conceptual simulation model for climate migration and
TRANSCRIPT
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 1 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
Conceptual Simulation Model for Climate Migration
and Population Health
Rafael Reuveny
Author’s affiliation:
Professor, School of Public and Environmental Affairs, Indiana University, Bloomington,
Indiana 47405, USA
Correspondence address: [email protected]
1. Introduction
The Intergovernmental Panel on Climate
Change (IPCC, 2014) says that climate
change is already harming human and natu-
ral systems. It expects worsening impacts
worldwide in this century, including rising
sea level; more agrarian diseases and pests,
drying lands; increased water stress; dam-
age to settlements; more frequent and in-
tense weather disasters; and declining crop
yields, incomes, and food security. The
report expects that poverty will expand and
deepen in the least developed countries
(LDCs) and countries with rising inequality.
It foresees adverse health outcomes, partic-
ularly for people who are socioeconomical-
ly, institutionally, politically, or otherwise
marginalized (e.g., based on class, gender,
age, ethnicity, and culture). The size and
timing of the impacts of climate change, the
IPCC says, will vary across countries de-
pending on factors such as hazard exposure,
adaptive ability, socioeconomic develop-
ment, risk attitudes, and demographic fea-
tures, but the LDCs are the most vulnerable
Abstract
Growing literature expects that environmental degradation due to climate change (combined with
non-environmental factors) will increasingly drive migration from affected areas in this century. It
reflects psychology that views ecological decline as reducing the quality of life. Another literature
projects that climate will have adverse health outcomes worldwide, including both physical and men-
tal, but the role of this climate migration in health, particularly population health, is under-discussed.
The paper assesses and illuminates the need for greater focus and work on climate migration by con-
ceptually modeling the causal flow from environmental degradation in an origin area, to leaving this
site, to the health of migrant and native populations in the host area. The conceptual modeling con-
denses and clarifies some of the questions at stake and suggests the need for future research including
the codification and empirical testing of this or similar models. This capability is illustrated by heu-
ristically simulating the model to contribute to emerging discussions on climate migration and popu-
lation health. The article assesses the results and applies them to comment on policies seeking to
promote population health in areas poised to receive many climate migrants in this century.
RESEARCH ARTICLE
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 2 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
due to their limited ability to cope and
adapt.
Facing such risks, people may respond in
one of three ways, depending on how they
view them psychologically. They may do
nothing, seeing them as non-issues or ac-
cepting they cannot change them. They may
adapt in several ways, including by moving.
Or they may mitigate causes with or with-
out adjustment, deciding to take their fate in
their hand and solve the problem at its core.
Researchers have documented all these es-
sentially psychological reactions by observ-
ing what people do, but I focus on perma-
nent relocation or migration as an adapta-
tion. Though climate change will not be the
sole driver of the decision to migrate in the
future, studies say it may have a strong in-
fluence (IPCC, 2014). Stern (2007), for
example, projects 200 million climate mi-
grants by 2050, Brown (2008) more than
200 million, Myers (2009) 250 million, and
Werz and Conley (2012) up to 1 billion.
Others say these numbers are too high but
agree they will not be negligible (Boano et
al., 2009; WDR, 2012).
In 2011, the US National Institute of Health
projected climate change would cause ill
health and, noting the dearth of related stud-
ies, called for interdisciplinary work in sev-
eral medical fields and crosscutting areas
such as climate migration and modeling
(NIH, 2011). Recent studies have called for
developing dynamic simulation models for
climate change and health (Hess, 2015;
Betts, 2016) or environmental health
(Rosen, 2016). Portier et al. (2013), Lancet
Commission (2015), and Wu et al. (2016)
also called to study the impact of climate
migration on the health of everyone in the
destination, all the while noting a wide gap
in knowledge in this area. Only a few stud-
ies examine the link between climate migra-
tion and health. The IPCC, for example,
lists one work for individual health out-
comes (IPCC, 2014: 11.8.4). I discuss a few
more studies in the next section, but the
topic has not been adequately addressed,
especially for population health.
This paper seeks to contribute to ongoing
discussions of climate migration by focus-
ing on population health for newcomers and
natives in a host area. It illustrates the need
for greater focus and research in this area
by developing a general conceptual simula-
tion model for the causal flow from envi-
ronmental decline to migration to popula-
tion health. This under-discussed and chal-
lenging topic is broad enough to be served
by many approaches. I take a social science
approach. The current model does not pre-
sent empirical data, but rather elucidates
connections among significant factors and
sets the stage for condensing and clarifying
some of the research and policy questions at
stake. Once codified, it could be honed and
specialized by empirical work and provide
insights via simulations, examples of which
I give later. I illustrate this point by heuris-
tically simulating the model for storylines
assuming current climate change trends and
by using the results to comment about pub-
lic policy looking to promote population
health in areas poised to receive many cli-
mate migrants in this century.
