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Gone with the wind: International migration [1]

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Date: 2019-07-01

“Where shall I go? What shall I do?” Scarlett O'Hara (Gone with the Wind)

The recent refugee crisis in Europe overshadows an ongoing global trend: international migration.1 The UN International Migration Report 2015 finds that 3.3% of the world's population, or about 250 million people, are migrants. Besides this increase in the level, the report also shows that the change in migration is accelerating. The effects of migration on destination and origin countries are substantial but complex. We can expect that the scope and impact of migration will increase in the future. In 2010 the World Migration Report projected 405 million international migrants by 2050. This appears to be a rather conservative estimate given increasing global mobility, emerging conflicts, and the predicted 200 million climate migrants by 2050 (cf. Stern Review).

The driving forces of migration are increasingly complex and change over time. We classifiy them into three, broad categories: (socio-)economic, political, and climate-related. Economic factors include better employment and economic opportunities in destination countries (e.g. Mayda, 2010; Ortega and Peri, 2009, Ortega and Peri, 2013). Political variables include freedom and warfare (e.g. Hatton and Williamson, 2003; Moore and Shellman, 2004; Adserà et al., 2016). Most recently, the effect of climate variables on migration have received more attention from policy makers and academics (e.g. Beine and Parsons, 2015; Cattaneo and Peri, 2016). The 2017 World Economic Forum, for example, has declared extreme weather as the most likely risk and second-most impactful one, only trailing weapons of mass destruction.2

There is a direct link between climate change and (i) increases in temperature and (ii) a higher incidence, likelihood, and frequency of weather-related disasters (see IPCC, 2012a; Peduzzi, 2005; Herring et al., 2016). Those changes to natural systems already have and are likely to have even more severe effects on countries. Higher temperatures reduce agricultural productivity (cf. Burke et al., 2015b), adversely affect crop yields (cf. Lesk et al., 2016) and, hence, increase agricultural income risk. Along this line, climate change will likely lead to water scarcity and threatens food production (cf. Wheeler and von Braun, 2013). Moreover, climate change will directly impact on health conditions (see WHO, 2009). Along this line, climate change could lead to increased civil unrest and climate-driven conflicts within affected countries due to increased rivalry over scarce resources. Burke et al. (2015a) provide a review of the climate-conflict literature. They conclude that temperature affects the likelihood of intergroup and interpersonal violence. In conclusion, climate change will render some areas untenable and will likely have a positive effect on migration out of affected countries.

This paper adds to the literature on the driving forces of migration into OECD countries. It is important to understand the determinants of migration as they will have different effects on the destination and origin country and to develop appropriate policy tools dealing with the expected increase in migration and the expected change in the relative importance of driving forces.

Our paper makes two contributions. First, we offer a joint analysis of the driving forces of migration capturing year-to-year variations and long-run effects. While the literature has discussed various driving forces of migration there is no study jointly explaining it by (socio-)economic, political, and climate-related variables using a large time dimension. We address this important gap in the literature. To do so, we build a rich panel data set of international, bilateral migration flows (regular, permanent migrants) between 16 OECD destination and 198 origin countries over the time span from 1980 to 2015 and include various potential driving forces.

Second, having identified the main drivers of migration, we further exploit the large time-dimension of our data set. Our research question is how migration dynamically responds to shocks to its key determinants. In order to address this question, we estimate a panel vectorautoregressive model (PVAR, for short). To the best of our knowledge, this is the first paper looking at the dynamic response of migration to shocks to its driving forces. Therefore, we drive the literature on the determinants of migration into a new direction, emphasizing the importance of the adjustment path of migration.

In contrast to papers using decennial averages for migration flows (cf. Beine and Parsons, 2015; Cattaneo and Peri, 2016), we use annual flows. While this reduces the number of country pairs, it allows us to more precisely link variations in climate variables with migration flows over time. It is, moreover, essential for our analysis of the dynamic response of migration to shocks, were the econometric method requires a large time dimension.

Our main findings are as follows. Our findings show that climate variables generates sizable, negative effects at origin countries. Our results show that higher temperatures increase migration flows and that people avoid warmer destination countries. We find that the number of weather-related disasters at origin increases migration flows to our set of OECD destination countries. Compared to the related literature we find much larger effects of temperature (up to three times) and disasters (about twice as large). This finding supports that a large time dimension is crucial in identifying the effects of climate variables on migration as most of the literature uses decennial averages finding substantially different results.

We then proceed and extend the literature on the non-linear effects of climate variables (cf. Bardsley and Hugo, 2010; Kniveton et al., 2012; McLeman, 2018). We find that countries that rely more heavily on agriculture experience more outward migration while richer origin countries experience less migration from temperature increases. In line with the findings of Howe et al. (2012), we find a significant interaction between temperature and the number of weather-related disasters. Supporting that inference about the long-run consequences of climate change is likely to be done by observing changes in local weather patterns. Finally, in line with the findings of Halliday (2006) we show that a decomposition of weather- and non-weather-related disasters into subcategories reveals different responses of migration to different types of disasters. This is important for policy makers as it implies that the policy response should vary with the type of disaster.

Finally, our panel VAR results show that the dynamic response of migration flows in our sample to shocks to its driving forces is very different across our three categories. We identify shocks using three instruments: volcanic activity for temperature, deaths due to epidemics for political freedom, and non-weather related disasters for income. The response of migration varies in the on-impact response, persistence, and overall adjustment path across different shocks. The response to temperature shocks is particularly interesting. Migration flows in our sample decrease for roughly 5 years before they increase for more then 20 years. This response can be potentially explained by binding liquidity constraints in the short-run and the difficulty to detect and internalize the effects of temperature shocks. Our findings support the “trapped population” concept. Papers such as Gray and Mueller (2012), Black et al. (2013), and Noy (2017) argue that climate shocks can reduce mobility while papers such as Munshi (2003) and Dillon et al. (2011) find an increase in mobility. Our panel VAR allows to add a new perspective to this issue: exploiting the dynamic dimension we find that climate shocks can reduce mobility and decrease migration in our sample.

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[1] Url: https://www.sciencedirect.com/science/article/abs/pii/S0921818118303308?dgcid=raven_sd_aip_email#!

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