Selection, adaptation and advantage. Later-life health and wellbeing of English

migrants to Australia

Word count: 8010

Abstract

This study investigates the long-term impact of English adult migration to Australia by

comparing health and wellbeing outcomes in later life of English migrants to their

counterparts who remained in England (non-migrants) and to native-born Australians. It

traces the influence of selection, adaptation and advantage as three mechanisms that can

influence migrant health in later life. The analysis utilises data for a cohort aged 60 to 64

years from the Australian Life Histories and Health (LHH) survey (n=1088), a sub-study of

the Australian 45 and Up Study, in combination with a matched cohort from the English

Longitudinal Study of Ageing (ELSA) (n=1139). Social rather than health characteristics

were found to play a role in the selection of English migrants. English migrants reported

higher subjective quality of life than English non-migrants, and better physical health than the

Australian-born, but their mental health outcomes did not significantly differ from the other

cohorts. The comparatively better later-life outcomes for the English migrants can partly be

linked to advantage, as they hold higher prestige jobs than Australian-born at lower levels of

education.

1. Introduction

Between 1945 and 1972, more than one million people migrated from the UK and Ireland to

Australia, making them the largest immigrant group in the country up until this day(ABS

2016). Earlier colonial settlers, transported convicts, and waves of British-assisted migration

to Australia had already established a firm British presence in Australia. However, the bulk of

Australian immigration and population growth occurred after World War II. Until the early

seventies, immigration to Australia was deliberately restricted to Europeans under the ‘White

Australia Policy, providing the context for a historically unique and ethnically specific mass

migration, further strengthened by the assisted migration schemes financed by the Australian

government (Jupp 1995). This paper focuses on the long-term impact of this specific

migration by comparing the health and wellbeing of baby boomers on entry to later life of

English who migrated to Australia, English who remained in England and Australian-born

who stayed in Australia.

A life course framework to study migrant health and wellbeing has the potential to yield

particularly valuable insights into the interrelations between health and place (Spallek et al.

2011). Adult migrants’ health and wellbeing in later life is influenced by early life in the

country of origin, as well as experiences as an adult in the host society. This exposure to

different country contexts at separate stages of the life course potentially results in marked

health differences relative to their country of origin, as well as their host country. To

disentangle the social processes underlying these possible differences, this paper focuses on

three social mechanisms: selection, adaptation and (cumulative) advantage. First, migrants

are a selective group in terms of the society they are leaving behind as well as the position

they take up on arrival in their host society (Borjas 1987). Comparing the characteristics of

migrants with those who stayed in the country of origin, provides an indication of those

factors, early on in the life course, associated with the migration selection process (Spallek et

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al. 2011; Kennedy et al. 2015). Second, the profile of migrants compared with the host

population tends to become less pronounced over time as adaption, integration and

acculturation take place (Berry 1997; Marmot & Syme 1976). However, if health differences

between the migrant and host populations persist long after migration into later life, this may

reflect relative lack of adaptation (Spallek et al. 2011). Third, examining the advantages (or

disadvantages) that migrants accumulate over their life course can enhance our understanding

of the factors driving these possible health differences between migrants and the host

population (Dannefer 2003; Purkayastha 2005). This paper proposes to trace the interplay

between these three mechanisms by examining them jointly, rather than empirically pinning

one of them down in detail.

The historical mass migration under study, between two countries with a shared cultural,

linguistic and ethnic background, was mainly economic and “chosen” in nature. This specific

case of chosen migration between two relatively wealthy countries, has a number of

theoretical implications for our study (Kennedy et al. 2015; Malmusi et al. 2010). Because of

the ease of immigration through the assisted migration schemes, which allowed British

citizens to travel to Australia for £10, the decision to migrate was mainly an individual one,

through a process of self-selection (Kennedy et al. 2015). The absence of marked differences

in language and culture, which often act as barriers to integration (Ager & Strang 2008),

would have made adaptation for English migrants easier. In terms of accumulation processes,

the combination of a general Anglophilia in the ex-colony (Jones 2001), white privilege in an

increasingly multicultural society (McIntosh 2003), and the high degree of social mobility in

Australia (Leigh 2007), created a fertile ground for the accumulation of advantages, rather

than disadvantages for English migrants to Australia. As such, this study has the potential to

illuminate our understanding of impacts on migrant health and wellbeing under “ideal”

circumstances.

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This study investigates health in line with the WHO definition, which sees health as a

multifaceted concept, encompassing positive aspects such as physical, mental and subjective

wellbeing, instead of the mere absence of disease (Grad 2002). The essential aspect of

physical wellbeing to maintaining autonomy and independence in daily life is functional

health, as it represent the influence chronic impairments have on everyday living (Verbrugge

& Jette 1994). Mental wellbeing relates to feelings which can be both positive (such as

happiness) or negative (such as feeling depressed), while subjective wellbeing is a personal

evaluation of one’s circumstances (Vanhoutte 2014). Different mechanisms underlie the

generation and maintenance of each dimension of health, which is why we consider each

aspect side by side in the same framework.

1.1 Selection

Selection, or the fact that “immigrants … do not make up a random sample of the population

from the countries of origin” (Borjas 1987, p. 531), is a key aspect in the study of migration

and migrant health (Akresh & Frank 2008; Borjas 1987; Kennedy et al. 2015; Lu & Qin

2014; Vandenheede et al. 2015). One important and often investigated criterion for selection

at the individual level is physical health: moving country typically requires being in good

physical health, and the labour migration policies responsible for most of the large-scale post-

war migration movements emphasized it as one of the main conditions for migration. In

addition to physical health a range of educational, social and psychological characteristics are

also likely to play a role in migrant selection (Borjas 1989; Kennedy et al. 2015). For

example, high educational levels or specific skills and professions can enable people to

migrate to countries with better living conditions (Kennedy et al. 2015). In the post-war

migration to Australia, being white, in good physical health and under 45 years old were the

main prerequisites for access to the assisted migration schemes, and British migrants were a

fairly select group, possessing skill and income levels above the British average (Appleyard

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1964). Rarely captured in migration statistics, individual differences in psychological or

personality characteristics, such as the propensity to take risks, the motivation to leave one’s

country and the resolve to succeed in the host society, are potentially very decisive in

determining who chooses to migrate.