In principle, climate migrants (and all mi-
grants for that matter) may move within and
across countries. Most authors say they will
originate mainly in LDCs and move within
their own countries, as this migration offers
the security of similar culture without legal
barriers and the excessive cost of migrating
to DCs (Brown, 2008; WDR, 2012). Others
mention these migrants may also move to
DCs (Carballo & Smith., 2008; Solana,
2008; Scheffran & Battaglini, 2011). Both
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 3 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
views are sensible, the latter not least since
the vast majority of the more than 700 mil-
lion potential migrants in the world, by far
mostly from LDCs, want to move to DCs
(Gallup, 2017). DCs share culture with
many LDCs due to their colonial pasts and
people from LDCs living in DCs may help
comrades back home to migrate, as many
do today. While today most people move
internally, LDC to DC migration is not
small. Currently, some 104 million docu-
mented people from LDCs live in DCs; up
to 42.6 million do so illegally1. Another 2.8
million move each year from LDCs to DCs
as temporary migrant workers or students,
and 425,000 seek asylum.2
By 2050, 900 million may join the LDCs’
workforce, while the DCs’ workforce may
fall by 75 million (UN, 2013). This gap will
likely continue to drive LDC to DC migra-
tion (Hugo, 2011; NIC, 2012). The UN
(2015) projects that the net gain in migrants
from LDCs to DCs (entry minus exit) be-
tween 2015 and 2050 will 91 million, or 2.6
million per year on average, regardless of
climate change. Against this backdrop, I
1 In 2010, 97.6 million migrants (and refu-
gees) from LDCs resided in DCs (World
Bank, 2011). In 2007-12, on average, 3.7
million migrants and refugees came to DCs
each year (OECD, 2013: Table 1.1; p. 21 for
2012), 68% of which (2.5 million) from
LDCs (OECD, Table 1.7). I get: 104.2 = 96.7
+ 3 x 2.5. The 42.6 million is from Reuveny
(2016).
2 In 2006-11, on average, 2.3 million short-
term migrant workers came each year to DCs
in 2006-11 (OECD, 2013: Table 1.5); for the
share of LDC origins, I use (56.9% + 68%)/2.
Most asylum seekers come from LDCs (Ta-
ble 1.6). In 2004-10, on average, 2.3 million
students came to DCs each year; 60% were
from LDCs (Table 1.8 and p. 36).
develop a conceptual simulation model for
climate migration as it impacts health for
the arriving and native populations in a host
area, regardless of its location. Section 2
outlines the literature. The next two parts
present the model and Section 5 simulates it
heuristically. The conclusion suggests fu-
ture research and says something about
public policy.
2. Prior Literature
The literature provides a natural starting
point to gain insight. I outline models of
actual migration, health outcomes for mi-
grants and residents in a host area, use of
health care by both, population health fac-
tors and simulation, violence and climate
change, and migrant-host violence. These
bodies of work may seem eclectic, but they
separately studied aspects of our problem. It
is too large for me to review it here thor-
oughly. I give examples of studies and re-
sults.
Migration models assume that potential
migrants behave according to the following
psychology. They choose to live in the
place maximizing their expected or per-
ceived net benefit (benefit – cost). This per-
ceived net gain is theorized to depend on
many factors, including the difficulty of
moving to a new place and the barriers fac-
ing people looking to enter this place. Em-
pirical models of actual migration find that
international, illegal, and internal migrants
move for similar reasons. The number of
migrants rises with pull factors such as in-
come, jobs, political stability, and peace
operating in the destination. It increases
when these forces decline in the sending
area, and when population size and, less so,
cumulative causation increase. It rises with
the extent of shared culture between the
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
origin and destination areas. Migration de-
clines when the cost of moving, entry barri-
ers to the destination and, less so, place
identity in the origin increase. See, for ex-
ample, Bratsberg (1995), Mayda (2010),
Fussell (2010), Weeks et al. (2011), Belot
& Ederveen (2012), Mendoza (2015), and
Flores et al. (2013). These models do not
include environmental forces.
A smaller subset does, controlling for those
other factors. Several studies model surveys
from some LDC, especially for internal
migration (Henry et al., 2004; Lewin et al.,
2012; Gray & Muller, 2012). These models
attempt to find how people thought about
the environmental problems when they de-
cided to move. Other studies model interna-
tional or internal migration counts that
come from official sources such as coun-
tries and international organizations
(Reuveny & Moore, 2009; Feng et al.,
2010; Marchiori et al., 2012; Liu & Shen,
2014). The findings of both types of models
show that environmental problems raise
emigration from, and reduce immigration
to, affected areas. Some survey models find
there is a threshold at which the severity of
hurdles lowers the ability to migrate. The
psychological attachment of people to plac-
es thus takes account of the state of the en-
vironment. People view a harsh environ-
ment as reducing the quality of life and so
may move away from it.
Many studies model the effect of migrant
status on health outcomes (e.g., overall
health status, physical health status, mental
health status, the occurrence of a particular
disease) based on individual sample surveys
run in a destination, typically a DC and
occasionally an LDC. In both types of
countries, the bulk of the immigrants are
coming from LDCs, or from rural areas in
the case of LDCs. Controlling for factors
like income, education, healthcare use, age,
and ethnicity, many find a healthy migrant
effect: migrants arrive healthier than na-
tives, which is usually ascribed to healthy
people self-selecting to be migrants. The
health gap is found to shrink over time. It
may turn into a deficit due to poverty, and
psychological and physical impacts of ac-
culturation stress, defined as the tension
associated with getting accustomed to a
new place, a new culture, and a new lan-
guage, and anti-immigrant host public atti-
tudes. See, for example, Rubalcava et al.
(2008), Maio & Kemp (2010), Chen (2011),
Domnich et al. (2013). Lower quality
healthcare to migrants than natives also
plays a role (Newbold, 2009; Grabovshi et
al., 2013; Frank et al., 2013). Others studies
find the healthy migrant effect does not
apply to groups such as refugees, illegal
migrants, rural-urban migrants, temporary
migrant workers, and child and pregnant
migrants. For example, see Huang &
Ledsky (2006); Hu et al. (2008), Bollini et
al., (2009), Magalhaes et al. (2011), Benach
et al. (2012), and Kiss et al. (2013).
