In this chapter, I review and synthesize the existing evidence relevant for the research questions of this dissertation. First, I briefly assess knowledge on the effects of hazards on (im)mobilities, underlying workings, and seminal studies in Latin America. Afterwards, I evaluate the literature on how migration in general—and climate-related cases in particular—can affect objective and subjective well-being (OWB and SWB). Finally, I discuss implications for this study. The chapter sets the stage for the subsequent discussion of the new empirical findings on Peru in chapters 5–7, which also contain detailed reviews of hazard-(im)mobility links in the respective study areas.

1 State of the Research on Links between Hazards and (Im)mobilities

1.1 Research Evolution and Landscape

Research on hazard-(im)mobility links has rapidly developed and applied varied foci (Flavell et al. 2020). After a first wave of research in the 1980s, studies have steadily risen and reached over 100 peer-reviewed outputs per year since 2008 (Ionesco et al. 2017). While first generation research focused on the causal contributions of hazards to migration (Jäger et al. 2009), second generation research expanded to more holistic assessments of migration as one among many livelihood strategies to address climatic and other stressors (McLeman & Gemenne 2018; Piguet 2013). Throughout, various streams of research have also mapped populations at risk of migration or projected future pathways (Clement et al. 2021; de Sherbinin & Bai 2018; Gemenne 2011; Warner et al. 2009). While scholars have also started to warn of risks linked to immobility in dangerous areas, this flipside of migration has remained studied less (Foresight 2011; Wiegel et al. 2019; Zickgraf 2018). Later, a third generation of studies has started to empirically assess the impacts of climate migration (e.g. Melde et al. 2017). I summarized existing synthesis studies in the field elsewhere (Bergmann 2019) and briefly repeat main findings here, especially from reviews published later (Table 4.1).

Table 4.1 Identified synthesis studies of hazard-migration linkages worldwide
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The synthesis of existing reviews certifies that knowledge about climate migration has improved, yet with marked gaps and biases. Key hazards remain understudied; key geographic gaps persist; North–South divides in research and funding agendas have endured (Piguet et al. 2018; Wrathall et al. 2018); and publication biases exist (Šedová et al. 2021). Additionally, methodological choices strongly shape results (Beine & Jeusette 2021; Šedová et al. 2021). The variety of concepts, designs, and methods used—and diverse spatial and temporal scales, units, and data structures—have made it difficult to compare results and draw conclusions (Borderon et al. 2018; van der Land et al. 2018; Vinke & Hoffmann 2020). While research mostly consists of case studies (Gemenne 2018; Piguet et al. 2018), meta-analyses have advanced recently (Beine & Jeusette 2021; Hoffmann et al. 2021). Conversely, climate immobilities remain understudied (Cundill et al. 2021; Hoffmann et al. 2020).

1.2 Identified Effects and Underlying Mechanisms

Meta-studies find significant positive and negative effects of hazards on migration, especially in low- and middle-income and agriculturally-dependent countries (Beine & Jeusette 2021; Hoffmann et al. 2020; Šedová et al. 2021). Most migration is within countries and over long distances, rather than localized (Kaczan & Orgill-Meyer 2020), often from rural to urban areas (Šedová et al. 2021).

Both environmental hazards and benefits can shape migration (Bendandi 2020). Hazards can inhibit and accelerate existing movements or create entirely new flows, depending on the hazard type and duration (Beine & Jeusette 2021; Borderon et al. 2019). Especially extreme temperatures, water-stress, and rainfall changes have significant effects (Berlemann & Steinhardt 2017; Hoffmann et al. 2020; Šedová et al. 2021). The severity of hazards affects flows nonlinearly, depending on people’s capabilities and vulnerabilities (Kaczan & Orgill-Meyer 2020). The impacts on migration also seem greater for recent hazards (Beine & Jeusette 2021; Šedová et al. 2021). The speed of hazards can create different outcomes. If gradual hazards make migration more or less likely remains disputed and depends on context (Borderon et al. 2019; Cattaneo et al. 2019; Zickgraf 2021). The most direct climate-migration link is displacement after abrupt extreme events (Cattaneo et al. 2019), which is usually short-term, short-distance, and shaped by gender and vulnerabilities (Berlemann & Steinhardt 2017; Cardona et al. 2012). Displacement can become permanent due to cumulative shocks and extraordinary disasters, particularly for at-risk households (Berlemann & Steinhardt 2017). Finally, abrupt extreme events can also drive in-migration, such as for reconstruction (Adger et al. 2014).

However, migration is not the default strategy. Even during abrupt disasters, displacement is often a last resort (Foresight 2011). Other people opt to stay despite gradual hazards. Alternative strategies include “on-farm adaptation, off-farm adaptation, informal credit, participation in risk-reducing networks, social protection policies, and international development assistance” (Cattaneo et al. 2019: 8). Additionally, climate impacts, such as reduced crop yields, can also decrease migration. Hazards that damage micro level factors such as health or wealth can reduce the ability to move even if hazards would make escape desirable (Choquette-Levy et al. 2021; Flavell et al. 2020; Kaczan & Orgill-Meyer 2020). As “vulnerability is inversely correlated with mobility”, and moving requires resources, hazards may trap the most affected (Adger et al. 2014: 767; Kaczan & Orgill-Meyer 2020; Morrissey 2014). A meta-study observes immobility in multiple contexts, especially where liquidity constraints are tangible, and underscores that entrapment risk is especially high in low-income countries and for women (Šedová et al. 2021). Obstacles tend to be particularly high for cross-border migration (Veronis et al. 2018). Finally, hazards can raise stayers’ vulnerabilities and cause downward spirals of poverty (Kaczan & Orgill-Meyer 2020).

While the evidence demonstrates that some people move to address climatic risks (McLeman 2016b), the underlying mechanisms are less clear. In many regions, such as the Sahel, migration is a common livelihood strategy and one among several options to respond to environmental change (Morrissey 2014). However, most reviews agree that establishing direct causation from hazards is difficult, because decisions to migrate are multicausal and climate risks interact with non-climatic stressors (Adger et al. 2014). As one example, migration due to sea level rise (SLR) is “multifaceted” and “further research is needed on the fundamental mechanisms underlying SLR migration, tipping points, thresholds and feedbacks, risk perception and migration” (Hauer et al. 2020: 28). A meta-analysis indicates that economic migration drivers are particularly sensitive to climate hazards, and income moderates and explains hazard-migration links partially (Hoffmann et al. 2020). For example, hazards can increase income variability or widen income gaps that propel migration (Cattaneo et al. 2019). As agrarian livelihoods are most likely to be affected (Borderon et al. 2019), changing rural wages and agricultural productivity are also key paths underlying migration (Berlemann & Steinhardt 2017; Falco et al. 2018). Additionally, climate-related conflict can moderate the relationships, but the direction of its influence is context-specific (Cattaneo et al. 2019; Hoffmann et al. 2020). Besides livelihood erosion, hazards can also threaten non-economic factors key for people’s place attachment and thereby drive migration, for example, by destroying cultural or spiritual ecosystem services such as sacred glaciers (Adger et al. 2014). These pathways remain understudied. Ultimately, (im)mobility dynamics seem to depend on time- and place-specific demographic, environmental, political, and socioeconomic contexts, alongside social differentiation, which makes multiple outcomes possible for any given hazard, occasionally in contradictory directions for different areas (Veronis et al. 2018). Both micro variables such as age, gender, and wealth as well as meso level factors such as social networks influence the propensity to move, often in heterogeneous ways (Borderon et al. 2019; Cattaneo et al. 2019). The interplay of hazards and structural changes can make migration more likely (Jónsson 2010) but institutions shape how such dynamics emerge (Morrissey 2014). Thus, while most reviews stress the multicausality in (im)mobilities, it is still not clearly understood under which exact circumstances people affected by climate change are compelled to move (O'Neill et al. 2022). Uncertainties relate to the nature and extent of climate impacts, including non-linear changes; the complexities of human vulnerability to these changes; adaptation possibilities; and the intricacies involved in migratory decisions (Flavell et al. 2020). The effect of prior migration experience—or the absence thereof—is another research gap (Findlay 2011; Henry et al. 2003). While direct environmental channels today still seem less vital than economic and social ones, studies usually do not account for indirect effects, interactions (both chain‐logical causation and independent operation), or complex pathways, and may thus underestimate the effects of hazards (Beine & Jeusette 2021; Borderon et al. 2019; Neumann & Hermans 2017; van der Land et al. 2018). Finally, solid theoretical frameworks that account for these complex links are scarce (Hunter et al. 2015; Piguet 2018). A seminal model highlights that hazards mostly shape economic, environmental, and political migration drivers, but micro and meso level variables also affect the initiation of movement (Black et al. 2011b).

1.3 Linkages in Latin America

In Latin America, the focus region of this study, a meta-analysis finds strong significant effects of hazards on migration (Hoffmann et al. 2020). Another study using 21 million census data points from eight South American countries (not including Peru) also observes significant effects of climate variability on migration, which is mostly directed toward cities. The effects are heterogeneous across gender, age, country of residence, and depend on baseline climatic conditions (Thiede et al. 2016).

Nonetheless, research on climate migration in Latin America is limited (Figure 4.1), and even more so on immobility (Castellanos et al. 2022; Piguet et al. 2018). The only dated, academic review on climate migration in the region concludes that empirical studies are scant, particularly for Andean countries such as Peru (Kaenzig & Piguet 2014).Footnote 1 Especially “the broad absence of water stress-migration research across the continent is worrisome” (Wrathall et al. 2018: 14), a gap I aim to respond to through this study. For the Andean states beyond Peru, most research exists for Ecuador (e.g. Gray 2007, 2010; Gray & Bilsborrow 2013) and Bolivia (Balderrama 2011; Brandt et al. 2016; Kaenzig 2011, 2015). Most available studies center on Central American migration—often as it relates to the US—and focus on tropical storms as well as on links between drought, heat, and food security (Baez et al. 2017a, 2017b; IDB et al. 2017; Spencer & Urquhart 2018).

