IDPs stay within their own country and remain under the protection of its government, even if that government is the reason for their displacement. They often move to areas where it is difficult for us to deliver humanitarian assistance and as a result, these people are among the most vulnerable in the world.

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In many Western countries, the total fertility rate (TFR) of immigrant women has declined over the last decades (Figure 1). In Norway, for instance, immigrant TFR fell from 2.6 children per women in 2000 to less than 2.0 in 2017. This may be due to several factors, such as immigrants’ adaptation to the fertility norms at destination, changing composition of immigrant women by area of origin, and other factors.

In a recent study, I try to disentangle the different possible causes of this decline (Tønnessen, 2019). Like many other Northern European countries, Norway has relatively high fertility (ranging between 1.98 and 1.62 in the study period) and an increasing share of immigrants (from 5.3 per cent in 2000 to 13.8% in 2017) from all over the world and, in the last decade, from Eastern EU countries like Poland and Lithuania in particular (Statistics Norway 2020a,b).

Newly arrived family migrants explain a lot

The results show that in line with findings from many other countries (see for instance Sobotka 2008), immigrant women from regions with high fertility (such as Africa and parts of Asia) often have higher fertility in Norway than women from low fertility countries. Also, immigrant women’s fertility often declines with time since arrival in the destination country, probably because of integration, so that, everything else equal, women who have been living in Norway for a long time have lower fertility than those who arrived only recently. For instance, in 2000, newly arrived immigrants (0-2 years since migration), from Africa and Asia had a TFR above 4 children per women, while women from the same regions who had entered the country more than ten years earlier had a TFR of around 2. These trends are not particularly surprising, of course, nor is the considerably lower fertility – about 2 children per woman – of women from the European Union or the US.

However, the decline in immigrant TFR in Norway from 2000 is due neither to an increased share of immigrant women from low fertility countries, nor to an increased share of women with long duration of stay. It is linked, instead, to the fact that the fertility of recent immigrants to Norway is considerably lower than it was 20 years ago. For instance, while in 2000 the fertility of newly arrived women from Eastern and South Eastern Asia was above 4 children per women, in 2017 it was below 2. The same holds for newly arrived women from Latin America (from 3.5 in 2000 to 2.0 in 2017) and from Western and Southern Asia (4.5 to 3.5).

Decomposition methods make it possible to quantify the effect of changes in composition by duration of stay and area of origin, and the effect of changing fertility within subgroups (by duration of stay and reason for migration). The results (Table 1) show that lower fertility among newly arrived immigrants explains 93% of the overall decline, and that this is mainly driven by women from Asia.

I further analyzed this fertility decline among newly arrived women by reason for migration. Women who migrate for family reasons appear to provide a key: their share among all newly arrived immigrant women declined in this period – for instance, from above 80% in 2000 to 55% in 2017 among women from Eastern and Southern Asia – and so did their fertility (see Figure 2). Among newly arrived family migrants from Asia, TFR fell by more than two births per woman from 2000 to 2017.

Further decompositions show that the lower TFR of newly arrived family migrants accounts for a large share of the total TFR decline among all immigrants in Norway. Among newly arrived immigrants from Western and Southern Asia only, lower fertility among family migrants accounts for 10% of the overall TFR decline, and among those from Eastern and South East Asia it accounts for 8%. Hence, decreased fertility among these two groups alone accounts for 18% of the TFR decrease of all immigrant women in Norway. This is a large effect from a relatively small group; at end-2017, they represented just 3% of all immigrant women of childbearing age (5% in 2000).

A large part of this fertility decline among newly arrived family migrants from non-western parts of the world may reflect fertility declines in origin due to factors such as family planning programs or more education for women. Such factors may, in turn, affect both the share of women who migrate for family reasons and the fertility of the family migrants. And although migrant women may be a select group compared to those who remain in origin, they may have been affected by the same secular changes in fertility norms and behaviors.

Fertility has declined in many origin areas

Fertility has declined substantially in many non-western countries. Such origin area trends are sometimes overlooked in studies of immigrant fertility. Although the newly arrived immigrant women grew up in the same origin areas as those who moved to Norway one or two decades ago, they grew up in a different time, with different fertility norms and patterns.

The fact that the decreased immigrant TFR in Norway is mainly driven by lower fertility among newly arrived women, possibly reflecting declining fertility trends in their countries of origin, may remind migration researchers to look for explanations of changes in immigrant fertility beyond the destination country and the characteristics of individual migrants.

