Climate-induced International Migration and Conflicts

1. Introduction

In the coming decades, climate change will expose hundreds of millions of people to its impacts. As summarized by the main scientific intergovernmental body on climate change in its latest report (IPCC 2014), both vulnerability and exposure will be extremely diverse across the globe. The migration of individuals and communities from the areas most exposed to environmental stress represents and will represent an important measure of adaptation to climate change. However, the resulting displacements may also produce security risks for receiving areas.

The direct link between climate change and emigration, on the one hand, and between climate change and violent conflicts, on the other, have been both researched individually. For example, Barrios et al. (2006) find that rain shortages increase internal migration from rural to urban areas in Sub-Saharan countries. Marchiori et al. (2011) report that temperature and rainfall anomalies affect both internal and international migration in sub-Saharan countries. They also predict that weather anomalies will produce an annual displacement of more than 11 million people by the end of the 21st century. Cai et al. (2016) report a positive effect of yearly temperatures on bilateral migration flows directed to OECD countries from both middle- and low-income countries, while Cattaneo and Peri (2016) find that the positive effect of rising temperature on emigration rates to urban areas and to other countries features only in middle-income economies. In very poor countries, higher temperatures reduce the probability of emigration to cities or to other countries, consistently with the presence of liquidity constraints. Conversely, Beine and Parsons (2015) find no statistically significant effect of climate-related factors such as extreme weather events, deviations and anomalies from the long-run averages, on bilateral international migration. The null effect features in all origin countries, independently of the level of income.

Regarding the second nexus, namely, between climate and violent conflicts, Miguel et al. (2004) is the first paper that includes rainfall-driven economic shocks in a conflict estimation for sub-Saharan countries. They find that lower rainfall levels and adverse rainfall shocks increase the likelihood of conflicts in African countries. Further evidence of a possible link comes from Hsiang et al. (2011) documenting more likely conflicts during hot and dry El Niño years than during cooler La Niña years. Harari and La Ferrara (2014) also report that climatic shocks increase conflicts in a geographically disaggregated analysis. Conducting a meta-analysis on quantitative studies, Hsiang et al. (2013) conclude that warming is associated with an increase in both the rate of interpersonal conflict and the rate of intergroup conflict. Burke et al. (2009) find a strong link between civil war and temperature in Africa, if one controls for country fixed effects and country-specific time trends only.

However, it appears that the results are not robust to different statistical assumptions and different specifications in the corresponding papers. For example, Ciccone (2011) reconsiders the link between rainfall and conflict found in Miguel et al. (2004). By taking into account the mean-reverting properties in rainfall, the author finds no robust link between civil conflicts and year-on-year rainfall growth or rainfall levels. Couttenier and Soubeyran (2014) document that any statistically significant relationship between climate variables and conflict vanishes if one takes into account yearly worldwide changes in civil war and the evolution of rainfall and temperature. When year fixed effects are included, the coefficient of the climatic variables drastically reduces and turns not significant compared with a specification with no year fixed effects. Buhaug (2010) finds that in African countries climate variability is poorly related to armed conflict. The article improves with respect to the previous literature by considering not only civil wars, which are battles with at least 1000 deaths, but also conflicts with a lower number of deaths, setting the threshold to 25 annual battle deaths. In addition, the author models the outbreak and incidence of civil war as distinct processes. To summarize, results seem to be highly sensitive to the way climate is modeled, to changes in the data and in the coding choices and to the use of yearly fixed effects that should take into account coincident movements in the aggregate measures of climate and civil conflict.

The possibility that climate, migration, and conflict are interlinked has been envisaged and discussed, in some of these studies, but only qualitatively. The causal link has not been adequately tested (Withagen 2014). Indeed, some of the above studies find that natural disasters increase the risk of conflicts, but do not explore the specific channels through which this relationship emerges. A sudden and mass influx of displaced people can be one of these channels, in particular when poor economic conditions and weak institutions characterize the receiving regions. Extreme climatic events such as floods, droughts, extreme heat may intensify the process of out-migration to a point that large and fast waves of migrants are not smoothly absorbed in destination countries, and this can ultimately make conflicts more likely. This threat could be exacerbated by competition over resources, ethnic tensions, distrust, demolition of social capital, and the existence of socioeconomic fault lines (Reuveny 2007). The threat could also arise if climatic shocks in hosting countries are correlated with shocks in sending locations. However, we should expect that people move from areas most exposed to climate shocks to less climate-vulnerable areas.

