Frejka, 2007).3 To the best of our knowledge, there are few to no studies that explicitly explore how fertility varies by religion in France, which likely reflects that France does not collect religion data in the census or other state-led surveys.

3 Nonetheless, this study has several important limitations. First, it does not have micro-level religion data, and thus assumes all immigrants from Muslim majority countries are Muslim. Furthermore, the analysis of the relationship between religiosity and fertility is conducted with data from the European Values Survey, which has a very small number of Muslim respondents primarily from countries with native-born Muslim populations (e.g. Bosnia, Russia,

Macedonia, Bulgaria) as opposed to migrant Muslim populations (e.g. France).

5 In what follows, we draw on and combine two separate but related literatures—one on religion and fertility and another on immigration and fertility—to develop a conceptual framework that explicates why religion might be associated with fertility in France specifically and Europe more broadly, with special attention to understanding fertility differentials among

Muslim populations. As Figure 1 shows, we propose there are three key pathways to explain religious differences in fertility; (i) differences in immigration status; (ii) differences in socio- economic status; and (iii) differences in socio-cultural background. Although these three pathways are not mutually exclusive they nonetheless represent distinct social processes, thus we explore them separately.

First, as Figure 1 demonstrates, fertility may be higher among Muslims because religion proxies for immigration status. Many European Muslim immigrants come from high-fertility countries and arrive at prime reproductive age. Fertility often peaks soon after migration due to an interrelationship of migration and family formation (Andersson, 2004; Lübke, 2014;

Milewski, 2007; Mussino & Strozza, 2012; Wolf, 2016). This rise in childbearing soon after arrival often inflates estimates of period fertility of first-generation migrant women (Sobotka,

2008). However, in the long term, fertility may adapt to the level of native populations

(Andersson, 2004; Milewski, 2007).

Although first-generation immigrants often have higher fertility than native populations in Europe (Afulani & Asunka, 2017; Andersson, 2004; Coleman & Dubuc, 2010; Milewski,

2007; 2010; Toulemon, 2004a), the fertility of the second-generation migrants typically decreases (Kulu et al., 2017; Milewski, 2010). Declining fertility among the descendants of the first generation is typically attributed to the influence of the host society and processes of adaptation and socialization (Afulani & Asunka, 2017). Thus, analysis that does not consider

6 immigrant generation (e.g. first versus second generation immigrants) may conflate these

important processes when assessing the relationship between religion and fertility. Nonetheless, there are several important exceptions to trend of fertility declines in the second generation in

Europe—including second-generation Turkish women in Sweden and second generation

Pakistani and Bangladeshi women in the United Kingdom (Andersson, 2004; Kulu et al., 2017).

Variation in fertility across immigrant descendant groups within host countries has been attributed to cultural factors such as norms related to family size and religiosity (Kulu et al.,

2017).

Generally speaking, these generational trends in fertility apply to France as well. While

first-generation women from other European countries tend to have lower fertility than native

women from metropolitan France, women from other origins, including Northern Africa, Asia

and Turkey, have higher fertility than native-French women (Héran & Pison, 2007). Nonetheless, the higher fertility of many first-generations immigrants from non-Western countries obscures the fact that their fertility is often between that of their origin country and that of native French women (Toulemon, 2004b) and that their overall contribution to national fertility rates is minimal (Héran & Pison, 2007). Furthermore, the fertility of the second generation often converges with native French women (Kulu et al., 2017; Pailhé, 2017), though there are exceptions to this trend—for example, descendants from Turkey have higher first and second birth transition rates than native French women, though these effects disappear when controlling for compositional effects (Pailhé, 2017).

Second, as Figure 1 shows, higher levels of Muslim fertility could be because both religion proxies for socio-economic disadvantage (Goldscheider, 1971). Western European

Muslim immigrant populations are disproportionately lower socio-economic status and face

7 obstacles accessing employment and education necessary for social mobility (Alba & Foner,

2015). For these reasons, along with longstanding socio-historical institutional structures,

religion is a barrier to inclusion in contemporary Europe. In France, both first and second generation immigrants have on average lower education than the native French population, though with some variation depending on country of origin (Ichou & Hamilton, 2013; Ichou et al., 2017). First-generation and second-generation immigrants in France also have higher unemployment rates compared to the native French population (Meurs, Pailhé, & Simon, 2006).

The disproportionately low SES of Muslim immigrants is relevant because lower socio- economic status women often have higher fertility and higher religiosity, thus making it difficult to disentangle the effects of socio-economic status on fertility from religion and religiosity

(Goldscheider, 1971). A large literature establishes that women’s education—a key measure of

SES—is negatively correlated with women’s fertility due to knowledge transmission, socioeconomic mobility, rising material aspirations, ideational changes, and the perception of children as costs (Becker & Lewis, 1974; Caldwell, 1980; Lestaheghe & Surkyn, 1988).

Nonetheless, socio-economic disadvantage may not fully account for religious-fertility differentials; evidence suggests that the association between religiosity and fertility is unaffected by controls for socio-economic status in both the United States and Europe (Frejka & Westoff,

2007a).

Third, as Figure 1 indicates, higher levels of Muslim fertility could be because religion is proxies for socio-cultural factors such as religiosity and/or family norms. First- and second- generation Muslim immigrants in Europe often have higher religiosity than that of native populations (Connor, 2009). In France, both first and second generation immigrants tend to be more religious than natives, although there is variation across countries of origin with lower

8 religiosity among immigrants from Europe and Southeast Asia and higher religiosity among immigrants from North Africa, Sub-Saharan Africa and Turkey (Simon & Tiberj, 2013). This is important because religiosity—rather than religion itself—may be associated with higher fertility. For example, religiosity is associated with significantly higher fertility among Christian populations in the United States and Europe (Berghammer, 2012; Frejka & Westoff, 2007a;

Hayford & Morgan, 2008; Philipov & Berghammer, 2007). Cross-national evidence from across

Europe also indicates that female immigrants from Muslim majority countries have higher religiosity and more “traditional” family values, which partially accounts for observed differences in fertility (Westoff & Frejka, 2007).