This can also be a reminder for policy makers and others not to draw too hasty conclusions about the effect of domestic policies by looking at trends in immigrant TFR. Although an immigrant woman’s fertility often declines with her duration of stay, thanks to successful integration for instance, this does not necessarily translate into a declining TFR for all immigrants, unless the share with long duration of stay increases.

The results of my research also point to the future: if fertility change in origin areas is a driver of fertility decline among many non-western newly arrived migrants, and if fertility continues to fall in important origin areas – as projected by the United Nations for high-fertility parts of the world (United Nations 2019) – we may expect further fertility declines among immigrants from these areas. Moreover, policies affecting fertility preferences in high-fertility parts of the world may, in turn, affect the fertility of Western countries’ own immigrant populations.

References

Sobotka, T. (2008). The rising importance of migrants for childbearing in Europe. Overview Chapter 7. Childbearing Trends and Policies in Europe. Demographic Research, Special Collection, 7, 225-248.

Statistics Norway (2020a). Births. Statistics from retrieved February 2020.

Statistics Norway (2020b). Immigrants and Norwegian-born to immigrant parents. Statistics from retrieved February 2020.

Tønnessen, M. (2019). Declined Total Fertility Rate Among Immigrants and the Role of Newly Arrived Women in Norway. European Journal of Population.

United Nations (2019). World population prospects: The 2019 revision. New York: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.

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When we study the social outcomes of international migrants – such as fertility, health or education – we typically compare them with three reference groups: (i) non-migrants in the destination country, (ii) non-migrants in the origin country (iii) and immigrants from the same origin country living in different destinations. Although there is more than one potential comparison group, migration scholars almost always compare immigrants with non-migrants in the countries they move to. This focus on destination comparisons is understandable; they are easier because they only require a single data source. Comparisons with origin, on the other hand, require at least two harmonized data sources containing similar information and of requisite quality; one source for immigrant outcomes in the destination country and another for non-migrant outcomes in the origin country.

Destination comparisons also reflect a greater interest in where immigrants are living now, how their lives change after arrival, and the links between migration, integration and inequality. Nevertheless, comparisons with non-migrants in the destination only tell us part of the story about immigrants’ lives. If we additionally make comparisons with non-migrants in origin countries, then this can help us to understand why the outcomes of immigrants differ from non-migrants in the destination country. Often, immigrants are said to be “selected” from their origin populations. This might mean that those who leave a country are, for example, more highly educated or healthier than those who stay behind. These two factors are not independent. If some people are more highly educated than others, then they are probably healthier too, and vice versa. Although destination comparisons can tell us a lot about the experiences of immigrants in their new country, they say little about selection unless we compare them with the population they are selected from.

Comparing immigrants with their origin country: mortality…

In a recent study (Wallace and Wilson 2019), we compared the mortality of immigrants in the United Kingdom (UK) with non-migrants in their origin countries. Our analysis is framed around the migrant mortality advantage – a term used to describe the frequently observed situation in which immigrants have lower overall mortality than non-migrant populations at destination. It is considered advantageous because immigrants, on average, will live longer than non-migrants. Researchers often suspect that selection plays a big role in the advantage. For example, if we find that immigrants from India – a country that ranks around 130^th in the world life expectancy rankings – have lower mortality than non-migrants in the UK – a country that ranks around 30^th in the world – then it’s likely that the immigrants have, on average, lower mortality than non-migrants in India.

In our study, we estimated relative mortality, versus origin, of immigrants from the 35 countries with the largest immigrant populations in the United Kingdom, by age and sex. We analysed data on deaths and population sizes from the Office for National Statistics (in the UK) alongside equivalent data for origin countries from the Human Mortality Database and the United Nations World Population Prospects. We also analysed equivalent data on the educational attainment – of immigrants versus their country of birth – as a more widely accepted measure of selection (Feliciano and Lanuza 2017) and a characteristic that remains quite stable with time (unlike mortality, which is directly affected by people’s experiences in the destination country (Ichou and Wallace 2018).

Figure 1 shows the age-specific mortality of immigrants relative to the population in their countries of birth. The far-left panel shows the “average” for all immigrants living in the UK plotted in black, with individual immigrant groups plotted in light grey. The red line indicates mortality in origin countries at a given age. Values below the red line indicate a mortality advantage, whereas those above the red line indicate excess mortality among immigrants. The other panels show four examples of immigrant groups from countries with different levels of the Human Development Index (HDI), ranging from low (Nigeria) to very high (the United States). These examples are fairly typical of the averages for each HDI category that we show in the full paper, and they illustrate three key findings. First of all, there is a common shape across panels, in which the advantage is largest at young adult ages and decreases with age. Second, the advantage at young adult ages is largest among immigrants from poorer countries and diminishes as we move up HDI categories. Third, we find that substantial mortality advantages exist at older ages (65+) for countries in the low and medium HDI categories, but not for groups in the high and very high categories.

