Abstract

Abstract This paper investigates the long-term implications of climate change on global migration and inequality. Accounting for the effects of changing temperatures, sea levels, and the frequency and intensity of natural disasters, we model the impact of climate change on productivity and utility in a dynamic general equilibrium framework. By endogenizing people’s migration decisions across millions of $5 \times 5$ km spatial cells, our approach sheds light on the magnitude and dyadic, education-specific structure of human migration induced by global warming. We find that climate change strongly intensifies global inequality and poverty, reinforces urbanization, and boosts migration from low- to high-latitude areas. Median projections suggest that climate change will induce a voluntary and a forced permanent relocation of 62 million working-age individuals over the course of the 21st century. Overall, under current international migration laws and policies, only a small fraction of people suffering from the negative effects of climate change manages to move beyond their homelands. We conclude that it is unlikely that climate shocks will induce massive international flows of migrants, except under combined extremely pessimistic climate scenarios and highly permissive migration policies. In contrast, poverty resulting from climate change is a real threat to all of us.

Highlights

  • How will long-term climate change ( CLC) affect human mobility over the course of the 21st century? This question has been the source of much controversy in recent literature and has gained unprecedented attention in public discourse as global warming projections for the coming decades get worse.1 Anthropogenic temperature changes and sea level rise constitute two major threats of CLC envisaged by climatologists (Stocker et al, 2013)

  • In the first set of simulations, we focus on two slow-onset mechanisms: changes in total factor productivity (TFP) driven by long-term variations in mean temperature, and forced displacements driven by the rise in the sea level

  • Aggregated effects of CLC are presented for Central Asia and the Rest of Europe (CARE), the Middle East and North Africa (MENA), Sub-Saharan Africa (SSA), Latin America and the Caribbean (LAC), OECD, East Asia and Pacific (EAP), and the world

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Summary

Introduction

How will long-term climate change ( CLC) affect human mobility over the course of the 21st century? This question has been the source of much controversy in recent literature and has gained unprecedented attention in public discourse as global warming projections for the coming decades get worse. Anthropogenic temperature changes and sea level rise constitute two major threats of CLC envisaged by climatologists (Stocker et al, 2013). We are looking for first-order effects of CLC on individuals and countries in a framework that takes into account the fact that the (endogenous) geography of skills affects migration choices through differences in incentives, fertility decisions, and migration costs Another related study is Shayegh (2017), which models the effect of CLC on fertility rates, income inequality and human capital accumulation in developing countries. As to the effects of CLC, DRH and Shayegh (2017) model the effect of the change in temperature on productivity Compared to these studies, we use an improved migration technology that combines the six advantages listed above, including an excellent predictive power for the period 1980 to 2010; we account for sea level rise, which affects countries differentially, and implement additional mechanisms of transmission, such as the costs of natural disasters, health and productivity effects of heat waves, and conflicts over resources.

Climate Damage Functions
Climate Scenarios
Channels of Transmission
Behavioral and Market Responses
Technology
Preferences
Individuals Raised in Non-flooded Areas
Forcibly Displaced People
Dynamics and Inter-temporal Equilibrium
Parameterization
Results
Slow-onset Mechanisms
Fast-onset Mechanisms and Conflicts
Conclusion
Temperature scenarios
Parameterization of the model
B Additional results with slow-onset mechanisms
20 Equatorial Guinea
C Modeling conflicts
20 Afghanistan
D Additional results with fast-onset mechanisms and conflicts
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