WITHDRAWN: Estimating the Disadvantages Facing Muslim Women in the Australian Labour Market

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Research in Social Stratification and Mobility journal homepage: www.elsevier.com/locate/rssm

Estimating the disadvantages facing muslim women in the Australian labour Market ⁎

Nabil Khattaba, Yousef Daoudb, , Anas Qaysiyac, Miriam Shaathc a

Doha Institute for Graduate Studies, Qatar Doha Institute for Graduate Studies, Qatar and Birzeit University, Palestine c Doha Institute for Graduate Studies, Qatar b

A R T I C LE I N FO

A B S T R A C T

Keywords: Australia Employment Human capital Migration penalties Muslim women Participation Occupation

This article examines performance of Muslim women in Australia in relation to three labour market outcomes: economic activity (participation), employment and occupational choice. It examines whether Muslim women face a penalty due to their religious background. Despite the large number of studies on this issue, there is not a single study that has examined the three labour market outcomes as we do in this study. This study analyses data obtained from the 2001 Australian Census, and found significant differences between Muslim women and mainstream Christian women in relation to economic activity (participation) and occupational choice. Muslim women were less likely to participate in the labour market and less likely to obtain managerial and professional jobs. However, the results show that Muslim women are as likely to be employed as the majority group. Human capital factors explained the entire difference between the groups in the case of employment, refuting religious discrimination as an explanation for this difference. However, the study suggests that the lack of differences in employment should be examined further as it might be masking other forms of disadvantages, e.g. overqualification. The results also show that qualifications have a positive impact on all labour market outcomes and by and large operate similarly among all groups.

1. Introduction Australia has for a long time been a popular destination for economic and humanitarian migrants from Asia, the Middle East and Africa, and it comes as no surprise therefore that the process of migration and the circumstances in which migrants live after they have migrated have been heavily discussed in the literature, not just in the Australian case, but in the West in general (the destination of migrants). Australia resettles approximately 14,000 refugees every year (Khawaja & Hebbani, 2018). Because of this, the Muslim population in Australia has significantly increased over the years and they are now the largest minority religion group and the second largest faith group after Christians. Their overall proportion has significantly increased from 1.5% in 2001 (Poynting & Mason, 2007) to 2.2% and 2.6% of the total population of Australia in 2011 and 2016 respectively (ABS, 2017). With the relative increase in their population, Muslim presence in the labour market has also increased. Studies examining the integration of Muslims in the Australian labour market in general, have pointed out that Muslims, and particularly Muslim women, face various barriers and disadvantages on the grounds of gender, ethnic, religious and

⁎

migration (Foroutan, 2015; Kabir, 2007; Khawaja & Hebbani, 2018; Syed, 2007). These disadvantages can be divided into two broad categories according to the labour market status (Foroutan, 2015; Khattab & Johnston, 2015); 1) barriers associated with the probability of finding a job (entering the labour market), given that the individual is seeking employment, and 2) disadvantages affecting people while in employment (occupational status and pay). Most studies on labour market integration of Muslims and other minorities in Australia have focused on the risk of unemployment pointing out that Muslims face a greater risk of unemployment than the rest of the population (Fozdar & Torezani, 2008; Kabir, 2007; Peucker, Roose, & Akbarzadeh, 2014). Studies focusing on women only have also suggested that Muslim women have the lowest rate of employment, which can be attributed to discrimination (Kabir, 2007) and human capital deficit (Foroutan & McDonald, 2008). The few studies that compared the occupational status of Muslim men and women to that of the rest of the population in Australia, have pointed out that Muslims are less likely to attain jobs within the managerial and professional categories (Foroutan & McDonald, 2008; Peucker et al., 2014). Furthermore, it appears that within the

Corresponding author. E-mail addresses: [email protected] (N. Khattab), [email protected] (Y. Daoud).

https://doi.org/10.1016/j.rssm.2018.11.008 Received 3 July 2018; Received in revised form 21 November 2018; Accepted 21 November 2018 0276-5624/ © 2018 Published by Elsevier Ltd.

Please cite this article as: Khattab, N., Research in Social Stratification and Mobility, https://doi.org/10.1016/j.rssm.2018.11.008