An equally selective return migration of unhealthy, less able, or less motivated migrants,

known as the “salmon bias” (Abraido-Lanza 1999), has been raised as a secondary selection,

reinforcing the primary selection. According to Appleyard (1964), about 25 percent of British

post-war migrants returned to the UK within a couple of years after migration, but about a

third to half of the returned re-emigrated to Australia, spawning the nick name of

“boomerang migrants”. This substantial return migration is probably due to the ease of

assisted migration, and reflects that many departed to Australia temporarily, never meaning to

stay permanently (Hammerton & Thomson 2005).

This paper examines the consequences of migration after a substantial period of time, such

that effects of selection should have stabilised. In sum, the selection hypothesis entails

migrants having better physical health, higher levels of human capital and higher motivation

than the general population in their country of origin (Chiswick 1999; Kennedy et al.

2015).The primary and most important selection is the selection of more healthy and able

English people as migrants. A secondary selection, of migrants who re-migrated to England,

after experiencing the challenges associated with settling and the possible temporary nature

of migration, possibly amplifies this primary selection process.

We expect English migrants to have been selected on the basis of health and social

background, and as such have had better childhood health, and high levels of education than

English who stayed in England. As selection effects are concerned primarily with comparing

those who migrate with those who remain in their country of origin, we make no hypothesis

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in relation to how English migrants might differ from Australians here, but rather explore

these differences below in section 1.3 on Advantage.

1.2 Adaptation

Adaptation is the process of change through which individuals become better suited to their

environments (Berry 1997). Acculturation to new cultural codes or integration in the

educational and labour markets reflect, respectively, the cultural and structural contexts to

which migrants have to adapt (Portes & Rivas 2011). Several types of adaptation can be

distinguished and depend on varying skills in different life domains. Successful psychological

adaptation, indicated by good mental health, depends strongly on personality characteristics,

life events and social support. Sociocultural adaptation can be facilitated by cultural

knowledge, social contact and intergroup attitudes, while acquiring a satisfying job can be

seen as economic adaptation (Berry 1997).

In the case of health, adaptation paradoxically involves the attenuation of migrants’ initial

advantages over the population of their host country. This so-called “Healthy Migrant Effect”

has been illustrated in terms of physical health (Marmot et al. 1984; AIHW 2002), chronic

illness (Kennedy et al. 2015), mental health (Wu & Schimmele 2005) and mortality

(Vandenheede et al. 2015; Abraido-Lanza 1999; Marmot et al. 1984). The health advantage is

not limited to migrants from a “poor” to a “rich” country, but also exists among migrants

within a country (Lu 2008), as well as for migrants from one “western” country to another

(Vandenheede et al. 2015). It erodes as differences with the host population wane with

increasing length of stay and over generations (Krupinski 1984; Malmusi et al. 2010;

Vandenheede et al. 2015), which points to adaptation as an important process underlying this

erosion. Adaptation in this sense captures the influence of current circumstances on earlier

determinants of health and wellbeing. Changes in diet, health behaviours and in the physical

environment, have been identified as the main causal pathways for adaptation of physical

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health (Krupinski 1984). Personal expectations, cultural values and changes in the social

environment are thought to influence more subjective mental health outcomes such as

psychological wellbeing (Escobar, Nervi, & Gara, 2000).

The health effects of migration processes are often distorted by the fact that migration

involves the meeting of two different cultures, which can entail language barriers, racism,

differential health care access as well as different health behaviours. However, for the

English baby boomers migrating to Australia, this was much less the case given the extent of

shared culture, language and ethnicity between Australian-born Australians and English

migrants (Kennedy et al. 2015). British migrants to Australia have been called invisible

migrants because of their similarity to Australian-born Australians and they face relatively

few cultural adaptations (Jupp 2004). Nevertheless, the change in circumstances still leaves

migrants with having to construct a new life and identity, although the story of the English

migrants is often one of success under these new conditions (Hammerton & Coleborne 2001).

In sum, we expect adaptation in psychological wellbeing and quality of life of the English

migrants to Australian population levels, as the initial shock of migrating and adapting is in

the past for most English baby boomer migrants, and would have led to a return to England

when insurmountable. In terms of health and illness, we expect either no or a small healthy

migrant effect, as this effect erodes over time, and most migrant baby boomers will have

resided in Australia for a long time.

1.3 Advantage

Cumulative (dis)advantage is the idea that “initial comparative advantage … [leads to]

successive increments of advantage such that the gaps between the haves and the have-nots

… widen” (Merton 1968, p. 606). As a key concept for the life course perspective, it

emphasises that early life conditions set in motion a complex cascade of direct and indirect

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influences that can accentuate disparities (Ben-shlomo & Kuh 2002; Kendig & Nazroo 2016;

Preston et al. 1998). Direct paths between childhood circumstances and adult health can have

both harmful or protective effects, while indirect paths can involve associated environmental

effects or selection processes (Preston et al. 1998). Long-term effects of early life health and

socioeconomic environment occur mainly indirectly, through mechanisms of accumulation of

advantage (Blane et al. 2004; Kendig et al. 2016; Shen & Zeng 2014; Vanhoutte & Nazroo

2016). From a life course perspective on inequality, disadvantage means exposure to risk,

while advantage means exposure to opportunity (Dannefer 1987). These indirect pathways of

selection to opportunities or risks are most straightforward in their effects on the labour

market in the context of career opportunities. As stated earlier, migrants often possess skills

and motivation that exceed those of the destination country’s population, which means they

have the potential to outperform them (Borjas 1987). Early advantage can lead to steeper,

more successful career paths, and better kinds of jobs with more prestige, which potentially

provide more material wealth and less exposure to risk and occupational hazards.