The positive association between religiosity and fertility could be because higher religiosity is often associated with stronger adherence to gender ideologies—such as high ideal family sizes and childbearing as central to women’s identity—that are also associated with higher fertility (Goldscheider, 1971; Hayford & Morgan, 2008). In addition, more religious people often adhere to religious institutions that regulate fertility behaviors via behavioral norms related to contraception use, abortion etc. (McQuillan, 2004). Nonetheless, the relationship between religiosity and fertility is not always straightforward. For example, the least religious women in Northern and Western Europe have equivalent (or sometimes higher) fertility to more religious women in the United States, and higher fertility than more religious women in Southern

Europe (Frejka & Westoff, 2007b).

Data & Sample

We use data from the French Trajectories and Origins (TeO) survey. The TeO is a cross- sectional survey of 22,000 respondents in metropolitan France collected by the National Institute

9 for Demographic Studies (INED) and the National Institute of Statistics and Economic Studies

(INSEE) between September 2008 and February 2009. The TeO sample includes detailed

information about immigration status, religion, religiosity, fertility, and socio-economic status.

Immigrant populations were oversampled to ensure adequate representation; thus, although the

TeO survey is not nationally representative, it is a unique data source for high quality micro-data

on religion, fertility, and immigration in contemporary France.

Our analytical sample is limited to female respondents with full information on religion,

religiosity, immigration status, and fertility.4 Following convention, we include only women

who were childless upon immigration to France among the immigrant sample.5 In total, this

provides sample of 8,811 female respondents: 19% are first generation immigrants; 55% are 2nd

generation immigrants (including the 1.5 generation e.g. women who immigrated before the age

of 16); and 26% are native born (Table 1).

Methods

Measures

Religion: We measure religion using self-reported religious affiliation. We include

indicators for Christian (including both Catholics and Protestants); Muslim; Other religion; and

no religious identification. In total, Christians comprise 42% of the sample, Muslims comprise

4 Following convention in event history models, we exclude respondents with twins from the analysis. Only 1.6%

(n=498) of the children in the full sample of 30,894 children were twins, thus it is unlikely that this exclusion would induce bias into our results.

5 Among first generation female migrants to France (n=5,599), 71% (n=3,984) had no children upon arrival to

France. Among the full sample of female respondents (n=11,194), 10%(n=1,104) had no children upon arrival to

France.

10 25% of the sample, other religions comprise 5% of the sample, and no religious identification

comprises 42% of the sample (Table 1). Among the Christians in the sample, 80% are Catholics

(n=2,992); 11% are other Christian denominations (n=416); 3% are Orthodox (n=97); 6% are

Protestants (n=234)—in supplementary models we disaggregate the Christian variable by

Catholic (described below). Among women who are other religion category 18% (n=76) are

Jewish; 53% (n=228) are Buddhists; 6% (n=26) are Hindus/Sikhs; 18% (n=75) and 23% (n=98)

are other categories. Throughout the analysis, we use Muslim as the reference category for the

analysis because we are particularly interested in differences between Muslims and other groups.

Immigration status: In line with pathway 1 from our conceptual framework (Figure 1), we control for women’s immigration status. First, we control for time since immigration as immigrants’ fertility may change with increasing duration of stay due to life cycle processes and/or adaptation processes. We include a continuous measure of years since arrival in France; women who are French natives and second-generation women who were born in France are coded as their current age.

In addition, we control for generational status, because second generation women may have different fertility trajectories then first generation women due to processes of adaptation and socialization (Afulani & Asunka, 2017). We include indicators for first generation immigrants, second-generation immigrants, and “native”-born population. First generation immigrants are women who immigrated after the age of 16. Second generation immigrants are either born in France or migrated before the age of 16; the latter group is often referred to as the

1.5 generation. We combine the 2nd generation and 1.5 categories for reasons of parsimony; but in subsequent models we disaggregate the two groups and our substantive findings are

11 unchanged (available upon request). Residents of French overseas territories (e.g. DOM/TOM)

are coded as native French because they have full citizenship rights.

Socio-economic characteristics: In line with pathway 2 from our conceptual framework

(Figure 1), we control for women’s socio-economic status. Our main measure of SES is the

educational status of both women and their partners. Education is a key measure of SES, with

important implications for earning potential and asset accumulation. Furthermore, education is

almost always completed prior to the initiation of childbearing, thus providing a measure of SES

that is not affected by the fertility outcomes under study in most cases. We include dichotomous

indicators for low education (less than high school education), medium education (completed

high school), and high education (some tertiary and above). Women who are not partnered are

coded as “no information” for the partner education variable.

In supplementary analyses, we also include controls for social origin with variables for

the respondent’s mother’s and father’s educational level. Although we exclude these variables

for reasons of parsimony, results are substantively unchanged by inclusion of these additional

controls and are available upon request.

Socio-cultural characteristics: In line with pathway 3 from our conceptual framework

(Figure 1), we control for women’s socio-cultural characteristics. For this analysis, we are particularly interested in; (i) family norms about fertility and family size; and (ii) religiosity.

First, we control for family norms about ideal family size with a continuous measure of stated ideal number of children in a family, constructed from the following question: “according to you what is the ideal number of children in a family?” 4% (n=315) of women in the sample give a response higher than 4 children; these responses are collapsed into a single measure of four or more children. Family norms may also be shaped by the local context in which one grew up and

12 culturally specific norms about childbearing. Thus, we include indicators for region of birth

including (i) Europe (including France); (ii) North Africa; (iii) Sub-Saharan Africa; (iv) Asia

(including Turkey); and (v) other regions (e.g. Americas, Oceania etc.)