Ghimire et al. (2015) is the only paper that analyzes the link between climate-related human displacement and civil conflict. The paper uses historical data for 126 countries during 1985–2009 and finds that the displacement of people due to floods is not a cause of new conflicts. However, they find that flood-induced migration contributes to prolonged existing conflicts. The paper focuses only on flood-induced displacement. It is well known, however, that other events, such as gradual changes in temperature, droughts, changing patterns of precipitations or heat waves can influence migration (IPCC 2014), and this can consequently generate conflicts (Hsiang et al. 2013). The number of displaced people and the risk of conflict could then be different from the figures reported in Ghimire et al. (2015), if one were to consider a broader definition of climate change as a driver of migration. Moreover, the article considers only internally displaced people, namely, people who move within the same country. It does not consider the effect of international migration on conflict in destination countries. This is an important distinction between the present article and the one by Ghimire et al. (2015).

The objective of the article is to analyze whether gradual changes in temperature and precipitations or the occurrence of extreme events such as flood, droughts, and storms have an impact on conflicts through the migration channel. To answer this question, a macro-level empirical estimation is conducted. The rest of the article is organized as follows. Section 2 describes the methodology to compute the measure of climate migrants. Section 3 presents a description of the data and the empirical specifications. Section 4 shows the results. Section 5 provides a summary and conclusions.

2. Methodology to Compute a Measure for Climate Migrants

Drawing on Cattaneo and Peri (2016), we estimate Equation (1) allowing for differential effects on emigration between low-, middle-, and high-income countries. All coefficients are estimated for three groups of countries, namely, low (L), middle (M), and high (H) income countries, using the World Bank income classification (World Bank 2016).

T and P are average temperature and precipitation in country c, over the decade t. F, D, and S are the number of floods, droughts, and storms that occurred during the decade t.²$φr,t$captures region-decade dummies to absorb regional factors of variation in economic conditions over time, thus alleviating potential omitted variable bias. Finally, $εc,t$is a random error term, clustered by country of origin.

Following Jones and Olken (2010), Dell et al. (2012), Couttenier and Soubeyran (2014), Hsiang et al. (2013), and Cattaneo and Peri (2016), no additional controls are added to Equation (1). Climatic variables affect many of the socioeconomic factors commonly included as control variables. Things like income, population, socio-political environment are themselves an outcome of climate. If these outcome variables are used as controls, we may draw mistaken conclusions about the relationship between climate and migration. The inclusion of these additional controls in addition to temperature, precipitations, droughts, floods, and storms would produce a bad control problem, also called over-controlling problem (Dell et al. 2012).

The estimation of Equation (1) applying decadal data allows one to capture medium-run impacts of climate change. Changes of temperature, precipitations, or incidence of extreme events over annual periods have a distinct effect from the medium-run changes. Adaptation is one crucial mechanism that drives a wedge between short-run and medium-run impacts. The advantage of taking decadal data is that one can partially embody the adaptation effects and can better identify medium-run effects.

Using the estimated parameters of temperature and precipitation from Equation (1), we build a predicted measure of the climate-induced emigration flows for every country in the sample. In a following step, we allocate the emigration flows departing from country c due to climate change to the different possible destinations j. To do so, we use the observed shares of total emigration flows from country c to country j, at time t, $Scj,t$⁠.

3. Data and Empirical Specification

The migration data are taken from Özden et al. (2011), which give bilateral migration stocks between 226 origin and destination countries for the last five census rounds, 1960–2000. The data are only available every 10 years. We compute net emigration flows as differences between stocks of foreigners in two consecutive censuses. Temperature and precipitation data are taken from Dell et al. (2012). The authors aggregate worldwide (terrestrial) monthly mean temperature and precipitation data at 0.5 × 0.5 degree resolution obtained from weather stations (Matsuura and Willmott 2007)³ using as weights 1990 population at 30 arc second resolution from the Global Rural-Urban Mapping Project (Balk et al. 2004). The country-level data for floods, storms, and droughts are taken from EM-DAT, an International Disaster Database compiled by the Centre for Research on the Epidemiology of Disasters (Guha-Sapir et al. 2015). Applying a simple two-period model in the spirit of Roy–Borjas (Roy 1951; Borjas 1987), Cattaneo and Peri (2016) provide theoretical predictions of the effect of temperature and emigration, which vary depending on the income of the origin country. In particular, the authors predict that an increase in average temperature increases emigration rates in middle-income countries, decreases emigration rates in poor countries, and should not affect emigration in rich countries.

