Climate change and international migration: Exploring the macroeconomic channel

large destination countries at high northern latitudes experience only small losses or even gains from climate change. When we assume that emigration rates respond to changes in origin GDPc according to the original migration hump function (assumption T0), we find a very different pattern than under assumption CR. Emigration is projected to increase for most countries, and most strongly so in many tropical and subtropical countries (Fig 4b). Decreases in emigration are found in high northern latitude countries. In other words, emigration is projected to increase from countries that experience economic damages; while it would decrease from countries that gain from the impacts of climate change. Again, this pattern can be explained through the emigration function implemented in our study. Indeed, under the baseline SSP 5 scenario, all countries have already crossed the peak of the migration-hump function and a negative impact would move them towards lower levels of GDPc, associated with higher rates of emigration. The opposite applies to countries like Canada, which are projected to experience a positive impact on the GDPc. PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 15 / 25

Fig 4. Country level, climate change impact on emigration under SSP5–8.5 scenario and short-term impact method for the three different assumptions regarding emigration rates. Positive values represent increase in emigration flows under the climate change impact case. The change is calculated as relative to a baseline scenario without climate change impact. Values represent the mean reached within the 30-years period of 3˚C global warming (see Methods), and are then averaged along the ensemble of GCMs. Panel (a) shows the change under the CR assumption, panel (b) covers the T0 assumption and panel (c) the TS assumption. https://doi.org/10.1371/journal.pone.0276764.g004 PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 16 / 25

The same process drives the results from the TS assumption, where results are similar as under the T0 assumption. However, since the GDPc distribution of the countries covers a por- tion of the emigration function closer to the peak, we find two main differences with the T0 assumption: Different countries in Africa and Southeast Asia show a decrease in emigration, and the range of values reached is smaller than under the T0 assumption (Fig 4c). The shifted Fig 5. As in Fig 4 but for immigration. https://doi.org/10.1371/journal.pone.0276764.g005 PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 17 / 25

emigration function in this case redefines the position of the peak, mapping a portion of the countries on its left branch, where a loss in GDPc turns into a decrease in emigration. While few countries may find themselves on the left branch of the new shifted migration-hump, many countries get closer to the peak, where rates of change in emigration rates are lower and therefore the same impact on the GDPc would produce smaller changes under the TS assumption. Immigration, under the CR case, shows a similar pattern of impact as observed for emigra- tion but with a larger number of countries showing a reduced immigration (Fig 5a). Since the only varying factor in the CR assumption is the destination country relative GDPc factor, these results largely resemble the figure of impacts of climate change on the GDPc level (Fig 1). Escaping from this pattern, in countries like Mexico, despite a strong negative impact on GDPc, immigration is projected to increase. Importantly, in our model we account separately for the return migration flows (Eq 6b). These flows, not depending directly on economic fac- tors, may play a fundamental role in bilateral migration channels characterized by a large return migration flow (e.g. USA to MEX), and drive results that diverge from common global patterns. Under the T0 assumption we find that immigration increases in almost all the coun- tries and reaches higher levels of impact (Fig 5b). Importantly, we find that many countries see, under the T0 assumption, an opposite impact on immigration compared to the CR case.

This highlights the fact that the contribution from the GDPc at the origin dominates over the destination country GDPc factor, not only in defining the patterns of change in emigration but also for those regarding immigration. Under the TS assumption, results are similar as for T0, even though the levels of impact on immigration are smaller (Fig 5c). Altogether, for both the assumptions that include a migration transition mechanism, i.e. T0 and TS, we find a global pattern of higher mobility compared to the CS case. Many countries see an increase in immigration despite the projection of large negative and asymmetric economic impacts, which would reduce their relative capacity to attract migrants. This means that migrants from countries that are impacted negatively may still choose to migrate to another country which also faces negative impacts. This illustrates a more complex mechanism than would be expected by a simplistic implication where “environmental migrants” would move from coun- tries that see damages from climate change to countries that benefit. Finally, complementing the results from the country level we also look at the bilateral flows between ten major world regions (Fig 6. We construct these figures by selecting the subset of bilateral flows between countries included in each region (S2 Table in S1 File). We sum up the country-level bilateral flows for each region for both the baseline case and climate change sce- nario. We then calculate the difference between them, for each bilateral flow between regions.