We create a measure of religiosity comprised of different dimensions of religious belief

and practice using principal components analysis (PCA). PCA transforms a series of variables

that are often highly correlated into a single measure that is not linearly correlated. In analysis of

the proportion of variance explained by each component of the PCA analysis we find that we

only need one “component” (e.g. in other words, one measure of religiosity created using PCA is

sufficient to capture most of the variation in the composite measures). The religiosity measure is constructed from five variables: (i) the frequency of religious attendance (0=never; 1= less frequent; 2=once a month; 3=once a week); (ii) the importance of religion in life (0=none; 1=a little; 2=somewhat important; 3=very important); (iii) the importance of religion in the respondent’s past education (0=none; 1=a little; 2=somewhat important; 3=very important); (iv) whether the respondent adheres to religious food recommendations (0=never/none;

1=sometimes; 2=always); and (v) whether the respondent wears a religious symbol

(0=never/none; 1=sometimes; 2=always). The Cronbach’s alpha for these five variables is 0.78.

The religiosity measure created using PCA takes continuous values from -1.05 (low religiosity) to 1.48 (high religiosity). Women who report having no religion nonetheless get a religiosity score based on their responses to these questions.

The major limitation of both the ideal family size and religiosity measures is that they are collected at survey and not prior to the initiation of childbearing. This is potentially problematic because ideal family size evolves throughout the life-course based on employment, relationships, existing offspring characteristics and other contextual factors (Bulatao, 1981; Heiland,

13 Prskawetz, & Sanderson, 2008; Lee, 1980; Liefbroer, 2008; Sarah R Hayford, 2009; Udry, 1983;

Yeatman, Sennott, & Culpepper, 2013). Furthermore, women may not want to report current children as unwanted, and thus may adjust their ideal family size based on current children

(Pritchett, 1994). Religiosity may also change over the life course, although panel data from

Europe suggests that fertility has limited effect on religious practice (Berghammer, 2012).

Despite these limitations, we believe both measures provide meaningful information about how socio-cultural norms may moderate the relationship between religion and fertility. As a robustness check we provide an assessment of how these measures vary differ between the oldest and most recent birth cohorts of the study, because if ex-post facto rationalization were driving responses to these questions were would expect large differences between the oldest birth cohort—who has presumably almost completed reproduction—and the most recent birth cohort—who is presumably early in the reproductive cycle. The mean value of both the ideal family size and religiosity variables are comparable for both the oldest and most recent cohorts

(see Appendix 1). For example, 2.6 (SD 0.8) is the average ideal family size for the 1950s cohort and 2.7 (SD 0.8) is the average ideal family size for the 1980s cohort (these results are statistically different at p<0.05). Likewise, 0.9 (SD 0.94) is the average religiosity for the 1950s cohort and 0.1 (SD 1) is the average religiosity for the 1980s cohort (these results are not statistically different).

Additional controls: All models other than the baseline also control for generational status because age may influence both fertility and immigration trajectories. We include indicators for the 1950s-birth cohort; 1960s birth-cohort; 1970s birth-cohort; and 1980s birth- cohort. In supplementary models, we also include controls for women’s current partnership status (partnered versus not partnered); results are the same and are available upon request.

14 However, we exclude this control because women who are not partnered is highly collinear with

the no educational information category. Of the 2,533 women with no information on partner’s

education 2,295 women do not currently have a partner and 238 women have a partner but are

missing information on his education.

Analytical approach

We explore the association between religion and the hazard of having a first, second, or third birth using Cox proportional hazard models; the hazard of having a second birth is conditional on having a first birth and the hazard of having a third birth is conditional on having a second birth. We run a series of nested models where we first show the hazard of birth for the different religious groups without any additional covariate controls to establish whether there are indeed religion-based fertility differentials. Next, we explore whether the hazard of first, second, and third birth is moderated upon controlling for the three major pathways of our conceptual framework (i) immigration status; (ii) socio-economic status; and (iii) socio-cultural factors.

In convention with the norms of the literature, entry into risk starts at the age of 15 for all respondents. Respondents are censored upon age of experiencing the event or age at the end of the survey.6 All events are measured in person months. In instances where we do not have exact

information about the month of an event we assume the event took place at the mid-point of the

year.7

6 Data collection took place between September 2008 and February 2009. Because we do not have information on the date of the interview we assume the interview took place on December 2008.

7 Month of birth is collected for all female respondents and all children of respondents who are currently living in the household. Month of birth information is not available for children of respondents who are not currently living in the household; 63% of respondents have at least one child who is currently living outside of the household.

15 The advantage of the Cox proportional hazards model is that it does not make assumptions about the probability of the distribution of event times (Allison, 2014), which is useful given the non-linearity of event times (see Figure 2). The main limitation of the Cox approach is that time is treated as continuous which would be particularly problematic if there are many ties (e.g. individuals experiencing an event at the same time). Following Allison, we deal with the possibility of multiple ties by running Cox models using the Efron approximation

(as opposed to the default Breslow approximation), which better deals with ties by adjusting the subsequent risk set using probability weights. As a further robustness test, we re-run all models using a logistic regression discrete-time event history approach where all events are measured in person-years; results of this analysis are substantively the same and are available upon request.

Results

Description of how key variables differ by religion

First, we provide descriptive information on how key variables from our analysis differ by religion to understand whether Muslim women differ on observable characteristics. Table 2 shows that there are large differences in immigration status depending on religion. For example,

Muslim women are significantly more likely to be immigrants than the other three groups; only

2% of Muslim women are native French, compared to 37% of Christian women, 10% of women of other religions, and 35% of women with no religion. This corresponds with the history of immigration in Europe where a first wave of low-skilled foreign workers arrived from Southern

Europe (predominately Christian) and North Africa (predominantly Muslim) following World

War II with more recent waves from Muslim countries in Asia and West Africa (Alba & Foner,

2015).