Given that a dichotomous variable fails to utilize a lot of information on conflict, in an alternative specification we use a count variable for the number of conflicts. Finally, following Cotet and Tsui (2013), we also measure conflicts by the military coup attempts, from the Center for Systemic Peace.

We use a standard battery of controls, which have been previously applied in the conflict literature (among others Fearon and Laitin 2003; Montalvo and Reynol-Querol 2005; Montalvo and Reynol-Querol 2008; Esteban et al. 2012; Esteban et al. 2015; Morelli and Rohner 2015). Fearon and Laitin (2003) provide a detailed description of the rationale for the inclusion of the full set of controls. We include the natural logarithm of GDP per capita. More economically developed countries have lower rates of conflicts because of cultural reasons, and because these states have greater financial, administrative, and military capabilities. Moreover, for poor people the opportunity costs of joining a guerrilla is lower and rural societies are more penetrated by central administrations. We add the natural logarithm of population (P). A large population makes it more difficult to control who is doing what at the local level and increases the number of potential rebels that can be recruited by the insurgents. We add an index of ethnic diversity (D) because ethnically fractionalized societies are more at risk of conflicts. We control for natural resource abundance (O), which identifies countries where fuel exports exceed one-third of merchandise exports. Natural resource abundance provides an opportunity for rebellion since these resources can be used to finance the war and increase the pay off in case of victory. Moreover, oil producers tend to have weaker state apparatuses. We control for whether a state was recently created (N), marking countries in the first 10 years of independence. The risk of conflicts should be higher in newly independent states because a newly independent state loses the support of the former imperial power, and has to rely on new and untested military capabilities. We include a vector of controls for institutional quality (I), using the Polity-2 score from the Polity IV database, because better institutions should foster peace. To allow for a nonlinear effect of institutional quality, we decompose this index to capture democracies, which are countries with an index higher than six, anocracies between minus five and plus five and autocracies lower than minus six. We add a control for non-contiguous states (G) because the existence of natural obstacles, like water, a frontier or long distances between the territorial base and the state center favors the insurgence of a conflict. We add a variable for mountainous terrain (M), which measures the percentage of territory covered by mountains. Mountains represent an opportunity for the insurgence of a conflict, since this terrain can favor the rebels. $φt$ is a decade fixed effect. All controls are averaged over a time period of 10 years of the decade t. In an alternative specification, we measure the controls in the year starting decade t.⁶ Table A1 in the Appendix provides a summary statistics of the variables, while Appendix C provides information on the different sources of the variables.

We use linear probability models in the estimations, as they have been extensively used in the literature (Cattaneo et al. 2015; Berman et al. 2017). They allow a straightforward computation of the marginal effects and are a convenient approximation to the underlying response probability (Wooldridge 2010).

As most of the existing empirical literature, we run pooled cross-country regressions without controlling for country fixed effects (see, for example, Fearon and Laitin 2003; Montalvo and Reynol-Querol 2005; Montalvo and Reynol-Querol 2008; Esteban et al. 2012; Esteban et al. 2015). We do so for two main reasons. First, by averaging all variables over 10 years, we remove a big part of the within-country variation in the controls. The inclusion of country fixed effects would likely remove the significance of most control variables in this context. Second, an important determinant of civil conflicts, such as ethnic fractionalization, is not a time-varying measure. Given that the inflows of migrants may alter the ethnic composition of the destination countries, the inclusion of the ethnic diversity is crucial. The effect of ethnic diversity would be absorbed by the migration variable, if one does not include this control in the regression. We also estimate different sets of specifications, which include regional fixed effects, or alternatively region-decade fixed effects. This is done following Esteban et al. (2015) and Montalvo and Reynol-Querol (2008).