The mechanism of increasing inequalities building on initial disparities can help us trace the

connection between selection and adaptation in migration processes. Selection establishes or

reinforces the initial differences between migrants and the host population, while adaptation

is a continuous taking stock of them. Understanding how these group differences enable

opportunities or enhance risk exposure in the context of work for a given level of education,

both of which are strong predictors of health and high wellbeing, is the core rationale for

studying cumulative disadvantage in migrants.

In the framework of this study, English migrants to Australia may have had a head start in

comparison with Australian-born Australians, unlike many other groups of migrants. Arriving

in a former colony, with its cultural compass firmly pointing towards England, gave English

migrants access to privileged networks and positions (Jones 2001), even if they were

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perceived to be “whinging Poms” by Australians, complaining about the harder conditions

and risks compared to the UK (Hammerton & Thomson 2005). As the majority of migrants

found work in urban environments, heavy industry or mining projects, all occupational

sectors with high job mobility and social promotion, it should be possible to trace differences

in occupational mobility between migrants and Australians within social strata. In conclusion,

studying processes of advantage can assist in understanding how selection at the time of

migration, through initial differences with the host population, can lead to further group

differences in later life even after many years of possible assimilation and adaptation.

We expect English migrants to have arrived with an initial health and socioeconomic

advantage over the Australian-born, due to positive selection, which should have been

converted into higher job prestige over the life course, resulting in better health and wellbeing

outcomes.

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2. Data and Methods

2.1 Data

We combined two datasets focused on ageing, that were designed with cross-national

comparisons in mind: the fifth wave of the English Longitudinal Study of Ageing (ELSA;

Steptoe, Breeze, Banks, & Nazroo, 2013) and the Australian Life Histories and Health (LHH)

survey (Kendig et al., 2014), both collected in 2011. ELSA is a longitudinal study which used

the Health Survey for England (Mindell et al., 2012) from 1998, 1999, and 2001 as a

sampling frame for its first wave in 2002, together containing 16,983 eligible respondents

aged 50 or over (Taylor, 2007). Of these eligible respondents, 11,392 took part in the survey

resulting in an individual fieldwork response rate of 67% for wave 1 core members (Taylor,

2007). In the fifth wave, 8,982 respondents from this first cohort were eligible to be

interviewed, of which 6,173 took part in the study, reflecting a study response rate

(conditional on participation in wave one) of 69% (Blake et al., 2015). The individual

response rate for first cohort respondents in wave 5, taking into account those who died or

moved out of the UK during the course of the study, is 78%, with 6,477 productive

interviews over 8,263 eligible respondents issued to the field (Blake et al., 2015). LHH was

conducted as a sub-study of the Sax Institute’s 45 and Up Study, which used Australia's

national public health insurance database (formerly Medicare Australia) as a sampling frame

to recruit over 260,000 residents aged 45 years and older from the state of New South Wales

(NSW), Australia (Banks et al., 2008). To be eligible for LHH, participants had to be born

between 1947 and 1951, have completed the baseline 45 and Up questionnaire in 2008, but

not taken part in a prior sub-study (Kendig et al. 2014). A stratified random sample of 2800

potential respondents who met these criteria was selected, with deliberate oversampling of

participants born in England, to enable comparisons with ELSA, and oversampling of males

to adjust for expected gender differences in participation; 1261 participants took part in the

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LHH survey, reflecting an overall response rate of 45%. An important difference between

both surveys lies in the method used to collect data: ELSA is collected mainly through a face-

to-face interview, with an additional self-completion paper questionnaire, while LHH was

collected through a mail survey and telephone interview (Kendig et al. 2014). A second

important difference between the studies is that ELSA is a national study focused on English

people aged 50 and up, while LHH is a specific sub-sample of the early baby boomers born

between 1947 and 1951 living in the state of New South Wales, Australia. However, the LHH

sample has been shown to be broadly representative of the Australian population, with the

exception of some over-representation of higher educated and professional classes (Kendig et

al. 2014). To ensure our findings are robust to these sample selection biases, we used weights

in all our analysis1.

To match the age range of the LHH cohort, only ELSA respondents (n=1139) born between

1947 and 1951 in England were included in this comparative analysis. The possibility of

examining English migrants who returned to England, was investigated. Although 16 percent

of the matched ELSA cohort reported residence outside of the UK during their lifetime, less

than one percent had lived in Australia. We excluded participants in LHH who migrated

before they were 18 years old (as we study adult migration), were born in the UK but not

England (to ensure comparability with ELSA), or migrated after the year 2000 (to ensure the

migration is not temporary and some adaptation has taken place), resulting in a sample of

1088 respondents (865 Australian-born, 223 English migrants). Most of the English migrants

(70.0%) in the LHH sample migrated to Australia in early adulthood before the age of 30,

compared to only eight percent who migrated after the age of 40. The historical timing of

1 ELSA provides standard weights (we used cross-sectional and self-completion weights, depending on the outcome). For LHH we calculated population weights based on birthplace (England or Australia), gender and level of education for this specific cohort using data provided by the Australian Bureau of Statistics (2011).