Figure 4.1
figure 1

Prior country case studies on climate change and migration in the Americas. (Note: Peru is marked red. The size of the circles indicates the relative number of case studies. Based on 532 articles from the CliMig database (1970–2016). One article can contain multiple sites. Cropped from Piguet and colleagues (2018: 369), edited by the author)

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The IPCC’s latest regional assessment observes with high confidence that Latin America is sensitive to climate migration dynamics, whose magnitude has been increasing due to water-related hazards, such as droughts, heavy rainfalls, floods, and storms. Climatic- and non-climatic drivers of migration interact in complex and partially indirect ways, while intersectional social factors influence propensities to move (Castellanos et al. 2022). Previous empirical studies agree that multiple hazards shape migration in the region (Kaenzig & Elizabeth Warn 2015; Kaenzig & Piguet 2012). Water hazards such as floods, glacier retreat, and rainfall variability affect many countries, but are “just one more factor added to social and political contexts that are sometimes steeped in deep inequality… and power dynamics” (Kaenzig & Piguet 2014: 171). Especially for gradual hazards, pathways are context-specific and multicausal. Vulnerabilities to water scarcity are high in the region but little evidence of effects on migration exist at present. Sea-level rise has not caused migration so far, but since many at-risk areas are urban and densely populated, pressure may rise. Besides temporary movements, some people use multiple residences to address hazards. Disaster displacement tends to be short-distance, short-term, and directed to urban destinations (Kaenzig & Piguet 2014). Movement often originates in climate-affected rural areas but tends to be a last resort. Immobility is a salient issue especially for the poor, who may end up trapped in vulnerable situations. Others, however, make a conscious decision to stay due to high place satisfaction (Castellanos et al. 2022). The IPCC stresses that the outcomes of climate migration in the region are uncertain. While urban opportunities exist, the authors have high confidence that moving can reproduce structural problems and deepen pre-existing vulnerabilities, especially for socially disadvantaged groups (Castellanos et al. 2022: 85–86).

Looking to the future, the IPCC expects that climate migration will continue to increase in Latin America (Castellanos et al. 2022). The most robust study projects that in a worst-case scenario, slow-onset hazards may force up to 17.1 million migrants to move within their Latin America countries by 2050, representing 2.6% of the region’s total population. They would leave areas with water stress and crop losses or those threatened by rising sea levels and storm surges. Yet, dedicated development action and climate mitigation could strongly change these numbers. For example, a more climate-friendly scenario would result in up to 9.4 million internal climate migrants (Rigaud et al. 2018).

In summary, existing studies indicate that climate migration is a major issue in Latin America and projections suggest that it will gain relevance in the future. However, the breadth and depth of the evidence remains limited, especially on varied forms of (im)mobilities and their differential outcomes, a gap this study strives to bridge. In the next section, I assess the general literature on how climate (im)mobilities can affect well-being. (Additionally, in chapters 5–7, I discuss migration dynamics specifically for my three case study areas and provide in-depth reviews of the relevant evidence on exposure, vulnerability, and climate-(im)mobility links in these zones).

2 State of the Evidence on Links between (Im)mobilities and Well-Being

Well-being is the state of people’s life situations, based on need fulfillment, their present evaluation thereof, and views of the future. Using this lens developed in Section 2.3, I first review possible effects of (im)mobilities on the fulfillment of relevant needs, namely development from a secure base, a space to live better, and social relatedness. Then, I assess the state of research on how (im)mobilities can affect people’s cognitive and emotional need evaluation for the present and views of the future.

First of all, the evidence on the effects of climate immobility worldwide and in Peru is limited. Global studies suggest that risks may be high for trapped, non-resilient populations in areas severely affected by climate impacts, who may suffer from cumulative damage, food insecurity, health issues, as well as hostility, racism, and violence (Brubaker et al. 2011; Herren 1991; Mallick & Schanze 2020; Schwerdtle et al. 2017; Sow et al. 2016). Even though the dominant discourse highlights that risks related to immobility include “people losing their assets, falling into poverty traps, or suffering from a lack of capital”, generalizations about impacts remain difficult because immobilities are multifaceted (Ayeb-Karlsson et al. 2018: 563). Since the empirical evidence on the impacts of immobilities is incomplete to such an extent, this review focuses on the effects of climate migration.

Second, research on climate migration is more readily available but rarely analyzes the impacts of such movements in destinations, as a systematic review of about 3,200 empirical studies finds (Rigaud et al. 2018; Zander et al. 2022). A prior review of mine distilled broad directions for Peru specifically (Bergmann et al. 2021a). (Chapters 5–7 provide more detailed reviews of hazard-(im)mobility links and effects in the Peruvian areas of interest in this study). The studies reveal that some migrants within Peru can escape hazards, diversify incomes, acquire new skills, and send remittances to their communities (Badjeck 2008; Lennox 2015; Milan & Ho 2014). Conversely, more studies in Peru seem to suggest negative impacts on those moving and staying. First, farmers whose skills are not transferable to cities can end up in informal jobs, food insecurity, precarious housing with limited access to basic services, and exposed to new climate risks (List 2016; Sherman et al. 2015). Second, migration can heighten vulnerabilities in sending areas by eroding local knowledge and adaptive capacity, removing workers from labor-intensive agriculture, and increasing workloads for non-migrant women (Lennox & Gowdy 2014; Milan & Ho 2014; Sperling et al. 2008). Overall, studies argue that the outcomes depend on hazard patterns, migration trajectories, and profiles of receiving areas. Household vulnerability is another key determinant (Ho & Milan 2012): relatively well-off households can raise resilience through temporal migration that facilitates diversification; less resilient households may gain the bare minimum through survival migration; but when the poor migrate, resilience often erodes, because they tend to end up lacking income for remittances while sending areas are deprived of labor. Beyond migration, studies agree that prior disaster displacement has had a high toll on people in Peru (e.g. Espinoza-Neyra et al. 2017; Rojas-Medina et al. 2008) and relocations have reduced well-being by failing to consider place attachment, land and social issues, and livelihood necessities (e.g. Pittaluga 2019; Sperling et al. 2008) (Table 4.2).

Table 4.2 Synthesis of available studies on climate migration outcomes in Peru
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Because insights into the effects of climate migration within Peru are limited, it is sensible to also review (a) studies on climate migration in other areas and (b) the broader literature on migration and well-being, which can provide insights by analogy. To gauge this broader state of research adequately, I prioritize meta-analyses and systematic reviews when available. Moreover, in this review I also distinguish between different migration types and conditions (compare Section 2.2.2) in the few cases for which such data exists, because these factors affect outcomes. Overall, people forced to relocate or move may have less capabilities than those freely choosing to migrate, and may thus achieve less positive results (Bartram 2015; de Haan 1999; UNDP 2009). Similarly, although gender and other social factors—and their intersections—can strongly influence migrants’ well-being, few studies disaggregate results accordingly (Fleury 2016; Selod & Shilpi 2021). Finally, when indicated, I also review results of cross-border migration because “remarkably little attention is given to the patterns of internal migration around the world…[and d]ata are relatively scarce and often out of date” (UNDP 2020: 33), especially for rural-to-rural and urban-to-urban migration (Selod & Shilpi 2021).

2.1 Effects on Objective Dimensions of Well-Being

I begin this review by analyzing data on how migration affects OWB, namely development from a secure base, a space to live better, and social relatedness (see Section 2.3).

2.1.1 Development from a Secure Base: Decent Livelihoods, Health and Food Security, and Educational Opportunities

Livelihoods are a key research interest in climate migration studies (Bardsley & Hugo 2010; Black et al. 2011a; McLeman 2016a; Tacoli 2009; Webber & Barnett 2010). Research emphasizes that the outcomes depend on intersectional variables, social mobility, and initial vulnerability profiles (Afifi et al. 2016; Jäger et al. 2009; Warner et al. 2009): people with greater resilience prior to moving can benefit from adaptive migration; others with less resources use migration for survival or for erosive coping; and the most deprived often lack capitals required to move. Climate impacts make beneficial conditions for migrants less likely, especially for forced migrants (Rigaud et al. 2018). Thus, the most affected may use migration in ways that raise vulnerability or end up trapped (Adger et al. 2015; Black et al. 2013; Zickgraf 2018). A cross-country study confirms these differentiated livelihood pathways. It finds that internal climate migrants mostly perceived positive effects on income and employment and around 40% of them learnt new skills; yet relocatees and forced migrants had fewer income sources, higher debts, and more vulnerabilities (Melde et al. 2017). Other reviews stress how relocations due to hazards can threaten livelihoods (Hino et al. 2017; Mazhin et al. 2020); in a global mapping, relocatees lost their revenues in one third of 308 cases (Bower & Weerasinghe 2021). Finally, disaster displacement can create chances for migrants “but most often it undermines their welfare” (IDMC 2021a: 18). In summary, migration may hold adaptive potential for more resilient groups but for more vulnerable ones, it threatens to be erosive coping or mere survival.

General migration studies complement these results. They indicate that cross-border migration to higher-income areas “typically brings about dramatic increases in the earnings of migrants” compared to areas of origin (Yang 2009: 6). Incomes are especially higher for voluntary migrants with higher capabilities (Dustmann & Glitz 2011; UNDP 2009) but the relationship holds for migrants in low-wage jobs or working below their skill levels (UNDP 2020). Migrants moving from lower to higher-income countries increase their incomes three to six times, and most migrants and refugees move from countries with lower to those with higher employment rates (World Bank 2018). Still, migrants are “at a severe economic disadvantage” compared with natives upon arrival, and wages and employment converge only over time (World Bank 2018: 22). Progress is slower for refugees and irregular immigrants, who are often excluded from formal labor (World Bank 2018). Camp-based refugees often live in dire conditions; their chances to progress are low as they face restrictions, although “refugee economies” usually develop (Alloush et al. 2017; Bruijn 2009; Turner 2016).