… and education

Figure 2 shows an almost identical analysis, but with a focus on education. Here, the far-left panel shows that immigrants almost always have higher rates of tertiary education than average members of the population in their country of birth. There are only a few exceptions (i.e. the light grey line is below the red line), with the clearest case being Lithuania (not singled out here). Generally, the size of the differentials falls as we move up HDI categories. For example, Nigerian immigrants are much more highly educated than their Nigerian origin population, whereas immigrants from the US are only somewhat more highly educated than the US average. Variation over age is likely to reflect cohort differences in migration, as well as cohort trends in the origin countries, including factors such as educational expansion.

Conclusions: selective selection at work (but weakening over time)

So, what do these findings tell us? We think that the patterns provide evidence consistent with selection. Immigrants have lower mortality than the population in their country of origin, on average. The lower the level of development in the origin country, the larger this advantage tends to be. This suggests that immigrants from poor countries need to be more strongly selected than immigrants from rich countries in order to generate a similar mortality advantage vs. the same destination population. Next, the advantage is largest at young adult ages and diminishes with age. Since the majority of immigrants arrive at young adult ages, this finding is consistent with the idea that selection effects are strongest shortly after immigrants arrive and weaken over time. In absence of data on duration of residence, it is at young adult ages that we come closest to capturing the extent of selection effects among immigrants, as these are the ages where the selection has just taken place (and exposure to life at destination is minimised). With time and a growing influence of destination-specific risk factors that affect mortality, the role of selection diminishes. Last, immigrants are also more highly educated, on average, than their origin populations. Given that education is a more stable measure of selection (because it is unlikely to change from young adulthood onwards), this result adds further support for our conclusions on mortality.

Overall, our findings represent an important reference point in the re-conceptualization of immigrant outcomes relative to origin country populations. We are not the first to make such comparisons (Marmot, Adelstein, and Bulusu 1984; Gadd et al. 2006), but our study is the first to do so for such a diverse array of countries and in such detail (i.e. by age, sex and origin country), even considering comparisons to destination. Given that selection seems to be an important explanation of the migrant mortality advantage, we should consider how the flows of selected people between countries – often from poor to rich countries – impacts positively on national mortality metrics in rich countries, negatively on national mortality in poor countries and potentially inflates the size of health inequalities between them.

Bibliography

Feliciano, C., and Y. R. Lanuza. 2017. “An immigrant paradox? Contextual attainment and intergenerational educational mobility.” Am Sociol Rev 82 (1):211-241.

Gadd, M., S. E. Johansson, J. Sundquist, and P. Wandell. 2006. “Are there differences in all-cause and coronary heart disease mortality between immigrants in Sweden and in their country of birth? A follow-up study of total populations.” BMC Public Health 6:102. doi: 10.1186/1471-2458-6-102.

Ichou, M., and M. Wallace. 2018. “The Healthy Migrant Effect: The role of educational selectivity in the good health of migrants.” Demographic Research 40 (4):61-94. doi: 10.4054/DemRes.2019.40.4.

Marmot, M.G., A.M. Adelstein, and L. Bulusu. 1984. “Lessons from the study of immigrant mortality.” The Lancet 323 (8392):1455-1457. doi: 10.1016/S0140-6736(84)91943-3.

Wallace, M., and B. Wilson. 2019. “Migrant Mortality Advantage Versus Origin and the Selection Hypothesis.” Population and Development Review 45 (4):767-+. doi: 10.1111/padr.12298.

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Are there distinctive patterns of ethnic residential segregation across the life course?

In a recent publication (Sabater and Catney 2018), we examine variations in ethnic residential segregation across the life course, represented in our study by particular age groups and ‘stages’ of life. We argue that taking such an approach is important in contexts with simultaneous growth of young and ageing minority ethnic populations for understanding the local dynamics of ethnic geographies.

Using harmonised small area data (8,546 wards) for England and Wales (2001-2011), we demonstrate the usefulness of our approach by applying two measures of segregation: the dissimilarity index (ID) and the isolation index (P*). These two commonly-employed measures capture two key dimensions of residential segregation (evenness and exposure), and allow straightforward comparisons of global, age group and birth-cohort segregation both nationally and internationally. In the study, we analyse the evolution of ethnic residential geographies for the eight largest and most stable categories from 2001 to 2011: White British, Other White, Indian, Pakistani, Bangladeshi, Black Caribbean, Black African and Chinese. Ethnicity data used in UK national statistics relies on individuals’ self-definition.

