Salmon Bias or Red Herring?
Salmon Bias or Red Herring?
Comparing Adult Mortality Risks (Ages 0 1
) between Natives 0 1
Internal Migrants: Stayers 0 1
Returnees 0 1
Movers in Rotterdam 0 1
the Netherlands 0 1
Paul Puschmann 0 1
Robyn Donrovich 0 1
Koen Matthijs 0 1
Paul Puschmann 0 1
0 Family and Population Studies, Centre for Sociological Research, KU Leuven , Parkstraat 45, 3000 Leuven , Belgium
1 Radboud Group for Historical Demography and Family History, Department of History, Radboud University , Erasmusplein 1, 6525 HTNijmegen , the Netherlands
The purpose of this research is to empirically test the salmon bias hypothesis, which states that the “healthy migrant” effect-referring to a situation in which migrants enjoy lower mortality risks than natives-is caused by selective returnmigration of the weak, sick, and elderly. Using a unique longitudinal micro-level database-the Historical Sample of the Netherlands-we tracked the life courses of internal migrants after they had left the city of Rotterdam, which allowed us to compare mortality risks of stayers, returnees, and movers using survival analysis for the study group as a whole, and also for men and women separately. Although migrants who stayed in the receiving society had significantly higher mortality risks than natives, no significant difference was found for migrants who returned to their municipality of birth (returnees). By contrast, migrants who left for another destination (movers) had much lower mortality risks than natives. Natives who left Rotterdam also had significantly lower mortality risks than natives who stayed in Rotterdam. Female migrants, in particular, who stayed in the receiving urban society paid a long-term health price. In the case of Rotterdam, the salmon bias hypothesis can be rejected because the lower mortality effect among migrants was not caused by selective return-migration. The healthy migrant effect is real and due to a positive selection effect: Healthier people are more likely to migrate.
Many European and North American studies report that migrants have lower mortality
risks than the native-born population
(Alter and Oris 2005; Markides and Eschbach
. This so-called healthy migrant effect is found in both contemporary and
historical settings, and it has been explained by differences in early life conditions
(Alter and Oris 2005)
, healthy lifestyles and behaviors
(Abraído-Lanza et al. 2005;
Lariscy et al. 2015)
, as well as in terms of selection effects (Oris and Alter 2001). With
regard to the latter, most studies focus on the idea of a positive selection effect in the
area of origin, in the sense that individuals who are young and healthy are more able
and more likely to move than the sick, weak, elderly, and disabled
(Khlat and Darmon
. For the nineteenth-century Belgian village of Sart,
Oris and Alter (2001)
observed, for example, that individuals from families that experienced death among
their members during the previous two years were much less likely to out-migrate,
compared with those from families in which everybody had survived.
Ever since the healthy migrant effect—initially referred to as an
epidemiological paradox and later as the Hispanic or Latino paradox—was discovered among
Latin American migrants in the US
(Markides and Coreil 1986)
, scholars have
doubted whether the results of such analyses are real, or if the healthy migrant
effect is merely a statistical artifact resulting from measurement errors or biases
toward healthy migrants. Not only are the results counterintuitive, and—at first
glance—hard to reconcile with long-standing insights into health and mortality,
but also the data on migrants are often of poorer quality than that of the native
(Razum 2006; Redstone Akresh and Frank 2008)
Doubts about the validity of the observed healthy migrant effect led to the
formulation of the salmon bias hypothesis, which states that the observed lower mortality
risks among migrants are the result of selective return-migration of the sick and elderly
and those who are unable to adapt to and endure harsh working and living conditions
(Deboosere and Gadeyne 2005)
. If migrants indeed have a tendency to go home before
they die, their deaths do not contribute to the national death statistics in the country of
study, but rather in the country of origin. If the second out-migration is not registered,
this would lead to a situation in which migrants become “statistically immortal” in the
society under study
(Abraído-Lanza et al. 1999)
. Even if out-migration of the sick is
registered, this can lead to measurement error since the presence of migrants in a
society who do not die there is likely to lead to an inflated denominator, causing an
artificially lowered mortality rate among migrants. These doubts suggest an
overestimation of the healthy migrant effect, or even question the very existence of a health
advantage of migrants.