Most internal migrants also move to wealthier areas (World Bank 2018) and have a “large urban premium” in income (although smaller than cross-border migrants), which holds across generations (Harttgen & Klasen 2009; Lucas 2021b; Selod & Shilpi 2021: 21). One study finds 25% returns for internal migrants (Lagakos 2020). Nonetheless, they initially earn less compared to locals and only gradually catch up (de Haan 1999). However, internal migrants are more often employed than locals (Tacoli 2007) and their movement also raises productivity and decreases inter-regional inequality in developing countries (Selod & Shilpi 2021). Overall, norms, policies, sociocultural distance, social capital, and information availability shape outcomes (Selod & Shilpi 2021). The results are often most challenging in adverse migration conditions. Specifically, many persons internally displaced due to conflict or disasters frequently face reduced employment, average incomes, and livelihoods (Cazabat 2020; World Bank 2017b). Similarly, relocatees often risk impoverishment, especially in forced cases (Arnall 2019; Cernea 2004; Piggott-McKellar et al. 2020; Wilmsen & Webber 2015), whereas voluntary cases can occasionally render higher incomes (Bazzi et al. 2016). The data seen so far emphasize that “very high” livelihood benefits and costs of internal migration can occur at once (Selod & Shilpi 2021: ii). Costs, including for transportation and subsistence, can reduce livelihood gains; such costs depend on trajectories, occupations, and individual variables (World Bank 2017b, 2018). For example, a review identifies jobs as the most salient migration-related stressor (Mak et al. 2021), and short-term employment migrants usually require long before making returns (Bedford et al. 2009). While migrants’ occupational quality can be low at arrival (depending on migration corridors and skillsets), it improves “fairly rapid” (World Bank 2018: 197). Nonetheless, migrants often have jobs below their skill levels, and their human capital is frequently under-utilized or lost (McAuliffe et al. 2019). Especially cross-border migrants with low levels of education have to work in problematic “3D” jobs, in “low-skilled occupations that are shunned by members of the host society – manual labour that is often dirty, difficult, dangerous and in relatively isolated places” (Bedford et al. 2009: 64; Martin 2005). Irregularity raises the risk of underemployment, low wages, high living costs, and exploitation (Baldwin-Edwards 2008). Similarly, such risks and discrimination are salient for female migrants, which often complicates their economic situation (Fleury 2016).

Few empirical studies on climate migrants’ health outcomes exist (Schwerdtle et al. 2020). Reviews find that water and food security act as key moderators but outcomes are varied and context-dependent (Hunter et al. 2021; Schwerdtle et al. 2020); “in the early stages[, they] are expected to be similar to the health outcomes associated with refugees”, including the risk of disease, disability, and loss of life (Mazhin et al. 2020: 97). Four major health concerns identified are (a) an altered distribution of infectious disease, partially due to high exposure during the journey or poor living and labor conditions especially in displacement and irregular settlements; (b) changes in non-communicable diseases, including food insecurity and diet-related diseases due to unfamiliar destinations and barriers in accessing healthcare; (c) psychosocial health challenges due to reduced social capital, deprivations, uncertainty, increased violence, and destruction of ecosystems or other resources key to migrants’ identities; and (d) unequal access to health care in urban destinations, in relocations, or after hazards damaged infrastructure (Mazhin et al. 2020; Schwerdtle et al. 2020). Overall, climate migrants’ profiles and conditions influence if moving is adaptive for health and food security (Schwerdtle et al. 2020; Warner & Afifi 2014). Results can thus be mixed: in a cross-country study, climate migrant households reported mostly positive effects on health and food security, including through better access to health care services (Melde et al. 2017), whereas a review in the Pacific finds that health worsened due to discrimination, poor housing, and livelihood barriers (Yates et al. 2021). Finally, forced climate migrants in particular bear severe health risks (McMichael et al. 2012); for example, mental health burdens after disaster displacement tend to be high, especially for women who experience violence and additional caregiving burdens (Rigaud et al. 2018).

General migration studies provide extensive, complementary results. International migrants seem to profit as they can gain access to better health facilities, staff, services, and health-enhancers, such as water and sanitation. For example, child mortality rates greatly decline across all migrant corridors (UNDP 2009, 2020). Mortality is also lower for migrants than for locals (Abubakar et al. 2018). Selection processes and sociocultural resources are reasons why most (particularly voluntary) immigrants in richer countries have better mortality and health than locals. Yet, this healthy immigrant paradox can cease the longer migrants stay due to acculturative stress and lifestyle changes (Markides & Rote 2015). Additionally, a meta-analysis finds that mortality premiums are clearer in high-income countries than for marginalized migrants in low- and middle-income countries (Aldridge et al. 2018). Finally, morbidity can be larger for some diseases and among specific migrant groups (Abubakar et al. 2018; Alidu & Grunfeld 2018). Next, rural-to-urban migrants often improve health through access to better services, yet they tend to remain behind locals (Harttgen & Klasen 2009). In addition, a review demonstrates that internal migrants in poorer countries improve health through income gains, but benefits “can be counterbalanced by adverse living conditions” (Selod & Shilpi 2021: 26). Especially marginalized, irregular, and female migrants’ tend to have constrained access to health services (Fleury 2016; UNDP 2020) and many migrants in 3D jobs suffer job-related hazards (Ratha et al. 2011). Moreover, two systematic reviews note that cardiovascular risk factors are greater for urban migrants than in rural groups (Hernández et al. 2012), and that migrants’ self-perceived health is often worse than that of non-migrants (Lu et al. 2020; Nielsen & Krasnik 2010). For forced migrants, food security depends strongly on the host context (Bruijn 2009); their health tends to be better in urban than in camp settings (Crea et al. 2015). Finally, migration also strongly affects mental health, and the effects seem to depend on context, coping strategies, and personal resilience (Mak et al. 2021; Siriwardhana et al. 2014). A natural experiment finds that cross-border migration can raise mental health (Stillman et al. 2015), whereas two meta-analyses find opposite effects at arrival, for those un- or underemployed, and for those with downward social mobility (Das-Munshi et al. 2012; Foo et al. 2018). A different review and a meta-analysis report increased post-traumatic stress disorders among migrants and refugees (Bustamante et al. 2018; Lindert et al. 2009). Some risks are gendered, and a review finds that female migrants have a greater risk of perinatal mental health issues than non-migrant women (Fellmeth et al. 2017). For developing countries specifically, a review corroborates that rural-to-urban migration often involves high psychological costs (Selod & Shilpi 2021). Taken together, the existing reviews identify key health determinants as (a) pre-migration trauma; (b) post-migration income, employment, housing, language skills and interpretation, communication, continuity of care, confidence, social support; (c) financial, legal, cultural, and spatial challenges; and (d) structural and political factors, such as discrimination, gender inequalities, and exclusion from services (Abubakar et al. 2018; Brandenberger et al. 2019; Hynie 2018).

Lastly, the UNESCO underscores that educational effects remain understudied, but most climate migrants are likely “to suffer exceptional educational vulnerabilities” due to trauma, infrastructure failure, or administrative, economic, and linguistic access hurdles (2020: 4). By contrast, in one of the few existing cross-country studies, climate migrant households reported mostly positive effects, including through better access to services; yet not all benefitted, and gains coincided with exclusion and discrimination (Melde et al. 2017). General migration studies provide additional insights. First, they show that educational selectivity shapes who migrates in the first place, and thereby also the outcomes (Bernard & Bell 2018). Second, migrants often move to improve children’s education (UNDP 2020), and even before they move, the prospect of migration can raise educational investments both for migrants and their children (Dustmann & Glitz 2011). Third, many migrants’ education profits from moving, especially when going from poorer to richer areas, where they have more years of schooling than at origin (Harttgen & Klasen 2009; UNDP 2020). Additionally, migrants from the poorest countries on average double school enrollment rates in richer countries (World Bank Group 2016). While migrants often improve educational attainment over generations, institutional and language barriers can slow the process of catching up with natives (Dustmann & Glitz 2011), and the accessibility of services is often gendered (Fleury 2016). Finally, educational effects for refugees depend on the host country and available international assistance (Bruijn 2009; World Bank 2020a).

2.1.2 A Space to Live Better: Adequate Housing, Basic Services, Pleasant Surroundings, Safety from Hazards, and Security

Climate migrants often move to cities (Adamo 2010; Black et al. 2011a), especially in the swiftly urbanizing Latin American region (Villa et al. 2017; Warn 2014). While these cities provide chances, risks also abound, including due to the effects of rapid urbanization, such as sprawl and irregular housing (Adger et al. 2020). Studies confirm that many climate migrants have constrained access to housing and basic infrastructure (Adger et al. 2020; Adger & Adams 2013; McMichael et al. 2012). For example, a cross-country study finds that their households fare worse than non-migrants concerning shelter and housing materials, which may raise vulnerabilities (Melde et al. 2017). In particular, disaster displaced persons frequently cannot satisfy basic shelter needs due to their definitive losses of portable assets and fixed capital (World Bank 2017b). Many displaced persons are forced to live in temporary shelter insufficient for lasting protection, which reinforces precarious living conditions and can trigger secondary displacement (IDMC 2020). Similarly, deficient shelter is widespread in planned relocations (Cernea 2004). A global mapping notes that main problems are “the availability and quality of infrastructure at the settlement site [as well as] architectural layout of homes and incompatibility with traditional ways of life or expectations” (Bower & Weerasinghe 2021: 9). General migration studies substantiate that housing conditions depend on context, such as financial resources, family sizes, and conditions of arrival and at destination. Key factors include legal status, family networks, property prices, the quality of information, social housing, and social benefits. Especially migrants who settle first in a new place, those with low incomes, and those subjected to discriminatory practices are at risk of substandard housing (OECD & EU 2015). Finally, most refugees live in cites, where they “share accommodation, live in non-functional public buildings, collective centres, in slums and irregular types of settlements; often their living conditions are poor, cramped and unsafe” (UNHCR 2014: 13). Refugee camps in rural areas imply their own challenges.