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migration of our sample is important to understand life course effects, with about 70 percent

of the migrants migrating between 1966 and 1980.

2.2 Outcome Measures

The main health and wellbeing outcomes assessed in both LHH and ELSA included measures

of quality of life, mental health and physical health. Quality of life was measured using the

validated 15-item version of the CASP-19 scale (Hyde et al. 2003; Vanhoutte 2014), while

the shortened 8-item Centre for Epidemiologic Studies – Depression Scale (CES-D scale;

Radloff 1977; Steptoe, Demakakos et al. 2012) was used as an indicator of poor mental

health, and the 10-item physical function scale from the Short Form Health Survey (SF-36;

Ware et al. 1994) was used as an indicator of physical health limitations. Quality of life

scores ranged from 6 to 45 (M =35.4, SD =7.3). Mental health scores from the CES-D ranged

from 0 to 8 (M =1.3, SD =1.9), indicating the total number of depressive symptoms

experienced in the past week. Given that about half the sample indicated having no functional

physical or health limitations, physical health was scored as a binary variable where 0 = no

physical limitations, and 1 = one or more physical limitations.

2.3 Retrospective Life Course Measures

As both the Australian and English survey populations were only contacted later in

adulthood, data on earlier life course influences were collected retrospectively in both the

LHH and ELSA studies. The measures included as explanatory variables in this analysis were

chosen to reflect important health and social influences from different stages of the life

course. Childhood health was assessed retrospectively with a single item asking ‘Would you

say that your health during your childhood was excellent, very good, good, fair, or poor?’

Answers were recoded into a binary measure, with 1 indicating excellent, very or good health

and 0 indicating fair or poor health. Social position in early adulthood was indicated by

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highest educational level (0 = high school or lower, 1 = certificate or diploma, 2 = degree or

higher), which has been closely linked to migration selection. It is important to keep in mind

that participation in secondary education was more common in Australia than in the UK in

the period our cohort was attending school: about 38% of 16 year olds were attending school

in Australia (Burke & Spaull 2001), compared to 25% in the UK (Bolton 2012). Social

position in late adulthood was indicated by participants’ current subjective social status,

partnership status and club membership. These three measures reflect two different types of

resources (social status and social support) important for later life health and wellbeing.

Subjective social status was measured by the MacArthur scale (Ostrove et al. 2000), which

asks the respondent where they would rank themselves on the social ladder, and is a

commonly used measure to look at social position in later life (Demakakos et al. 2008).

Scores ranged from 0 to 10 (M =6.2, SD =1.7), with higher scores indicating higher social

position. As a measure of social support, having a partner (0 = no, 1 = yes) can be an

important resource when coping with the stresses of later life (Cohen & Wills 1985). Club

membership can help us understand how sociability and social connections beyond having a

partner might contribute to later life health and wellbeing (Jivraj et al. 2016).

Table 1 below lists the descriptive statistics of both outcome and explanatory variables by

group. There were significant group differences in physical health limitations and quality of

life, but not in the number of depressive symptoms. Self-rated health as a child did not

significantly differ between the three cohorts, but all the other explanatory variables did.

Interestingly, English non-migrants had the greatest proportion of people with either the

highest or lowest educational qualifications, while English migrants had the highest

proportion of certificate and diploma qualifications, though this was similar to the Australian-

born cohort. In terms of their current situation, English non-migrants have a lower subjective

social status, and lower levels of club membership. Australian-born are more likely to be

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partnered and be a member of a club, while English migrants have the highest subjective

social status, and are least likely to be partnered.

***Table 1 about here***

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2.4 Methods

A three-step analytic approach was used to examine how the processes of selection,

adaptation and accumulation might affect health and wellbeing in later life.

Selection

The first step was to establish whether there were selection differences at the time of

migration between English migrants to Australia and English non-migrants on the one hand,

and Australian-born Australians on the other hand. To do so, we examined whether

information valid at the time of migration, such as age, gender, level of education, and

childhood health can predict group membership in a multinomial logistic regression. This

will establish which characteristics played a role in the selection for migration as well as

clarify the position of English migrants in comparison to Australians when they arrived.

Adaptation

The second step aimed to explain current differences in later life health outcomes between the

three different cohorts (Australian-born Australians, English migrants and English non-

migrants) by investigating the relative importance of early life, education and current

economic and social circumstances for the three separate outcomes: quality of life, mental

health (depressive symptoms), and physical function. This allows us to investigate to what

extent adaptation, in the form of current life circumstances, influences the advantage in terms

of health and wellbeing established earlier in the life course. Each outcome was assessed

separately using a series of multiple linear or logistic regressions. Model 1 for each outcome

examined group differences in age and gender. Controlling for age and group differences,

each consecutive model examines (a set of) variables related to a specific life phase,

respectively childhood circumstances (Model 2), educational achievement (Model 3) and

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current circumstances (Model 4). In the final model (Model 5), all predictors were entered

simultaneously to examine the unique contribution of each variable.

Advantage

The third step was to examine whether there was evidence of advantage by looking at mid-

life differences between English migrants and Australians. Given the generally cumulative

nature of occupational prestige and work trajectories, cohort differences in occupational

prestige of participants’ main job were examined, within educational strata, using the

Australian socioeconomic index (AUSEI06) (McMillan et al. 2009). This data was only

available for English migrants and Australians from the LHH sample.