In this paper we test the salmon bias hypothesis for internal migrants, both men and
women, in Rotterdam during the latter half of the nineteenth and the early twentieth
centuries by systematically comparing mortality risks among stayers and leavers,
subdividing the latter category into returnees (migrants who return to their municipality
of birth) and movers (migrants who moved to another destination than their
municipality of birth). Given that women at the time had different migration patterns than
men—they moved more often, but over shorter distances
(Greefs and Winter 2016)
and faced diverging mortality risks in later life
, we look at mortality
risks not only for the entire study group, but also separately for women and for men.
Rotterdam was selected as a case study to test the salmon bias hypothesis for two
main reasons. First in a previous study
(Puschmann et al. 2016)
comparing adult (ages
30+) mortality risks among migrants and natives in Antwerp, Rotterdam, and
Stockholm (1850–1930), we found that the healthy migrant effect was particularly strong
among internal migrants who moved to Rotterdam. We revealed that the health
advantage of migrants in the port cities under study was related to, among others, the
early life environment and positive selection effects. With respect to the latter, our
previous findings showed an inverse relationship between migration distance and
mortality risks. This led us to the conclusion that the most physically and mentally fit
were more likely to migrate over long distances. Although we censored individual
migrants upon out-migration, we wanted to test in a more systematic way whether
selective return migration had biased the results of our event history analysis. This
decision was strengthened by the fact that we found that certain subgroups of migrants
in the population actually experienced excess mortality. In the city of Rotterdam this
was the case for Italian and Italian-speaking Swiss immigrant men.
The second reason for choosing Rotterdam to test the salmon bias hypothesis
is related to the nature of the data. The Historical Sample of the Netherlands
allows us to follow the life course of migrants (and
natives) who left Rotterdam, at least as long as they moved within the national
borders (97% of all internal migrants). Typically, the life courses of leavers are
truncated upon out-migration in historical and contemporary datasets.
Consequently, previous studies have, at best, only been able to estimate to what degree
selective return-migration might have biased their results. Such estimations have
led to contradictory and inconclusive results. Whereas some studies report that
the healthy migrant effect is indeed caused by selective return-migration of the
sick, weak, and elderly
(Lu and Qin 2014)
, others found that this phenomenon
only partially contributed to the observed effect
(Khlat and Courbage 1996;
Turra and Elo 2008)
, while still others reached the conclusion that it did not
have an impact at all
(Abraído-Lanza et al. 1999; Deboosere and Gadeyne 2005;
Wallace and Kulu 2014)
Because the data allow us to track the final migration destination of internal migrants
within the Netherlands, this case study can shed light on whether the salmon bias
hypothesis can (partly) explain the mortality advantage of internal migrants we
observed in our previous study
(Puschmann et al. 2016)
Like other European port cities at the time, Rotterdam received growing numbers
of internal migrants during the latter half of the nineteenth and the early twentieth
centuries. Thanks to urban in-migration and natural population growth, the
population of Rotterdam increased from just over 90,000 inhabitants in 1850 to
332,000 in 1900, reaching 598,000 inhabitants by 1930. The majority of the
internal migrants originated from the rural municipalities of the province of
Zuid-Holland, which meant that the largest share of the newcomers in Rotterdam
were peasants and agricultural laborers who were born in Rotterdam’s direct
hinterland. The Dutch provinces of Noord-Brabant and Zeeland were also
important sending areas
(Van der Harst 2006)
. Among the urban immigrants in
Rotterdam, women slightly outnumbered men
, and single women
were particularly active as domestic servants (Bras 2003). Declining opportunities
in the agricultural sector and the gradual destruction of the family economy were
the main push factors for both men and women. The share of international
migrants was stable at around 3% between 1850 and 1930 and consisted mainly
of Germans, which is not surprising given the important trade relations with the
Rotterdam’s attractiveness to migrants was mainly related to the growing
employment opportunities in the port sector, industry, construction, and services.
Thanks to the construction of the Nieuwe Waterweg—a direct connection between
Rotterdam and the North Sea—and the high-speed industrialization of the German
Ruhr and Rhine areas, Rotterdam swiftly turned into Europe’s largest port city.