The computation of ethnic residential segregation with an age dimension is equivalent to the summary or ‘global’ calculation for all groups, although the analysis relies on an index value for each age group. The use of age for the analysis of ethnic residential segregation increases our knowledge about the spatial incorporation of each group because it highlights two important characteristics about an individual: their place in the life cycle – whether a young adult, middle-aged or older – and their membership in a cohort of individuals who were born at a similar time. Further, since the characteristics of younger or older adults may differ at a given period, the use of birth-cohorts provides one way to examine the trajectory of residential segregation of ethnic groups as they pass through life course phases, including when household sizes may be growing or reducing.

Results on age-specific ethnic segregation clearly demonstrate that this simple demographic approach to analysing segregation by age groups can provide an important contribution to the ethnic segregation debate. Most studies using global measures depict segregation as either low, moderate or high, yet this analysis reveals significant differences in segregation by age between ethnic groups. For instance, the oldest age group (60-64) in this study is the most residentially segregated group in 2011 (measured here by the dissimilarity index), particularly for the Bangladeshi (79.8%), Pakistani (75.8%), Black African (72.5%), Black Caribbean (70.5%) and Indian (66.7%) groups, whose overall segregation can be considered as high (figure 1).

Perhaps more importantly, the results from figure 1 also highlight variations in segregation across the life course, represented here by particular age groups and ‘stages’ of life. Three distinctive phases can be identified, with higher levels of segregation at the youngest and oldest age categories (those within the 0-19 and 45-64 ranges), and lower levels of segregation for the ‘middle’ age categories (within the 20-44 ranges). It can be seen that the youngest group is more residentially segregated compared to the ‘middle’ age group. This is the result of clustering with their immediate family members in the same household, a situation which, in turn, is determined by the forces of choice and constraint on parents/families. Of course, while most children in the youngest group are likely to live in the same household with their parents, not all individuals in the ‘middle’ age categories are parents, thus the differences that we observe between these two age categories can be interpreted in terms of the impact of household composition and family location on residential segregation. Crucially, these phases are to a large extent common to all ethnic groups, and the consistency in relative levels of segregation found for the global values are generally observable across all age categories. The only departure from the common trends is the distinctive segregation patterning of the Chinese ethnic group aged 20-24, whose segregation (58.6%) is associated with overseas migration to UK universities as well as post-student retention, particularly in urban centres across England and Wales.

Cohort/generational change in terms of spatial mixing

Another part of our study is to examine how the residential segregation of ethnic groups evolves with age. This is an important aspect because it allows us to see whether or not there are cohort/generational changes in terms of spatial mixing for all ethnic groups. Figure 2 shows change in segregation across all wards in England and Wales since 2001, in terms of unevenness (ID) and exposure (P*) of ethnic groups by cohorts.

The analysis of ID across birth-cohorts indicates similar changes in geographical spread during the decade for all ethnic groups. First, the youngest cohort, which refers to children living with their parents, and older cohorts in their 40s, 50s and 60s, have experienced marginal changes in unevenness. Meanwhile, a clear decrease in unevenness is observed among cohorts in their 20s and 30s in 2011. For instance, ID values show a substantial percentage point decrease for birth-cohorts 10-14 in 2001 and 20-24 in 2011, particularly among Black African (-19.9), Black Caribbean (-12.6), Indian (-10.1), Bangladeshi (-8.5) and Pakistani (-7.9) groups.

The examination of ID values by cohorts shows a changing experience of ethnic segregation as people age. In a similar fashion to analyses of residential mobility by age, our results demonstrate that residential segregation decreases during young adulthood for all cohorts, then increases during the late 20s and early 30s, and continues to increase across mid-life until retirement. For instance, greater residential segregation in terms of unevenness can be seen for the White British groups who at the start of the 2001-2011 period, were aged 35-39 (+0.2), 40-44 (+0.6), 45-49 (+1.9) and 50-54 (+3.1). Similarly, a pattern of increased segregation is identifiable among the oldest cohorts (i.e. aged 50-54 in 2001) of most minority ethnic groups. Nonetheless, the results also indicate a lower geographical spread during the decade at somewhat younger ages for some minority ethnic groups – for instance, among Pakistani and Bangladeshi in their late 20s and early 30s – ranging from +1.4 (Pakistani aged 25-29 and Bangladeshi aged 30-34 in 2001) to +3.6 (Pakistani and Bangladeshi aged 35-39 in 2001).