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group in relation to women’s activities in the public sphere and the gender division of labour (Boeckmann, Misra, & Budig, 2014; Crompton & Harris, 1998). Although the decision is internal to the household or at the individual level, it is often the case that labour market conditions do affect women’s decision to participate. The discouragement mechanism could result from “spatial barriers” to participation; (Preston & McLafferty, 1999) provide a review of advances in spatial barriers to participation. Their review pointed that geographic location of jobs exerts a time constraint because of the household workload. In addition, the job search model postulated that the probability of finding affects the intensity of the job search, thus a lower probability of finding a job may lead to discouragement. The job search model has been utilized to show that the discouragement takes place in two phases (Van Ham & Büchel, 2006), the decision to participate and the probability to participate when a job is available. They find evidence that labour market conditions do affect female labour force participation in the western part of Germany. However, finding a job very much depends on labour market opportunities that are often external to women and are out of their control. In other words, while cultural considerations might determine the decision of some women to become economically active, finding a job depends primarily on labour market opportunities and employers’ tastes and practices (Darity & Mason, 1998; Ridgeway, 1997). However, a few studies have asserted that employment opportunities and employers’ recruitment practices can very often be contingent upon the cultural background and visibility of the job seeker. For example, Muslim women who display their cultural and religious identity through clothing (e.g. wearing the hijab) are subjected to discrimination (Ghumman & Ryan, 2013; Kabir, 2007; Lovat et al., 2013), depicting a very strong link between the labour market behaviour and outcomes for Muslim women on the one hand, and their cultural background and preferences on the other hand. The term culture is used here in a broad sense referring to a wide range of possible influences of local traditions and religious affiliation. The most common account for the role of culture is that traditional cultures (and religiosity) might restrict labour market behaviour of women in two different ways. First, it does that through shaping women’s gender roles and beliefs (Abdelkerim & Grace, 2012, Al Ariss, Akram, Özbilgin, & Suutari, 2012; Read, 2003, 2004). For example, in her study on American-Arab women, Read (2004): 52) argues that the labour force participation (or lack of it) among American-Arab women can be attributed to ‘traditional cultural norms that prioritise women's family obligations over their economic activity, and to ethnic and religious social networks that encourage the maintenance of traditional gender roles’. Similarly, in studying labour market behaviour amongst Pakistani and Bangladeshi women in the UK, Dale, Fieldhouse, Shaheen, and Kalra, (2002) found that many older women who do not work, think that women should not be working outside the home and that this practice is in line with the Islamic faith. A number of studies focusing on Muslim women in Australia have also identified cultural norms, religious and cultural pressures and patriarchy as major challenges shaping the employment experience of Muslim women (Ali, Malik, Pereira, & Ariss, 2017; Foroutan, 2015). These factors are likely to restrict women’s labour market supply by placing them within the private sphere and the home domain (e.g. see Spierings, 2014). Likewise, these factors can potentially restrict women’s labour market supply through shaping their norms in relation to the kind of jobs that women can or cannot do. The main concern here is not whether women can or cannot work outside the house, but the concern is how can they do so while adhering to their cultural norms and meeting their family obligations (Baker, 2002; Khattab, 2002). These restrictions are not exclusive for Muslim women only, but many women from various ethnic and class backgrounds experience different forms of gender segregation in the work place (Anker, 1998; Boeckmann et al., 2014; Charles & Grusky, 2004; Crompton & Harris, 1998). Many Muslim and non-Muslim women would take on certain jobs that would also allow

workplace, Muslim women face various additional challenges and difficulties due to cultural differences and the display of their religious identity through clothing and other forms of behaviour (Syed & Pio, 2010). Given the aforementioned studies, the conclusion that Muslims in general and Muslim women in particular are significantly disadvantaged in the Australian labour market compared to their Australian counterparts is inescapable. However, these studies are not without drawbacks. For example, when the employment rate is examined in some of these studies (e.g. Foroutan & McDonald, 2008; Foroutan, 2015), it is not clear whether the dependent variable (employment) referes to employment versus unemployment, or labour market participation versus non-participation (economically active versus inactive). Other studies are descriptive in nature (Peucker et al., 2014) employing methodologies that make it very difficult to delve deeper into the mechanisms behind the differences between Muslims and the rest of the population1. Moreover, most of these studies have only focused on two labour market outcomes, employment and occupational status, rendering other important outcomes such as economic activity (participation), overqualification and income from work fully uncovered. None of these studies have estimated the exact penalty Muslim women face, making it almost impossible to know whether this penalty (the extent of disadvantage) is fixed across the different labour market outcomes. The paper addresses this gap by examining the performance of Muslim women in relation to three labour market outcomes: economic activity (labour market participation), unemployment and occupational status2. We argue that the disadvantage of Muslim women varies across labour market outcomes rendering it severe in relation to some outcomes and lighter in others3. We also argue that because the Muslim community in Australia is ethnically diverse, and that Islam is more associated with Arabs rather than other ethnicities and races (Abdelkerim & Grace, 2012; Poynting & Mason, 2007), the extent of disadvanatge is expected to be contingent upon the ethnic or regional background. The labour market disadvantages facing Muslim women are not unique to Australia; for example (Biffl, 2008) documents that Belgium has a higher native-migrant (mostly Morrocan and Turkish migrants) employment gap relative to other Western European countries. The situation is not different in Canada and the United States (see for example (GHAZAL READ, 2004) on the US and (Wilkins‐Laflamme, 2018) on Canada4). The literature on the UK is more abundant ((Miaari, Khattab, & Johnston, 2018) and (Khattab & Hussein, 2018) to name a few. In what follows, we will discuss the theoretical background and review the literature. 2. Cultural and structural perspectives There is a fundamental difference between the decision that is made by a woman to become economically active (join the labour market) and her ability to find a job. For many women, the decision to become economically active depends primarily on internal factors and considerations such as the financial status of the household, but more importantly, the cultural codes within the particular household or 1 This refers to the usage of descriptive as well as qualitative interviews in the referenced article. No hypothesis of differences along religion lines were tested. 2 This type of coverage has been applied to the UK (Miaari, Sami, Nabil Khattab and Ron Johnston. 2018. "Religion and Ethnicity at Work: A Study of British Muslim Women’s Labour Market Performance." Quality & Quantity:1-29., In the US, no articles were found dealing with the three outcomes jointly. 3 Based on literature review below (Kabir & Evans, 2002; Kabir (2007), and Lovat et al., 2013) it is found that the biggest disadvantage facing Muslim women is getting employed. 4 This study finds that Muslims in general report experience of discrimination against them; this is not a study on Muslim women and their labour market outcomes. The literature on this topic in Canada is very scarce.