Many models compare health care use rates
by migrants and natives using surveys, con-
trolling for health and other factors. Most
find a lower use for migrants (Regidor et
al., 2009; Pylypchuk & Hudson, 2009;
Magalhaes et al., 2011; Fan et al., 2013).
Some find migrants have a higher use rate
of emergency rooms for primary care
(Buron et al., 2008) or no gap for public
health care (Pylypchuk & Hudson,
Newbold, 2009; Wadswarth, 2013).
We saw above that migrant health falls over
time. The results for the lower health care
can thus reflect barriers to healthcare ac-
cess. In DCs, studies find various obstacles
to migrant access, including language, user
fees, discrimination and cultural insensitivi-
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
ty in the health care system, perceived bias,
feelings of being insulted and of hopeless-
ness, problems in obtaining insurance, and
long delays in health care centers for the
uninsured. Illegal immigrants face all these
barriers, as well as fear of being detected by
healthcare personnel, reported, and deport-
ed. See the studies by Magalhaes et al.
(2011), Maio & Kemp (2010), Edge &
Newbold (2013), Rechel et al. (2014), and
Frank et al. (2013). We again see that psy-
chological factors (fear, insult, hopeless-
ness, perceived discrimination, cultural in-
sensitivity) play a role.
Another factor in health care use is legal
eligibility. Laws naturally change across
countries. In general, landed (legal) mi-
grants, including refugees, have the most
entitlement to access. Temporary migrant
workers and asylum seekers have less ac-
cess than legal migrants, and illegal mi-
grants have almost no access. See the arti-
cles by Magalhaes et al. (2011), Rechel et
al. (2014), Zimmerman et al. (2011),
Fortuny & Chaudry (2011), and Bustamante
& Wees (2012). In LDCs, migrants often
have no access to healthcare (WHO, 2010,
HDR, 2009). Internal migrants have full
access, except when access is tied to resi-
dence areas, as in China (Mou et al., 2013),
Vietnam (UN–Vietnam, 2011), and Russia
(Ionikan & Rakovskaya, 2012).
The health and healthcare models noted
above and others like them use the individ-
ual as a unit of analysis. Other studies mod-
el population health for a country or a re-
gion. Though they do not look at migration,
they are relevant here, given the paper’s
focus on population health. Studies examine
population health measures such as mortali-
ty, disability-adjusted life years, or rate and
burden of symptoms for some disease. Con-
trolling for demographic factors, they find
population health rises with access to and
quality of healthcare, environmental health
services (e.g., waste removal, sanitation),
social capital, income, education, democra-
cy, and welfare programs, and declines with
harmful behaviors, joblessness, and envi-
ronmental degradation. See, for example,
Baltagi et al. (2012), Kim & Saada (2013),
Muntaner et al. (2013), WHO (2014), Park
et al. (2015), and Friis (2018).
Emerging research uses simulation models
for studying population health when exper-
iments are not workable, especially for pro-
jecting needs and delivery of healthcare and
environmental health services, forecasting
effects of practices, assessing existing abili-
ties to address crises, predicting the spread
of infectious diseases, and evaluating the
cost-effectiveness of delivery systems.
Since population health issues are multifac-
eted, these models use many variables, in-
cluding healthcare features, epidemiological
factors, public health policies, upstream
health causes, health risks, socioeconomic
forces, and environmental aspects. Their
development typically begins with a con-
ceptual model, followed by equations, pro-
gramming, choice of parameters and trajec-
tories for exogenous variables, and tests
discussed in Section 4 (Kopec et al., 2013;
Okhmatovskaia et al., 2012; Levy, 2014).
Another emerging strand reviews outcomes
for rural-urban migrants in LDCs, migrants
from LDCs residing in DCs, refugees, peo-
ple displaced by disasters, and people
moved by governments to make way for
development projects. It assumes these out-
comes will resemble, in turn, those obtained
for people planning migration in anticipa-
tion of, or in reaction to, climate change
impacts, people displaced by weather disas-
ters, and people relocated by governments
to reduce exposure to climate change. Re-
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
views of the currently observed outcomes
suggest that affluent areas may face more
exposure to communicable diseases carried
by migrants from deprived regions. People
relocated or displaced may see new infec-
tions, stress, trauma, and poverty, but those
who plan their migration may fare relatively
better (Carballo & Smith., 2008; Sherbinin
et al., 2011; McMichael et al., 2012; Licker
& Oppenheimer, 2013).
Finally, two nascent research strands link
migration and climate change to armed vio-
lence, respectively, which can naturally
impact health. One stand associates the ar-
rival of newcomers with violent events in a
host area, controlling for other factors. Re-
sults show refugee flow from nearby states
(Salehyan & Gleditsch, 2006) and internal
migration (Fearon & Latin, 2011) raise the
risk of civil war in a host area. Movement
linked to an ecological decline in an area of
origin (among other factors) increases the
risk of riots, revolt, and warfare in a desti-
nation area (Reuveny, 2007, 2008;
Bhavnani & Lacina, 2015). Interstate im-
migration raises the risk of clashes with
natives (Reuveny; Dancygier & Laitin,
2014), terror attacks (Choi & Salehyan,
2013) and coups (Gebremedhin &
Mavisakalyan, 2013). In a second strand,
many link climate change impacts to rising
risk of armed violence (NIC, 2012; IPCC,
2014), while others say they can also cata-
lyze acts of violence (CNA, 2014; DOD,
2014). Some writers downplay the risk of
disorder, arguing the extent of climate
change is unclear, and say people can re-
solve problems, that the current migration is
mostly peaceful, and sociopolitical integra-
tion of migrants can ease tensions
(Salehyan, 2008; Theisen et al., 2013).