An additional concern is represented by the fact that our control of interest, climate-induced migration (⁠$IM̂$⁠), is a generated regressor (Pagan 1984). Inference in case of generated regressors is problematic, as the sampling variation of the vector of parameters of the first step estimation is unknown. To address this problem we employ bootstrapped standard errors. To address concerns of unobserved heterogeneity between countries leading to over-stated significance levels, standard errors are also clustered by country.

4. Results

Table 1 provides the estimated coefficients of Equation (1). Column (1) displays results of a specification which includes interaction between decade and region fixed effects to control for regional factors of variation in economic conditions over time. Column (2) adds also decade fixed effects interacted with a high-income country dummy, to capture differential time variation in the group of countries considered as ‘high income’ relative to those considered as ‘middle’ or ‘low’ income. Rich countries should behave differently compared to other groups, as agriculture represents only a small source of income and the rural population is a small percentage of the total compared to middle- and low-income countries.

The point estimates are quite stable across specifications. Moreover, they indicate differential impacts of climate change depending on the income level of the origin countries.⁷ Migration from middle-income countries is positively driven by the incidence of floods and storms and decreases with higher precipitation. On the contrary, the coefficient of average temperature is not statistically significant at the conventional levels. These findings indicate that in middle-income countries, fast-onset rather than slow-onset events encourage emigration. The existing literature agrees on the fact that migration responses to slow-onset environmental change differ from those to rapid-onset events. For example, by analyzing four different articles, Fussell et al. (2014) conclude that rapid-onset weather events produce evacuation or displacement of entire households. This type of migration can occur over a longer period of time even if return episodes are sometimes documented. On the contrary, slow-onset events tend to produce short distance and temporary migration. This is a type of migration that the present analysis does not capture, given that migration data are taken from censuses. An additional flooding event in middle-income countries increases the net emigration flows by 16,564 persons. As the average emigration flows from middle-income countries are 234,910, an additional flood would increase the emigration flows by about 7%.

Conversely, extreme events do not produce an increase in migration from low-income countries. Even if we would expect to see migration in response to extreme events, such as floods, from poor countries as well, as floods destroy homes and farms, it could be that this type of climatic shock is so detrimental that it traps very poor people and makes them unable to migrate. Floods worsen the liquidity constraint of people who live near subsistence in poor countries, implying a reduced ability to pay for migration costs. Environmental change on the one hand increases the incentive to move, but on the other it can also limit the capacity to do so (Black et al. 2011). Climate change can reduce mobility through resource constraints (Fussell et al. 2014). An increase in precipitation, on the contrary, reduces emigration from low-income countries. In many countries, where farmers barely have access to irrigation, precipitation represents an environmental amenity. This is the case for low- and middle-income countries, and less so for high-income countries, where modern agriculture technologies and irrigation systems are largely available. This would explain the null effect of precipitation in rich countries. Finally, migration from high-income countries is positively affected by floods. The estimated parameter for high-income countries is quite comparable with the one for middle income, as an additional flooding event would increase the emigration flows from high-income countries by about 15,461.

The negative and statistically significant coefficient of drought in high-income countries is quite puzzling. However, this effect could be due to a problem of small cell bias. The occurrence of droughts in high-income countries is generally quite low and, on average, it is lower than in poor and middle-income countries. However, in rich countries the variable registers the occurrence of one case of four droughts (Australia) and one case of five droughts in the decade (USA). While these two cases represent only 1% of the total cases for high-income countries, the occurrence of four and five droughts in a decade is a considerably high number, also for low- and middle-income countries.

Applying the estimated coefficients of the climatic variables to the three blocks of countries, a predicted flow of migrants moving from country c is computed. Figure 1 compares the average total emigration rates and the average climate-induced emigration rates for each country in the sample, using the population of the origin country at the beginning of each decade at the denominator. The number of climate migrants is computed using the estimated parameters presented in specification 2 of Table 1. For many countries (plausibly low-income countries), the predicted climate-induced emigration rates are very low, close to zero. This result is not a surprise, insofar as climate change will decrease the income in very poor countries, thus generating a poverty trap and lowering the probability of emigration. The points located in the center of the plot represent middle-income countries and indicate positive outflows of climate-induced migrants per capita.