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qualifications (Scandone, 2018). The author argued that for many second-generation minority students in Britain, labour market discrimination is a fact of life which they have to live with. However, knowing that they are going to face employment discrimination does not deter them from pursuing higher education, but reinforces their educational and social mobility aspirations even further. It is fairly possible that Muslim women in Australia have developed the same perception, which can explain their higher than average level of qualifications as we will see in this paper. Thus, it is important, given the well-established discrimination against them, to examine the extent to which this affects their labour market performance. Likewise, given their higher than average level of qualifications, it is important to examine whether they also face the disadvantage of being overqualified as a recent qualitative study has indicated (Syed & Pio, 2010).

them to meet their caring responsibilities (England, 2005). In the case of Muslim women, some cultural norms and expectations might only reinforce this trend further by directing them towards certain jobs, working hours and labour markets (Spierings, Smits, & Verloo, 2010). Meeting all these structural and cultural requirements would result in fewer employment opportunities over which Muslim women have to compete. The second way by which culture is linked to employment prospects among Muslim women is by making them visible in the public sphere through clothing, language, names and other social practices. Within a specific historical context, for example when Islamophobia in a society (e.g. Australia) is on the increase, the cultural appearance or the religious background not only becomes important in a discrete way, but more decisively it injects its significance into other social cleavers such as gender or class, which exponentiates the impact of each of these divisions and together they shape the labour market opportunities of Muslim women (Brah & Phoenix, 2013; Brah, 1993). In our case, in an era of increasing debates over the integration (or lack of it) of Muslims in the West and of growing Islamophobia, Muslim women not only have to respond to their gender identity (as women) and the influences of patriarchy (Walby, 1989), but also to their religious identities. They all become relevant, not separately, but in a complex way that intersects them with each other, which is likely to result in greater risk of unemployment. In a recent study by Ali et al. (2017) on Muslim women in Australia, it was argued that these women have to deal with complex challenges posed by the interweaving of ethnicity, religion, patriarchy, gender and migration, along with different family aspirations and lifestyles. They show how these different identities and constraints restrict employment opportunities due to discrimination, confirming earlier findings that explained how the disadvantaged position of Muslim women in the Australian labour market was due to ethnic and religious discrimination (Kabir & Evans, 2002). Another study by Kabir (2007) shows how when Muslim and Christian unemployment levels are compared, Muslims are at a disadvantage, even if they are from the same ethnicity. Even though this comparison was not done on migrants (it was done on Australian-born individuals), it is an indication of one of the levels of discrimination Muslims face because of their religion, let alone when other layers of discrimination are added (gender for example). These finding were confirmed later on by another study that examined direct discrimination against various groups including Muslims, asserting that Muslim veiled women were also discriminated against, even if they have the qualifications and English language proficiency to qualify for employment (Lovat et al., 2013). However, a number of studies have argued that some of the labour market difficulties facing migrants and first generation ethnic minorities are likely to diminish with the length of stay in the host country and over generations (for example see Borjas, 1994; Chiswick, 1978, 1999; Chiswick & Miller, 2002). They argue that the initial disadvantages are likely to be associated with a lack of language skills, knowledge and familiarity with the system of the new host country, especially in relation to how the labour markets operate. Of these potential factors it seems that the most important is the devaluation of their skills and qualifications that were obtained in the source countries (Lerner & Menahem, 2003; Nielsen, 2011). A recent study on the labour market experience of Muslim women in Britain found that qualifications obtained in the UK and English proficiency were very important in determining the labour market participation of women in general and Muslim women in particular (Khattab, Johnston, & Manley, 2017). This study also suggested that the effect of family formation and the presence of dependent children on Muslim women’s labour force participation was moderated by education; in that those with higher qualifications were able to remain more economically active after getting married and having children than women with lower qualifications. Additionally, a recent study depicted a strong relationship between perceived discrimination and the motivation to gain further