3. Motivation and Basic Design
Section 2 suggests that predicting popula-
tion health for climate migrant and host
populations is complicated, not least since it
involves many forces, including environ-
mental decline, environmental health,
health care use, healthcare quality, barriers
to accessing healthcare, socioeconomic and
demographic factors, and, possibly, vio-
lence. These forces are, to some extent,
stochastic, introducing uncertainty. Studies
statistically model them one at a time, either
as a dependent or an independent variable,
but this suggests they evolve as a complex
dynamic system or in response to one an-
other and other factors. Studying our sys-
tem in controlled experiments would consti-
tute the first best approach, but this is not
possible. Alternatively, a simulation model
can project trajectories of endogenous vari-
ables under various assumed policies and
scenarios for exogenous factors, accounting
for uncertainty, interdependencies, nonline-
arities, delayed effects, and feedbacks
(Fone et al., 2004; Levy, 2014; Smith et al.,
2014). This section motivates such a model
for this paper and explains its basic design.
A simulation model can usefully address
planning and policy questions related to
climate migration and population health.
For example, it can forecast the effects of a
particular flow or stock of climate migrants
on the host and migrant population health
over time. It can assess whether existing
healthcare services and access eligibilities
suffice to sustain a certain population health
level and whether there are specific lever-
age points in the system for which minor
changes would create substantial beneficial
effects. The model can make similar projec-
tions for environmental health. Also useful
is the model’s ability to outline population
health effects based on origin and destina-
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
tion features (e.g., income, internal, interna-
tional), types of climate movers (migrants,
displaced by disasters, moved by a govern-
ment), and types of armed violence and
climate impacts.
The development of simulation models typ-
ically begins with a conceptual design. This
stage specifies links between endogenous
variables (rely on each other for causal in-
puts) in response to exogenous variables
(evolve on their own). Conceptual simula-
tion models are usually displayed graphical-
ly using arrows with – and + signs to show
the direction of effects (Carpiano & Daley,
2006). The next section takes this approach
for a model at the population level of analy-
sis for the migrant and host people, respec-
tively.
Population health measures typically
change gradually over time, suggesting us-
ing periods a few months to a few years, as
I do here (Upraising et al., 2011). Our mod-
el simplifies by including one origin-
destination pair and by treating migrants as
one entity. A more elaborated model could
consist of several origins and destinations
and migrant subsets (e.g., international,
internal, illegal, age-stratified, ethnically
grouped). I will discuss extensions in the
last section. Some exogenous variables may
be endogenous themselves, but for reasons
of practicality and usefulness, one cannot
model all possibilities in this regard.
The endogenous forces here reflect our goal
and the literature. Migration and population
health are naturally endogenous. Section 2
outlined that healthcare, environmental
health, and immigration affect health;
health and being a migrant impacts health
care use; migration and environmental deg-
radation affect violence, and degradation
and acts of violence affect movement. All
these forces influence population size,
which affects needed services, the environ-
ment, and violence. Meanwhile, violence
harms the environment, health, and
healthcare services, and raises the need for
healthcare (Altare & Guha-Sapir, 2013).
Accordingly, I model migration flow, and
population, violence, healthcare services,
environmental health services, and popula-
tion health by origin and destination as en-
dogenous variables. The other factors dis-
cussed in Section 2 (e.g., wage, socioeco-
nomic development, ethnic composition,
environmental degradation) are modeled as
exogenous in both sites, evolving according
to pre-defined scenarios (not unlike the
IPCC scenarios for projecting climate
change). The modeled levels of some of the
exogenous variables can change somewhat
at runtime around their given levels depend-
ing on computations engaged by the user,
making the model flexible for any number
of specific questions. These variables then,
are not entirely exogenous in the model, but
the model does not capture determinants of
their time.
Many of the variables are multifaceted, so I
define them as indices, by the site. For ex-
ample, Healthcare services (HCS) include
inputs such as primary, emergency, and
dental care, and Environmental health ser-
vices (EHS) waste removal, food safety,
sanitation, and pollution control. Violence
includes crime, clash, terrorist act, civil
war, and war and so on. For health, the In-
stitute of Medicine (2011) suggests an in-
dex summing up health problems weighted
by their severity, for society at a given time;
the model defines population health as a
stock variable rising when this cluster falls.
The exogenous environmental degradation
index includes climate change impacts, en-
vironmental health issues (e.g., waste, pol-
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
lution), declining resources, and factors like
urban heat island (cities are warmer than
rural areas since urban surfaces release
more heat than natural, and denser setups
hinder heat dissipation). To the extent that
climate migrants will move to cities (as
most migrants do today), degradation in the
destination may disproportionally reflect
floods (many cities are in low-lying coastal
and river areas), urban heat island and heat
waves, and environmental health problems.
The model does not require building indi-
ces; it interprets some user-provided inputs
as indices and requires users to read outputs
as such. Using it to say something about
reality may need index creation; see Section
5. The next section describes the conceptual
model.
4. Conceptual Simulation Model
The computations evolve period by period.
The user sets initial levels for some of the
endogenous variables and defines scenarios
for the exogenous variables. The user also
decides whether to include violence as a
variable and chooses whether to engage
processes that change some of the exoge-
nous variables around their scenario levels.