Given that one of the most important attributes of birth-cohorts is the number of people born into the group, the number of arrivals from abroad, and the mortality of that group, the index of isolation (P*) is also employed here to highlight birth-cohort differences in population composition between ethnic groups. While the results indicate that the larger volume of births, particularly among some groups such as the Bangladeshi and Pakistani group, and streams of (family) immigration combine to produce marginal changes in residential segregation for birth-cohorts in their late 20s and early 30s, the most remarkable change in P* over the decade is a decrease for most birth-cohorts in their teens and 20s. The latter reflects widespread decreases in the average local population of ethnic minorities due to out-migration from ethnic concentration areas, associated with migration from cities, particularly for those at the family-building life stage (Sabater and Finney, 2014).

Meanwhile, older birth-cohorts of all ethnic groups experience greater neighbourhood segregation. This is because many older people, especially those entering pre-retirement ages, have largely settled in their neighbourhoods and aged in place. While for many older cohorts neighbourhood attachment and belonging may have contributed to these settlement patterns of ethnic concentration, for others it may reflect the outcome of cumulative disadvantages, particularly with regard to the housing market. Although the gradual, if slow, dispersal of all ethnic groups has contributed to desegregation over time, it is important to highlight that exclusionary forces such as racial stereotyping and discrimination have also played a crucial role in reinforcing minority ethnic concentration among older cohorts.

Implications

Most work on ethnic residential segregation fails to consider that the residential patterning of ethnic minorities for any place becomes more complex if age structures of recent immigrants are juxtaposed with those of second- and third-generation minority groups. A useful way to overcome this problem is to establish whether ethnic residential segregation at different times and contexts varies by age groups (i.e. between people who were born at different periods) and birth-cohorts (i.e. between people who were born in the same period), and whether there are differences or similarities between ethnic groups at key stages of the life course.

References

A. Sabater & G. Catney,  (2018). Unpacking Summary Measures of Ethnic Residential Segregation Using an Age Group and Age Cohort Perspective. European Journal of Population, vol. First Online, pp. 1-29. DOI: 10.1007/s10680-018-9475-3 [Open Access]

A. Sabater &  N. Finney (2014). Demographic Understandings of Changes in Ethnic Residential Segregation Across the Life Course. In C. Lloyd ,  I. Shuttleworth and D. Wong, (eds), Social segregation: concepts, processes and outcomes. Bristol: Policy Press, 269-300.

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In recent times, the growth of racially diverse U.S. cities and suburbs has been unprecedented (Lee et al. 2014; Parisi et al. 2015). However, according to Putnam (2007, p. 149), “people living in ethnically diverse settings appear to ‘hunker down’—that is, to pull in like a turtle.” That is, U.S. whites may live together with other minorities in the same communities but at the same time live apart from them with mostly white neighbors. Is this really the case?

Whites and minorities living in same cities and suburbs

As far back as Gunnar Myrdal’s An American Dilemma (1944), changing race relations and integration arguably have depended less on racial and ethnic minorities (i.e., what they do) than on white reactions to racial and ethnic change. Here, we shift the question by asking whether whites are more likely than in the past both to live in racially diverse places and to have minority neighbors living nearby. To answer this question, we rely mainly on the standardized entropy score E, which ranges between 0 (complete homogeneity, i.e. complete segregation) and 100 (highest possible presence of all racial groups in all places), keeping into account the proportion of people of the various racial groups: in other words, the index is comparable, over time and across places. (For the details, please see Lichter, Parisi, and Taquino 2017.)

As shown in Table 1, whites on average lived in central cities with a mean entropy score E of 66.0 in 2010, up from 48.4 in 1990. In other words, they were significantly less segregated than they were 20 years before. Moreover, the variation in white exposure to diversity in central cities has converged over the past two decades to a point where racial differences in E—at least within central cities—are comparatively small, ranging from 65.3 among blacks to 70.9 among Hispanics in 2010. These findings contrast with the estimates for 1990, when whites were living in the least diverse (i.e., most segregated) central cities (48.4). These patterns of racial convergence were also apparent in suburban areas. Whites, by far, were living in the least diverse suburbs in 1990 (E = 26.5). By 2010, however, whites lived in suburban places with E scores of 46.4 on average, compared with 48.3 among blacks and 50.3 among Hispanics.

Clearly, at the place level—in both cities and suburbs—whites are increasingly exposed to other racial and ethnic populations in much the same way as nonwhites overall. This, as we document in Lichter, Parisi, and Taquino (2017), is largely a function of broader metropolitan demographic and economic characteristics. That is, metropolitan-level characteristics rather than individual characteristics (e.g., income) play the dominant role in explaining individual exposure to diverse central cities. Large-scale demographic change has swamped individual volition.