3. The context of the study As previously mentioned, Australia is and has been a popular destination for Muslim migrants from various parts of Asia and Africa and the number of Muslims living in Australia is increasing5. It is worth mentioning here that Muslims in 1911 made up only 0.09% of the Australian population, the majority of whom were Afghan and Indians who started immigrating in the 1880s (Kabir, 2007). The Muslim community in Australia is also very diverse, since they are either Australian born, or originate from different countries such as Lebanon, Turkey, Afghanistan, Bosnia, Palestine, India, Pakistan, Indonesia and many others, where 14.7% and 11.5% of the Muslim population were born in Pakistan and Afghanistan respectively, according to the 2016 census. Between 1901 and 1973, structures were put in place that maintained the “Whiteness” of Australia and curtailed the efforts of nonwhites and Muslims from entering Australia. So, for example, all individuals of colour who tried to enter Australia had to submit to a medical examination upfront and to a dictation test of 50 words, in any language chosen by the immigration officer, which was usually administered in a language that could not be understood by the applicants to ensure their failure (Kabir & Evans, 2002: 71). However, a “Multicultural” period emerged in 1973 with the need for labour, resulting in an influx of immigrants some of which were Muslims. Despite this apparent need, Muslims continuously found themselves at a social and economic disadvantage, especially compared to their immigrant counterparts who do not have visibly distinctive features. In 1991, the unemployment level of Middle Eastern-born Muslim males was 47%, where as it was 34% for Christian counterparts (Kabir & Evans, 2002: 72). The 1996 census showed that “unemployment levels of Australianborn Muslim men and women (23%) were more than three times higher than that of Australian-born Christian men (8%) and women (7%) respectively” (Kabir & Evans, 2002: 78). In 2001, even though unemployment levels amongst Muslims decreased, it remained higher than their non-Muslim counterparts, where it was 18.5% for Muslims and 6.8% for the national total (Kabir, 2007: 1290) and the unemployment rate of Muslims continues to be double that of the national average today. Such examples clearly support the argument that religion and ethnicity play a major role in putting migrants at a disadvantage in being employed. Their visibly distinctive features often make employers overlook their experience and qualifications. Of course, the more negative the media attention Muslims got due to acts of terrorism, the more challenging it became for them. It is within such context that Muslim women are endeavouring to find employment and to integrate socially. In what follows we will present our estimation 5 The percentage of Muslims almost doubled between 2006 and 2016; According to the census conducted by the Australian Bureau of Statistics, the percentage of Muslims almost increased from 1.7% of the Australian population in 2006, to 2.6% in 2016.

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Table 1 Sample Socio-Economic Indicators for Women between 15 and 65 (proportion).

Table 2 Sample Labor Market Outcomes for Women between 15 and 65 (proportion).

Income

Christian

Muslim

Other

Occupation

Christian

Muslim

Other

Negative $0-$199 $200-$299 $300-$399 $400-$599 $600-$799 $800-$999 $1,000-$1,249 $1,250-$1,499 $1,500-$1,999 $2000 or more NA and visitors N*

0.005 0.197 0.094 0.093 0.141 0.127 0.09 0.084 0.053 0.056 0.032 0.027 44397

0.017 0.323 0.188 0.093 0.117 0.08 0.048 0.037 0.021 0.011 0.014 0.051 1568

0.006 0.209 0.096 0.09 0.137 0.124 0.087 0.083 0.052 0.056 0.034 0.026 20058

Managers Professionals Technicians and Trades Workers Community and Personal Service Workers Clerical and Administrative Workers Sales Workers Machinery Operators and Drivers Labourers N*

0.094 0.240 0.043 0.146 0.265 0.127 0.013 0.070 29476

0.082 0.208 0.059 0.161 0.176 0.167 0.029 0.118 510

0.098 0.280 0.053 0.144 0.217 0.120 0.016 0.072 12998

Labor force status Unemployed Employed N*

0.048 0.952 31475

0.116 0.884 596

0.070 0.930 14182

Education Postgraduate Degree Level Graduate Diploma and Graduate Certificate Level Bachelor’s degree Level Advanced Diploma and Diploma Level Certificate Level N*

0.058 0.049 0.345 0.222 0.325 21065

0.15 0.027 0.415 0.18 0.228 627

0.117 0.057 0.394 0.183 0.249 10825

Participation not participate Participate N*

0.286 0.714 44064

0.611 0.389 1531

0.289 0.711 19949

Marital status ever married Married never married N*

0.156 0.517 0.327 44397

0.133 0.598 0.269 1568

0.133 0.41 0.457 20057

Education Postgraduate Degree Level Graduate Diploma and Graduate Certificate Level Bachelor Degree Level Advanced Diploma and Diploma Level Certificate Level N*

0.058 0.049 0.345 0.222 0.325 21065

0.150 0.027 0.415 0.180 0.228 627

0.117 0.057 0.394 0.183 0.249 10825

Gender** Male Female N*

0.472 0.528 84123

0.511 0.489 3209

0.522 0.478 41999

*represents the number of non-missing observation on each variable.

evidence that they are similar in human capital and other facets, but dissimilar in other ones. We begin with showing the distribution of each religion group on educational attainment. The statistics in Fig. 1(a) show that there is a larger share of Muslim women at the postgraduate degree as well as the bachelor’s degree level and they have lower proportions in the rest of the educational levels. This distribution should work in their favor at least in the occupational choice and income, assuming all other covariates are similar. Panel (b) of the figure shows the contrary. Muslim women seem to be more concentrated in the occupations8 that require less education rather than higher degrees. This is not true for technicians and trades workers and community and personal services occupations. This implies that there may be obstacles that inhibits their access to higher occupations. Although unemployment, education, and participation are so interrelated, and the relations may be bi-directional, a quick look at Fig. 2 resembles what is typically found on the experience of Muslim women in the West. Participation rate is half that of Christian and Other, while unemployment is almost double. This essentially shows that a lot more of them are not working, which makes the economic cost of not working much higher at the country level9. The implications for income inequality at the group level are obvious. Table 1 shows that Muslim women have higher proportions at lower income levels and lower proportions at higher income levels, which means a weighted average weekly income will be lower for Muslim women. In fact, the weighted average for Muslim women is 68% of Christians and 65% of Other. The emerging picture is as follows: Muslim women are more educated, most

* Represents the number of non-missing observation on each variable. ** The estimation sample includes females only.

strategy and discuss the data that are used for the analysis.