The total flows of climate-induced migrants from country c are then allocated to the different destination countries j. We use the observed bilateral shares of total emigration flows for the allocation. We are aware that this is a strong assumption, as climate-induced migrants may not follow the existing routes of emigration of ‘conventional’ migrants. For example, the adverse effect of climate change may induce a massive loss of habitat across the world and, as a consequence, new patterns of emigration may be generated (Sassen 2014). These new flows may not follow routinized flows that have become chain migrations. It is also true, however, that the empirical evidence strongly suggests that the networks of family, friends, or community members ease subsequent emigration waves. The established networks, by providing information and assistance, reduce the emigration costs and limit the risks involved in the emigration process. The presence of a network should drive the location of migrants and should matter for both ordinary and climate migrants.

We run Equation (3) for a panel of 124 countries in our sample. Table 2 presents the estimated results for the incidence of conflicts. In this specification we test the drivers of the intensity of a conflict, represented by battles with less than 25 deaths. We use the inflows of climate migrants generated from Equation (1) and presented in Table 1, column (2). This and the following tables have the following structure. Column (1) runs a pooled ordinary least squares (OLS) with decade fixed effects (FE). Column (2) uses decade and region-fixed effects. Column (3) uses interactions between decade and region-fixed effects. Standard errors are bootstrapped with 200 replications.

The estimated parameters displayed in Table 2 are robust to the inclusion of the different types of fixed effects. The point estimates display a little variation, but the sign and significance of the coefficients is mainly unchanged in the different specifications. The coefficients are in agreement with the main findings of the existing literature.

The variable ethnic fractionalization has a strong and positive effect, confirming that highly fractionalized societies are more at risk of conflicts (Esteban et al. 2012; Morelli and Rohner 2015).⁸ Using the coefficient of specifications (3) and (4), an increase in ethnic diversity by 1 standard deviation (equal to 29 percentage points) makes a risk of civil conflict 10 percentage points more likely.

Better institutions should foster peace, but this hypothesis does not find empirical support in the present analysis. Autocratic countries are equally likely to experience a civil conflict than democracies and anocracies. The present data only marginally support the hypothesis that natural resource abundance increases the risk of conflicts. The coefficient has the correct sign but it is statistically not significant in many specifications. The coefficient of new state indicates that countries that recently gained independence are 17 percentage points less likely to be in conflict. We find support to the hypothesis that the existence of natural obstacles favors the insurgence of a conflict. Being a non-contiguous state increases the risks of civil conflict by 15 percentage points. The size of the population enters with a positive sign. A 10% increase in the population augments the risk of conflict by around 0.5 percentage points. Income per capita is negatively correlated with the incidence of civil conflicts. A 10% increase in the GDP per capita decreases the incidence of conflicts by 1.4 percentage points. Mountains could represent an opportunity for the insurgence of a conflict, but while the coefficient supporting this hypothesis is positive, it is not statistically significant. As far as our main control variable is concerned, we find no statistically significant effect of climate migrants on conflicts.

Table 3 presents the estimated parameters for conflict onset. This specification shows what triggers a ‘fresh’ episode of violence. The coefficients are almost invariant with respect to the conflict incidence specification.

As in Frankel and Romer (1999) and the subsequent literature, once we have estimated the gravity migration regression as in Equation (4), we generate the fitted values for the log of bilateral migration flows for each pair of countries in each year. We aggregate these predicted flows across destination j to compute the instrument for the inflows of climate-induced migrants (⁠$IM̂$⁠).

Following the migration bilateral literature (Anderson 2011; Beine et al. 2011; Bertoli and Fernández-Huertas Moraga 2013; Beine et al. 2016) the dependent variable $mcj,t$ is the natural logarithm of (climate-induced) migration flows from country c to destination j in decade t. The choice of the bilateral geography controls to be included follows the standard in the literature. We take, however, only a small subset of controls to avoid a violation of the exclusion restriction in the instrumental variable model. We use the natural logarithm of population size in 1960 as a lagged measure (⁠$Pj1960$⁠), a dummy for whether the origin and destination countries share a border (B), an official language (L), and colonial history (C). We add a variable for the difference in Agro-Ecological Zones between origin and destination (EAZ), to capture the extent to which migrants choose destinations with a climate similar to/different from the one of the origin country.