Next, some climate migrants, displaced persons, and relocatees succeed in reducing exposure to hazards at least initially (Melde et al. 2017). Yet, others “tend to cluster in low-cost locations exposed to environmental hazards” (Adger et al. 2020: 397), and migrants writ-large continue to move toward areas highly exposed to climate hazards, such as coastal or deltaic cities exposed to sea-level rise (de Sherbinin et al. 2012; Foresight 2011; McGranahan et al. 2007). Cities also tend to expose migrants to unfamiliar environmental stressors, such as water and air pollution (Carrasco-Escobar et al. 2020; Chen et al. 2013) or urban heat stress (Tuholske et al. 2021). Therefore, the IPCC names new hazard exposure in destinations as one illustrative cause why certain forms of climate migration can “compromise human security” (Adger et al. 2014: 758). Similarly, a global relocation mapping highlights the risks related to secondary hazard exposure in many, but not all cases (Bower & Weerasinghe 2021). Within cities, many migrants also have higher vulnerabilities to hazards than locals because they often live in densely populated areas, and have no access to basic services or lack political voice (Adger et al. 2020; Adger & Adams 2013; McMichael et al. 2012). In general, disaster displacement risk is significant in many urban areas (IDMC 2021c). Disasters are more likely to harm the poorer segments of urban populations with limited political voice and subjected to structural disadvantages, which often includes migrants (de Sherbinin et al. 2007; Hallegatte et al. 2017). Migrants may also lack knowledge of hazards in new settings and fail to receive or understand early warnings (Melde et al. 2017). Even so, few cities account for the vulnerabilities of climate migrants (Gemenne et al. 2020) and their climate risks in destinations remain underreserached.

Beyond hazards, climate migrant households reported also greater human insecurity than non-migrants in one cross-country survey (Melde et al. 2017). A review on general migration reveals that women, especially those with irregular status, are at high risk of violence, sexual exploitation, and forced prostitution (Fleury 2016). Interpersonal and self-directed violence are also rife after disasters and during displacement (Asgary et al. 2013; Bruijn 2009; IDMC 2020; McMichael et al. 2012; Rezaeian 2013; True 2013). While women displaced by disasters face high risks, men can also end up in vulnerable situations, for example, when norms demand male risk-taking behavior (Demetriades & Esplen 2008; Rigaud et al. 2018). Finally, insecurity is equally frequent in relocations. For example, a review in the Pacific region finds that relocations can raise conflicts between ethnic groups or over land and fishing rights (Yates et al. 2021). Relocatees may also experience organized violence or be relocated by force, occasionally for political or economic motives (Webber & Barnett 2010).

2.1.3 Social Relatedness

Even though the effects of climate migration on social capital and networks are salient, they remain underexplored. First, when suffering climate impacts, social factors affect capacities to remain in place or to migrate and the related outcomes (Nawrotzki et al. 2015; Webber & Barnett 2010). Second, migration itself also affects social relatedness, but the results are inconclusive. For example, most climate migrant households stated positive effects on family relations in one cross-country study (Melde et al. 2017). Yet, other studies argue that social exclusion is frequent for climate migrants in many cities (Adger et al. 2020). Moreover, reviews point out that displacement and relocations frequently fragment social networks and cohesion, which contributes to psychosocial issues, and may engender frictions, tensions, and discord among generations (Bower & Weerasinghe 2021; Schwerdtle et al. 2020). In relocations specifically, community support and social support are key to mitigate challenges and ease adjustment to unfamiliar settings; however, the possibility to maintain social relationships hinges upon community structure and relocation design (Yates et al. 2021).

Expanding these findings, reviews on general migration confirm that social networks are key for support in destinations (Munshi 2020; Selod & Shilpi 2021) and for recovery after displacement (World Bank 2017b). Social impacts depend on changing family and household dynamics throughout migration (Bedford et al. 2009). The distance to family members and translocal relationships can cause psychological distress for migrants (Selod & Shilpi 2021), and losing social support can have strong mental health repercussions (Fellmeth et al. 2017; Hynie 2018). Female migration can occur as a self-sacrifice for the family, but can also strain family ties, for example when it challenges patriarchal norms or when it involves translocal parenting. Female migrants with legal status and formal employment have better chances to become socially empowered (Fleury 2016). After disasters and displacement, social relations can change profoundly (Bonanno et al. 2010; Cohan & Cole 2002). For example, mental diseases and worsened parenting efficacy or support can harm family functioning (Green et al. 1991; Hackbarth et al. 2012; McDermott et al. 2010; McDermott & Cobham 2012; McFarlane 1987; Pfefferbaum et al. 2016). Successive displacements are common and can also disrupt social capital and networks, because building social ties requires years (World Bank 2017b). On the contrary, supportive primary relationships as well as family or community cohesion and support raise forced migrants’ resilience against mental illnesses (Siriwardhana et al. 2014).

2.1.4 Effects on Sending Communities

Climate change, migration, and development are intertwined across localities (Lucas 2021a). Generally speaking, emigration can bolster development for sending communities, but it also implies costs and losses, so that the net effects on adaptive capacities in hometowns remain debated (Vinke et al. 2020): Webber and Barnett argue that “in most cases, and in aggregate, migration seems to contribute positively to the capacity of those left behind to adapt to climate change” (2010: 22), whereas others argue that emigration can increase precarity (Porst & Sakdapolrak 2018).

First, economic effects for sending households “are usually mixed… and in large measure [migration] can be seen as either virtuous or vicious for development” (Katseli et al. 2006; Martin 2005: 190). While sending areas of climate migrants often profit, the effects depend on the context and phase of migration and tend to be unevenly distributed (Rigaud et al. 2018; Webber & Barnett 2010). Since cross-border migration is too costly for many people, it can initially raise inequality in sending areas (see below on remittances); yet over time, the relationship between emigration and inequality at home seems to be U-shaped (McKenzie & Rapoport 2007). Wages in sending countries can increase for workers with similar, substitutable skills, while those with different or complementary skills stand to lose (World Bank 2018). A systematic review finds that international migration reduces labor force in rural areas, which can trigger child labor, land control losses, and a feminization of agriculture at home (Obi et al. 2020). Emigration can both reduce the labor force and erode traditions at home and thus raise food insecurity (Kothari 2003; Sherman et al. 2015). While emigration can also generate employment at home, labor availability, service delivery, and equality may decrease if many highly-skilled persons migrate (Katseli et al. 2006). Finally, human capital effects depend on context and can include brain drain (loss of human capital due to high-skill emigration), brain gain (increase in human capital investment), or brain circulation (knowledge diffusion and economic integration) (Beine et al. 2008; Kone & Özden 2017; McAuliffe et al. 2019).

Second, financial flows such as remittances, diaspora bonds, and fundraising by hometown associations can support relatives at home (Mendola 2012; Mohapatra et al. 2012). Reviews highlight that remittances can raise expenditures and reduce poverty for migrant households (Obi et al. 2020; Ratha et al. 2011), and children with migrant parents have usually more household assets (DeWaard et al. 2018). Multiplier effects due to remittances spending and changes in the labor market can also reach non-recipients (Katseli et al. 2006; Mendola 2012). As often-stable inflows, remittances can provide insurance against shocks or buffer them; they can thus also support recovery from—and to some extent preparedness to—climate impacts (Banerjee et al. 2017; Bendandi & Pauw 2016; Rigaud et al. 2018). Migrant networks may also aid home areas by delivering or organizing humanitarian or development projects, information, and political action (ADB 2012; Webber & Barnett 2010). Moreover, hometowns may profit from social remittances, such as technology and skill transfers (de Haas 2009; Levitt & Lamba-Nieves 2011). For example, migration can facilitate the spread of mobile phones (Hübler 2016; Kothari 2003). For climate migration, results are inconclusive: one cross-country study finds positive effects of remittances on poverty reduction but less so on adaptive capacity (Melde et al. 2017), while another one is more positive on the adaptive benefits (Scheffran et al. 2012). However, remittances also imply challenges. First, they can create dependencies (de Haas 2010a). Second, remittances may raise inequalities since the poorest often cannot migrate or their migrant members cannot remit (Le Dé et al. 2013; Schade et al. 2016; Tacoli 2011), but such effects remain debated (Azizi 2021). Third, benefits “might come at substantial social costs to the migrants and their families” (Ratha et al. 2011: para 1). For example, short-term employment migrants often have to service loans and fees for long, and thus, “the absence of regular remittances because of loan repayments poses a double burden on the left-behind” (Bedford et al. 2009: 64).

Third, remittances can have several health effects. While they are primarily spent on food, more affluent recipients also invest in health care and housing (UNDP 2009). Two reviews confirm that remittances often improve educational and health indicators, including food security (Obi et al. 2020; Ratha et al. 2011). A different review qualifies that remittances can reduce food insecurity and underweight, but not chronic undernourishment, while they may also increase unhealthy food intake (Thow et al. 2016). For climate migration, a cross-country study finds that it can stabilize food consumption in sending areas, yet outcomes depend on the profiles of migrants (Warner & Afifi 2014). Besides remittances, visits and returns can also improve health knowledge and preventative health care in sending areas (Hildebrandt & McKenzie 2005; UNDP 2009; Yang 2009). Nevertheless, debates continue if some groups staying behind can suffer from increased morbidity for certain diseases or conditions (Abubakar et al. 2018). For example, while one review does not find different or worse outcomes for the physical health of children with absent parents (Abubakar et al. 2018), another meta-analysis reports that migration strongly worsened nutrition and mental health of left-behind children and adolescents “with no evidence of any benefit” (Fellmeth et al. 2018: 1). The health of left-behind, older family members in rural areas can also deteriorate (e.g. Ao et al. 2016).

Fourth, the effects of emigration on education at home can be mixed. They partially depend on “changes in family composition and the role of women within the family and society” (Katseli et al. 2006: 9). Especially wealthier households also spend remittances on education, which raises schooling rates and reduces child labor (Lucas 2021c; UNDP 2009; Yang 2009). Additionally, high-wage emigration can create brain gain when it raises stayers’ desires to move and thus incentivizes spending on education (Dustmann & Glitz 2011; Kone & Özden 2017), but not always, for example, when more housework reduces school attendance and attainment (McKenzie & Rapoport 2011).