4. Data and sample descriptive statistics For the empirical part of the study, we use a Basic 1% Census Sample File (CSF) obtained from the most recent Australian Census of Population and Housing available, which was administrated on Census Night, 9 August 2011. The 1% Basic CSF contains data on 87,798 dwellings, 93,002 families and 215,597 persons. Census of Population and Housing provides information on all the variables of interest including religious affiliation, labour market participation, employment, qualifications, migration history, ethnic background and many other relevant variables. The sample we analyze was restricted to women between the age of 15 and 65. Tables 1 and 2 provide ratios (proportions) based on count statistics. The following is a brief description of variables in the model: Income is total weekly income in US dollars; it has twelve categories ranging from negative to $ 2000 or more.6 Although this variable was not included in the regressions, it serves to corporate the results of the regressions in Tables 3 and 4. The remaining variables (such as religion, education, occupation, labour force status…etc) are all micro-level nominal variables. We show the distributions in Tables 1 and 2 since the aim is to provide an initial assessment of whether there are significant differences across the religion lines.7 The literature on religion and labour market outcomes (Abdelkerim & Grace, 2012; Kabir, 2007; Peucker et al., 2014) has documented that Muslims seem to encounter significant disadvantages in the labour market. Our aim is to provide

8 This includes sales workers, machinery operators and drivers, and labourer’s. A complete detail of these occupations can be found at http://www. abs.gov.au/ausstats/[email protected]/0/C535B3808974AB51CA2575DF002DA70A? opendocument. 9 The economic cost referred to here is the forgone output that could have been produced had these resources been employed. The economic cost also addresses non-participation. It is documented in the economic literature that output growth has increased to increased female labor force participation. See for example a recent report on the relation between participation and growth in the United States, https://www.stlouisfed.org/on-the-economy/2018/august/ gdp-labor-force-participation-economic-growth”.

6 Negative income is included to account for income from un-incorporated businesses; it also includes rental income and wage income. 7 Missing values were excluded, and the descriptive statistics were not weighted as there are no survey weights for census data.

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Table 3 Logit Estimates of Female Unemployment and Participation. Unemployment

Religion No. of Children

Marital Status Educational Attainment

Year Arrived Language Spoken at Home

Model 1 0.87*** 0.37***

Muslim Other One Two Three or more Married Never married BA Diploma Secondary Other Arrived 2001-2005 Arrived 2006-2011 Other European Southwest & Central Asian Languages Asian, Other Age FE Ethnicity FE Constant Observations

participation status Model 2 0.32 0.21**

Model 3 0.23 0.15 0.17 0.32* 0.48*** −0.29* −0.00 −0.08 0.04 0.50** 0.34** 0.27* 0.78*** 0.13 0.70* 0.60*** Yes Yes −2.43*** 12456 −3123.20 −2908.8 428.88 0.0687 0.00

−0.02 0.11 0.54*** 0.47***

0.15 1.11*** 0.89*** No No −3.41*** 12456 −3123.20 −3017.2 212.10 0.034 0.00

No No −2.81*** 12456 −3123.20 −3101.5 43.42 0.007 0.00

Log Likelihood Constant model log likelihood chi2 pr_2 p

Model 1 −1.24*** −0.04

Model 2 −0.74*** −0.06

−0.21** −0.37*** −0.55*** −1.31***

No No 0.76*** 18855 −12078.9 −11895.7 366.57 0.0152 0.00

−0.51*** −1.11*** −0.56*** No No 1.87*** 18855 −12078.9 −11159.0 1839.88 0.0762 0.00

Model 3 −0.77*** −0.14*** −0.75*** −0.82*** −1.11*** 0.05 0.00 −0.19** −0.29*** −0.46*** −1.07*** −0.26*** −0.71*** −0.51*** −0.85*** −0.35*** Yes Yes 1.42*** 18855 −12078.9 −10413.7 3330.53 0.138 0.00

Significance levels are: * (5%), ** (1%), and *** (.1%). Base outcomes are: Postgraduate Degree Level for education, None for number of children, ever married for marital status, 2000 or before for year arrived, English for language spoken at home, Christian (including no religion) for religion definition 2, and Christian (including more restrictions) for religion definition 3. ‡ Definition 1 is Christian, definition 2 adds white no religion to Christian, definitions 3 adds citizenship and English language spoken at home, and at least one parent born in Australia to definition 2. Table 4 Multinomial Logit for Occupational Choice. Model 1 nd

2 Muslim Other BA Diploma Secondary Other Other European Southwest & Central Asian Languages Asian, Other Arrived 2001-2005 Arrived 2006-2011 One Two Three or more Married Never married Age FE Ethnicity FE Constant Observations log-likelihood constant model log-likelihood current model Chi-Sq Psudo R2 Pr (Chi-sq)

outcome

Model 2 rd

3

outcome

th

4

outcome

nd

2

outcome

Model 3 rd

3

outcome

th

4

outcome

0.43** −0.09

−0.09 −0.34***

0.58*** −0.05

0.50** 0.06 0.65*** 2.00*** 3.08*** 2.58*** 0.37*** 0.26 0.78***

0.02 −0.14** 0.20* 1.27*** 1.97*** 2.25*** 0.18* 0.15 0.35***

0.43* 0.03 1.07*** 2.22*** 3.56*** 4.32*** 0.75*** 0.52* 1.45***

No No −0.65*** 11416 −14800.54 −14760.71 79.65 0.003 0.00

No No 0.005

No No −1.18***

No No −2.64*** 11416 −14800.54 −13141.71 3317.66 0.112 0.00

No No −1.31***

No No −4.56***

2nd outcome

3rd outcome

4th outcome

0.36* 0.00 0.65*** 2.07*** 3.18*** 2.63*** 0.34*** 0.00 0.44*** 0.27** 0.70*** 0.09 0.25** 0.38*** −0.32*** −0.34** Yes Yes −1.07** 11416 −14800.54 −12919.08 3762.9 0.127 0.00