The endogenous flow variables are given
value by algebraic equations each period
and the stock variables by equations of mo-
tion. Given space limits, I discuss here only
a few of the exogenous effects. The model
is presented using two figures, one for each
of its parts. The design differentiates be-
tween migrant and host populations in the
destination, but the graphical presentation
combines them for improved ease of under-
standing.
Figure 1 shows the Environment and Mo-
bility part or module. Boxes are variables,
arrows causal effects, + and – the direction
of impact, +/– nonlinear effects, and indica-
tors with no sign effects whose signs are set
based on logic valued at runtime. Origin
Degradation and Destination Degradation
are stock indices of problems.
Origin/Destination Violence are flow indi-
ces of events.
Origin/Destination Population are stocks of
residents. The Origin and Destination Popu-
lation Health denote the health stocks de-
fined in Section 3. Migrant Flow indicates
the number of migrants per period. Needed
EHS and Provided EHS are flow indices of
environmental health services required and
received in the destination, defined on the
same metric. EHS Quality varies from one,
denoting 100% success at fixing problems
in one period (perfect), to zero (no effect).
Origin Degradation follows a scenario
whose levels can be attuned in runtime to
rise with Origin Violence and Population.
Origin Population Health follows a scenario
whose levels can be adjusted to fall as
Origin Violence and Degradation rise.
Origin Population rises due to its natural
birth rate and when Origin Population
Health rises (indicating fewer health prob-
lems). It falls due to its natural death rate
and Migrant Flow, and when Origin Vio-
lence and Degradation rise. The natural
birth and death rates are parameters.
Origin Violence follows a probabilistic pro-
cess in which its likelihood and intensity (if
realized) rise with Origin Degradation,
Origin Population, the prior probability of
violence, and the previously attained Origin
Violence.
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Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
Migrant Flow increases with Origin Popula-
tion and the Host and Migrant Populations
(Destination Population in Figure 1). It falls
as Destination Violence and Degradation
and Origin Population Health rise and rises
with the Host/Migrant Population Health,
showing people’s preference for peace,
healthy society, and environmental quality.
Migrant Flow increases with Origin Vio-
lence and Degradation until they reach their
respective turning point levels, above which
their effects reverse. At the outset of vio-
lence or degradation, the desire to leave due
to these forces is enough to overcome diffi-
culties in moving due to the injury, death,
and financial distress they cause. Once the
two factors grow large enough, these im-
pediments to departure dwarf the desire to
migrate.
In the destination, Migrant Population rises
due to its birth rate and Migrant Flow and if
Migrant Population Health rises. It falls due
to its death rate, more degradation or vio-
lence, and if Migrant Population Health
falls below a critical level.
Host Population is modeled similarly with-
out Migrant Flow. The migrant and host
population health levels come from the
module presented in Figure 2.
Destination Degradation follows a scenario
whose levels can be attuned to rise in
runtime with Destination Violence,
Host/Migrant Population, and Host/Migrant
Needed EHS (showing a decline in envi-
ronmental health). This variable can be at-
tuned in runtime to fall as Host/Migrant
Provided EHS and Host/Migrant EHS
Quality rise.
Host/Migrant Needed EHS, Host/Migrant
Provided EHS, and Host/Migrant EHS
Quality come from Figure 2.
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 10 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
Destination Violence is set by a probabilis-
tic process whose likelihood and intensity
(if realized) rise with Migrant Population,
Host Population, Destination Degradation,
and occurrence of violence in the prior pe-
riod, as well as when Migrant Flow rises,
suggesting that high inflow is less condu-
cive to a peaceful adjustment.
Figure 2 shows the Migration and Health
part of the model. Needed/Provided HCS
are flow indices of healthcare types. HCS
Capacity is the maximum HCS flow the
health care system can deliver with its cur-
rent resources in the destination (e.g., hos-
pital beds, doctors, medicines), all meas-
ured on the same metric. HCS Barriers var-
ies from one, denoting no barriers to access
HCS in the destination, to zero for no ac-
cess. EHS Capacity and EHS Barriers are
similarly defined. HCS Quality varies from
one (perfect) to zero (completely ineffec-
tive). Health Change is a change in Destina-
tion Population Health, per period, where
both variables represent all aspects of
health, both psychological and physical.
Other variables are as defined above.
Migrant Needed HCS falls as Migrant Pop-
ulation Health and Origin Population
Health rise, as healthier people need a lower
level of HCS, and rises with Migrant Popu-
lation and Destination Violence/Degra-
dation.
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 11 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
Migrant Needed EHS rises with Destination
Degradation and Violence and Migrant
Population.
Host Needed HCS/EHS are set likewise (excluding Origin Population Health).
HCS Capacity, Migrant HCS Quality and
HCS Barriers, and the associated EHS and the Host variables, follow scenarios.
Destination Violence can be engaged to
reduce the capacity and quality at runtime,
as its occurrence may harm providers and
facilities.
For the Migrant Provided HCS, if HCS Ca-
pacity is larger than or equal to the sum of
Migrant Needed HCS and Host Need HCS,
both adjusted for barriers (Migrant Needed
HCS x Migrant HCS Barriers + Host Need-
ed HCS x Host HCS Barriers), groups get
their barriers-modified needs. That is, Mi-
grant (or Host) Provided HCS equals Mi-
grant (or Host) Needed HCS x Migrant (or
Host) HCS Barriers.3 Else, capacity is in-
sufficient, and the Migrant (or Host) Pro-
vided HCS follows a capacity allocation
scenario.
The model sets the Migrant and Host Pro-vided EHS using similar logic.