Fifth, the social effects of emigration on stayers differ and are shaped by age and gender. If men move, female stayers may experience greater autonomy, but also increased workload and emotional strains (Abdurazakova 2013); if women move, men may have to assume (often-unfamiliar) household and child caring duties, which can be challenging if norms frame such work as feminine (Fleury 2016). Long separations of children from parents and of couples create social strains in developing countries worldwide (Lucas 2021c). Ultimately, extended family networks are a key mediator for children and older adults to manage the absence of migrants (Bedford et al. 2009).

Lastly, the effects of migration on the environment at home are not fixed. Environmental stress can be reduced as population pressure is lowered, land abandoned, and agriculture de-intensified; yet weakened local resource management after emigration and rising consumption due to remittances can create opposite effects (Gray & Bilsborrow 2014; Rigaud et al. 2018; Scheffran et al. 2012).

2.1.5 Effects on Receiving Communities

While not the focus of this study, I also briefly review the possible effects of immigration on receiving areas. Overall, such effects depend on migrants’ social factors as well as markets, structures, and policies at their destination (Rigaud et al. 2018; Webber & Barnett 2010). While climate migrants are often “used as ‘scapegoats’ for a host of larger socioeconomic structural issues” and portrayed as creating “competition, tensions and conflict” in receiving areas, reality is more complex (Melde et al. 2017: 11). First, migration usually creates net economic benefits for receiving countries (Golding et al. 2018; McKinsey&Company 2016; World Bank 2018) and refugees also contribute to growth if they can work (Betts et al. 2014). Yet, unreceptive settings can induce brain waste (McAuliffe et al. 2019). Aggregate labor markets effects are often marginal, defined by the degree of skill complementarity between migrants and non-migrants (Clemens et al. 2018; OECD 2018; Ruhs 2015). If migration restocks the labor force, it supports wealth, taxes, public goods, pensions, and care systems; if labor is scarce overall or in certain segments, results can be mixed and may include labor market segmentation (Golding et al. 2018; McAuliffe et al. 2019; Webber & Barnett 2010). For example, meta-analyses in richer countries find that immigration affects wages and work marginally while native job losses are small at most (Longhi et al. 2008, 2010). However, a review argues that the effects can differ for internal migration in developing countries; because their labor markets tend to be more “dualistic and isolated… with a predominance of unskilled workers, migration could be more likely to depress local wages and the employment rate” in the short term (the longer term remains unclear) (Selod & Shilpi 2021: 29). The impacts of displacement camps for host areas are also mixed (Cazabat 2020). Such camps can increase wealth for nearby rural households, but also raise food prices and reduce wealth in urban areas (Alix-Garcia et al. 2018; Alix-Garcia & Saah 2010; Taylor et al. 2016). Regarding health and education, the evidence is mixed. Immigration creates additional demand for such services and may overcharge them, but it can also provide benefits. For example, the immigration of health workers is essential to maintain care systems in many countries (Connell 2010; Kingma 2018; Stilwell et al. 2004). As another health effect, migration and travel can, under certain circumstances, be one factor in the spread of communicable and infectious diseases in transit and receiving areas (Bedford et al. 2009; Heymann 2007; Tognotti 2013). Next, concerning a space to live better, findings are mixed. Internal migration is key for urbanization, a salient process in in the Global South (Cerrutti & Bertoncello 2003; Murillo 2014; UNDESA 2018; Villa et al. 2017). Urbanization, in turn, can cause “negative externalities, such as high unemployment, strained infrastructure, and environmental degradation”; however, cities can also help reduce poverty and environmental harm and are “often unfairly stigmatized” (Marcotullio et al. 2012; Rigaud et al. 2018: 35). How immigration affects urban adaptive capacities remains unclear (Barnett & Adger 2018), while its effects on local environments depend on context: ensuing clustered population growth can result in land-use change or degradation and pollution (Bilsborrow 1992; Hugo 1996), but not necessarily so (Muradian 2006; Price & Feldmeyer 2012). Finally, in receiving areas, immigration also has complex and context-dependent fiscal impacts (Rowthorn 2008), as well as effects on innovation, foreign direct investment, official development assistance, and trade flows (Egger et al. 2012; McAuliffe et al. 2019; Webber & Barnett 2010); on demography (Coleman 2008; Rodríguez-Vignoli & Rowe 2018); on social cohesion and diversity (Bauloz et al. 2019; Reitz et al. 2009); and on conflicts (Burrows & Kinney 2016).

2.2 Effects on SWB

SWB—present emotional and cognitive evaluations of need fulfillment alongside views of the future—offers an additional analytical lens (see Section 2.3). Below, I first review the evidence on the general changeability of SWB and its effects before examining the links between migration and SWB.

2.2.1 Changeability and Effects of SWB

The evidence demonstrates that while certain factors stabilize present SWB, other processes can change it enduringly (Veenhoven 2015). Change factors exist at the macro and individual levels, with different durations of effects; the interaction of all the factors discussed below determines SWB (Diener et al. 2017a). First, the large SWB differences between countries is partially due to varied macro conditions, such as wealth, rule of law, freedom, inequality, corruption, and climate (Rentfrow 2018; Tay et al. 2014). This livability of the surroundings may explain about three quarters of the SWB variance between states (Veenhoven 2015). Research on the effects of the physical environment is limited; it suggests that SWB is highest in moderate climate zones; air quality can slightly decrease SWB; and differences between rural and urban areas are marginal (Veenhoven 2015).

Second, besides macro conditions, individual factors shape SWB, and people’s life-ability may explain up to half of the variance (Veenhoven 2015). Genes and related personality and proficiencies fix a certain “set range” of long-term SWB (Diener & Biswas‐Diener 2008: 162) but how strongly this range is “set” remains disputed (Anglim et al. 2020; Bartels & Boomsma 2009; Neve et al. 2012; Røysamb et al. 2018). Age and gender may have effects (Biermann et al. 2022; Senik 2015; Veenhoven 2015). Beyond these stable, inner life-ability factors, change can occur at the individual level for three reasons. To start with, a review and a meta-analysis find that psychological interventions can partially raise people’s SWB over the long term (Koydemir et al. 2021; Solanes et al. 2021). In addition, brief stimuli such as weather, season, moods, random events, and preceding questions can change SWB self-reports shortly, but appropriate survey design and data aggregation over time can reduce their influence (Diener et al. 2017a). Moreover, certain life events and changes in life circumstances can alter SWB, although the magnitude of effects has been debated (Cummins 2014). Hedonic adaptation level theory stipulates that people get used to new stimuli as well as to recurring or constant situations (see Section 2.3). Yet, while most people adapt to adverse or positive situations to a certain extent, a meta-analysis emphasizes that SWB is not static or always adaptable (Luhmann et al. 2012). Recent life events can shape well-being for some time (Suh et al. 1996) and certain major life (or course of life) events can shift SWB levels lastingly (Luhmann et al. 2012). Meta-analyses and longitudinal research find that major life events concern more the cognitive than the emotional dimension of SWB, and can have diverging impacts on these two features. They do not necessarily depend on the perceived desirability of the events. And their effects are more pronounced for certain individuals than others (Anglim et al. 2020; Luhmann et al. 2012; Luhmann et al. 2013; Yap et al. 2014).Footnote 2 A longitudinal, nationally representative study in Australia reveals that positive events, such as monetary gains and retirement can lastingly raise cognitive satisfaction, although followed by emotional adaptation (Kettlewell et al. 2020). Conversely, so-called scarring due to certain strong negative life events, such as unemployment, can enduringly reduce SWB even after remedy of the events (Jovanović 2019; Mousteri et al. 2018). For example, SWB can return to previous states after people are widowed or separated, but health shocks and financial losses decrease cognitive satisfaction and emotional balance without hedonic adaptation (Kettlewell et al. 2020). Migration figures among the life events that can shift present SWB beyond adaptation, as discussed in detail further below (Kettlewell et al. 2020; Luhmann et al. 2012; Luhmann et al. 2013).

Similarly, subjective future-oriented states can change to some degree, although a certain disposition for hope(lessness)Footnote 3 seems stable (Hellman et al. 2013). Hope has diverse internal and external sources (Pleeging et al. 2021b), some of which are relatively firm (such as biologyFootnote 4 and social factors or culture and history), whereas others appear more variable (such as past experiences and choices or social networks and work). Therefore, hope can be lost due to major life transitions, traumatic events, loss, failure, alienation, and lack of social connections, control, or progress (Edey & Jevne 2003; Snyder 2002). However, strong evidence simultaneously demonstrates that specific interventions can raise hope (Hernandez & Overholser 2021; Long et al. 2020).

Feeling well and being content with life in the present and having hope for the future are not only states that people desire but also have major downstream effects on them, as converging empirical evidence proves. Present SWB can raise work productivity, creativity, and how people contribute to their organizations; support health and longevity; improve caring social relationships; as well as increase virtuous behavior, such as volunteering and donations (Diener et al. 2017a; Diener et al. 2017b; Diener et al. 2018b). Due to these beneficial ripple effects, more positive emotions and cognitive satisfaction are mostly desirable, yet not at all costs, without limits, or in all situations (see Section 2.3). Likewise, having hope or not strongly affects several aspects of life. According to a review, possible internal effects of hope include motivation, personal development, positive feelings and comfort, meaning and purpose, or spiritual experiences, but also disappointment. Potential external effects include shared feelings of identity, socially shared capital, smoothed social interaction, virtuous or ethical behavior, or mobilizing large groups, but also societal disillusionment and abuse or manipulation of people (Pleeging et al. 2021b). Views of the future, such as outcome expectancies, are a key determinator of action and behavior (Feather 1992; Wigfield & Cambria 2021; Wigfield & Eccles 2000).Footnote 5 Hope and optimism are significantly correlated with better performance and success at work, improved social relations, and increased psychological and physical health, whereas hopelessness and pessimism deepen perceptions of vulnerability, uncontrollability, and unpredictability, and block action and engagement (Carver & Scheier 2014; Cheavens & Guter 2017; Forgeard & Seligman 2012; Long et al. 2020). Hope and positive future self-views also strongly predict SWB in the present (Lu et al. 2018; Satici 2016; Werner 2012)Footnote 6 and the relationship seems bidirectional (Long et al. 2020; Pleeging et al. 2021a). Similar as for present SWB, more optimism and hope are typically beneficial, but exceptions exist (see Section 2.3). Finally, how people subjectively perceive their future time also affects well-being (Kooij et al. 2018). One reason is that different future time perspectives result in different motivations for action (Lens et al. 2012).Footnote 7 For example, children focus on exploring and learning, but people who perceive their future time as limited increasingly prioritize emotionally meaningful goals (Liao & Carstensen 2018).