Given that the geographic characteristics are time-invariant, to identify the evolution of bilateral migration flows in time, we add time fixed effects and interactions between bilateral distance and time dummies as in Feyrer (2009). These interactions should capture common advances in communication and transportation that reduced the costs of migration. Being common to all countries, these shocks should be exogenous with respect to any specific country. At the same time, they have different effects across country pairs, as they depend on the relative pair distance. In particular, we add the natural logarithm of bilateral (geodesic) distance (lnD) interacted with decade dummies (I).

We also add a vector of decade fixed effects (⁠$ϕt$⁠) and origin-decade fixed effects (⁠$ϕct$⁠) to account for multilateral resistance that arises from time-varying common origin shocks to migration which influence migrants' location decisions (Anderson and Van Wincoop 2003; Bertoli and Fernández-Huertas Moraga 2013; Ortega and Peri 2013). Standard errors are clustered at the origin-destination pair level. To discard potential sources of endogeneity bias, destination fixed effects are not included, as they could absorb some destination country characteristics that are correlated with conflicts. We run both OLS and a PPML (pseudo-Poisson maximum likelihood) estimator following Santos-Silva and Tenreyro (2006). PPML estimator addresses important heteroscedasticity and selection bias issues.

To mitigate concerns about the exclusion restrictions of the other geographic controls, we use only bilateral and not unilateral geography variables in the gravity Equation (4). Moreover, following Rodriguez and Rodrik (2001) in the context of international trade, we include in the second-stage baseline model a set of variables that should control for the main pathways between geography and conflicts. This is done in the event that relative bilateral geography variables are correlated with absolute (unilateral) geography characteristics, and thus may be correlated with conflicts. For this reason, we add in the conflict Equation (3), geography and disease variables, along with institutional quality.

Table 4 reports the estimated parameters of PPML and OLS gravity models for environmental migration. The point estimates are qualitatively comparable across the PPML and OLS models and have the expected signs. Geographical distance is negatively correlated with bilateral climate-induced migration flows, while linguistic links, common border, and common colonial past are associated with larger migration flows. Large destination economies tend to attract people. Interestingly, difference in the Agro-Ecological Zone between origin and destination boosts emigration, indicating that individuals tend to choose destinations with a different climate.

We construct the instrument for climate migrants using the fitted values of the log of bilateral migration flows for each pair of countries in each year. Although we can compute predictions for both the OLS and PPML gravity models, we will use only the predicted flows produced by the OLS model. Only the OLS gravity-based instrument proves to be a powerful instrument in the second stage, as indicated by the reported F-tests.

Tables 5 and 6 show the Two-Stage least squares (2SLS) estimates of climate-induced migration on conflict incidence and onset, respectively, which use the fitted values of the log of bilateral migration flows as an instrument in the first-stage regression. For the full sequence of columns, the tables have the structure described for Table 2. The nonsignificant coefficient of climate migrants is confirmed also by the 2SLS estimations. Based on this finding, we cannot find empirical evidence of climate migrants as an additional driver of tension in destination countries. In the incidence specification, the positive and statistically significant coefficients of ethnic diversity and population and the negative coefficient of GDP per capita are robust to the 2SLS approach.

Finally, we also estimate Equation (3) with country fixed effects. While this approach constrains the explanatory variables of conflicts to time-varying variables, it may offer a more robust estimate of the effect of climate‐induced migration on conflict. The use of country fixed effects should address unobserved heterogeneity between countries and omitted variables. The estimations are presented in Table 7 and confirm the non-statistically significant effect of climate-induced migration.

We conducted a series of robustness checks. First, given that the data on migration flows are available on a decade basis, the time for the estimations is divided into 10-year sub-periods. In the baseline specifications, we averaged all controls over the 10-year sub-periods. As a robustness check, we follow Esteban et al. (2012) and measure the controls in the first year of each period. The results are presented in Table A2, for both OLS (Panels A, C) and 2SLS (Panels B, D) estimations using both the incidence (Panels A, B) and onset (Panels C, D) dependent variables. The coefficients of climate migrants are not statistically significant in this alternative approach.