Whites and minorities living on the same blocks

However, an important question remains: Are U.S. whites—especially those exposed to rapidly diversifying places—increasingly exposed to racial and ethnic minority neighbors living next door? Table 2 provides information on whether whites in diverse places in 2010 actually have neighbors who are racial minorities. Overall, these data show that white householders in the PSID (Panel Study of Income Dynamics) on average live on city blocks that are 31.9 percent nonwhite. As expected, these percentages are higher in principal cities (41.2 percent) than in suburban communities (25.9 percent).

No evidence of hunkering down appears over time. In fact, the reverse is true: net of individual and metropolitan characteristics, the proximity of whites to racial minorities living on the same block increased throughout the 20-year study period in both cities and suburban places (data shown in Lichter, Parisi, and Taquino, 2017).

Implications for minority integration

Racial residential segregation is the linchpin of America’s system of racial and ethnic stratification and inequality (Massey 2016). Our analyses showed that rising diversity is a dominant demographic trend that is spatially widespread, affecting virtually all segments of U.S. society. Growing diversity at the place level—in cities and suburbs—has involved most demographic and economic segments of the U.S. white population over the past 20 years. And, more significantly, it seemingly has trumped most behavioral explanations that emphasize invasion-and-succession processes (i.e., white flight). The commonplace idea of whites clustering together or barricading themselves against a new invasion of racial and ethnic minorities seems, on its face, to be an exaggeration of demographic reality.

Still, most whites today live on all-white or predominantly white blocks. Living with minority neighbors may be increasingly commonplace among U.S. whites, but it is far from a universal experience. Spatial integration among America’s white population is unfolding differently at multiple levels of geography—across metropolitan areas and between and within cities and suburbs. Racial segregation and integration have taken on new forms.

References

Lee, Barrett A., John Iceland, and Chad R. Farrell. 2014. “Is ethnoracial residential integration on the rise? Evidence from metropolitan and micropolitan America since 1980,” in Diversity and Disparities: America Enters a New Century (p. 415–56), edited by J. Logan. Russell Sage Foundation.

Lichter, Daniel T., Domenico Parisi, and Michael C. Taquino. 2017. “Together but Apart: Do US Whites Live in Racially Diverse Cities and Neighborhoods?” Population and Development Review 43(2): 229–255.

Massey, Douglas S. 2016. “Residential segregation is the linchpin of racial stratification,” City & Community 15: 4–7.

Myrdal, Gunnar. 1944. An American Dilemma. Harper and Bros.

Putnam, Robert D. 2007. “E pluribus unum: Diversity and community in the twenty-first century: The 2006 Johan Skytte Prize lecture,” Scandinavian Political Studies 30: 137–174.

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In this short essay we argue that Trump’s wall will not work. It will not reduce the number of undocumented immigrants in the United States. It will have the opposite effect; it will increase their number. We first examine demographic data on immigration to the U.S., both legal and undocumented.

Visa overstayers in the US

As of 2015 there were around 44 million persons living in the U.S. who were born in another country. Three-quarters, or around 33 million, are lawful immigrants, also known as “legal” or “authorized” immigrants. They include naturalized citizens, persons granted lawful permanent or temporary residence status (e.g., as workers or students), and persons granted asylum or admitted as refugees.

The remaining 25 percent of the U.S. foreign-born population, or just over 11 million people, are unauthorized immigrants, also known as “illegal” or “undocumented” immigrants. These are the 11.1 million “illegals” that President Trump is always referring to, whom he wants to deport back across the southern border.

Apparently unknown to President Trump is the fact that around two-fifths of these 11.1 million undocumented immigrants, or almost 4.5 million, are what are known as visa overstayers. They entered the U.S. with legal passports and legal visas but either stayed past their visa expiration dates or otherwise violated the terms of their admission into the U.S. Most flew in legally from Asia, Europe and other continents and entered at major airports in San Francisco, New York, Los Angeles, Houston, and elsewhere. Trump’s Wall is not high enough to keep them out.

In the U.S. presently, there is no plan to address the issue of undocumented immigration via visa overstayers. The U.S. Department of Homeland Security does not match entry and exit records of persons coming into and leaving the U.S. The Congress mandated an electronic entry-exit system more than 20 years ago, but it has not been implemented because of objections from the tourism industry and other groups. A biometric entry/exit system would likely be able to keep tabs on most of the people entering and exiting the U.S. and would probably reduce the number of visa overstayers. But the implementation of such a system is not in the plans of President Trump. He only wants to build a wall. Hence the numbers of visa overstayers will likely remain at around 4 to 5 million despite Trump’s wall.