2.2.2 Migration and Present SWB

While many migration theories presume that people move to maximize their standard of living (Brettell & Hollifield 2014b; King 2012; Massey et al. 1993; Piguet 2018), more complete measures of well-being are gradually entering academic debate and policy frameworks (Laczko & Appave 2013). Migration requires wide-reaching adjustments in daily lives, and OWB effects do not necessarily converge with SWB outcomesFootnote 8; the related literature is relatively new but has advanced over the past decade (Haindorfer 2019a; Helliwell et al. 2018b; Hendriks & Commandeur 2018).

The SWB concept has to my knowledge not yet been directly applied to climate migration, but indirect evidence suggests that it can provide valuable additional insights. For example, one review finds that climate migration and relocation in the Pacific can render “new hope and reliefs from fears of … hazards” but “even the least disruptive movements caused significant stress”, and can trigger strong fear, sadness, distress, and resentment by disrupting relationships to land, culture, identity, and social networks (Yates et al. 2021: 1). Studies only focused on OWB would miss such key findings.

Given the shortage of SWB studies on climate migration, research on general migration provides supplementary, but occasionally mixed results, as discussed below. Most studies focus on cross-border free and improvement migration to rich destinations, but less so on internal, distress, or survival movements (Hendriks & Commandeur 2018; Knight & Gunatilaka 2018). Existing analyses are mostly quantitative, cross-sectional comparisons between migrants and stayers (non-migrants), matched stayers, or natives at one point in time (Bartram 2015; Hendriks & Bartram 2019; Laczko & Appave 2013). Comparisons to matched stayers can reveal information that comparisons with natives may conceal due to intrinsic differences between the migrant and the local population (such as culture or language). Nonetheless, these cross-sectional approaches cannot unveil how migrants’ present SWB develops over time, and may entail further problems. In comparisons with locals, ecological fallacy can be an issue; in comparisons with stayers (and for longitudinal studies), selectivity and endogeneity can be problematic (Bartram et al. 2013; Haindorfer 2019a). For example, studies cannot rule out that people who migrate are inclined to more or less happiness than stayers. Various studies try to control for selection bias through statistical methods such as matching, which cannot fully account for the problem (Hendriks 2015). A few natural experiments exist that avoid selection bias (e.g. Stillman et al. 2015), and longitudinal studies also provide robust insights (e.g. Chen et al. 2019)

To start with, the evidence highlights that migration conditions strongly affect SWB (Bartram et al. 2013; Hendriks 2015). The degree of voluntariness is one key factor (Bartram 2015). Survival or distress migration often challenges mental health for long, including through suffering and trauma (McMichael et al. 2012; Murray et al. 2008; Schwerdtle et al. 2017). Conversely, free or improvement migrants may have higher capabilities and better chances to meet their goals. However, they could also have greater expectations and thus be more likely to be disappointed after settling. The exact SWB effects depend on drivers, reasons, and events behind migration, and “[i]t would be desirable to investigate the happiness consequences of migration by migration motives” (Nowok et al. 2013: 999). Yet, SWB data on forced migration remains “virtually nonexistent” (Hendriks & Bartram 2019: 286). A meta-analysis finds that SWB effects are often positive for voluntary movements but results are unclear for involuntary cases (Luhmann et al. 2012). Gallup data suggests that refugees in Germany raised their SWB, but less so than other migrants (Helliwell et al. 2018b). By contrast, a longitudinal study in the UK (2002–2012) shows that voluntary migrants maintain high levels of well-being, but older internal migrants who move involuntarily (for example, due to health reasons or a split from the partner) also decrease the well-being decline that can be linked to ageing (Finney & Marshall 2018). Beyond voluntariness, the spatial, cultural, and linguistic distance covered by migrants are other key factors shaping SWB conditions. Thus, below, I first discuss results on internal migration, the focus of this dissertation, and complement them with findings on cross-border flows.

For internal migrants, the evidence is still limited for poorer countries (Helliwell et al. 2018b). Nevertheless, various high-quality longitudinal studies and cross-sectional analyses offer mostly matching results. To begin with, two longitudinal studies for poorer countries indicate negative effects. Research in Indonesia (2000–2007) highlights that people with higher aspirations self-select into internal migration, but even economic success may fail to raise their SWB (Czaika & Vothknecht 2014). Similarly, a study in rural Pakistan (1991–2013) finds that migrants are 12–14 percentage points less likely to feel happy or calm despite strong economic gains, even when accounting for selection effects (Chen et al. 2019). For richer countries, longitudinal studies render more mixed results. An analysis in South Africa (2008–2012) indicates that rural–urban migration raises incomes but reduces SWB by 8.3% four years after moving, even if controlled for self-selection (Mulcahy & Kollamparambil 2016). Similarly, a nationally representative study in Australia (2002–2016) finds that moving homes lastingly reduces cognitive satisfaction but not emotional balances (Kettlewell et al. 2020). Yet in other countries, results seem more positive. Data in Great Britain (1996–2008) documents that migrants’ SWB strongly declines before moving, then increases back to initial levels after settling, but does not rise further (Findlay & Nowok 2012; Nowok et al. 2013). The authors control for selection effects and find no SWB differences due to gender or spatial distance. A study in Germany (1985–2016) identifies a causal SWB effect of migration, even when controlling for selection effects. Both genders but only long-distance migrants experience an anticipation effect of migration that reduces SWB before and shortly after moving (for example, due to preparation stress or prior overestimation of negative effects). Yet after settling, male migrants reach lasting SWB gains of 0.35 points on a scale from 0 to 10, whereas women return to initial set points. Urban destinations raise SWB more permanently than rural ones (Kratz 2020). Similarly, in a Finnish study (1966, ‘67, ‘80, ‘97), only rural–urban male migrants reported significantly higher SWB than non-migrants (Ek et al. 2008). Moreover, a cross-sectional analysis in China shows that migrants achieve higher incomes but lower SWB than stayers in rural areas (Knight & Gunatilaka 2010, 2018). Related, a small-N study in Germany accounting for selection effects finds that internal migrants have less SWB than locals (Hendriks et al. 2016), similarly as in China (Cheng et al. 2014) and Turkey (Aksel et al. 2007). Finally, explicit questions on migration success exist but are still rare; they may also be prone to various cognitive biases (Haindorfer 2019b). While most interregional Nordic migrants in Europe (1999–2001) self-reported satisfaction with migration outcomes (Lundholm & Malmberg 2006), in Thailand, a study with explicit before-after migration questions finds that slightly more permanent migrants raise satisfaction than not, but results are worse for temporary migrants (de Jong et al. 2002).

Additionally, studies on cross-border migration provide partly transferable insights for internal migration.Footnote 9 One natural experiment study of a visa lottery for migrants from Tonga to New Zealand finds that despite large gains in OWB, migrants’ SWB declined and was “0.8 points lower than they would have been in Tonga, about four years after migrating” (Stillman et al. 2015: 11). Longitudinal data are scarce. One study of Russian migrants in Finland (2008–2013) finds that SWB rose half a year after migration, but then stabilized, while self-esteem declined (Lönnqvist et al. 2015; Mähönen et al. 2013). Other longitudinal data (1990–2014) exists for migration between former East and West Germany after reunification, which falls in between international and internal migration (Melzer 2011; Melzer & Muffels 2012, 2017). Most East-to-West migrants experienced a SWB decline around the first move and a sharp rise during the first year of settling, after which SWB plateaus, despite strong income profits. All migrants gain SWB compared to former East German peers (men more than women) but remain slightly below the level of locals. Conversely, West-to-East migrants’ SWB decreases after settling but remains higher than that of locals. Furthermore, cross-sectional studies add mixed results. Gallup data of 156 countries (2005–2017) documents that many migrants moving to happier countries tend to increase happiness, unlike those reaching unhappier countries, even if figuring in selection effects (Helliwell et al. 2018a). Several studies also suggest that migrants fare better than similar stayers (Bartram et al. 2013). For example, migrants moving from post-socialist states to richer countries raise their SWB compared to matched stayers (Nikolova & Graham 2015). In a similar fashion, the SWB of most but not all immigrants in Canada and the UK increases relative to stayers, particularly strongly for migrants from countries with lower average SWB (Frank et al. 2016; Helliwell et al. 2020). In like manner, a study using Latin America Gallup data shows that international emigrants became modestly happier than comparable stayers once living abroad, while before moving, they had used to be unhappier (but wealthier) than those wishing to stay. SWB gains were greater for Latin American than non-Latin American destinations and highest for middle-aged migrants in their working years (Graham & Nikolova 2018). However, conflicting data exists; specifically, older studies suggest that Latin American emigrants realize income gains but remain less satisfied than stayers (Graham 2016; Graham & Markowitz 2011). On the contrary, studies agree that only few international migrants reach similar SWB levels as locals (Bartram et al. 2013; Hendriks & Commandeur 2018). In Canada and the UK, migrants’ SWB approaches that of natives but remains slightly lower (Helliwell et al. 2020) and in thirteen European countries, it stays below that of natives (Safi 2010; Vroome & Hooghe 2014). Scholars suspect that the context influences outcomes, and that migrants remain unhappier than (even comparable) natives in richer countries, while they have more comparable levels in poorer countries (Bartram et al. 2013; Safi 2010). Lastly, regarding the question of adaptation, data suggests that international migrants’ SWB plateaus over time and the second generation may not be more satisfied than their parents (Helliwell et al. 2018b; Safi 2010). However, studies on SWB adaptation after migration are rare and more conclusive findings on long-term effects are “clearly needed” (Luhmann et al. 2012: 610).