Migrant Population Health equals the sum
of its prior level and two other terms. The
first, Migrant Health Change, is the expres-
sion Migrant Provided HCS x Migrant HCS
Quality ‒ Migrant Needed HCS; the setting
of Migrant Provided HCS indicates it is
either zero (for free access and perfect ser-
3 The idea behind the expression Needed
HCS x HCS Barriers (by group) is this. HCS
Barriers is in the range 0 to 1. If HCS Barriers
= 1, all the group’s needs take part in figuring
out if the total need tops the capacity. If HCS
Barriers = 0, a group cannot access HCS, so
the model ignores its needed HCS. If 0 <
HCS Barriers < 1, the model considers a part
of the demand.
vices) or negative (for all combinations of
barriers and quality). The second term
comes from changes in exogenous varia-
bles, by the group, which can be greater
than zero (e.g., representing exercise) or smaller than zero (e.g., capturing smoking).
The model sets the Host Population Health
using a similar algorithm.
5. Heuristic Simulations
The computation requires computer pro-
gramming and setting parameters and exog-
enous variables. Testing uses the following
steps, though they may not always be feasi-
ble. First, check the face validity of equa-
tions and outputs. Second, verify input-
output consistency. Third, check the con-
sistency among all outputs in response to an
input. Fourth, validate the reasonableness of
outputs for different parameters and exoge-
nous values. Fifth, check compatibility with
other simulations. Sixth, check compatibil-
ity with empirical data. Seventh, evaluate
the desirability of outputs from implement-
ing model-based decisions. See, for exam-
ple, Kopec et al. (2013), Okhmatovskaia et
al. (2012) and Levy (2014).
The first four steps of the testing are work-
able here. The fifth activity is feasible to the
extent that similar models exist. Stage six is
achievable to the degree that empirical data
exist for situations like those simulated. The
seventh step is feasible only if controlled
trialing is. We can say the conceptual de-
sign of the model in this article has some
face validity, as it reflects the literature.
Testing needs a computerized simulation.
However, the current model captures and
clarifies some of the issues at stake, so we
can try to gain insight by applying the mod-el in heuristic (or qualitative) simulation.
Applying the simulation to real-life situa-
tions requires linking variables and parame-
ters to practical measures and coming up
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 12 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
with relevant storylines. We cannot observe
index variables, but we can use proxies to
represent them. Alternatively, we can com-
pute indices from their lower-level factors
(e.g., as weighted sums). The second possi-
bility is not easy; our population health in-
dex, for example, requires collecting data
and finding weights. The storyline is a set
of assumptions defining numerical parame-
ters, scenarios for the trajectories of the
exogenous variables, and initial values for
endogenous variables that require them. In
general, this set is chosen to suit research
needs. This section applies the model in
heuristic simulation to illustrate some of the
insights it can give and distill some issues
for future research. In this approach, we
would need to make assumptions to resolve
competing effects in equations as we go
(which a codified model would determine
numerically).
Simple Storyline without Violence
This storyline depicts a simple case of mi-
gration from a poor origin to an affluent
destination, without violence. We present
its parameters, scenarios, and initial values.
Parameters
1. The origin and migrant populations
have the same birth and death rate (as they belong to the same group).
2. The origin and migrant birth rates
are higher than their respective
death rates, in line with observed
population growth patterns.
3. The birth rate of the host population is also larger than its death rate.
4. The net birth rate (birth rate – death
rate) in the origin area is higher than
that in the destination, in line with
data from poorer and wealthier re-gions, respectively.
5. The processes adjusting exogenous
variables in runtime (Section 4) are
activated.
Scenarios for exogenous variables
1. The economic features do not
change, and the destination’s econ-
omy is doing better than the origin’s
economy (e.g., the standard of liv-ing, wages, jobs).
2. Origin Degradation follows four
phases.
a. It grows slowly.
b. It rises faster and climbs
above its turning point effect
on migration.
c. It falls fast to the level ob-
tained had the slow growth
continued in phase b.
d. It grows as in step a.
This scenario depicts weather disas-
ters occurring on top of more gradu-
al degradation due to climate change
(e.g., sea level rising with a hurri-
cane making landfall and dissolving.
3. Destination Degradation does not
change and is lower than the initial
Origin Degradation.
4. Origin Population Health is constant
and smaller than the initial Host
Population Health.
5. There are no HCS/EHS Mi-grant/Host Barriers.
6. Migrant/Host HCS/EHS Quality is
perfect.
7. The HCS and EHS Capacities do
not change, which is a sensible in
the short run and, depending on
technical and financial abilities,
even longer, as it takes time to add
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 13 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
capacity-builders (e.g., doctors,
hospitals, labs, sewage pipes, waste
treatment).
8. Other factors are fixed and similar in both sites.
Initial values for endogenous variables
1. Migrant Population is positive (mi-grants live in the destination).
2. Host Population is larger than Mi-
grant Population (usually the case).
3. Origin Population is larger than
Host Population (depicting some-
thing like migration from Mexico to
the State of Texas or from rural
China to Beijing).
4. Migrant Population Health equals
the Host Population Health (as there
are no barriers to accessing perfect
HCS and EHS), and both are much
greater than Origin Population Health at the start of its scenario.
5. Destination Degradation is smaller
than the initial scenario level for Origin Degradation.
This setup simplifies things for ease of un-
derstanding and to make a point below. In
reality, environmental recovery after ex-
treme weather is often partial and slow,
access to HCS and access to EHS face bar-
riers, HCS and EHS are imperfect, and the
exogenous forces change over time and
place. I relax some these assumptions later to allow for more nuanced analysis.