3. Results

3.1 Selection

The results of the multinomial logistic regression presented in Table 2 shows that compared

to the English non-migrants (who stayed in England), English migrants to Australia were

born slightly later and tended to be better educated, but were no different in terms of gender

and childhood health. This suggests a social selection of the educated rather than a health

selection of English migrants to Australia. A second point worth noting is that the English

with a certificate or diploma were more likely to migrate, but not those with a degree. This

underlines the non-linear relation between education and emigration, with only a specific

level of education leading to higher chances of migration. The only significant difference

between English migrants and Australian-born Australians is that English migrants are more

likely to be female.

***Table 2 about here***

3.2 Adaptation

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The results from our regression analysis of quality of life (Table 3) show that quality of life

of English migrants is higher than that of both Australians and English non-migrants (Model

1). This advantage remains when controlling for early life circumstances (Model 2),

education (Model 3), current circumstances (Model 4), and all of these together in the final

model (Model 5). The difference with the English non-migrants who remained in England

shrinks substantially when current circumstances are included in the model (Models 4 and 5),

suggesting that this may be an important explanatory factor for differences in quality of life.

Across the three cohorts, the influence of early life circumstances and education diminishes

when current measures are included in the final model. This suggests an indirect pathway

from favourable childhood circumstances and better education to higher social status in

midlife as a plausible life course mechanism leading to better quality of life.

***Table 3, 4 and 5 about here***

The prevalence of depressive symptoms did not differ significantly between the three cohorts

(see Table 4). While gender and education were significant predictors when entered

separately (Models 2 and 3), only early life and current circumstances remained significant in

the final model, though the effect of early life was slightly attenuated. Using (zero-inflated)

poisson regression, to account for the non-normal distribution of CES-D, yielded

substantively similar, but slightly weaker associations. Consistent with other literature

(Pinquart & Sörensen 2000), it appears that differences in current social status and

partnership mediate gender, early life, and educational differences.

Table 5 shows the analysis investigating physical health in later life. Compared to English

migrants, Australians are more likely to report having one or more physical function

limitations. This difference is not attenuated when controlling for possible other influences

across the models. Similarly, gender, childhood health and education were all significant

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predictors of physical health limitations when analysed either separately or simultaneously in

the final model. Subjective social status has a non-linear association with physical health in

the full model: while middle social status was related to fewer limitations than low social

status, high social status was not.The fact that the association between social status and

physical health weakens in the full model suggests the role of current circumstances in later

life health is partly determined by earlier segments of the life course, such as education.

Except for mental health, for which host and origin society have similar levels, English

migrants have not adapted to the Australian-born levels of quality of life and physical health,

so that their “migrant effect” has not attenuated.

3.3 Advantage

The third step in our analysis explores differences in occupational prestige between English

migrants and Australians by educational level, as an examination of cumulative advantage.

***Figure 1 about here***

Compared to English migrants, Australians scored significantly lower in occupational

prestige (50.3 v. 55.4, t(1081)=-2.8, p=.006). Further investigation stratified by educational

level (Figure 1) illustrates that this is mainly true for the least educated groups. While

differences are absent for middle and high educational levels, in the lowest educational group

the English migrants tend to have been working in positions with more societal prestige than

the Australian-born (49.8 v. 42.1, t(266)=-2.6, p=.012). Supporting our hypothesis of

cumulative mid-life differences in occupational class between English migrants and

Australians, English migrants were less likely than Australian-born Australians to report

having had a paid job for six months or more that involved doing heavy labour or physically

demanding work (22.9% v. 30.0%, χ2 = 4.43, p = .035), workplace danger or injury risk

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(22.3% v. 33.3%, χ2 = 10.07, p = .002), leaving the job because of ill health or disability

(4.5% vs. 11.5%, χ2 = 9.58, p = .002), or feeling that their health had been affected by their

job (7.2% v. 18.8%, χ2 = 17.34, p < .001). It is possible that the cumulative effects of these

occupational differences may lay at the basis of the later life differences in physical

limitations between the English migrants and Australian-born.

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4. Discussion and conclusions

This study has investigated how three interrelating mechanisms connected to migration -

selection, adaptation and cumulative advantage - can help in understanding how health and

wellbeing of adult migrants differs from that of their counterparts in their host country as well

as their country of origin.

The first section of results suggests that selection at time of migration was based on social

rather than health criteria, with educational level rather than childhood health being a

significant early life predictor of migration to Australia. An important caveat here is that

childhood health is retrospectively reported, and biases in this reporting might vary between

migrants and non-migrants. The fact that we see a specific level of education, having a

certificate or diploma, associated with migration, rather than higher levels of education

leading to higher chances of migration, reinforces the social selection interpretation. Aspiring

middle classes, with an above average level of education for English standards, were more

likely to emigrate, in line with the demographic evidence from the period of peak migration

(Appleyard 1964). The higher educated English elite might have seen more risks than

benefits associated with migration, while those with a lower educational level might not have

possessed the necessary resources to build up a new life in Australia. We stress that education

should be seen as a relative rather than absolute selection criterion: English migrants lost their

educational advantage when arriving in Australia, as average levels of education for

Australian born participants were higher.

A limitation of our study is that only “successful” migrants are observed, which reflects the

‘salmon bias’, and that we cannot investigate experiences during the substantial period

between migration and observation of outcomes. While the literature on English immigration

to Australia does suggest substantial return migration, as well as re-emigration (Appleyard

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1964; Richardson 1974; Hugo 1994; Hammerton & Thomson 2005), our representative

sample of baby boomers living in England shows little evidence of return migration from

Australia to England, with less than one percent reporting having lived in Australia for at

least 6 months. This is consistent with findings from empirical studies of return migration

that suggest that “salmon bias” is too weak to substantially alter the size and direction of

selection effects (Abraido-Lanza 1999; Lu & Qin 2014; Turra & Elo 2008).