More, not fewer, EWIs in the foreseeable future

What about the other 6 to 7 million undocumented immigrants in the U.S.? Who are they? How did they enter the United States? Where are they from? These are the so-called “illegal” immigrants everyone refers to. They are formally referred to by immigration officials as EWIs, or persons who “entered without inspection”. They entered the U.S. without detection or used fraudulent documents when crossing the border. Almost all of them entered at the U.S.-Mexico border, and until recently most of them were from Mexico.

Why won’t Trump’s wall keep out the EWIs, irrespective of their country of origin? Why will Trump’s wall result in an increase in the number of EWIs and not a decrease?

The stereotype of the undocumented immigrant crossing the Mexican-U.S. border no longer matches the contemporary realities of immigration. Over the decades most of the EWIs who entered the U.S. over the southern border were what are known as circular migrants. The came to the U.S. mostly for low-level jobs in agriculture and construction and related areas, stayed for several months, maybe a year, earned their money and returned home. Many were seasonal agricultural workers from Mexico who, for instance, followed harvests from California’s Central Valley to Washington’s Yakima Valley (Morrison and Poston, 2017).

Douglas Massey and his colleagues (2016) have documented these immigration patterns. They have shown that increased border enforcement has seriously disrupted the circular flow of workers who used to come and go, mainly just to California and Texas. Increased border surveillance “has raised the costs of undocumented border crossing, requiring the undocumented immigrants to stay longer in the U.S. so to make the trip profitable.” With greater border enforcement and surveillance, the costs of crossing the border have increased. As a result, the migrants have minimized the border crossing, “not by remaining in Mexico but by staying in the United States.” Simply put, the migrants are no longer circular migrants; they are now being transformed into a permanently settled population of unauthorized immigrants (Massey et al., 2016; Hotchkiss, 2016).

More than just a conjecture

Two charts demonstrate this relationship. Figure 1 presents annual estimates produced by demographers at the Pew Research Center in Washington, DC of the number of undocumented immigrants in the U.S. from 1990 to 2014 (Krogstand et al., 2016). Undocumented immigrants in the U.S. rose from 3.5 million in 1990 to 11.1 million in 2014. The 24-year period between 1990 and 2014 saw a phenomenal increase, of more than 210 percent.

The second chart presents data on U.S. Border Patrol appropriations (Argueta, 2016). It shows clearly that border enforcement and surveillance appropriations have grown steadily over the period of 1990 to 2015 from $263 million in 1990 to $1.4 billion in 2002 to $3.8 billion in 2015.

Relating the data in the first chart with the data in the second shows that the rapid escalation of border surveillance and enforcement initiated in an attempt to halt the flow of undocumented immigration over the Mexico-U.S. border has not worked. Militarizing the border has been associated with an increase, rather than a decrease, in the number of unauthorized immigrants. Militarization has transformed undocumented migration into the U.S. from a circular flow of migrants into, and out of, a few states, to a permanent settlement of migrants in virtually all the states of the continental U.S.

Trump’s wall may well make this relationship even stronger. Immigration to the U.S. is highly selective of the staunchest and the most motivated. Only the strongest and most advantaged will attempt the crossing. Demographers are well-aware of the selectivity of migration (Poston and Bouvier, 2017). With Trump’s wall the journey to the U.S. will become more dangerous and many migrations will fail. But eventually most attempts will be successful. Trump’s wall will not keep out of the U.S. this strong-willed, motivated, and talented population. The specter of a Trump’s wall will cause the would-be circular migrants to settle and to stay in the U.S., and not return to their homes in Mexico and other countries in Central America (Morrison and Poston, 2017).

We foresee that within ten years of such a wall being erected, there will be at least as many EWIs as there are now (6 to 7 million), and perhaps several million more. This means that along with the 5 million visa overstayers, there will be at a minimum a total of over 11 million undocumented immigrants in the U.S., and possibly many more, most of whom transformed into a permanently settled population of residents. Thus, a $20 billion investment intended to wall people out of the U.S. will have kept people in the U.S. Trump’s wall won’t work.

References

Ainsley, J.E., 2017. “Trump Border Wall to Cost $21.6 Billion, Take 3.5 Years to Build: Internal Report.” Reuters World News, February 9, 2017.

Argueta, C.V., 2016. “Border Security: Immigration Enforcement Between Ports of Entry.” Congressional Research Service, April 19.

Hotchkiss, M. 2016. “Tighter Enforcement along the U.S.-Mexico Border Backfired.” Phys.Org News, April 21, 2016.