The port clearly functioned as an important pull factor for male internal and
international migrants. By 1909, about 55% of the city’s working population
was engaged in the port sector
(Van de Laar 2000)
. The growth of the port went
hand in hand with the revival of old industries and the advent of new industries,
which usually concentrated on the handling of raw materials which arrived in the
port. The construction of railways and tramways, as well as the introduction of
busses, facilitated the migration of thousands of migrants but slowed down
migration in the course of the first half of the twentieth century, as it allowed
growing numbers of people to commute to Rotterdam
Bouman and Bouman (1955)
showed that especially rural-to-urban migrants
had a hard time integrating in Rotterdam. They put forward that newcomers were
uprooted and ended up in a struggle for survival, as they landed in badly paid
and dangerous jobs in the port and in construction, which made it difficult for
them to make a living. Simultaneously they no longer could count on the social
network in their home village, and it was difficult to create new social ties in
Rotterdam, especially since the newcomers were—because of their dialect, low
socioeconomic status, and different lifestyle—being looked down upon by the
native Rotterdam population. More recent research has led to considerable
changes to this picture. Internal migrants indeed entered the labor market at
lower levels, but stayers were able to catch up with natives and even to
outperform them in the long run
. Nevertheless, being born
in the countryside was associated with substantially lower social status. Next, the
facts that internal migrants in Rotterdam disproportionally stayed single, and that
those who married did so on average at a later age, show that the social
integration of internal migrants was indeed hampered, although this time
ruralto-urban migrants were not disfavored relative to urban-to-urban migrants.
Internal migrants in other port cities, including Antwerp and Stockholm, faced similar
(Puschmann et al. 2015)
The data for the analyses was retrieved from the Data Set Life Courses Release 2010.01
from the Historical Sample of the Netherlands, a large historical demographic database
with life course information on individuals born in the period 1812–1922
. The data are derived from the Dutch population registers as
well as the vital registration of births, marriages, and deaths. The data collection began
with a random sample of birth certificates, and the database makers aimed to
“reconstruct” as many full life courses as possible. The Data Set Life Courses Release
2010.01 consists of 44,252 life courses, of which 62% are complete. Since the database
managers have provided start and end dates for the periods in which the life course
information of the research person is complete, we can determine the risk period for all
individuals, with both full and partial life courses, in our survival models.
We selected all individuals who lived in Rotterdam and its suburbs at some point
between 1850 and 1940 (after their thirtieth birthday). This resulted in a dataset
consisting of 1,452 research persons (756 natives and 696 migrants). Research persons
who were born in Rotterdam are considered natives; individuals who were born
elsewhere in the Netherlands and moved at some point to Rotterdam are migrants.
The distinction between stayers, returnees, and movers is based on a combination of
observed places of death, the declared destination of the last out-migration as it was
specified in the population register of Rotterdam, and the place where a person was last
recorded. The latter two criteria were only taken into account if the person was still
alive at the end of the research period, or in case the place of death was unknown
(n = 281). An overview of the classification of the research persons into natives,
migrants, stayers, leavers, returnees, and movers is presented in Fig. 1.
For all individuals, all life course information from the database was retrieved, and
individuals were only censored if they were still alive at the end of the study period or if
they left the country. In our study group, 67% of all life courses are complete.
We included several fixed and time-varying variables in our analyses. The variable
migration status is coded as native for those born in Rotterdam and migrant for those
born outside of Rotterdam (but within the Netherlands). The variable stayers/leavers
divides the study group into those who stayed in Rotterdam and those who left the city.
Age at arrival notes the age that the migrant first arrived in the city and is divided into
four categories: ages <15, 15–24, 25+, or unknown. In the analyses that include both
men and women in the study group, we distinguish sex as women and men. Since birth
dates spanned 60 years, we used birth cohort to categorize research persons into groups
born 1850–1869, 1870–1889, and 1890–1910. Two variables were treated as
timevarying, being updated from age 30 until the end of analysis time: civil status and
occupation. Civil status was grouped into four categories: unmarried, married,
separated/widowed, and unknown. Occupation is based on the HISCO codification
Leeuwen et al. 2002)
and recoded into HISCLASS
(Van Leeuwen and Maas 2011)
further categorized into four groups: professionals, foremen and skilled, day laborers
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and unskilled, and unknown. Finally, in order to identify the study population as
natives, stayers, returnees, or movers, we included the variable last destination. This
variable is first grouped as natives, migrants who stayed in Rotterdam, migrants who
returned home, and migrants who migrated somewhere else. Later we further divided
the native population into two groups: those who stayed and those who left.