Let us turn to the heuristic simulation. As-
suming the effect of birth is larger than the
total population-reducing impact in Figure
1, Origin Population rises in the simulated
timeframe (as occurs in less developed are-
as). Origin Degradation increases as de-
scribed earlier, reducing Origin Population
Health below its scenario level in each
point in time. The net effect on Migrant
Flow is assumed to be positive in the time-
frame (capturing, for example, rural-urban
migration in LDCs or LDC to DC migra-
tion). The Migrant Flow peaks just before
Origin Degradation rises above its turning
point in phase b of its scenario. Migrant
Population increases due to Migrant Flow,
and Migrant Population and Host Popula-
tion grow due to their net birth. Destination
Degradation increases with both popula-
tions and works to reduce their size. Styl-
ized facts suggest the positive effects out-
weigh the negative, so Migrant Population
and Host Population rise at least for a
while; notably, the longevity of this effect
depends on what happens to their health measures.
As we enter Figure 2 in the early periods,
Destination Degradation and Migrant Popu-
lation have risen from their previous levels.
As a result, since other exogenous variables
do not change, and, for migrants, since
Origin Population Health declines, Migrant
Needed HCS/EHS and Host Needed
HCS/EHS rise. The barriers-adjusted needs
for both services equal the requirements
themselves, as Migrant/Host HCS/EHS Barriers are all set to one (no barriers).
There are now four possibilities.
Both of the Total needs are less than or
equal to their capacities. The provided
HCS/EHS are set at the needed HCS/EHS
levels, per group, respectively. The Migrant
Health Change and the Host Health Change
are zero (as HCS/EHS quality is perfect and
the exogenous health factors do not
change), so Migrant Host Population and Host Population Health do not change.
The total Needed EHS top their capacity,
and the entire Needed HCS do not. The
provided EHS follow the EHS capacity
allocation scenario and are too low for at
least one groups, so Degradation Destina-
tion rises (e.g., overrun sewage, piling
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 14 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
waste). As a result, the total needed HCS
increases. The total HCS provision equals
the overall need, so the Migrant and Host
changes are zero, and the related population
health indices do not change.
The Needed HCS grow above their capacity
level and Needed EHS do not: HCS provi-
sion follows its capacity allocation scenario.
Some needs go unmet, so population health
falls for at least one of the two groups. As a
result, the total needed HCS rise, and popu-
lation health continues to decline for at least one group.
The Needed HCS and Needed EHS top their
capacity levels: Here, there are again four
possibilities. The Needed EHS may first
surpass the capacity, the Needed HCS may
exceed its provision-capacity first, or both
needs may simultaneously top their provi-
sion-capacities, but the gist is similar. When
a total requirement for services exceeds its
associated capacity, some people see their
needs go unmet. Unmet Needed EHS raises
environmental degradation for at least one
of the groups, which further increases
Needed HCS and EHS. Unmet Needed
HCS reduces population health for at least one of the groups.
The needed services are likeliest to first
surpass capacity in or after phase (B) of the
origin’s degradation scenario. Hereafter, the
fall in population health hastens its decline.
In effect, the migration influx pushes the
society beyond the tipping point for health.
Unlike a natural system crossing an irre-
versible ecological tipping point, however,
the community recovers. Once Migrant
Population Health and Host Population
Health (depending on the capacity alloca-
tion scenario) fall below critical thresholds,
their death rates rise. Ultimately, either
population or both begin to fall, working to
reduce Migrant Flow. These declines lessen
the total needed HCS below HCS Capacity,
and the society returns to health normalcy, albeit with fewer people.
Adding Violence to the Simple Storyline
The occurrence of violence is likelier in the
area of origin than in the destination area
since the origin’s degradation, and popula-
tion, grow faster than in the destination and
since the sending area is relatively more
impoverished. Not shown in Figures 1 and
2, the standard of living and job prospects
fall with the likelihood, and the realized
level, of violence, the former since the oc-
currence of violence reduces will to invest
economically (more risk), and the latter due to the associated damages.
As Origin Degradation and Origin Popula-
tion increase, the likelihood of Origin Vio-
lence and, if realized, intensity rise, particu-
larly in phase (B) of the degradation scenar-
io. If violence occurs in a period, its likeli-
hood rises in the next period, Origin Popu-
lation Health falls more than under peace,
and the standard of living and job prospects
decline. Origin Degradation now grows also
due to violence. The Origin Population
grows, assuming its net birth effect still
exceeds the impact of the population-
reducing factors, though less than under
peace due to violence-induced death and
injury. Migrant Flow tops the one under the
peace storyline, indicating that people leave
due to the rising tension, violence, and the
related reductions in the standard of living,
population health, and job prospects, in line
with empirical observation. As a result,
Migrant Population now rises faster. The
arriving migrants in the current case are less
healthy than those arriving in the peace case
because the Origin Population Health is lower due to the violence.
The likelihood of violence and realized vio-
lence in the destination rise with Migrant
Flow. We assume they are smaller than
their counterparts in the sending area. The
reasons for this assumption are that the
standard of living in the destination is high-
er than that in the sending area, and the deg-
radation and population growth rates are
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 15 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
lower than their sending area counterparts.