Krogstand, J.M., J.S. Passel and D. Cohn, 2016. “5 Facts About Illegal Immigration in the U.S.” Pew Research Center, November 3, 2016.

Massey, D.S., J. Durand, and K.A. Pren, 2016. “Why Border Enforcement Backfired.” American Journal of Sociology 121: 1557-1600.

Morrison, P.A. and D.L. Poston, Jr., 2017. “Three Myths of U.S. Immigration: The Reality? A Border Wall Would Keep Undocumented in the U.S. — Not Out of It,” San Antonio Express-News (March 5): F1.

Poston, D.L., Jr., and L.F. Bouvier, 2017. Population and Society: An Introduction to Demography. 2^nd edition. New York: Cambridge University Press.

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Trend of global displacement and proportion displaced (1996-2015 end year).

Fonte: http://www.unhcr.org/statistics/unhcrstats/576408cd7/unhcr-global-trends-2015.html

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*Content belongs to the United Nations, CNN, BBC World News, VICE News, WH.gov. Recorded footage by Anita Pico.

The post The many faces of migration. A short film appeared first on N-IUSSP.

The aim of our work is to produce a database and to investigate in what way the phenomenon of migration has been represented in movies. We are interested in studying what messages (migratory themes, places, subjects involved) and what form of cinematographic language (genre) are mainly used and what aspects of migration are most represented in film production.

The European Migratory Films Database (EMFD) contains films, with the exception of certain genres (fantasy, cartoons, horror, science fiction, documentaries) produced between 1991 and 2010, in at least one European country, on migratory themes.

We drew information from several sources: the IMDB database; the filmography produced by the Fondazione ISMU, the filmography published by Berghahn and Sternberg (2010) and two important cinema guides (Mereghetti 2010; Morandini et al. 2010).

We selected the movies into our database by querying our main source (the IMDB – International Movie Database) with a few keywords, notably: immigrant, immigration, emigrant, emigration, migrant, migration, prejudice, racial prejudice, cultural clash, refugee, interracial, multicultural, racism. We had actually started with a shorter list, but it kept growing, since we included new terms as we found them in the IMDB review of each of the movies, which can be assimilated to a snowball sampling procedure.

For each film, from the plot and the information provided in the IMDB database, we identified both qualitative and/or quantitative variables:  the year and the countries of production, the original language, the length, the genre, and whether the film was taken from a book or based on a true story. We then added information about the gender and nationality of the filmmaker and any participation in a festival or cinema competition. From the plot the following main variables were then identified: country in which the film was set; direction of the migratory flow (countries of origin and destination); period of reference of the story (starting and ending decade); typology of the main character(s) of the movie (single, family, relatives, friends, couple, community, acquaintances); gender and age of the protagonist (child, adolescent, young, adult, elderly); cause of migration.

The EMFD consists of 256 films: it is in Excel format and is available for research (on request to the authors).

2. Main Results 

Since the first 5-years of our time window (1991-95), the total film production has been growing, with a slowdown in the final period (Figure 1). This trend has been determined largely by the production in Western and, less so, in Northern Europe, which are areas with a “mature” migratory history (Bonifazi et al. 2008).

Focusing on the characteristics of the movies (Table 1) the most used genre is drama, followed by comedy. Only in a minority of cases (15%) is the film taken from books, narratives or true stories. A strong and obvious connection between the country of location and the country of production appears (84%), but the films for which we observed a coincidence between the country of origin of the filmmaker (or country of origin of the migration) and the country of production are much less frequent (30% in the first case and 34% in the second).

Table 2 shows the distribution of some noteworthy variables related to the plot. Single and young people are more often described; the lead role is generally performed by a man and the countries of origin are indeed the countries from where most immigrants in Europe come. Indeed, the films represent both migrations within and from outside Europe, and some may appear as countries of both as origin and destination (e.g. Italy). The stories take place mainly taken in periods that coincide, or at least partly overlap, with the twenty years considered in the EMFD.

References

Berghahn, D. and Sternberg, C. (2010). European cinema in motion: migrant and diasporic film in contemporary Europe. Basingstoke: Palgrave Macmillan.

Bonifazi, C., Okolski, M., Schoorl, J. and Simon, P. (eds). (2008). International Migration in Europe. Amsterdam: University Press.

Mereghetti, P. (2010). Il Mereghetti. Dizionario dei film 2011, 3 vol., Baldini Castoldi Dalai.

Morandini L., Morandini L. and Morandini M. (2010). Il Morandini 2011. Dizionario dei film, Zanichelli.

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