All things considered, theory and data point to several determinants of migrants’ present SWB. Theory holds that SWB can be shaped by (a) adaptation to new situations, (b) social comparisons, or (c) altered aspirations after settling (see Section 2.2.3). Existing data illuminates that these three mechanisms can but do not necessarily overlap (Haindorfer 2019a; Melzer & Muffels 2017). Gains compared to own past living standards seem key, followed by comparisons to peers in home villages and destinations as well as self-ranking in society at large (Bartram 2010; Clark & Senik 2010; Gelatt 2013; Melzer & Muffels 2017; Senik 2009). Along the same vein, several empirical studies explain migrants’ worse SWB compared to stayers—or the lack of full SWB convergence—by false expectations, lacking information, unrealized or rising aspirations, shifting reference groups, and relative deprivation (Chen et al. 2019; Czaika & Vothknecht 2014; Knight & Gunatilaka 2018; Mulcahy & Kollamparambil 2016). Many of them focus on how income changes affect migrants’ SWB (Haindorfer 2019a), although income is not the major driver of SWB and may thus have limited explanatory power (Hendriks & Commandeur 2018). Some studies link migrants’ SWB gains to income increases and find no evidence for hedonic adaptation (Melzer & Muffels 2017), but others argue that even high-income gains do not raise SWB (Chen et al. 2019; Stillman et al. 2015). One well-established explanation could be that comparison effects result in complex SWB effects.Footnote 10 Social comparisons and footprint effects seem to influence SWB simultaneously. On the one hand, the SWB in migrants’ areas of origin—and thus the conditions into which they were born—have “small but significant footprint effects” for their SWB even years after settling (Helliwell et al. 2020: 1637). On the other hand, migrants’ SWB also depends on the average SWB of locals, to which they tend to converge to a certain degree. Data suggests that “on average, a migrant gains in happiness about three-quarters of the difference in average happiness between the country of origin and the destination country” (Helliwell et al. 2018b: 6). Lastly, SWB also depends on macro conditions in destinations, including socioeconomic and governance variables (Hendriks & Bartram 2016). For instance, hurdles for migrants’ SWB include adverse living conditions, social and emotional costs of adjusting to a new life, discrimination, language difficulties, and less time spent on SWB-lifting activities (Chen et al. 2019; Helliwell et al. 2018b; Hendriks et al. 2016; Safi 2010; Texidó & Warn 2013).

Moving also influences the SWB of migrants’ kin who live elsewhere. The effects are context-dependent, but studies suggest that costs of moving (such as emotions related to family separation) can be high even when compared with the benefits (such as receiving remittances) (Laczko & Appave 2013). For example, a Gallup survey in 156 countries (2015–2017) shows that partial household emigration creates mixed SWB effects for remaining family members: on average, they improve life satisfaction and positive feelings, but simultaneously feel more negative emotions such as sadness, worry, and anger, especially due to temporary migration (Hendriks et al. 2018). Likewise, a study using Gallup data for 114 countries (2009–2011) finds that stayers experience higher satisfaction and positive emotions and yet also report more stress and depression. Remittances can enhance SWB gains—especially in poorer and more unequal societies and for poor respondents—but do not offset adverse effects, and impacts are less negative in areas where migration is common (Ivlevs et al. 2019). Results seem consistent in Latin America, where families with migrant members benefit from remittances and make modest satisfaction gains, but also report more depression than others (Graham & Nikolova 2018). By the same token, an earlier study finds that cross-border migration from Latin America raises satisfaction of family members staying (probably linked to remittances) but decreases nutritional security at the same time (Cárdenas et al. 2009). Other studies identify mostly negative effects: one systematic review with studies mostly on internal migration in China and in 15 other low- and middle-income countries highlights that children, adolescents, older parents, and spouses left behind suffer from physical and mental health issues (Paudyal & Tunprasert 2018). Certain effects can be gendered; a study in Central Asia documents that after male emigration, women can suffer from added care burdens and may feel isolated, deprived, and sad (Abdurazakova 2013).

2.2.3 Migration, Climate Change, and Views of the Future

The links between migration and time have only recently gained more attention (Baas & Yeoh 2019; Cwerner 2001; Griffiths et al. 2013), however, especially migrants’ views of the future remain “quite understudied” (Boccagni 2017: 2). A small number of studies exist on the objects and the subjects of migrants’ imagined futures; the determinants of baseline hopes and fears, their changeability after migration, and relevant mechanisms; their functions; and the heterogeneity of outlooks to the future.

First, the objects of imagined futures after migration are diverse but tend to include employment, income, education, better living conditions, upward social mobility, safe return and investment, or legalization (Boccagni 2017; Portes et al. 1978; van Meeteren et al. 2009; Wake et al. 2019; Yeboah 2021). Migrants do not only imagine a future for themselves, but subjects of their views of the future frequently involve peers. Primarily, views of the future tend to involve other generations. Even when migration displaces or postpones migrants’ own hopes over time, many of them still hold externalized hope for their children (Boccagni 2016, 2017; Pine 2014; Wake et al. 2019).

Next, migrants’ initial views of the future depend on socioeconomic and demographic factors as well as personal factors. Often, people with high aspirations self-select into migration (Czaika & Vothknecht 2012), but a lack of information can bias their expectancies (Knight & Gunatilaka 2010). Still, views of the future are not static during migrants’ life course: imagined futures often appear open, blurred, and accessible early after moving but over the course of migration, relative deprivation and worsened social status can gradually flatten these imaginations and make them more uncertain, ambivalent, and closed (Boccagni 2017). Other studies confirm that risks and uncertainty created by migration can alter views of the future (Wake et al. 2019; Williams & Baláž 2012), sporadically creating oscillations between hope and despair (Pettit & Ruijtenberg 2019). Notwithstanding, even forced migration can create hope if refugees feel safe and welcome and improve their living conditions (Siriwardhana et al. 2014). Finally, rural-to-urban migrants may increase their aspirations for the future, for example, regarding asset wealth, once they become established (Chen et al. 2019).

Migrants create future expectations through various mechanisms, which include chances and problems in the present, such as poverty, inadequate living conditions, discrimination, and exclusion (Koo 2012; Ming et al. 2021; Pettit & Ruijtenberg 2019; Wake et al. 2019). Views of the future also depend on personal-level processes, such as evaluations of past accomplishments and skills (Portes et al. 1978), as well as comparisons between migrants’ present utility of life and the socially-expected utility in life course stages (Hu et al. 2020). Moreover, a ‘stress-is-enhancing’ mindset and the belief in upward social mobility are coping mechanisms that can mitigate hopelessness in deprived rural-to-urban migrants (Ming et al. 2021). In a similar fashion, climate relocatees who trust in god’s provision or protection often hold hope and determination for a better future (Yates et al. 2021).

Critically, views of the future have varied functions for migrants. Hope often motivates action, although not always in ways stringent with desired outcomes (Boccagni 2017). Moreover, hope can provide consolation. Many low-wage migrants endure hardship in the present and near future only because they are oriented toward hopes for the long-term future (Pine 2014), and hope helps forced migrants to navigate post-displacement challenges (Umer & Elliot 2021; Yohani & Larsen 2009). Nevertheless, being hopeful is not unequivocally positive. For example, detained asylum seekers often suffer from extreme uncertainty and despair, yet overfocusing on the unrealistic hope of moving to safety can further diminish daily functioning (Turner 2017). In general, uncertainty or unpredictability—for example regarding duration of migration, employment or legal status—can also affect migrants’ decisions, such as if to enter serious social relationships (Griffiths et al. 2013).

Overall, the limited existing literature suggests that migrants’ hopes and fears regarding the future are heterogeneous. For example, studied Rohingya refugees in Bangladesh were “cautious and constrained” regarding future expectancies (Wake et al. 2019: 10); rural-to-urban migrants in China suffered from low expectations, high hopelessness, and high uncertainty for their futures (Knight & Gunatilaka 2010; Koo 2012; Ming et al. 2021); and in Ghana, young rural-to-urban migrants’ held hopes despite constraints (Yeboah 2021). A review of climate relocations in the Pacific documents this heterogeneity. It finds that relocatees frequently suffered from anxiety due to the uncertainty of their futures and the unfamiliarity of their new environment as well as from fear of near hazards and concerns about intensifying climate change. Simultaneously, some relocatees gained a sense of safety after relocation, and many held hope and determination for a better future (Yates et al. 2021).

While few (climate) migration studies examine people’s views of the future, increasing evidence highlights that climate change itself strongly affects future expectations (Doherty & Clayton 2011; Helm et al. 2018; Manning & Clayton 2018), and one can reasonably assume that some of the insights from this literature also apply to migrants from areas affected by climate hazards. Climate change shapes people’ future expectations through various mechanisms. On the negative end, general concerns about uncontrollable, unpredictable, and uncertain climatic changes can induce negative views of the future, perceived helplessness, and inaction (Albrecht 2011; Hayes et al. 2018). Moreover, approaching or already experienced climate impacts can create uncertainty, generalized anxiety, hopelessness, feeling of doom, resignation or fatalism (Bennett & McMichael 2010; Clayton 2020; Fritze et al. 2008; Hayes et al. 2018). As one example, despair can ensue when climate impacts destroy homes or cherished environments (Albrecht et al. 2007; Albrecht 2011; Tschakert & Tutu 2010; Warsini et al. 2014). On the more positive end, hope is possible as long as people perceive chances to take action (Fritze et al. 2008), and vice versa, active, constructive hopes can raise psychological adaptation or motivate action (Hayes et al. 2018; Ojala 2012). Even climate anxiety is not maladaptive per se, as long as it does not induce rumination or block action (Clayton 2020). Furthermore, climate threats can raise cooperation, compassion, and bonding that ultimately contribute to optimism (Edwards & Wiseman 2011; Hayes et al. 2018; Ramsay & Manderson 2011). Lastly, optimism, hope, and faith protect against disaster trauma and help people to cope with related severe losses (Cherry et al. 2017; Hackbarth et al. 2012; Hirono & Blake 2017).