The importance of ethnic diversity as a driver of civil conflict has been largely recognized on a theoretical ground. However, support to this hypothesis has been empirically weak, insofar as many papers find no statistically significant relationship between ethnic fractionalization and conflict. Some authors have argued that the link could be nonlinear, as both highly homogeneous and highly heterogeneous societies may be characterized by low tension and violence. If this is so, an index of polarization rather than fragmentation could be more correlated to conflicts (Montalvo and Reynol-Querol 2005). Esteban et al. (2012), drawing on Esteban and Ray (2011), emphasize that conflict intensity is connected to different measures of ethnic distribution, which include both ethnic diversity and ethnic polarization. For this reason, as a robustness check, we add to the baseline specification a measure of ethnic polarization along with ethnic fragmentation. The non-statistically significant coefficient of climate migrants is robust to the inclusion of the variable ethnic polarization (Table A3).

The validity of the 2SLS estimations rests on the assumption that geography is a determinant of conflict only through migration. However, a multitude of channels, of which migration is only one, could link geography and conflict. To avoid a violation of the exclusion restriction, only bilateral (and not unilateral) geography variables are used in the gravity Equation (4). Moreover, drawing on Rodriguez and Rodrik (2001) and Ortega and Peri (2014), in the structural Equation (3), we account for the main channels through which geography directly affects conflict. One potential pathway is represented by the type of country institution, in that geography influences the quality of an institution and this strongly influences the probability of a conflict occurring. We already account for this variable in the baseline specification. Therefore, we also include a very broad set of geography and disease variables. These are controls for the presence of yellow fever, absolute latitude, mean elevation above the sea level, average distance to the coast, percent of land in the tropics, percent of population within 100 km from ice-free coast, percent of population with malaria in 1994 and a landlocked dummy. Table A4 presents the estimated results. The coefficients of climate migrants remain statistically nonsignificant.

The main objective of this article is to estimate the effect of climate-induced migration on conflicts. However, as the existing literature suggests, in addition to conflict potentially being caused by climate migrants, the country’s own climatic activity may lead to conflict as well. For this reason we add in the conflict specification some controls for climate to reduce concerns about omitted variable bias. We use the natural logarithm of average temperature and precipitation in the decade and the total number of extreme events such as storm, flood, drought, and extreme heat. The estimated coefficient of climate migrants is still statistically nonsignificant, as indicated in Table A5. On the contrary, we find a robust, positive, and statistically significant coefficient of average temperature both on the incidence and onset of civil conflicts. This finding is in agreement with the existing literature that finds a strong link between civil war and temperature (Burke et al. 2009; Hsiang et al. 2013).

In the baseline specification, we measure conflicts using the 25 deaths cutoff. This low threshold ensures that even small and intermediate conflict events are captured. For robustness check and in agreement with some of the existing literature, we use alternative definitions. First, we increase the minimum threshold to 1,000 or more battle deaths in a year, to capture the onset/existence of a war. Major conflicts such as wars are of particular relevance because of their critical damaging consequences. Second, given that a dichotomous variable fails to utilize a lot of information on conflict, we also use a continuous variable for the number of conflicts. We build a variable which counts all conflicting events involving more than 25 deaths per year experienced by a country in a decade. Given the count nature of the dependent variable, we employ a Poisson regression. Third, in line with Burke et al. (2009) and Cotet and Tsui (2013), we employ military coup attempts as a dependent variable. This variable records all successful, attempted, plotted and alleged coup events without imposing any threshold.

The results of these alternative specifications are presented in Tables A6–A8. The OLS coefficient of climate migrants in the civil war incidence specification is negative and statistically significant (Panel A, Table A6). This finding is likely connected to a reverse causality problem, in that the existence of a large conflict at destination discourages the inflows of climate migrants. Once the reverse-causality is properly taken into account by a 2SLS estimation, the coefficient of climate migrants becomes zero (Panel B). In the civil war onset specification on the contrary, neither the OLS nor the 2SLS coefficients of climate migrants are statistically significant (Panels C and D). The null effect of climate migrants is robust to the use of a count variable for conflict (Table A7) or a coup attempt variable (Table A8).