16 Consistent with the literature on educational disadvantages faced by Muslim immigrants to France (Ichou et al., 2017; Ichou & Hamilton, 2013), there are key differences in education depending on religion. Muslim women are significantly more likely to be in the low education category than all other groups; 54% of Muslim women are in this category compared to 44% of

Christian women, 42% of women from other religions, and 40% of women with no religions. At the same time, Muslim women are significantly less likely to be in the high education category than all other groups; only 23% of Muslim women are in this category compared to 35% of

Christian women, 33% of women from other religions, and 35% of women with no religions.

In line with existing literature on how Muslim immigrants in Europe (including in

France) have higher religiosity than native populations (Connor, 2009; Simon & Tiberj, 2013) we also see large differentials in religiosity depending on religion in our sample. Muslim women’s score on the religiosity index is on average significantly higher than the score of any of the other religious groups (e.g. Christians or women from other religions). The high religiosity of Muslim women is particularly striking in a country like France where secularism is prevalent.

For example, about 28 percent of our sample reports having no religion (Table 1). Although we do not know if this is comparable with national averages because France does not collect religious information in official statistics, the high levels of secularism in France have been well- documented (Halman & Draulans, 2006). Consistent with the literature that suggests that religiosity and family norms are often correlated, we also find that Muslim women have significantly higher ideal family sizes than any other group.

Taken together, these descriptive results suggest that Muslim women differ from their counterparts of other religious backgrounds in several important dimensions that likely have implications for their fertility. For example, on average, they are more likely to be immigrants,

17 less likely to be well-educated, and more likely to have higher religiosity and higher ideal family sizes.

Analysis of whether religion is associated with fertility

Next, we explore whether there are fertility differentials by religion. We run a series of nested models where we first show the hazard of birth for the different religious groups without any additional covariate controls to establish whether the baseline hazard of having a first, second, or third birth differs by religion. Kaplan-Meier survival estimates show that the survival experience of all four religious groups is similar for the risk of having a first birth—although

Muslim women have slightly lower survival than the other three groups (Figure 2, right panel).

According to the Cox models, being Christian—as compared to being Muslim—is associated with a 88% lower expected hazard of first birth (p<0.001) and being no religion—as compared to being Muslim—is associated with a 81% lower expected hazard of first birth (p<0.001).

However, there is no significant difference between being another religion—as compared to being Muslim—and the expected hazard of first birth (Table 3). Post-estimation tests of significance indicate no significant difference between the other religion and Christian coefficients and between the other religion and no religion coefficients; however, there is a significant difference between the no religion and Christian coefficients (p<0.05).

Figure 2, central panel, shows that Muslim women have somewhat lower survival than other groups for the risk of having a second birth (conditional on having a first birth). The survival of the other religion group is also somewhat lower than the survival of the Christian or no religion groups (which are comparable). According to the Cox models, being Christian—as compared to being Muslim—is associated with a 64% lower expected hazard of second birth

18 (p<0.001); being no religion—as compared to being Muslim—is associated with a 78% lower expected hazard of second birth (p<0.01); and being another religion—as compared to being

Muslim—is associated with a 62% lower expected hazard of second birth (p<0.001) (Table 4).

Post-estimation tests of significance indicate there is a significant difference between the other religion and Christian coefficients (p<0.01) and between the other religion and no religion coefficients (p<0.01); however, there is no significant difference between the no religion and

Christian coefficients.

Figure 2, right panel, shows that most dramatic divergences become apparent for the risk of having a third birth (conditional on having a second birth). Muslim women have considerably lower survival than the other three groups for the risk of having a third birth. Women from the other religion category also have lower survival for the risk of having a third birth compared to women with no religion or Christian women, although their survival is still higher than that of

Muslim women. Christian women and women with no religion have similar survival experiences for the risk of having a third birth. According to the Cox models, being Christian— as compared to being Muslim—is associated with a 36% lower expected hazard of third birth

(p<0.001); being no religion—as compared to being Muslim—is associated with a 57% lower expected hazard of third birth (p<0.001); and being another religion—as compared to being

Muslim—is associated with a 39% lower expected hazard of third birth (p<0.001) (Table 5).

Post-estimation tests of significance indicate there is a significant difference between the other religion and Christian coefficients (p<0.001) and between the other religion and no religion coefficients (p<0.01); however, there is no significant difference between the no religion and

Christian coefficients.

19 Thus, Muslim women on average have higher expected hazards of first, second, and third births than any other religious groups; nonetheless, these baseline estimates do not consider the myriad ways in which Muslim women differ from their non-Muslim counterparts in terms of immigration status, socio-economic status, and socio-cultural factors (see Table 2), a topic that is explored in the preceding section.

Analysis of what moderates the relationship between religion and fertility

In the next part of the analysis we explore whether the hazard of birth is moderated upon controlling for the three pathways from our conceptual framework. In line with the first pathway, we start by adding controls for immigration status—including immigrant generation and time since arrival to France—to our baseline model (we also add controls for birth cohort).

Net of immigration status, religion is still an important predictor of the hazard of first, second, and third births. Being Christian—as compared to being Muslim—is associated with a 74% lower expected hazard of first birth (p<0.001), a 68% lower expected hazard of second birth

(p<0.001), and a 39% lower expected hazard of third birth (p<0.001) (Tables 3-5). Being other religion—as compared to being Muslim—is associated with a 84% lower expected hazard of first birth (p<0.05), a 81% lower expected hazard of second birth (p<0.01), and a 58% lower expected hazard of third birth (p<0.001) (Tables 3-5). Being no religion—as compared to being

Muslim—is associated with a 71% lower expected hazard of first birth (p<0.001), a 66% lower expected hazard of second birth (p<0.001), and a 43% lower expected hazard of third birth

(p<0.001) (Tables 3-5).

We also find generation status is an important predictor of the hazard of first birth.

Specifically, being first generation and second generation are both significantly associated with a

20 higher expected hazard of first birth compared to native born populations (Table 3).