In a second step we investigated to which extent English migrants have adapted to Australian

levels of later life quality of life, mental health and physical function. By examining if group

differences in later life exist, and if they can be explained by factors operating at different

stages of the life course, we place adaptation in contrast with the early life selection

mechanism. Differences between English migrants, non-migrants and Australians were found

in terms of subjective quality of life and physical function limitations, and remained after

controlling for all covariates; however, no differences were found in terms of depressive

symptoms. The latter finding is consistent with prior epidemiological research (Wilhelm et al.

2003), which finds no differences in depressive symptoms between England and Australia.

English migrants reported significantly higher quality of life than both English non-migrants

and Australians, being closer to their fellow Australians than to the English. The strongest

explanatory factor was current circumstances, which substantially weakened associations

with earlier aspects of the life course. In line with recent studies (Kendig et al. 2016;

Niedzwiedz et al. 2012; Vanhoutte & Nazroo 2016), this suggests a life course pathway to

quality of life might be operating from good early life circumstances, through higher

educational achievement, resulting in higher subjective social status, which translates into

higher quality of life.

21

English migrants had better physical function than Australians, on a similar level as English

non-migrants. Childhood health, educational level and subjective social status all had strong

independent associations with later life physical function. The substantial effects of different

life course episodes suggest a series of cumulative ‘insults’ as well as protective factors could

be at play in later life physical function, with exposure to risk in each life phase having a

broadly independent effect (Ben-shlomo & Kuh 2002; Singh-Manoux et al. 2004). In line

with the literature (Abraido-Lanza 1999; Lu & Qin 2014; Vandenheede et al. 2015) our

findings confirm the “healthy migrant” hypothesis, although the results from the analysis on

selection suggests this may be due to social rather than health selection at time of migration.

While the healthy migrant effect has been observed to diminish over time (Turra & Elo 2008;

Vandenheede et al. 2015), it is remarkable that it is still noticeable in a cohort which migrated

on average 34 years ago.

The third step of our analysis used data only available for Australians and English migrants to

explore the nature of cumulative advantage, by investigating differences in occupational

prestige between both groups. English migrants tended to have relatively higher prestige jobs

than Australians, particularly among those with lower levels of education. Given that jobs

requiring only minimal levels of education provide less income, as well as being most risky

in terms of occupational hazards, it is plausible that the more prestigious career paths of

English migrants may play a role both in their higher subjective quality of life and lower

physical health limitations relative to Australians. One possible explanation is that English

with specific skill sets were encouraged to migrate in order to fill particular gaps in the labour

market (Hammerton & Thomson 2005). A second possibility is that English migrants

benefitted from positive bias, in a combination of white privilege (compared to other

European migrants) and general Anglophilia in Australia, which allowed them to climb the

22

social ladder faster (Jones 2001). These initial advantages over time developed into

substantially better career paths, as we have shown.

One of the more important limitations of our study is that the English migrant sample is too

small to explore interactions in detail. Specifically investigating how gender affects the

mechanisms found would be very relevant. In addition, our analysis is limited by the

available comparable information in both surveys, and future comparative research would

benefit from examining a broader array of variables from each life stage. As we use

retrospective data, it is possible that bias in childhood health reporting does not fully account

for childhood health circumstances.

The added value of this study is that it illustrates the relevance of examining migration from a

life course perspective, investigating health and wellbeing in later life according to the

exposure to different country contexts at distinct life stages. A further contribution is that it

demonstrates how multiple datasets can be combined in innovative ways to answer questions

and enable cross-national comparisons that would be difficult to examine with a single

primary data source.

Our study opens up some broader points of discussion about the health effects of migration,

as we examined the specific migration from England to Australia under the ‘White Australia’

policy and its aftermath. The complicated intertwining of selection, adaptation and

cumulative (dis)advantage shaped the life course of English migrants to Australia and defines

the extent to which their later life health differs from those who remained in England and

their Australian-born counterparts. We illustrate that migrants are not necessarily caught

between catching up with the host society and distancing themselves from their country of

origin, but that it is possible to adopt the best characteristics of both worlds. For the specific

migration examined in this paper, a strong narrative of cumulative advantage could be made:

23

English citizens make the decision to migrate, and enjoy a relatively high level of health and

wellbeing after taking part in building contemporary Australia.

24

Bibliography

Abraido-Lanza, A., 1999. The Latino mortality paradox: a test of the“ salmon bias” and healthy migrant hypotheses. American journal of public health, 89(10), pp.1543–1548. Available at: http://ajph.aphapublications.org/doi/abs/10.2105/AJPH.89.10.1543 [Accessed April 1, 2015].

Ager, A. & Strang, A., 2008. Understanding integration: A conceptual framework. Journal of Refugee Studies, 21(2), pp.166–191.

AIHW, 2002. Australian health inequalities: birthplace, Canberra.Akresh, I.R. & Frank, R., 2008. Health selection among new immigrants. American journal

of public health, 98(11), pp.2058–64.Appleyard, R.T., 1964. British Emigration to Australia, Canberra: Australian National

University Press.Ben-shlomo, Y. & Kuh, D., 2002. A life course approach to chronic disease epidemiology :

conceptual models , empirical challenges and interdisciplinary perspectives. International journal of epidemiology, 31(6), pp.285–293.

Berry, J., 1997. Immigration, acculturation, and adaptation. Applied psychology, 46(1), pp.5–68. Available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1464-0597.1997.tb01087.x/full [Accessed April 7, 2015].

Blake, M. et al., 2015. The dynamics of ageing : The 2010 English Longitudinal Study of Ageing ( Wave 5 ) Technical Report, London.