Under the CR assumption, we find that climate change increases migration to North America, Europe, East Asia, and the Former Soviet Union, from most or all other world regions (Fig 6a). All other between-region flows see a decrease due to climate change. In line with the global- level results discussed above, the climate change effect on migration is overall relatively small under this assumption. Under the T0 assumption, we find much larger effects, mostly increases, in both migration between and within regions and within them (Fig 6b). These increases generally amplify exist- ing migration routes, i.e. we find increases in migration from poorer to richer regions of the world. However, we also find increases e.g. in migration from different parts of Asia to Africa. Note that these effects are expressed as percentage of the baseline flow size, so that large rela- tive changes also in small flows become prominently visible. Thus, these figures are indicative of the relative effect of climate change on between-country flows, but do not show which regions are more affected than others in absolute terms. When the climate change effect is instead expressed in absolute terms, it becomes visible that changes in flows to poor regions PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 18 / 25

Fig 6. Impact of climate change on bilateral migration flows between ten major world regions under SSP5–8.5 scenario, short-term impact method and the three assumptions regarding emigration rates. https://doi.org/10.1371/journal.pone.0276764.g006 PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 19 / 25

like Africa are almost completely due to internal migration, which increases substantially in all regions (S5 Fig in S1 File). When considering the TS assumption we find very similar results as in T0 but with values reaching lower levels of relative difference (Fig 6c). Assumptions T0 and TS, compared to the CR case, producing projections of increasing regional bilateral migration flows show that, under the migration transition assumption, economic constraints at the origin country are a major factor inhibiting global migration movements and in general driving the patterns of both emigration and immigration response to economic climate change impacts. Values, in percentage, show the difference between the climate change case and the baseline in relative terms of the baseline case. Increases represent cases where migration under the climate change impact scenario is larger than in the baseline scenario. The external thicker arc defines the region of origin while the smaller internal arc shows the region of destination. Arrows point to the destination region. Flows represented are mean values for the period of 3˚C global warm- ing and averaged along the climate models dimension. 4 Discussion Given the severe and widespread impacts of climate change, and its potential to alter patterns of economic productivity and development, knock-on effects on international migration appear likely. In particular, if countries undergo a migration transition along with rising pro- ductivity, then productivity losses (or gains) due to climate change would slow down (or speed

on the climate model and socio-economic development scenario. Again, this global number partly hides the full effect of climate change because some flows get increased while others get decreased. Finally, assuming that the emigration-GDPc relation is dynamic and shifts along with global average income (TS) leads to results that are in between the CR and T0 assump- tions; again affecting migration flows by several percentage points for many climate models and scenarios. Our results rely on two different methods for calculating economic effects from climate change impacts [13]. Expanding on previous approaches (e.g. [12, 39]), these methods are thought to improve the analysis on both spatial and temporal scales. Indeed, on the spatial scale they use subnational GDPc data, abandoning the long standing approach using national level GDPc. On the temporal scale they disentangle weather variability impacts (annual scale) PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 20 / 25