Nonetheless, there is no significant association between being first or second generation and the hazard of second birth or between being second generation and the hazard of second birth

(Tables 4-5). Being first generation is even significantly associated with a lower hazard of third birth compared to native women, although this finding becomes null in subsequent models. The fact that generational status is not a significant predictor of second and third births could be explained by the overall high fertility in France among native-born women which is attributed a history of pro-natalist policies (e.g. allowances, tax deductions, child daycare etc.) (Pailhé,

2008). Although there has been an increasing tendency to delay parenthood in France, the native-born total fertility rate has remained relatively stable at 1.8 over the last few decades

(Toulemon, Pailhé, & Rossier, 2008).

In line with the second pathway of the conceptual framework we next add controls for socio-economic status—including the education of the respondent and her partner. Net of immigration status and socio-economic status, religion remains a significant predictor of the hazard of first, second, and third births. Being Christian—as compared to being Muslim—is associated with a 84% lower expected hazard of first birth (p<0.001), a 65% lower expected hazard of second birth (p<0.001), and a 41% lower expected hazard of third birth (p<0.001)

(Tables 3-5). Being other religion—as compared to being Muslim—is associated with a 79% lower expected hazard of second birth (p<0.01), and a 62% lower expected hazard of third birth

(p<0.001) (Tables 3-5). Being no religion—as compared to being Muslim—is associated with a

83% lower expected hazard of first birth (p<0.001), a 65% lower expected hazard of second birth

(p<0.001), and a 46% lower expected hazard of third birth (p<0.001) (Tables 3-5).

21 There is also a negative association between education and women’s hazard of birth, which is consistent with sizeable a literature on the negative effects of education on fertility.

Having a medium or high level of education (compared to low education) is associated with a

lower expected hazard of first and third birth for women (Tables 3 & 5). Likewise, having a

partner with medium or high level of education (compared to low education) is associated with a

lower expected hazard of first birth (Table 3). Throughout all three models, having a partner

with no education information is significantly associated with a lowered expected hazard of first,

second, and third birth, which is likely because most women in this category are not partnered.

Finally, in accordance with the third pathway of the conceptual framework, we add controls for socio-cultural factors—including ideal family size, region of birth, and religiosity.

Upon adding these controls, we find that being other religion—compared to being Muslim—and no religion—compared to being Muslim—are no longer significantly associated with the hazard of having a first, second, or third birth (Tables 3-5). Being Christian—compared to being

Muslim—is not significantly associated with the hazard of first birth, however being Christian— compared to being Muslim is associated with a 87% lower expected hazard of second birth

(p<0.01) and a 69% lower expected hazard of third birth (p<0.001) (Tables 4-5). In supplementary models, we disaggregate the Christian variable into “Catholic” and “other

Christian” and find that the Catholic variable—but not the other Christian variable—is statistically different from the Muslim variable for the hazards of second and third births (see

Appendix 2).

This last set of models suggests that socio-cultural factors are important predictors of fertility. For example, ideal family size is associated with a significantly higher expected hazard of first, second, and third birth (p<0.001) and religiosity is associated with a significantly higher

22 expected hazard of third birth (p<0.001) (Tables 3-5). Because religiosity and ideal family size

are correlated (correlation of 0.29), we also explore whether these two variables are jointly

significant and find religiosity and ideal family size are jointly significant across all three

models.

Consistent with Pailhé (2017), region of birth is also an important predictor of fertility.

Being born in Asia is associated with a significantly higher hazard of first, second, and third birth compared to being born in Europe; being born in Sub-Saharan Africa is associated with a significantly higher hazard of first birth compared to being born in Europe; being born in North

Africa is associated with a significantly higher hazard of second, and third birth compared to being born in Europe (Tables 3-5). The fact that region of birth is significant net of controls for religion suggests that there may be important cross-national differences in the fertility-religion relationship.

Thus, most (though not all) of the associations between Muslim and the hazard of first, second, and third births become null upon controlling for religiosity, ideal family size, and region of birth; this suggests that socio-cultural factors—rather than religion itself—are associated with higher fertility. This is particularly relevant, since As Table 2 shows on average

Muslim women have higher religiosity and larger ideal family sizes then other religious groups in the study.

Supplementary analyses

In supplementary models, we include an interaction being the Muslim coefficient and the generation status indicators, to formally test whether the coefficients are statistically different across the different generations (for ease of interpretation we include the Muslim variable in the

model and use the Christian category as references). Throughout all three models, the Muslim

23 coefficient is not statistically significant, which means that native-born Muslims do not have significantly different hazards of first, second, or third births than any of the other groups

(Appendix 3).

The first-generation variable is statistically significant for the hazard of first birth, which indicates that being a non-Muslim first generation women is associated with a significantly higher expected hazard of first birth compared to being a non-Muslim native-born woman.

Nonetheless, first generation variable is not statistically significant for the hazard of second or third birth, which indicates that non-Muslim first generation women do not have significantly different second and third birth hazards than non-Muslim native born women. Finally, none of the interaction terms between Muslim and generational status are statistically significant, which suggests that the association between being Muslim and the expected hazards of first, second, and third birth does not significantly differ across the different generations.

Discussion

Although the fertility patterns of women migrating from high-fertility predominantly

Muslim countries to Europe have garnered considerable media, research, and policy attention, limited research explores what accounts for religious differences in fertility in the context of

European immigration. Drawing on this research gap, we explored the relationship between religion and fertility among immigrant and native populations in France using data from the

French Trajectories and Origins (TeO) survey. First, we developed a conceptual framework to explicate why religion might be associated with fertility including: (i) immigration status; (ii) socio-economic status; and (iii) socio-cultural factors. We showed descriptively that on average,

Muslim women were more likely to be immigrants, less likely to be well-educated, and more

24 likely to have higher religiosity and higher ideal family sizes, all of which have important implications for fertility.

In the main part of our analysis, we ran a series of nested cox proportional hazard models where we first showed that Muslim women on average had higher expected hazards of first, second, and third births than other religious groups. Next, we explored whether the hazard of birth was moderated upon controlling for the three pathways from our conceptual framework.