We conduct survival analysis, first making use of Kaplan-Meier survival estimates to
get an initial impression of the mortality differences for different categories of natives
and migrants. In order to adjust for other factors, such as birth cohort, age at first
inmigration, civil status, and occupation, we fit Gompertz proportional hazard models
with all-cause mortality specified (ages 30+) as the failure event. The Gompertz model
was chosen because it has been shown to fit adult human mortality well between ages
30 and 90
(Cleves et al. 2008)
. Further, on the basis of AIC/BIC testing criteria, the
Gompertz model best fit our data (with the lowest AIC and BIC scores) compared with
other parametric and semi-parametric models that were also tested (Cox proportional
hazards, Weibull, and the exponential model).
By including the survival history of migrants upon departure, a more formal way of
testing the salmon bias hypothesis than has been the case in previous studies is possible.
In this way, we compare the mortality risks of three different groups of migrants—
stayers, returnees, and movers—with that of the native population in order to determine
if the observed healthy migrant effect was a result of selective out-migration. In the first
analysis, we divide the migrant population into stayers and leavers. Next, we further
divide the leavers into migrants who returned to their municipality of birth and migrants
who moved to another destination. If we find that returnees have much higher mortality
risks than the native population, we can confirm the salmon bias hypothesis. If our
findings show, by contrast, no significant difference between natives and returnees or a
lower mortality risk among the latter category we will reject the hypothesis and
conclude that it is a “red herring.” Finally, we add a new element to the discussion
by dividing the native population also into stayers and leavers. Too often, natives have
been considered as a static category, even though a considerable share became migrants
in the course of their lives. It is worth evaluating whether mortality risks also differed
between natives who never left Rotterdam and natives who did leave since the healthy
migrant effect suggests a self-selection mechanism in terms of health in the place of
origin. Consequently, we should be able to find such a mechanism for their native
counterparts. Our findings based on this distinction of separate categories of natives
(those who stayed and those who left) are displayed in the Results.
We aimed for parsimonious models in order to maximize the statistical power for the
newly added variables since our sample is relatively small. In order to benefit from the
largest sample size possible, our main effects models are first presented for both sexes
combined. We opt for nested models in which we include only those variables that
improve the fit of the model, which are organized in a series of six models in which
each additional variable was tested by use of log likelihood ratio tests. Based on these
tests, two variables that we tested were not included in the analyses because they did
not lead to a better fit: urban-rural birthplace and distance from birthplace. The final
model, incorporating the main variables of interest and other controls from early and
later life, leads to the best fit. See the Electronic Supplementary Materials (Table
ESM1) for descriptive statistics of all variables.
Next, we present the full models which we ran separately for men and women, given
that there are sex differentials in mortality, in general, as well as that variables related to
migration may differ for men and women—for example, propensities to migrate, reason
to migrate, distance traveled, and ages at migration
(cf. Greefs and Winter 2016;
Greenwood 2008; Mourits 2017)
. These models were also designed as nested, but
for simplification we present only the full models (the nested models are shown in
Tables ESM-2 and 3). To more directly compare men and women with each other, we
included interaction terms to measure how male and female natives and different
groups of migrants differed in terms of mortality risk.
Mortality Risks among Natives, Stayers, and Leavers
Figure 2 shows the Kaplan-Meier survival estimates for natives and migrants, with the
latter category subdivided into stayers and leavers. The graph shows that stayers have
higher mortality risks than natives shortly after they enter the risk set, which might be
related to the stress of first moving to an alien environment. However, between 25 and
35 years of analysis time the mortality risk of stayers is lower than that of the natives.
From 37 years on their survival rates drop below those of the natives, suggesting that
the stayers paid a long-term price for their move to the city. This might be related to the
lack of a social network in a society with no national pension and health care system, in
which the elderly and sick were dependent on care from their family, friends, and
neighbors. However, the experience of the leavers is completely different. For the first
10 analysis years their mortality risks are comparable to those of natives, but
subsequently their survival rates become considerably higher than those of the natives
and the stayers. It seems therefore that this group was particularly healthy.
In Table 1, our nested models for both sexes are presented. Model I contains
only the migration status variable. Unsurprisingly, migrants had a lower (though
insignificant) risk of dying than natives (HR = 0.89; 95%; CI: 0.74–1.07). In
Model II the stayer-leaver variable is included and the migration status variable
becomes significant and its effect size strengthens (HR = 0.79; 95% CI: 0.63–
0.99). Stayers have a 37% higher mortality risk compared with leavers, significant
at the 5% level (95% CI: 1.02–1.85). In Model III age at in-migration is added.