Blane, D.B. et al., 2004. Life course influences on quality of life in early old age. Social science & medicine (1982), 58(11), pp.2171–9. Available at: http://www.ncbi.nlm.nih.gov/pubmed/15047075 [Accessed January 8, 2013].

Bolton, P., 2012. Education: historical statistics,Borjas, G., 1987. Self-selection and the earnings of immigrants. American Economic Review,

77(4), pp.531–553. Available at: http://www.nber.org/papers/w2248 [Accessed May 7, 2014].

Borjas, G.J., 1989. Economic Theory and International Migration. International Migration Review, 23(3), pp.457–485.

Burke, G. & Spaull, A.D., 2001. Australian schools: participation and funding 1901 to 2000. In 2001 Year Book Australia. Canberra, ACT: Australian Bureau of Statistics, pp. 433–446.

Chiswick, B., 1999. Are immigrants favorably self-selected? American Economic Review, 89(2). Available at: http://www.jstor.org/stable/117103 [Accessed April 1, 2015].

Cohen, S. & Wills, T. a, 1985. Stress, social support, and the buffering hypothesis. Psychological bulletin, 98(2), pp.310–57. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21176439.

Dannefer, D., 1987. Aging as intracohort differentiation: Accentuation, the Matthew effect, and the life course. Sociological Forum, 2(2), pp.211–236. Available at: http://link.springer.com/article/10.1007/BF01124164 [Accessed November 27, 2014].

Dannefer, D., 2003. Cumulative advantage/disadvantage and the life course: Cross-fertilizing age and social science theory. The Journals of Gerontology Series B: Social Sciences, 58(6), pp.327–337. Available at: http://psychsocgerontology.oxfordjournals.org/content/58/6/S327.short [Accessed

25

October 22, 2014].Demakakos, P. et al., 2008. Socioeconomic status and health: the role of subjective social

status. Social science & medicine, 67(2), pp.330–40. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2547480&tool=pmcentrez&rendertype=abstract [Accessed July 31, 2011].

Escobar, J.I., Nervi, C.H. & Gara, M. a, 2000. Immigration and mental health: Mexican Americans in the United States. Harvard review of psychiatry, 8(2), pp.64–72.

Grad, F.P., 2002. The preamble of the constitution of the World Health Organization. Bulletin of the World Health Organization, 80(12), pp.981–982.

Hammerton, a J. & Coleborne, C., 2001. Ten-pound poms revisited: Battlers’ tales and British migration to Australia, 1947-1971. Journal of Australian Studies, 25(January 2015), pp.86–96.

Hammerton, a J. & Thomson, A., 2005. Ten pound Poms. Australia’s invisible migrants, Manchester: Manchester University Press.

Hugo, G., 1994. The economic implications of emigration from Australia, Canberra, ACT: Australian Government Publishing Service.

Hyde, M. et al., 2003. A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19). Aging & mental health, 7(3), pp.186–94. Available at: http://www.ncbi.nlm.nih.gov/pubmed/12775399 [Accessed August 2, 2011].

Jivraj, S., Nazroo, J. & Barnes, M., 2016. Short- and long-term determinants of social detachment in later life Short- and long-term determinants of social detachment in later life. Ageing and Society, 36(5), pp.924–945.

Jones, K.W., 2001. Accent on Privilege. English Identities and Anglophilia in the U.S., Philadelphia: Temple Univeristy Press.

Jupp, J., 1995. From’White Australia’to’part of Asia’: recent shifts in Australian immigration policy towards the region. International Migration Review, 29(1), pp.207–228. Available at: http://www.jstor.org/stable/2547002 [Accessed March 13, 2015].

Jupp, J., 2004. The English in Australia, Cambridge: Cambridge University Press.Kendig, H.L. et al., 2014. Adapting data collection methods in the Australian Life Histories

and Health Survey : a retrospective life course study. BMJ Open, 4, p.e004476. Available at: doi:10.1136/bmjopen-2013-004476.

Kendig, H.L. et al., 2016. Pathways to Well-Being in Later Life : Socioeconomic and Health Determinants Across the Life Course of Australian Baby Boomers. Journal of Population Ageing, 9, pp.49–67.

Kendig, H.L. & Nazroo, J., 2016. Life Course Influences on Inequalities in Later Life : Comparative Perspectives. Journal of Population Ageing, 9(1–2), pp.1–7.

Kennedy, S. et al., 2015. The Healthy Immigrant Effect: Patterns and Evidence from Four Countries. Journal of International Migration and Integration, 16(2), pp.317–332.

Krupinski, J., 1984. Changing patterns of migration and their influence on the health of migrants. Social Science & Medicine, 18(Table 2), pp.927–937.

Leigh, A., 2007. Intergenerational Mobility in Australia. The B.E. Journal of Economic Analysis & Policy, 7(2), p.28.

Lu, Y., 2008. Test of the “healthy migrant hypothesis”: A longitudinal analysis of health

26

selectivity of internal migration in Indonesia. Social Science and Medicine, 67(8), pp.1331–1339.

Lu, Y. & Qin, L., 2014. Healthy migrant and salmon bias hypotheses: a study of health and internal migration in China. Social science & medicine (1982), 102, pp.41–8. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24565140 [Accessed April 1, 2015].

Malmusi, D., Borrell, C. & Benach, J., 2010. Migration-related health inequalities: Showing the complex interactions between gender, social class and place of origin. Social Science and Medicine, 71(9), pp.1610–1619.

Marmot, M., Adelstein, A.M. & Bulusu, L., 1984. Lessons from the study of immigrant mortality. The Lancet, pp.1455–1457.

Marmot, M. & Syme, L.S., 1976. Acculturation and coronary heart disease in japanese americans. American Journal of Epidemiology, 104(3), pp.225–247.