We found Muslim women had significantly higher hazards of first, second, and third birth than women of other religious backgrounds, net of controls for immigration status and socio- economic characteristics. However, the association between being Muslim—compared to no religion or other religion— and the hazard of first, second, and third birth was no longer statistically significant upon controlling for key socio-cultural variables of religiosity and ideal family size. This suggested that socio-cultural differences between Muslim and women of other religious backgrounds helped to account for observed religion-fertility differentials.

Nonetheless, the Muslim coefficient was significantly different than the Catholic coefficient

(though not the other Christian coefficient) for the hazard of second and third births. It is difficult to know why this would be the case since Catholic women do not differ from women with no religion on observed socio-demographic characteristics, thus further analyses should investigate whether there are unobserved factors that might account for these differentials.

Our analysis supported a literature that suggested that religiosity and associated gender and family norms—as opposed to religion itself— are correlated with higher fertility, although we extended this literature which has typically focused on Christian populations in Europe and the United States (Berghammer, 2012; Frejka & Westoff, 2007a; Hayford & Morgan, 2008;

Philipov & Berghammer, 2007). Our analyses also confirmed cross-national findings that

25 fertility differentials from immigrant groups can be partially attributed to higher religiosity and

more “traditional” family values among female immigrants from Muslim majority countries in

Europe (Westoff & Frejka, 2007), although our study advances knowledge by using micro-level

religion data and focusing on immigrant populations coming from outside of Europe. Taken

together, our results demonstrated the importance of considering a wide range of factors—

including socio-economic status, socio-cultural characteristics, and immigration status—when exploring the relationship between religion and fertility. Although France provides a rich case study, it is not representative of Europe and further analysis is needed to understand if similar trends are observed in other European contexts.

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29 Figure 1. Conceptual framework on the relationship between religion and fertility

Pathway 1: Time since arrival Immigration status Generation

Pathway 2: Religious Fertility background Socioeconomic status

Pathway 3: Religiosity Socio-cultural background Family norms

30 Figure 2. Kaplan Meier survival estimates of parity progression for hazard of first birth (left), second birth (center), and third birth (right) disaggregated by religion

1.00 1.00 1.00 0.75 0.75 0.75 0.50 0.50 0.50 First birth First Thirdbirth Secondbirth 0.25 0.25 0.25 0.00 0.00 0.00 0 100 200 300 400 500 0 100 200 300 400 500 0 100 200 300 400 500 Analysis time (person-months) Analysis time (person-months) Analysis time (person-months)

Christian Muslim Christian Muslim Christian Muslim Other religion No religion Other religion No religion Other religion No religion

31 Table 1. Descriptive statistics Mean Std. Dev. First birth (0-1) 0.63 0.48 Second birth (0-1)* 0.70 0.46 Third birth (0-1)** 0.41 0.49 Christian (0-1) 0.42 0.49 Muslim (0-1) 0.25 0.43 Other religion (0-1) 0.05 0.21 No religion (0-1) 0.28 0.45 Years since arrival in France 0.26 0.44 Native (0-1) 29.43 12.35 2nd generation (0-1) 0.55 0.50 1st generation (0-1) 0.19 0.39 1950/1959 birth cohort (0-1) 0.13 0.33 1960/1969 birth cohort (0-1) 0.25 0.43 1970/1979 birth cohort (0-1) 0.30 0.46 1980/1989 birth cohort (0-1) 0.32 0.47 Low education (0-1) 0.45 0.50 Medium education (0-1) 0.23 0.42 High education (0-1) 0.32 0.47 Partner low education (0-1) 0.35 0.48 Partner medium education (0-1) 0.12 0.33 Partner high education (0-1) 0.24 0.43 No info partner education (01) 0.29 0.45 Ideal family size 2.61 0.79 Born in Europe (0-1) 0.77 0.42 Born in North African (0-1) 0.09 0.28 Born in Sub-Saharan Africa (0-1)0.05 0.23 Born in Asia (0-1) 0.07 0.26 Born in other region (0-1) 0.02 0.13 Religiosity 0.04 0.99 N 8,811 * Conditional on having first birth (n= 5,554) **Conditional on having second birth (n= 3,899)

32 Table 2. Means of variables disagregated by religion with bivariate t-test comparision with Muslim. Muslim Christian Other religion No religion Mean Mean Pr(|T| > |t|) Mean Pr(|T| > |t|) Mean Pr(|T| > |t|) Years since arrival in France 22.95 32.53 *** 26.31 *** 31.05 *** Native (0-1) 0.02 0.37 *** 0.10 *** 0.35 *** 2nd generation (0-1) 0.67 0.46 *** 0.61 ** 0.56 *** 1st generation (0-1) 0.31 0.18 *** 0.29 0.10 *** 1950/1959 birth cohort (0-1) 0.07 0.19 *** 0.13 *** 0.09 ** 1960/1969 birth cohort (0-1) 0.16 0.29 *** 0.27 *** 0.26 *** 1970/1979 birth cohort (0-1) 0.32 0.29 ** 0.28 0.31 1980/1989 birth cohort (0-1) 0.45 0.23 *** 0.32 *** 0.34 *** Low education (0-1) 0.54 0.44 *** 0.42 *** 0.40 *** Medium education (0-1) 0.23 0.22 0.25 0.24 High education (0-1) 0.23 0.35 *** 0.33 *** 0.35 *** Partner low education (0-1) 0.38 0.36 0.31 ** 0.32 *** Partner medium education (0-1) 0.11 0.13 * 0.12 0.13 * Partner high education (0-1) 0.16 0.26 *** 0.29 *** 0.26 *** No info partner education (01) 0.34 0.25 *** 0.28 ** 0.30 ** Ideal family size 2.96 2.53 *** 2.67 *** 2.40 *** Born in Europe (0-1) 0.53 0.87 *** 0.52 0.87 *** Born in North African (0-1) 0.29 0.01 *** 0.04 *** 0.03 *** Born in Sub-Saharan Africa (0-1) 0.07 0.07 0.03 *** 0.02 *** Born in Asia (0-1) 0.10 0.03 *** 0.41 *** 0.06 *** Born in other region (0-1) 0.01 0.03 *** 0.01 0.02 *** Religiosity 0.91 0.00 *** 0.54 *** -1.05 *** N 2,197 3,739 428 2,447