The migration status variable now loses its significance and the effect changes
(HR: 1.02; 95% CI: 0.79–1.30), although the stayer-leaver variable stays
significant and the effect remains stable. Migrants who arrived before their fifteenth
birthday have a much lower risk of dying (HR: 0.26; 95% CI: 0.08–0.62) than the
reference category of migrants who moved to Rotterdam after their twenty-fifth
birthday, which is significant at the 5% level. The same is true for migrants who
arrived between their fifteenth and twenty-fifth birthdays, but the effect size is
considerably smaller (HR: 0.62; 95% CI: 0.35–1.07). Migrants who arrived at
unknown ages had a significantly higher risk of dying (at the 10% level) compared
with the reference category of migrants arriving at the ages of 25+ (HR: 0.62; 95%
CI: 0.97–1.90). Sex is adjusted for in Model IV, which has no major influence on
the other variables (only unknown age at arrival becomes more significant). In
Model V we adjust for birth cohort, significant at the 0.01% level, which suggests
an increase in the mortality risk for each successive cohort, most likely related to
industrialization. In Model VI the time-varying covariates civil status and
occupation were added to the models. These adjustments led to a stronger healthy
migrant effect (although the variable stays insignificant) and an increase in the
hazard ratio of the stayers, now with 83% higher mortality risk than leavers. The
effects for age at arrival weakened, and the category of ages 15–24 becomes
insignificant. The unknown age category, however, becomes stronger and highly
significant. As it turns out, singles had a higher mortality risk than the reference
category of married people (HR: 1.58; 95% CI: 1.18–2.09), and widowed and
separated individuals also had a higher mortality risk (HR: 1.99; 95% CI: 1.42–
2.66). An even stronger effect was found for individuals with unknown marital
status (HR: 3.01; 95% CI: 2.37–3.81). For occupation we found that the foremen
and skilled workers had higher mortality risk than the reference category of
professionals (HR: 1.30; 95% CI: 0.97–1.72).
Given that migration variables could have a different relationship with adult
mortality for men and women, Table 2 shows the full models separately by sex. A
first observation is that, apart from the marital status variable, the effects are in the
same direction, but less strong for men than for women, and the results are
considerably more often significant among women. The latter is most likely
related to the somewhat smaller sample size and the fewer number of failures
(deaths) among men.
Only for women do we find a significant mortality advantage for the migrants
compared with the reference category of natives (HR: 0.75; 95% CI: 0.54–1.05). For
men the HR is 0.86, but not significant (95% CI: 0.57–1.31). Among the women the
stayers had a 2.3 times higher mortality risk than the leavers, significant at the 0.001
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Stayers/leavers (migrants only)
Age at arrival
Civil status (time–varying)
Widowed / separated
Foremen and skilled
Day laborers and unskilled
Controlled for age
Exponentiated coefficients and confidence intervals in brackets
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001
level (95% CI: 1.58–3.56). Among the men the effect was in the same direction, but
weaker and not significant (HR: 1.37; CI: 0.75–2.51). The younger the migrant women
and men were when they arrived in the city, the lower their mortality risks were.
Although this effect was only significant for women in the age category <15 (at the
0.1% level), this finding is particularly strong (with a HR of 0.19) relative to the
reference category of ages 25+. For both men and women, migrants who arrived at an
unknown age had a much higher (and highly statistically significant) mortality risk.
Next, for men and women we observe significantly higher mortality risks for the cohort
1870–1889 and 1890–1910 compared with the reference cohort of 1850–1869.
Unmarried men and women had higher mortality risks than married individuals. Widowed
and separated women had a highly significant higher mortality risk compared with
married women (HR: 2.35; 95% CI: 1.70–3.29). Additionally, men and women with an
unknown marital status had a significantly higher mortality risk compared with the
reference categories of married men and married women. Regarding occupation, we
found significant effects only among women with an unknown occupation. They had a
higher mortality risk compared with the reference category of professionals (HR: 1.58;
95% CI: 1.02–2.44).
Mortality Risks among Natives, Stayers, Returnees, and Movers
Figure 3 shows the Kaplan-Meier curves for natives, migrants who stayed in
Rotterdam, movers, and return migrants. The curves show that movers had much
lower mortality risks than natives and migrants who stayed in Rotterdam. The
survival rates of return migrants were somewhat below that of natives and
migrants who stayed in Rotterdam, but the difference was not as pronounced
as one would expect on the basis of the salmon bias hypothesis. After 20 years
of analysis time the survival estimates become less reliable owing to small
sample size. Judging on the basis of these K-M curves, the healthy migrant
effect is caused by the group of movers, who have much lower mortality risks
than all other groups.
