New Zealand, Sweden, Switzerland, Australia, and Norway are the most attractive countries for highly skilled migrants in 2023. The United States would have been ranked second if visa policies were not considered.

Note: Values closer to 1(0) represent higher (lower) attractiveness. 
Source: OECD, Migration Policy Debates, N°29, March 2023.

The post Did you know? appeared first on N-IUSSP.

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.

The post Who is an Internally Displaced Person? appeared first on N-IUSSP.

The post Did you know? appeared first on N-IUSSP.

Before China’s transition to a market-oriented economy in the late 1970s, internal migration was strictly controlled by a hukou system (household registration system). As a result, migration within China was minimal and urbanization low. However, during the last few decades, urbanization in China has developed at an unprecedented pace: the percentage urban rose from less than 18% in 1978, to more than 57% in 2016 (Figure 1).

This trend has been driven mainly by massive internal migration following the relaxation of restrictive migration policies, and China’s reform and opening-up in the late 1970s (Liang, Van Luong, and Chen 2008). More than 100 million children are affected by internal migration in contemporary China. The majority are the so-called left-behind children (looked after by relatives in rural areas while their parents move to the city), but more than a third are migrant children who move with their parents (Duan et al. 2013). Although recent changes in government policies have improved educational opportunities for migrant children (Table 1), their choices in the city are still limited in subtle ways. Using data from a nationally representative survey, in a recent study we examined the continuing challenges faced by migrant families in getting their children (aged 6-12) enrolled in urban public schools (Liang et al. 2020).

Schooling options for migrant children in urban China

For public school education, children’s opportunities in Chinese cities are closely related to their hukou status. Migrant children, who lack a local hukou in destination cities, are not on an equal footing with their resident peers. Since the late 1980s and early 1990s, many migrant parents have had to pay large amounts of money for “education endorsement fees” to local public schools in order to enroll their children. While changes in education policy eventually banned such practices (in 2004), there are still schools that charge extra money to migrant parents.

As a result, many migrant families, especially the most socioeconomically disadvantaged ones, have no choice but to register their children in schools for migrant children organized by migrant parents or migrant entrepreneurs. Their affordable tuition, flexible payment schedule, and physical proximity make these migrant schools very attractive to migrant families. However, they often have poor infrastructure and less qualified teachers, and in some cases they do not even meet safety standards, so are exposed to the risk of government intervention and shutdown. As such, migrant children attending these schools face multiple uncertainties and their education may suffer in the long run.

Private schools are a third, and better, option but as is the case in most developed countries, they often charge very high tuition fees and are thus out of reach for ordinary migrant families. Overall, like their local counterparts, most migrant parents would prefer to enroll their children in local public schools. Given the more favorable outcomes for migrant children attending public schools (versus migrant schools), education policies also now require city government to accommodate migrant children’s education in the local public education system.

Ongoing challenges for migrant children

We examined two questions related to migrant children’s education using data from the 2012 China National Migration Population Dynamics Monitoring Survey and findings from our fieldwork in selected cities in China:

1) Do migrant parents enroll their children in public or migrant schools?

2) Do migrant parents pay any fees for public school enrollment?

As shown in Table 2, the majority of migrant children are enrolled in local public schools, but a non-negligible share (6.8%) are still attending special migrant schools. Despite government policy explicitly prohibiting public schools from charging enrollment fees, about 20% of migrant families paid such fees in 2012. While adaptation in the destination city can help migrant families navigate the local public school system, our fieldwork shows that they also face other more subtle hurdles. Even though some migrant families can afford to get their children enrolled, they often have to rely on some special “social capital” to do so. In nearly all cases, migrant parents have to find a “broker” , often a local resident, to make contact with school officials and facilitate the enrollment of their child in a public school.

Moreover, we also find significant differences across regions. Compared to children moving to other parts of China, migrant children residing in eastern China, which is more socioeconomically developed, are less likely to be enrolled in public schools and, when they are, their parents are more likely to pay for their education. However paradoxical these patterns may seem at first glance, they actually reflect a sad reality: large cities in eastern China, especially Beijing and Shanghai, are ignoring policies ensuring equality of educational opportunity and have begun to use a new strategy to control and discourage immigration.

Apart from these findings, there are other major concerns for both scholars and policy makers. For instance, among all the 6 to 12-year-old migrant children in our study, around 11% were not enrolled in school when the survey was conducted. While this could be due to disruption during initial settlement of migrant children, especially for the youngest among them, it also shows that more effort is needed to grant these children the same educational opportunities as their urban (resident) peers.

References

Duan, Chengrong, Lidan Lv, Jing Guo, and Zongping Wang. 2013. “Left behind Children in Rural China: Evidence from the 2010 Chinese Population Census.” Population Journal 35(3):37–49.

Liang, Zai, Hy Van Luong, and Yiu Por Chen. 2008. “Urbanization in China in the 1990s: Patterns and Regional Variations.” Pp. 205-225 in John R. Logan (ed.) Urban China in Transition. Blackwell.

Liang, Zai, Zhongshan Yue, Yuanfei Li, Qiao Li, and Aihua Zhou. 2020. “Choices or Constraints: Education of Migrant Children in Urban China.” Population Research and Policy Review 39(4):671–90.

The post Migrant children’s schooling in urban China appeared first on N-IUSSP.

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.

The post Why has migrant fertility in Norway declined? appeared first on N-IUSSP.

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.

The post Migrant mortality advantage and the selection hypothesis appeared first on N-IUSSP.

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.

The post The significance of age to the study of ethnic residential segregation appeared first on N-IUSSP.

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.

The post The demography of Trump’s wall appeared first on N-IUSSP.