One could argue that the null effect of climate‐induced international migration on conflict is due to the strong control that leaders from receiving countries have over the borders. These strong controls may constrain the inflow of migrants and reduce the possibility that climate migrants induce conflicts at destinations. An exception, however, might be represented by destination countries in Africa or South Asia, where borders are more porous. For this reason, in a robustness check we restrict the analysis to this subset of destination countries. The empirical analyses for Africa and South Asia only are reported in Table A9. The coefficient of climate migrants is not statistically different from zero. It should be noted, however, that the F-test for the power of the instrument in the first-stage estimation is in some specifications smaller than the value suggested by Staiger and Stock (1997) as a rule of thumb to assess the relevance of the instruments.

5. Conclusions

Human migration is an important response to environmental stress, but at the same time it could produce substantial indirect effects. One such indirect effect could be the existence of a link between climate-induced migration and conflicts. Competition over resources, ethnic tensions, distrust, demolition of social capital, crossing of fault lines have been identified as possible bridging factors between conflicts and climate-induced migration. In this article, we test the possibility that climate change, through migration, could cause new conflicts or fuel existing ones. A macro-level empirical estimation is conducted to provide an answer to this question.

Given that data on climate migrants are not available, as the reasons for migration are not generally recorded, we used an auxiliary regression to generate the number of migrants that are driven by climatic factors, such as average temperature and precipitation, but also floods, droughts, and storms. We also address endogeneity concerns due to a reverse causality between migration and conflicts. The article finds no statistically significant effect of climate migrants on conflicts. The result is robust to alternative specifications, to alternative definitions of conflicts, whether onset or incidence, and to different thresholds based on the number of deaths, to the inclusion of geographic and climate controls, and to the inclusion of country fixed effects.

In this article, we measure the incidence of these extreme events by counting the number of floods, droughts, and storms that occurred during the decade. The data used to capture these events come from the EM-DAT, which however has some drawbacks. The data are mostly provided by insurance companies and thus suffer from incomplete reporting because small events or events occurring in poor countries are under-sampled. This problem is particularly serious before 1990, which is when a large part of the present analysis is conducted. This limitation could partially explain why climate migrants do not increase the risk of civil conflict in this study.

Moreover, destination countries can resort to effective policies of land allocation and dispute resolution that prevent the rise of civil conflicts. This could be an additional reason explaining why climate migrants are not responsible for increasing disputes. That said, it could be the case that even if a destination country does not experience civil conflicts, characterized by a certain number of battle-related deaths, it may be susceptible to less intense social unrest or to a larger number of crimes due to climate migrants.

Another limitation of the present study is that it considers only international migration. This type of migration is only a fraction of the total and could differ from internal moves from a variety of aspects, including the income and skill profiles of those who leave. Internally displaced persons could have a different effect on conflicts given that they tend to be poorer and unskilled, and could boost urbanization without growth. This may explain why the findings of this article differ from the results of Ghimire et al. (2015), where a causal link between the number of people internally displaced by large floods and civil conflict is reported.

Finally, it is also possible that when population density increases as a result of migrant inflows, agriculture begins to intensify. Migrants can contribute to this technology shift by bringing new knowledge and skills. This process may introduce an additional channel between migration and conflict, which offsets the potential negative impact of land scarcity. Two forces, one leading to an increase in economic efficiency and the other causing higher competition over resources, may work in opposite directions and explain the null effect of climate migrants of this article. The analysis of potential benefit of climate-induced migration could represent a possible future extension of the present article.

Migration is an important instrument of adaptation to climate change. At the same time it is crucial to promote other forms of adaptation by increasing the access to energy, improving the efficiency of agricultural production and the water supply systems. Alternative ways to cope with climate change are needed, as migration cannot be the single solution to address climate-related risks. This is particularly important, as the indirect impacts of climatic stress through massive migration could be as substantial as the direct ones.

Acknowledgment

The research leading to these results has received funding from the Italian Ministry of Education, University and Research and the Italian Ministry of Environment, Land and Sea under the GEMINA project. We thank Mark Skidmore, Michel Beine, Ilan Noy, Max Steinhardt, Michael Berlemann, workshop participants to the CESifo Venice Summer Institute on Climate Change and Migration and one anonymous referee for comments and suggestions.

Footnotes

References