McIntosh, P., 2003. White Privilege and Male Privilege. Privilege: A Reader, pp.147–160.McMillan, J., Beavis, a. & Jones, F.L., 2009. The AUSEI06: A new socioeconomic index for

Australia. Journal of Sociology, 45(2), pp.123–149.Merton, R., 1968. The Matthew effect in science. Science, 159(3810), pp.56–63. Available at:

http://www.unc.edu/~fbaum/teaching/PLSC541_Fall06/Merton_Science_1968.pdf [Accessed December 12, 2014].

Mindell, J. et al., 2012. Cohort profile: The health survey for england. International Journal of Epidemiology, 41(6), pp.1585–1593.

Niedzwiedz, C.L. et al., 2012. Life course socio-economic position and quality of life in adulthood: a systematic review of life course models. BMC public health, 12(1), p.628. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3490823&tool=pmcentrez&rendertype=abstract [Accessed November 27, 2012].

Ostrove, J.M. et al., 2000. Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women. Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 19(6), pp.613–618.

Pinquart, M. & Sörensen, S., 2000. Influences of socioeconomic status, social network, and competence on subjective well-being in later life: a meta-analysis. Psychology and aging, 15(2), pp.187–224. Available at: http://www.ncbi.nlm.nih.gov/pubmed/10879576.

Portes, A. & Rivas, A., 2011. The adaptation of migrant children. Future of Children, 21(1), pp.219–246.

Preston, S.H., Hill, M.E. & Drevenstedt, G.L., 1998. Childhood conditions that predict survival to advanced ages among African-Americans. Social science & medicine (1982), 47(9), pp.1231–46. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9783866.

Purkayastha, B., 2005. Skilled migration and cumulative disadvantage : the case of highly qualified Asian Indian immigrant women in the US. Geoforum, 36, pp.181–196.

Radloff, L.S., 1977. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied psychological measurement, 1(3), pp.285–401.

Richardson, A., 1974. British Immigrants and Australia: a Psycho-social inquiry, Canberra, ACT: ANU Press.

Shen, K. & Zeng, Y., 2014. Direct and indirect effects of childhood conditions on survival

27

and health among male and female elderly in China. Social Science & Medicine, 119, pp.207–214. Available at: http://dx.doi.org/10.1016/j.socscimed.2014.07.003.

Singh-Manoux, A. et al., 2004. Socioeconomic trajectories across the life course and health outcomes in midlife: Evidence for the accumulation hypothesis? International Journal of Epidemiology, 33(5), pp.1072–1079. Available at: http://www.ncbi.nlm.nih.gov/pubmed/15256527 [Accessed November 12, 2012].

Spallek, J., Zeeb, H. & Razum, O., 2011. What do we have to know from migrants’ past exposures to understand their health status? a life course approach. Emerging Themes in Epidemiology, 8(1), pp.1–8. Available at: http://dx.doi.org/10.1186/1742-7622-8-6.

Statistics, A.B. of, 2016. Migration, Australia, 2015-16 cat. no. 3412.0. Available at: http://www.abs.gov.au/ausstats/abs@.nsf/mf/3412.0.

Steptoe, A. et al., 2013. Cohort profile: the English Longitudinal Study of Ageing. International journal of epidemiology, 42(6), pp.1640–8. Available at: http://www.ncbi.nlm.nih.gov/pubmed/23143611 [Accessed September 20, 2014].

Taylor, R. et al, 2007. Health, wealth and lifestyles of the older population in England: The 2002 English Longitudinal Study of Ageing Technical Report. UK Data Archive Study Number 5050 English Longitudinal Study of Ageing, (5050). Available at: https://keats.kcl.ac.uk/pluginfile.php/1366705/mod_resource/content/1/5050_Wave_1_Technical_Report.pdf.

Turra, C.M. & Elo, I.T., 2008. The Impact of Salmon Bias on the Hispanic Mortality Advantage: New Evidence from Social Security Data. Population research and policy review, 27(5), pp.515–530. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2546603&tool=pmcentrez&rendertype=abstract [Accessed April 1, 2015].

Vandenheede, H. et al., 2015. Mortality in adult immigrants in the 2000s in Belgium: a test of the “healthy-migrant” and the “migration-as-rapid-health-transition” hypotheses. Tropical Medicine & International Health, 20(12), pp.1832–1845. Available at: http://doi.wiley.com/10.1111/tmi.12610.

Vanhoutte, B., 2014. The Multidimensional Structure of Subjective Well-Being In Later Life. Journal of population ageing, 7, pp.1–20. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4102758&tool=pmcentrez&rendertype=abstract [Accessed August 28, 2014].

Vanhoutte, B. & Nazroo, J., 2016. Life Course Pathways to Later Life Wellbeing: A Comparative Study of the Role of Socio-Economic Position in England and the U.S. Journal of population ageing, 9(1), pp.157–177. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4785197&tool=pmcentrez&rendertype=abstract.

Verbrugge, L. & Jette, A., 1994. The disablement process. Social science & medicine, 38(1). Available at: http://www.sciencedirect.com/science/article/pii/0277953694902941 [Accessed April 9, 2013].

Ware, J., Kosinski, M. & Keller, S., 1994. SF-36 physical and mental health summary scales: A user’s manual ., Boston, MA: The Health Institute, New England Medical Centre.

Wilhelm, K. et al., 2003. Prevalence and correlates of DSM-IV major depression in an Australian national survey. Journal of Affective Disorders, 75, pp.155–162.

Wu, Z. & Schimmele, C.M., 2005. The Healthy Migrant Effect on Depression: Variation over

28

Time? Canadian Studies in Population, 32(2), pp.271–295.

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