33 Table 3. Cox proportional hazard models of the relationship between religion and hazard of first birth; results presented as hazard ratios. (1) (2) (3) (4) First birth First birth First birth First birth

Christian (ref= Muslim) 0.88*** 0.74*** 0.84*** 0.94 (0.03) (0.03) (0.03) (0.04) Other religion (ref= Muslim) 0.91 0.84* 0.96 0.94 (0.06) (0.06) (0.06) (0.07) No religion (ref= no religion) 0.81*** 0.71*** 0.83*** 0.97 (0.03) (0.03) (0.03) (0.06) Years since arrival in France 0.95 0.95 0.94 (0.03) (0.03) (0.03) Second generation (ref= native) 2.07*** 1.97*** 1.84*** (0.17) (0.17) (0.15) First generation (ref= native) 1.04*** 1.04*** 1.04*** (0.00) (0.00) (0.00) Cohort 60/69 (ref= cohort 50/59) 1.17*** 1.24*** 1.27*** (0.06) (0.06) (0.06) Cohort 70/79 (ref= cohort 50/59) 1.43*** 1.70*** 1.78*** (0.09) (0.11) (0.12) Cohort 80/89 (ref= cohort 50/59) 1.71*** 2.28*** 2.40*** (0.16) (0.21) (0.23) Medium education (ref= low) 0.65*** 0.65*** (0.02) (0.02) High education (ref= low) 0.46*** 0.46*** (0.02) (0.02) Partner medium education (ref= low) 0.82*** 0.81*** (0.04) (0.04) Partner high education (ref= low) 0.77*** 0.76*** (0.03) (0.03) Partner no education info (ref= low) 0.45*** 0.45*** (0.02) (0.02) Ideal family size 1.23*** (0.02) Born in North Africa (ref=Europe) 1.05 (0.06) Born in SS Africa (ref=Europe) 1.22** (0.08) Born in Asia (ref=Europe) 1.30*** (0.08) Born in other region (ref=Europe) 1.01 (0.11) Religiosity 1.03 (0.02)

Observations 8,811 8,811 8,811 8,811 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05

34 Table 4. Cox proportional hazard models of the relationship between religion and hazard of second birth; results presented as hazard ratios. (1) (2) (3) (4) Second birth Second birth Second birth Second birth

Christian (ref= Muslim) 0.64*** 0.68*** 0.65*** 0.87** (0.03) (0.03) (0.03) (0.05) Other religion (ref= Muslim) 0.78** 0.81** 0.79** 0.89 (0.06) (0.06) (0.06) (0.08) No religion (ref= no religion) 0.62*** 0.66*** 0.65*** 0.92 (0.03) (0.03) (0.03) (0.07) Years since arrival in France 0.98 0.97 1.00 (0.04) (0.04) (0.04) Second generation (ref= native) 1.13 1.12 1.10 (0.11) (0.11) (0.10) First generation (ref= native) 1.00 1.00 1.00 (0.00) (0.00) (0.00) Cohort 60/69 (ref= cohort 50/59) 0.98 0.98 1.03 (0.05) (0.05) (0.06) Cohort 70/79 (ref= cohort 50/59) 1.15 1.14 1.26** (0.09) (0.09) (0.10) Cohort 80/89 (ref= cohort 50/59) 0.97 0.97 1.09 (0.11) (0.11) (0.13) Medium education (ref= low) 0.94 0.96 (0.04) (0.04) High education (ref= low) 1.03 1.05 (0.05) (0.05) Partner medium education (ref= low) 0.92 0.92 (0.05) (0.05) Partner high education (ref= low) 0.94 0.92 (0.04) (0.04) Partner no education info (ref= low) 0.64*** 0.63*** (0.03) (0.03) Ideal family size 1.44*** (0.03) Born in North Africa (ref=Europe) 1.25** (0.09) Born in SS Africa (ref=Europe) 0.91 (0.07) Born in Asia (ref=Europe) 1.31*** (0.09) Born in other region (ref=Europe) 1.25 (0.16) Religiosity 1.03 (0.03)

Observations 5,549 5,549 5,549 5,549 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05

35 Table 5. Cox proportional hazard models of the relationship between religion and hazard of third birth; results presented as hazard ratios.

(1) (2) (3) (4) Third birth Third birth Third birth Third birth

Christian (ref= Muslim) 0.36*** 0.39*** 0.41*** 0.69*** (0.02) (0.03) (0.03) (0.06) Other religion (ref= Muslim) 0.57*** 0.58*** 0.62*** 0.80 (0.06) (0.07) (0.07) (0.10) No religion (ref= no religion) 0.39*** 0.43*** 0.46*** 1.00 (0.03) (0.03) (0.04) (0.11) Years since arrival in France 0.90 0.88 0.94 (0.06) (0.06) (0.06) Second generation (ref= native) 0.95 0.94 0.89 (0.13) (0.13) (0.12) First generation (ref= native) 0.99* 0.99* 0.99 (0.01) (0.01) (0.01) Cohort 60/69 (ref= cohort 50/59) 0.82** 0.85* 0.94 (0.06) (0.07) (0.08) Cohort 70/79 (ref= cohort 50/59) 0.90 0.95 1.08 (0.10) (0.10) (0.13) Cohort 80/89 (ref= cohort 50/59) 0.70 0.71 0.88 (0.14) (0.14) (0.19) Medium education (ref= low) 0.74*** 0.79** (0.06) (0.06) High education (ref= low) 0.70*** 0.70*** (0.05) (0.06) Partner medium education (ref= low) 0.90 0.94 (0.08) (0.08) Partner high education (ref= low) 1.00 0.99 (0.08) (0.07) Partner no education info (ref= low) 0.78*** 0.73*** (0.06) (0.05) Ideal family size 1.99*** (0.07) Born in North Africa (ref=Europe) 1.34** (0.13) Born in SS Africa (ref=Europe) 1.05 (0.13) Born in Asia (ref=Europe) 1.31** (0.13) Born in other region (ref=Europe) 1.14 (0.23) Religiosity 1.18*** (0.04)