from climate impacts (decades). Despite the improvements, these methods follow the majority of recent studies (e.g. [40–45]), focusing only on the temperature variable and its effects on the economic activity. Recently it has been shown that this channel is not the only one through which climate impacts the economy [46]. Therefore our estimates, using two different methods and one climate variable to calculate the GDPc impacts, give an indication of the possible range of outcomes, but not a complete one. The migration model, on the other hand, includes several important features that set it apart from previous studies [47–51]: (i) it considers bilateral flows between more than 170 countries globally, resulting in an unprecedented spatial coverage, (ii) it considers separately return and transit migration flows, augmenting the resolution on the type of bilateral migra- tion flows with respect to previous works, (iii) it represents the empirically observed non-lin- ear relationship between origin-country incomes and emigration rates, through the “migration hump” function. Nonetheless, our modeling approach also involves important simplifications and limita- tions. For instance, our projections of baseline population distribution, broken down by place of birth, rely on country-level natural population change rates applied to all migrant stocks uniformly. They ignore the effects of transient migration that would alter the migrant stock distribution between now and the future time period analyzed; effects that would need to be

taken into account for a quantitative prediction exercise. However, for our present analysis, we consider this simplification useful because it facilitates comparison between future and present migration patterns, both assuming the same initial distribution of relative migrant stocks. The same goes, importantly, for feedbacks from altered migration patterns on economic develop- ment and GDP [8], which we ignore for the sake of simplicity and transparency in our analysis of climate change effects. It is important to stress out that our modeling study focuses only on international migration. Internal, i.e. within-country, migration is an important phenomenon that would likely face impacts due to future climate change. Our global approach, and coun- try-level population and GDPc data do not allow for carrying out the same study at a higher spatial resolution. International migration models often include dyadic variables such as geographical dis- tance, colonial links, or common language, between origin and destination country. However, these can become insignificant once bilateral migrant networks are explicitly accounted for by including a diaspora variable ([52], p. 508). This is also the case for the present model specifica- tion, where these dyadic variables were estimated to be insignificant, and therefore neglected [26]. Moreover, the geographical distance in particular shows up more significantly in log- transformed models, while untransformed models such as ours predict more limited effects of distance [53, 54].

We acknowledge that international migration might be driven also by other macroeco- nomic factors as wage differentials and employment rates. We have opted for using only the GDPc because of the global coverage of GDPc projections data and the fact that we want to maintain the model parsimonious in the number of covariates. Moreover, by including the migration “hump function” we manage to capture a more complex dynamics of migration than that suggested by neoclassical economic theory [55] which would project decreasing migration flows as the wage differential decreases [8]. Indeed, observed migration flows have shown a more complex dynamics, with emigration rates from developing countries increasing during a first stage of economic development [15, 56]. The model also does not account for differences in within-country income distributions, nor for changes in the shape of these distributions; i.e. changes in within-country inequality. These distributions clearly differ between countries, and how they will change is likely going to depend on climate change impacts too. Another important effect comes from immigration PLOS ONE Climate change and international migration: Exploring the macroeconomic channel PLOS ONE | https://doi.org/10.1371/journal.pone.0276764 November 16, 2022 21 / 25

policies, which affect not only immigration into high income countries [36], but also return migration from these countries [37]. In our model, they are lumped together with other unob- served, country-specific variables in country-specific scaling factors. Implementing these dif- ferent dimensions of variation will be an important step in our future works for improving our analysis. Despite these limitations, our results for the first time provide a transparent and nuanced analysis of the potential impacts of future climate change on international migration via one specific channel: the impact on national economic productivity. Our results should be inter- preted as an exploratory step towards a more comprehensive and mechanistic approach of quantifying the impact of climate change on international migration, that should serve to inform future model development and data analysis in the field. Further research is needed for improving the understanding and modeling of the mechanisms driving international migra- tion, and the related pathways of climate change impacts on migration. Supporting information S1 File. Collection of data, tables and figures used as supporting information. (PDF) Author Contributions Conceptualization: Albano Rikani, Katja Frieler, Jacob Schewe. Data curation: Albano Rikani. Formal analysis: Albano Rikani. Funding acquisition: Katja Frieler, Jacob Schewe. Methodology: Albano Rikani. Project administration: Jacob Schewe. Resources: Katja Frieler. Supervision: Jacob Schewe. Validation: Albano Rikani. Visualization: Albano Rikani.

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