Next, Table 3 shows the three fully adjusted Gompertz models for both sexes
and for women and men separately. In the first model (both sexes combined),
migrants who stayed in Rotterdam had a significantly higher mortality risk than
the native population (HR: 1.67; 95% CI: 1.18–2.21). However, no significant
Natives (ref) 1 1 1
Migrants who stayed in Rotterdam 1.67** [1.18–2.21] 1.85** [1.29–2.66] 1.163 [0.58–2.33]
Migrants who returned to home town 1.13 [0.72–1.98] 1.05 [0.54–2.05] 1.416 [0.63–3.18]
Migrants who moved elsewhere 0.76* [0.59–0.99] 0.71* [0.50–1.00] 0.821 [0.53–1.26]
Controlled for age, age at arrival, birth cohort, and time-varying civil status and time-varying occupation
Sex is controlled for in the model including both men and women
Exponentiated coefficients and confidence intervals in brackets
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001
difference in the mortality risks between natives and return migrants was found,
and the effect size is so small that it cannot account for any healthy migrant effect
(HR: 1.13; 95% CI: 0.72–1.98). Movers, by contrast, had a considerably lower
mortality risk than the native population (HR: 0.76; 95% CI: 0.59–1.00) and was
highly significant. For the women we observe the same results, but the effects for
stayers (HR: 1.85; 95% CI: 1.29–2.66) and movers (HR: 0.71; 95% CI: 0.50–1.00)
were stronger, while the effect size for the returnees was even smaller. In the
model including only men, no significant results were found.
Extending this analysis, in order to compare both men and women among
natives, stayers, returnees, and movers, we ran a model including interaction terms
for sex and the last destination variable (Fig. 4). Compared with native men, only
returnees had elevated mortality risks, with about 15% higher mortality, but the
result was not significant (HR: 1.15; 95% CI: 0.52–2.50). Both stayers (HR: 0.83)
and movers (HR: 0.70) had lower mortality risks, with the latter category
significant at the 10% level. For women, relative to the reference category of native
men, there were significantly lower mortality risks for natives (HR: 0.73; 95% CI:
0.56–0.94) and for movers (HR: 0.56; 95% CI: 0.38–0.80). Female returnees also
had lower mortality risks, though not statistically significant (HR: 0.85; 95% CI:
0.43–1.65). The only female group with excess mortality was found for stayers,
with just over 40% higher mortality risk compared with native men, significant at
the 10% level (HR: 1.42; 95% CI: 0.98–2.04). The latter result is quite striking. It
suggests that women who stayed in Rotterdam were a less favorable selection of
the population of origin in terms of health and human capital, and/or that they paid
a higher health price for their migration. The former is underlined by a recent
study by Hilde Greefs and Anne Winter, in which they showed that women who
moved over a longer distance to Antwerp in the latter half of the nineteenth
century were—contrary to men—more often from a more modest background
(Greefs and Winter 2016)
. At the same time it is not unthinkable that women—
because of their limited human capital—became marginalized upon arrival in
Rotterdam. Others with more means might have moved on, whereas those who
stayed in contact with their family of origin and the community in which they
grew up might have returned after domestic service.
Comparing Mortality Risks of Stayers and Leavers for both Migrants and Natives
Finally, we divide the native population also into stayers and leavers. The
KaplanMeier curves in Fig. 5 show that natives who left Rotterdam had higher survival
probabilities than natives who stayed. The K-M curve of the leaving natives is similar
to that of migrants who moved to another destination in the Netherlands. However,
between 15 and 35 years of analysis time, the survival probabilities of the leaving
natives were even lower than that of the moving migrants.
Next, Fig. 6 shows the Gompertz model with the distinction between stayers
and leavers among the native population in three models: both sexes combined,
only women, and only men. The models are adjusted for age, age at arrival, sex
(in the combined model), birth cohort, and time-varying civil status and
occupation. Compared with the reference category of native stayers, natives who left
Rotterdam have highly significant relative mortality risks at just under HR = 0.6
across all models. Similarly, nearly the same results, in terms of strength and
significance, were found for migrants who left for another destination. No
significant differences were found for return migrants relative to the reference category
in all three models. Overall, men and women had similar results with the
exception of migrants who stayed in Rotterdam. Female migrants who stayed had
around 47% higher relative mortality risk than female natives, significant at the
5% level (HR: 1.47).