3 Summary and Implications for this Study

In this section, I briefly synthesize the main review findings on climate migration and infer implications for this study. Conversely, the effects of climate immobilities remain a key research gap.

The evidence on how migration can alter OWB is most robust for effects on livelihoods and health, while education, social ties, security, as well as hazard exposure and vulnerability are explored less. There are also few studies that comprehensively assess the interrelations of OWB variables and their links with SWB. These areas indicate research gaps that this study partially aims to bridge.

First, the existing evidence suggests that climate migration may facilitate development from a secure base only under certain conditions. Forced migrants and relocatees are more likely to suffer losses in income, education, and health than voluntary ones. Gains are smaller for internal than for cross-border migrants. Worldwide, many internal migrants make stepwise livelihood gains, but the conditions of moving, household vulnerabilities, intersectional social factors, and the context of reception determine if such migration is adaptive or maladaptive. Migration also implies major costs, and the risks of un- or underemployment, discrimination, and precarious jobs are high especially for workers with rural skillsets and women. While well-prepared migrants often improve education and health through higher incomes and better services, catching up with locals is rare, and the accessibility of services depends on intersectional factors. Additionally, adverse living conditions can offset health gains. Migrants can face a high incidence of physical and mental diseases and unequal access to health care. Mental health issues are notably high for marginalized, unemployed, and female migrants.

Second, regarding a space to live better, this review shows that many migrants—especially forced ones and relocatees—live in unfavorable areas with constrained access to adequate housing or basic infrastructure, at least initially. Poor conditions are more likely for first-movers, low-income migrants, and those subjected to discriminatory structures. Cities can provide opportunities for climate migrants, but adverse living conditions can also create higher vulnerability to hazards. While some migrants succeed in reducing exposure to hazards by moving, especially poor people often have few choices but to settle in zones highly exposed to unfamiliar hazards. Insecurity, including interpersonal or organized violence, is frequent for climate migrants and often gendered.

Third, results on social relatedness are inconclusive. Studies find positive effects on family relationships for some climate migrants, whereas in other cases, exclusion and fragmentation of social networks or cohesion occur. In general, impacts depend on changing family and household dynamics, the circumstances of moving, and the experience of settling in a new place. The risk of losing social support is tangible for numerous migrants, especially as a result of forced and recurring movements.

Furthermore, climate migration also affects sending communities.Footnote 11 To begin with, economic impacts depend on the migration context and phase. They can be positive but are often unevenly distributed. How emigration influences labor markets at home depends on who and how many people leave. While remittances frequently reduce poverty and can contribute to health, food security, education, and adaptive capacities, positive effects are less likely for the poor and exacerbating inequalities are possible. Further effects of emigration on education and health are mixed and include threats of increased morbidity, such as mental illnesses for at-risk groups. Finally, migration may empower stayers but can also cause social strains; the impacts depend on the individual migration journey, intersectional factors, and the use of extended family networks.

Taken together, these complex findings illuminate why controversies on the adaptive potential and risk of migration have persisted (Bettini & Gioli 2016; McLeman 2016a; Vinke 2019). This study aims to expand prior findings by analyzing objective and subjective dimensions of well-being in Peru.

The review synthesized that present SWB partially moves within a stable range defined by genes, personality, and proficiencies, but can change according to circumstances at the macro and individual levels. The evidence suggests that hedonic adaptation occurs in some but not all situations, yet how it arises and with which limits remains less clear. However, migration figures among those major life events that can shift SWB beyond adaptation. Similarly, people have a partially stable disposition for hope(lessness), but several processes can raise or reduce positive views of the future. Present SWB and views of the future seem to influence each other to some degree. Moreover, both can have significant downstream effects on people’s health, social relationships, work, and other domains of life. However, while positive SWB and views of the future are generally beneficial, exceptions exist.

The empirical findings demonstrate that gains and losses of migrants’ present SWB are not uniform and depend on the reasons for moving, the conditions of the journey, the migration corridor, the duration of stay, as well as the conditions in source and destination areas. The limited data suggests that forced migration likely generates most SWB challenges, although many studies only provide averages, which conceal changes for specific groups of migrants. For internal migration, the breadth and depth of the evidence are robust. Various high-quality longitudinal studies emphasize that even if migrants gain economically, rising or unrealized aspirations, relative deprivation, and factors such as emotional, social, and health costs reduce their SWB in all investigated poor countries. In richer countries, longitudinal studies also find an anticipation decline in SWB before migrants implement their moves. After this decline, some studies report a return to initial levels, while others find that (male) migrants can raise SWB enduringly. Cross-sectional studies, within the discussed limitations, reveal that many migrants remain behind the SWB of both stayers and locals despite income gains, for similar reasons as cited above. Studies on subjective migration success are rare and have their own biases but suggest that migrants may perceive their endeavors more positively than SWB data would indicate. Next, for cross-border migration, a natural experiment provides strong evidence that migrants’ SWB can fall despite OWB profits, while the few available longitudinal studies suggest that SWB decreases before moving, then improves, and later plateaus. Cross-sectional analyses yield mixed findings and suggest that the direction of specific migration streams matters: migrants moving from countries with lower to higher average SWB often improve their SWB compared to that of stayers, probably due to OWB gains, unlike those migrating to unhappier countries. However, migrants can concurrently lose SWB through social comparisons in destinations that induce relative deprivation. In happy countries, migrants’ SWB converges with that of locals over time to some degree, but only few reach similar SWB levels, partially because they take footprint effects of lower SWB averages from their areas of origin with them. Conversely, in unhappy countries, migrants are often happier than locals. Few studies exist on long-term SWB effects of migration and hedonic adaptation; they suggest that SWB may remain flat over time even for the second generation, possibly due to changes in reference groups.

Next, the evidence of SWB effects on stayers is limited and inconclusive. Results seem to depend on specific migration experiences, structures in areas of origin, household characteristics, and intersectional factors. Most existing analyses suggest that emigration can raise cognitive satisfaction and positive feelings (although not in uniform ways) but simultaneously create negative feelings, such as anger, depression, sadness, and worry. Other studies, however, find mainly negative effects.

Lastly, the evidence on migrants’ views of the future is limited. Still, it seems agreed that many migrants live orientated toward the future and that the subjects of their imagined futures can be relational and intergenerational. The objects of migrants’ views of the future are frequently diverse—often revolving around upward mobility—and their aspirations high, but potentially biased. Imagined futures can strongly change across life course trajectories and phases of migration, but the direction of changes is neither linear nor predetermined because both external circumstances and personal-level processes influence these imaginations. Depending on the circumstances of moving and settling, the point in time, as well as individual factors, resultant views of the future can vary greatly. For climate migrants, climate change is likely a key influence on their views of the future. Linked general climate concerns, felt unpredictability and uncontrollability, as well as impending or past impacts can create pessimism. Conversely, hope can be a key catalyst for action against climate change while actions and linked outcomes, such as bonding, can on occasion create optimism. Finally, hope and optimism can buffer adverse psychological effects of climate change to a certain degree.

In brief, these findings have several implications for this study of climate (im)mobilities within Peru. The review yielded a breadth of evidence on migration, but implications on immobility are uncertain. First, the evidence suggests that internal migration can have mixed effects on people’s OWB. Key determinants include how voluntarily and how prepared migrants can leave; under what conditions they move and into which contexts of reception they arrive; the time passed since settling; and how intersectional factors shape (dis-)advantages throughout the process. Some gains seem possible under positive conditions of moving—which may be true for some of the more anticipatory migrants from Peru’s highlands—but even then, high costs will likely accompany these gains. Enhancing development from a secure base and finding a space to live better may prove difficult for those climate migrants in Peru with preexisting vulnerabilities and those moving under distress or survival conditions. Here, the new case studies on flood-driven displacement in Peru’s Costa and relocation in the Selva promise insights. Moreover, mixed effects on social relatedness are possible. Finally, the effects on sending communities depend on the strength of translocal ties; remittances are one central effect but should not detract from others, including those on social relationships. The case study in the Peruvian highlands includes both migrants and stayers and may thus render new insights.

Second, the review does not provide clear expectations for this study regarding SWB effects. Since context matters, it is valuable that the case studies performed in this dissertation cover different forms and distances of movement and considerably varied contexts of reception, ranging from large cities to previously uninhabited spaces. As in other poor countries, most involuntary migrants and rural-to-urban migrants are bound to lose present SWB. Only a few migrants—especially men and those moving under more positive conditions—might lose SWB shortly before migrating, but then make gains beyond the prior set range. It is possible that climate migrants’ SWB will remain worse than that of stayers and locals. Additionally, negative feelings are likely for stayers, but may be accompanied by positive feelings and gains in satisfaction, especially for poorer recipients of remittances in areas where migration is common. The review also does not offer clear implications concerning climate migrants’ views of the future. Strong stressors, including those related to climate change, could negatively shape outlooks to the future, but personal-level factors may create buffering effects. More positive views of the future are possible where migrants perceive opportunities, agency, and pathways to desired futures, including for next generations. Critically, if the affected people end up holding more hope or fear could have feedback effects on OWB.

Third, the review shows that migrants’ OWB and SWB can converge or diverge, stressing the need to analyze both jointly. In this study, I will assess who among the affected people experiences (a) true well-being, (b) deprivation, (c) dissonance, and (d) adjustment and for which reasons (see Section 2.3). Comparison theory may offer one promising explanation for differences. Finally, present SWB and views of the future can also converge to varying degrees depending on external and personal-level factors, and this study intends to add insights into the array of possible outcomes.