−0.06 −0.17** 0.18* 1.29*** 1.98*** 2.22*** 0.19* 0.08 0.16 0.02 0.06 0.03 0.05 −0.02 −0.09 −0.24* Yes Yes 0.58

0.54* 0.02 1.10*** 2.26*** 3.64*** 4.33*** 0.81*** 0.51 0.92*** 0.14 0.92*** 0.27* 0.16 0.36** −0.22* −0.28* Yes Yes −3.33***

Base outcome is managers and professionals, the 2nd, 3rd, and fourth outcomes are technical, clerical-sales, and manual occupations respectively. The reference groups are for religion Christian, for education MA+, for language English, for year arrived 2000 and before, for No. of children none, for marital status never married. Significance levels are: * (5%), ** (1%), *** (.1%).

of them are either unemployed or not participating (discouraged or otherwise), and when they do work, they are in lower level occupations and make two-thirds of what the rest of the population do. In the

following section, we provide inferences on each of these descriptive statistics indicators.

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Fig. 1. (a): Distribution of Women by Education and Religion. (b): Distribution of Women by Occupation and Religion.

Fig. 2. Unemployment and Labor Force Participation.

5. Empirical model

Yi = β0 + β1 Xi + β2 Reli + ei

We estimate three equations individually, the first is on unemployment, the second on participation, and the third is on occupational choice. To capture whether it is religion alone or some combination with ethnicity, we estimate the model in three different measurements of religion: the first considers all denominations of Christianity as Christian. The second incorporates non-religious to Christians considering them as locals who turned non-religious, but share many of the same socio-economic characteristics. The third definition also adds those who speak English at home, with at least one parent who is born in Australia and is an Australian citizen.

Where Yi can be either an unemployment dummy (1 for unemployed), participation (1 for participant) dummy, or occupation (1 for Manager/ professional, 2 for Technicians, 3 for Clerical and Sales, and 4 for Manual Jobs. X is a set of control variables (age, number of children, marital status, year of arrival). For each dependent variable, we provide three specifications: the first includes religion only, the second incorporates the human capital covariates (education and language spoken at home), and finally we add socio-economic covariates (number of children, marital status, year of arrival, age fixed effect, and ethnicity fixed effect). From a theoretical viewpoint, one would expect 6

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Unlike unemployment, the progressive addition of new explanatory variables to the model did not change the effect (sign and significance) of being a Muslim woman on the probability of participation. To the contrary, the other group became significant after adding the socioeconomic control variables. The introduction of human capital and socio-economic control variables did however reduce the magnitude of the effect of religion on the probability of participation by approximately 40%. We find that the probability of participation is lower for Muslim and Other relative to Christian, and more so for Muslim women (see Fig. 3(b)). This would suggest that Muslim women are not facing a disadvantage in terms of unemployment, and they choose not to participate because they value their time at home to be higher than what it would be in the market. Another possible explanation is that lack of participation is due to not having child care services for women with children. It remains to be investigated further whether their lack of participation is due to personal/family choice or a form of discouragement. The results indicate that the number of children strongly reduces the participation tendency; one would consider that Muslim women may have more children on average than the rest which supports the notion of choosing to stay at home11. Thus, we ran the regressions12 for participation and unemployment (not reported) and found the following: for the participation decision, the number of children is negative and significant for all three groups, and more negative for Muslim women. For unemployment, the coefficient is negative and significant for Christian, positive and not significant for Muslim, and negative and not significant for the Other group. All levels of educational attainment are less likely to participate relative to the highest level (graduate degrees). Late arrivals to the country are less likely to participate (but also less likely to be employed). And finally, marital status is not significant in explaining the participation. The occupational outcome also shows that Muslim women are more likely to fall in any of the reported occupational outcomes relative to Christian women (except for clerical and sales). This result does not change by including human capital and other socio-economic control blocks. The reverse is true for Other who are significantly less likely to be in sales and administrative workers category, but just as likely to fall in the other two occupations relative to managerial occupations. Does this imply that Muslim and Other have unequal opportunities? The evidence in Fig. 4 (top left panel) shows that although the probability falls with lower education for all groups in the managers and professionals’ category, Muslim, Christian, and Other are just as likely to be in any occupation given that differences in each category are not significant (random). Having said that, the probability for Muslim women is lower in managerial and professional positions, and continues to have the highest probability in manual work occupations particularly when their education is low.