Discussion, Policy Implications, and Future Research
The analyses in this paper show that we can reject the salmon bias hypothesis for
our specific case study. Even though we found some elevated mortality risk for
male returnees at first glance, the observed lower mortality among internal
migrants in Rotterdam are real and were not caused by selective return migration of
the sick, weak, and elderly because no significant difference in mortality risks
between returnees and the native population was found. The salmon bias
hypothesis is thus a red herring in the case of late-nineteenth- and early-twentieth-century
Rotterdam. Movers experienced the lowest relative mortality risks of the entire
population under study. If we factor in that natives who left Rotterdam also had
significantly lower mortality risks than natives who stayed in the Dutch port city,
we can only conclude that migration and good health are even more strongly
correlated than we could imagine on the basis of the previous studies: The
healthier people were, the more they moved, and this was true for both men and
women, who experienced similar effects in terms of their migration patterns.
Although men and women experienced similar effects overall, we further
investigated sex differences since the motivation to move and the patterns of
migration differed between men and women. We find one significant distinction
in our analyses between men and women. Female migrants who stayed in
Rotterdam had a higher mortality risk (than female natives of Rotterdam), but men did
not (they had slightly lower mortality risk compared with male natives). This
seems to suggest that for migrant women the selection effect was not as strong as
it was for men; the difference between migrant women and those who stayed in
their region of origin was not as great
(cf. Greefs and Winter 2016)
because of their limited human capital or the lack of a social network, they might
have ended up in trouble in the city, which could have prevented them from
returning home or moving to another destination. One might think about domestic
servants who gave birth to an illegitimate child or about women who ended up in
prostitution. The literature on migration in this era suggests indeed that female
migrants were disproportionally engaged in out-of-wedlock fertility and
(Fuchs and Moch 1990; Moch 2003)
. However, in the case of Rotterdam such
claims would require further investigation.
The fact that we did not find a salmon bias effect in our data does not mean that this
result can be automatically extrapolated to other populations in other times and regions.
As always, the historical context in which human behavior is being shaped has to be
taken into account. Certain migrant populations might be more inclined to move to their
home region once they fall seriously ill, and this could in fact lead to a real salmon bias
effect. It is therefore crucial to replicate formal tests of the salmon bias hypothesis for
other populations, and to study migration and mortality patterns against the background
of the societies migrants came from as well as the societies they moved to. This means
that future research would do well to take the culture, religion, traditions, and family
systems of the migrant populations under study into account.
The results of this study also have an important implication for contemporary health
policy. The healthy migrant effect suggests that all migrant groups fare better than the
native population, but this is only true for those migrants who are most mobile. The
effect, therefore, likely underestimates the health problems among migrants who live in
a receiving society for a longer period of time. The fact that stayers fare much worse
than movers also suggests that migrants pay a health price for adaptation. In light of our
findings, future studies on migrant health should distinguish between stayers and
leavers and, within those groups, between men and women.
Acknowledgments We would like to thank George Alter (ICPSR, University of Michigan), Romola
Davenport (University of Cambridge), Angélique Janssens (Radboud University; Maastricht University),
Jan Kok (Radboud University Nijmegen), Kees Mandemakers (International Institute of Social History;
Erasmus University Rotterdam), Alice Reid (University of Cambridge), Richard Smith (University of
Cambridge), and Jan Van Bavel (KU Leuven) for their useful suggestions and encouragements. We are
grateful to Research Foundation Flanders (FWO) for the financial support we received which enabled us to
conduct this research.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
Paul Puschmann (PhD) is an assistant professor of economic, social, and demographic history at the
Radboud Group for Historical Demography and Family History, Radboud University, the Netherlands. He
is also affiliated with the research group Family and Population Studies, KU Leuven, Belgium, where he
earned his PhD degree on a thesis dealing with processes of social inclusion and exclusion of urban
inmigrants in northwestern European port cities, 1850–1930. Paul is associate editor of Historical Life Course
Robyn Donrovich (MSc) has a background in economics and demography. She is a doctoral candidate in the
research group Family and Population Studies, KU Leuven, Belgium. Robyn is preparing a dissertation on the
association between family-related factors and adult mortality in Northwestern Europe in the nineteenth and
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