Things now unravel faster than under
peace, especially if the immigration contin-
ues. The occurrence of violence in the des-
tination increases the needed HCS and EHS
and reduces the EHS and HCS capacities
and qualities due to damages, so the total
required HCS or total required EHS hit the
maximum provisions sooner. Depending on
the capacity allocation scenario, the migrant
or host population health fall sooner below
the threshold under which the death rate
rises and, joined by the violence, bring
about an earlier decline in the two popula-
tions.
Adding Partially Accessible and Imper-
fect services to the Simple Storyline
A more realistic storyline with less than
fully accessible and perfect services, keep-
ing other things equal, may exhibit an even
faster decline in population health for the
migrant and host populations than in the
above two cases. Rasing the HCS capacity
above the largest total needed HCS can help
resolve this problem in the model, but this
takes time. In the meantime, the society
suffers until things return to health normal-
cy following population decline.
6. Conclusion
This paper develops a conceptual simula-
tion model of the causal flow from an eco-
logical decline in an origin area to migra-
tion to population health in a host area to
examine. It centers on the need and provi-
sion of HCS and EHS, policies defining
access to services, and the possibility of
violence. The work then simulates the mod-
el heuristically for three types of scenarios.
It would be interesting to compare the re-
sults to those obtained by other models of
this type. The literature has not offered
work in this area, and so this comparison is still not workable.
Though all models are imprecise, this one
sacrifices some realism for the sake of clari-
ty. Future research may extend it. For ex-
ample, one could take account of demo-
graphic differences (e.g., gender) and types
of newcomers (e.g., internal, international,
illegal, temporary workers, displaced by
disasters, relocated by a state). One may
add sites and have the distance traveled for
migration decline when health falls (as
moving is not easy). Other extensions may
include adding sites, health and environ-
mental problems worsening or improving
on their own, services taking effect with
delays and uncertainty, groups infecting
each other, and delays in building capacity.
Extensions could address specific ques-
tions, creating insights that may be useful
for policymakers and future research. What
we have here already illustrated potential
perils for population health in the case of
large-scale climate migration under con-
servative parameters. Problems can arise in
the current model even in a peaceful world
in which the HCS and EHS are perfect and accessing them is free.
The capacity is a vital issue. Even the best
HCS and EHS can only provide so much
care to the people relying on them at any
given point in time and raising their capaci-
ties to provide more care is an involved and
costly endeavor. Societies receiving many
climate migrants could face difficult choic-
es. Since the HCS or EHS capacities may
not suffice to meet the needs of everyone, at
least in the short run of a few years, alloca-
tion policies would need to determine
whose needs must go unmet.
The possibility suggested by the model of a
need to chose whose health care to priori-
tize is unsettling. There are ways to mitigate
this risk. For example, proactively raising
population health would delay any potential
capacity overrun and create a higher base-
line should this occur. One way to apply
this policy is to provide HCS to everyone,
regardless of pay, but this raises pressure on
Reuveny R. Archives of Psychology, vol. 2, issue 4, April 2018 Page 16 of 22
Copyright © 2018, Archives of Psychology. All rights reserved. http://www.archivesofpsychology.org
the existing capacity, causing new prob-
lems. Building up the HCS and EHS ca-
pacities is maybe a panacea but requires
more resources like providers, hospitals,
sewage pipes, and waste treatment facili-
ties, which are costly and cannot be put in
place overnight. Success would require be-
ginning projects in advance of observed
needs and keeping capacities at levels suffi-
cient to meet projected rather than the cur-
rent requirements. Society should maintain
its untapped capacity structures in a state of
readiness; health care and environmental
health resources, much like standing ar-
mies, depreciate over time. Allocating cli-
mate migrants across sites could reduce
pressure on any one place; a model with
many destinations could suggest an alloca-
tion that achieves a certain level of popula-tion health across them.
Another option is to relieve climate change
impacts in origins. For example, we know
how to build seawalls against rising sea
level, strengthen structures facing weather
disasters, and reduce dependence on the
environment for livelihood through diversi-
fying incomes to fields demanding fewer
natural resources, but these schemes are
costly and technical, and many of the most
exposed regions are also poor. External aid
could meet some preparedness needs and
help promote economic development in
impoverished areas, provided they are large
enough, giving potential climate migrants
more reasons to stay in their current place.
With the ongoing fossil fuel-based energy
paradigm, however, economic development
in the more impoverished regions would
likely intensify climate change unless ac-
companied by a reduction of carbon emis-sions in the more affluent areas.
Applying such policies requires national
and subnational governments to shift means
from other goals (e.g., acquire more arms,
increase income) to aid, so wealthier socie-
ties might be tempted to focus on keeping
climate migrants out by reinforcing current
entry barriers to immigration. This ap-
proach can keep immigration in check in
the short run, but it risks tearing the social
fabric of societies who use it, for it inher-
ently necessitates a policy of ignoring hu-man suffering.
These adaptations and others are feasible,
but their consideration and application sig-
nal an acceptance of climate change as a
force majeure. Policies are most effective if
they attack problems at their core, other
things being the same. Just as we seek to
cure diseases, rather than live with their
symptoms, so should we be working to stop
climate change, not adapt to its myriad ad-
verse effects. Only when diseases are incur-
able or when the impact of treatment is
worse than the disease do we solely try to
alleviate symptoms. The cure for climate
change – mitigation of greenhouse gas
emissions – is understood and readily im-
plementable and nowhere near as costly as
the expected impacts of the problem itself;
it may be painful but in no way fatal. In-
formed observers know that mitigation is
ultimately superior to adaptation. That this
knowledge has not catalyzed a significant
policy shift toward reduction of carbon
emissions suggests a failure of our systems
of governance, a severe shortcoming that demands examination.
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