that endogeneity and/or omitted variables may bias the results. The age and ethnicity fixed effects are included to capture unobservable factors not included in the regression. While the human capital covariates are the primary determinants of labour market outcomes considered in this study, the inclusion of religion only in the first specification allows us to identify the net effect of religion after the other covariates are included, and whether it remains significant or not. The categories for each of those covariates are reported in Tables 3 and 4. The number of children applies to females only and reflects the number of children ever born to a woman. As for marital status, it distinguishes between currently married, never married singles, and ever married which includes widows and divorced and separated. Our estimation strategy identifies the human capital covariates as the primary determinant of the three labour market outcomes considered. In particular, the literature identifies schooling and participation as endogenous (Heitmueller, 2007), which if not properly accounted for, the estimates are biased. The literature points that having more children affects women’s decision to participate by altering their reservation wage relative to the market wage; hence the decision to not participate10. But there are also ethnic and age differences among women that capture unobservable factors (such as the reservation wage). We use a logistic regression to test whether religious affiliation affects the probability of being employed or being a participant. Table 3 presents model estimates for unemployment for three different variants; the first introduces religion only, the second adds human capital covariates, and the third adds several socio-economic control variables. Likewise, the next 3 columns are for participation. Our results indicate that religion increases the probability of unemployment only when human capital and other control variables are unaccounted for (Model 1 of unemployment). For “Other” religious affiliation, they continue to be more likely to be unemployed even after adding the human capital covariates (column 2). It was only after we added ethnicity and other personal characteristics that it ceased to matter. What this translates into is that the “Other” group, which includes no religion, Buddhists, etc., is explained by marital status, number of children, age, and year of arrival to the country. The effect of those variables was finding its way onto unemployment through religion. But once they were accounted for, religion became insignificant. For Muslims, the education and language spoken at home had a strong enough effect to render religion insignificant in explaining the probability of unemployment. Although the coefficients in model 3 are positive, they are not significant. We also notice that educational levels of Secondary and Other raises the probability of unemployment relative to graduates with a Master’s degree and above. For this reason, we present the predicted probability of being unemployed by religion and level of education. Diploma holders (below BA) have the lowest probability of unemployment irrespective of religion; one possible explanation is that this group focuses on vocational and skilled trades education for which there is a higher demand in the labour market. Christians (and Other) are the most likely to be employed; although the confidence intervals overlap indicating no significant statistical differences. Marital status affects employment probability positively for married women relative to ever married, which includes widowed and divorced and separated. The year of arrival and language spoken at home seem to play a significant role as well. The more recent the arrival is, the more likely to be unemployed relative to older arrivals (or equivalently the less likely to be employed). Females with Southwest & Central Asian languages spoken at home have an employment disadvantage compared to those who speak English at home.

6. Discussion This paper depicts the experience of Muslim women in the Australian labour market by comparing their performance with mainstream Australian women in three labour market outcomes: participation, unemployment and occupational attainment. While previous studies show that the labour market experience of Muslim women in Australia varies by ethnicity, country of origin and other relevant factors (Foroutan, 2015), in this study we analysed Muslim women as one group. In order to investigate the existence of a ‘Muslim penalty’, it is crucial that we compare between all Muslims with the majority population, whereby Muslims are considered as one homogenous group. It is 11

A cross tabulation of number of children and religion shows that 31% of Muslim women have 3 or more children, for Christian and Other the figures are 26% and 16%. On the other hand, the proportion for zero children, the figures are 33%, 37%, and 47% for Christian, Muslim, and Other respectively. 12 By religious group.

10

IV estimators are often used to account for endogeneity; this has not been applied in this case due to the lack of suitable instruments. While this study does not claim causality, we acknowledge the limitations this poses on interpreting the results of this model. 7

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Fig. 3. Predicted probabilities of unemployment (a) and participation (b).

Fig. 4. Predicted probability of being in an occupation by religion.

Given that applying for jobs requires applicants to write their names, but not necessarily their ethnicity or racial group, applicants with Muslim and Arab names will be more likely to be discriminated against (Blommaert, Coenders, & Van Tubergen, 2014). Therefore, estimating the disadvantage for all Muslim women as one group seemed well justified.

well established that all Muslim women, especially those wearing the hijab and displaying their religious identity via different symbols and practices, are more likely to get harassed and abused in public spaces in Australia (Kabir, 2007; Poynting & Noble, 2004). In this respect, the media and the Islamophobic discourse do not distinguish between Muslims, surely not by their ethnic background or country of origin. 8