Observations 3,879 3,879 3,879 3,879 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05

36 Appendix 1. Comparision of mean values of ideal family size and religisoty by birth cohort. Ideal family size Obs Mean Std. Dev. Pr(|T| > |t|) 1950s cohort 1,134 2.63 0.80 1980s cohort 2,812 2.69 0.80 Difference -0.06 *

Religiosity Obs Mean Std. Dev. Pr(|T| > |t|) 1950s cohort 1,134 0.08 0.94 1980s cohort 2,812 0.10 1.03 Difference -0.02

37 Appendix 2. Cox proportional hazard models of the relationship between religion and hazard of first, second, and third birth disaggregating by Catholic and other Christian; results presented as hazard ratios. (1) (2) (3) First birth Second birth Third birth

Catholic (ref=Muslim) 0.95 0.90* 0.71*** (0.04) (0.05) (0.06) Other Christian (ref=Muslim) 0.99 0.94 0.91 (0.05) (0.06) (0.10) Other religion (ref= Muslim) 0.95 0.91 0.82 (0.07) (0.08) (0.10) No religion (ref= no religion) 0.99 0.95 1.03 (0.06) (0.07) (0.11) Years since arrival in France 0.95 1.01 0.94 (0.03) (0.04) (0.06) Second generation (ref= native) 1.84*** 1.10 0.89 (0.15) (0.10) (0.12) First generation (ref= native) 1.04*** 1.00 0.99 (0.00) (0.00) (0.01) Cohort 60/69 (ref= cohort 50/59) 1.27*** 1.03 0.95 (0.06) (0.06) (0.08) Cohort 70/79 (ref= cohort 50/59) 1.79*** 1.27** 1.10 (0.12) (0.10) (0.13) Cohort 80/89 (ref= cohort 50/59) 2.42*** 1.11 0.91 (0.23) (0.13) (0.19) Medium education (ref= low) 0.65*** 0.96 0.79** (0.02) (0.04) (0.06) High education (ref= low) 0.46*** 1.05 0.70*** (0.02) (0.05) (0.05) Partner medium education (ref= low) 0.81*** 0.92 0.94 (0.04) (0.05) (0.08) Partner high education (ref= low) 0.76*** 0.92 0.98 (0.03) (0.04) (0.07) Partner no education info (ref= low) 0.45*** 0.63*** 0.73*** (0.02) (0.03) (0.05) Ideal family size 1.23*** 1.44*** 1.99*** (0.02) (0.03) (0.07) Born in North Africa (ref=Europe) 1.06 1.28*** 1.38*** (0.06) (0.09) (0.13) Born in SS Africa (ref=Europe) 1.22** 0.91 1.04 (0.08) (0.07) (0.12) Born in Asia (ref=Europe) 1.31*** 1.33*** 1.33** (0.08) (0.09) (0.13) Born in other region (ref=Europe) 1.00 1.25 1.11 (0.11) (0.16) (0.23) Religiosity 1.03 1.04 1.18*** (0.02) (0.03) (0.04)

Observations 8,811 5,549 3,879 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05

38 Appendix 3. Cox proportional hazard models of the relationship between religion and hazard of first, second, and third birth including interactions between Mulsim and generational status; results presented as hazard ratios. (1) (2) (3) First birth Second birth Third birth

Muslim 1.24 0.96 1.49 (0.26) (0.25) (0.44) Second generation 0.98 1.02 0.95 (0.03) (0.04) (0.07) First generation 1.67*** 1.01 0.86 (0.14) (0.10) (0.13) Muslim*second generation 0.77 1.13 0.94 (0.17) (0.29) (0.29) Muslim*First generation 1.12 1.41 1.03 (0.25) (0.37) (0.32) Other religion 1.02 1.04 1.17 (0.07) (0.08) (0.14) No religion 1.04 1.06 1.45*** (0.04) (0.05) (0.12) Years since arrival in France 1.04*** 1.00 0.99 (0.00) (0.00) (0.01) Cohort 60/69 (ref= cohort 50/59) 1.26*** 1.01 0.94 (0.06) (0.06) (0.08) Cohort 70/79 (ref= cohort 50/59) 1.76*** 1.24** 1.07 (0.12) (0.10) (0.13) Cohort 80/89 (ref= cohort 50/59) 2.38*** 1.07 0.88 (0.22) (0.13) (0.18) Medium education (ref= low) 0.66*** 0.96 0.79** (0.03) (0.04) (0.06) High education (ref= low) 0.46*** 1.06 0.71*** (0.02) (0.05) (0.06) Partner medium education (ref= low) 0.81*** 0.92 0.94 (0.04) (0.05) (0.08) Partner high education (ref= low) 0.76*** 0.92 0.99 (0.03) (0.04) (0.08) Partner no education info (ref= low) 0.45*** 0.64*** 0.74*** (0.02) (0.03) (0.05) Ideal family size 1.23*** 1.44*** 1.99*** (0.02) (0.03) (0.07) Born in North Africa (ref=Europe) 0.95 1.18* 1.32** (0.06) (0.09) (0.13) Born in SS Africa (ref=Europe) 1.20** 0.91 1.05 (0.08) (0.07) (0.13) Born in Asia (ref=Europe) 1.32*** 1.31*** 1.31** (0.08) (0.09) (0.13) Born in other region (ref=Europe) 1.04 1.29 1.14 (0.11) (0.17) (0.24) Religiosity 1.04 1.04 1.18*** (0.02) (0.03) (0.04)

Observations 8,811 5,549 3,879 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05