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2017), and women in general (Boeckmann et al., 2014). However, when we added the factors that are associated with human capital, the coefficient of religion dropped substantially and lost its statistical significance altogether, suggesting that the employment gap between Muslim and Christian women in Australia is explained by qualifications, language skills and time of arrival. In other words, the evidence suggests that the employment gap between the groups is not due to religious or ethnic discrimination, but due to the human capital deficit among Muslim women. This finding lends a great deal of support to the human capital and assimilation argument claiming that labour market disadvantages of migrants are temporary and are due to their human capital deficit (Chiswick & Huang, 2008; Koopmans, 2016). We also controlled for region of birth since there is ample evidence (Foroutan, 2008) that there are differences among Muslims themselves. The evidence not reported suggests that differences are not statistically significant. The base region is Oceania (which is basically Australia and New Zealand), all other regions are statistically not significant except Muslim women from the Americas who tend to be more likely to be unemployed at the 5% level. While this is an important finding, it raises a question simultaneously. Why do religious backgrounds or affiliations appear to be significant in determining economic activity (participation) and occupational attainment, but not in the case of employment? One possible answer is that participation is affected by factors that operate through religion, including cultural expectations, gender attitudes and family preferences. Whereas, in seeking and obtaining managerial and professional jobs, Muslim women are placed in direct competition with their Christian Australian counterparts. Given that the supply of qualified individuals to fill these positions (managerial and professional occupations) exceeds the demand, many employers would be reluctant to offer these jobs to individuals that belong to groups against whom employers hold negative perceptions, or prefer individuals that meet certain criteria, for example belonging to a certain ethnic or religious group (Becker, 1957). Furthermore, a recent study on Muslim men and women in the UK suggested that there is an increased penalty against Muslims, including women, when moving from finding any job to obtaining a better job within the managerial and professional occupations (Khattab & Johnston, 2015). In other words, if Muslim women accept jobs that are below their skill level because they are denied positions within the managerial and professional category, it is likely then that this explains why Muslim women are not disadvantaged in relation to employment, but they are in relation to occupational attainment. The results of this study also provide a strong evidence for the effect of qualifications on all the three labour market outcomes. The impact of qualifications operates similarly among Muslims, Christians and Other religious groups, in that higher qualifications are associated with higher level of economic activity, higher level rate employment and with higher probability of obtaining managerial and professional jobs. Yet, the occupational returns for qualifications among Muslim women were lower, which as we suggested, might be due to structural barriers. Although the study found positive effects of language skills, qualifications and the time of arrival on employment, it is possible that this particular result (the lack of employment penalty) might be masking a different kind of labour market disadvantage, for example overqualification as was pointed out recently in relation to Eastern European migrants in the UK (Khattab & Fox, 2016). It is possible though that improving human capital further, while also enhancing one’s knowledge in the host society (Australia), might improve employment prospects and opportunities, but without expanding these opportunities within the upper echelon of the labour market, the risk of disadvantages is likely to persist, though in other forms. That said, this study would benefit from and would be complemented by a qualitative method, in order to learn directly from Muslim women about their employment experiences and limitations in Australia.

The results show, as expected, that Muslim women have a substantially lower rate of labour market participation and are less likely to obtain managerial and professional jobs relative to majority Australian women. These findings come as no surprise given previous studies on this issue (Foroutan, 2015; Kabir & Evans, 2002; Lovat et al., 2013). The low rate of participation is likely to be an outcome of various forces and processes including cultural norms, preferences and patriarchy on the one hand (Ali et al., 2017; Foroutan, 2015), but also the hostile antiMuslim climate spreading in Australia (Kabir, 2007) and the lack of equal employment opportunities (Syed, 2007). These factors are likely to restrict women’s labour market supply by placing them, either by choice or reluctantly, within the private sphere and the home domain, which is in line with many previous studies on this issue in other western societies (Abdelhadi, 2017; Foroutan, 2015; Khattab & Hussein, 2017). However, the disadvantage of Muslim women in relation to occupational attainment and their lower likelihood of obtaining managerial and professional jobs than the majority group is likely to be due to structural barriers including discrimination on ethnic and religious grounds (Evans & Bowlby, 2000; Ghumman & Ryan, 2013; Khattab & Hussein, 2017; Perry, 2014; Syed, 2007). This result is very interesting given the educational profile of Muslim women, which is better than that of mainstream Australian women. It is likely that compared to the majority group, their qualifications do not yield the same level of occupational returns. If this turns out to be true, then we should expect them to also be overqualified. The fact that Muslim women’s probability of having jobs falling within the category of ‘Technicians and Trades Workers and Community and Personal Service Worker’, was substantially higher than the probability among Christian women points in that direction. In this study we did not analyze patterns of overqualification, so further studies are required in order to uncover the whole range of labour market situations and experiences among Muslim women in Australia. Contrary to the two labour market outcomes discussed above (participation and occupational attainment), the results of this study show that once we control for human capital and other individual and life circumstantial differences (Table 3), the initial differences found between Muslims and Christians in employment disappeared. Muslim women and majority Christian Australian women were as likely to be employed, contradicting previous research on the unemployment/employment gap between Muslims in general and the majority Australians (Lovat et al., 2013; Peucker et al., 2014). This is also out of line with studies in other western economies (Gracia, Vázquez-Quesada, & Van de Werfhorst, 2016; Heath, Rothon, & Kilpi, 2008; Miaari et al., 2018) (see also other articles in this edition). This is a very surprising finding, and it is important to know what exactly explains the employment gap between Muslim and Christian women in Australia; is it the individual ascriptive differences, the differences in human capital or both? To answer this question, we rerun the logit model, this time with unemployment focus using a 3-step method where we first ran the model with religion as the only explanatory variable. In the second step we added the human capital factors of qualifications, and language skills, and in the last step we added ascriptive factors of age, marital status and number of children and time of arrival to Australia ethnicity13. The results are presented in Table 3 and 4 below. The results are self-evident; religion appears to be very significant in the first model, and its size has even slightly increased in the second model when we controlled for age, marital status and children. This shows that family circumstances are an important factor in determining the employment prospects among Muslim women (Khattab et al.,

13 The data initially had 37 categories, these were grouped into 7 categories; these are: Oceania (5 countries), Europe (14 countries), Middle East and North Africa (3 categories), Asia (8 countries), Americas (5 regions), Sub Saharan Africa (2 categories), Other (those visiting or not defined).

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