Climate Change and Migration I. Martínez-Zarzoso, C. Muris and A. Backhaus University of Göttingen

Outline Motivation Literature Modelling framework Empirical strategy Data, variables and main results Conclusions

Motivation Nexus climate change and migration addressed since the early 1990s by political scientist, environmentalist and demographers Substantial media coverage but limited academic research Mainly case studies for specific regions/countries and time episodes (World Bank, 2010) The number of empirical studies quantifying this impact is scarce

Motivation (cont) Natural disasters and extreme events as drivers: Marchiori, Maystadt and Schumacher, 2011; Warner, Stal, Dun and Afifi, 2009 In this paper we focus on permanent migration due to gradual climate change Similar approach to Dell et al (2008), but our impact variable is migration instead of economic growth Target variables: temperature and precipitation

Literature Traditional determinants of migration: Warin and Svaton (2008), Ruyssen, Everaert, and Rayp (2011) Migration and natural disasters: Alexeev, Good and Reuveny (2010) International migration and climate: Afifi and Wagner (2008) Gravity model augmented with environmental factors for a cross-section of countries in 2000

Modelling framework The neoclassical approach to migration: a rational individual that takes his decision to migrate on purely economic grounds and acts independently of other social entities: Borjas (2005) The net gain to migration N can be expressed as j denote countries, j=0,1; a denote age, M is the costs of moving from source to destination, PV present value of the earning stream The individual migrates if N>0

Modelling framework (cont) Main factors that determine migration: Income origin and destination  +, (-) Unemployment rates or and dest  +, (-) Travel cost  (-) Immigr policies destination, stability origin Cultural similarities: colonial rel, trade, (+) Others: Inequality, capital market imperf, demography

Empirical strategy Gravity model with economic variables derived from neoclassical theory: demographic, geographic and cultural controls and the trade share: Note that coeff of time invariant ij var (dist, contig, lang, samecont) cannot be directly estimated when pair-FE are added

Empirical strategy (cont) A second specification will be estimated to model time-variant multilateral resistance, as suggested in the trade literature

Empirical strategy (cont) A third specification adds dynamics as suggested recently (Dunlevy, 1993, Ruyssen et al., 2011)

Data and Variables Migration flows and stocks in destination countries are mainly from the OECD’s International Migration Database (IMD) Average temperature and average precipitation both are from Dell et al (2008) Geospatial software used to aggregate both variables to the country-year level Inflows from 1995 till 2008, but climate variables only available until 2006 Other var: WDI Sample period: , 19 destinations and 161 origins

VariableObsMeanStd. Dev.MinMax ln_inflows ln_stocks ln_emig_rate ln_wtemperature_origin ln_wprecipitation_origin ln_gdp_destination ln_gdp_origin ln_pop_origin Demographic pressure Unemployment origin unemployment destination Trade_to_gdp ln_distance Contiguity Same_continent Language Colony EU membership Summary statistics

Main Results Static Model dep var : ln_migration_flow LSDV with time dummies LSDV with time and country dummies LSDV with time and pair dummies LSDV with host country- and-time and pair dummies Independent variables:M1M2M3M4 ln_wtemperature_origin se (0.082)(0.206)(0.146)(0.132) ln_wprecipitation_origin **-0.045*-0.047** se (0.049)(0.030)(0.025)(0.022) dep var: ln_migration_stock LSDV with time dummies LSDV with time and country dummies LSDV with time and pair dummies LSDV with host country-and- time and pair dummies Independent variables:M1M2M3M4 ln_wtemperature_origin **0.389**0.395** se (0.133)(0.259)(0.172)(0.161) ln_wprecipitation_origin se(0.079)(0.038)(0.023)(0.021)

Main Results Static Model (cont) dep var: ln_migration_rate LSDV with time dummies LSDV with time and country dummies LSDV with time and pair dummies LSDV with host country-and-time and pair dummies Independent variables:M1M2M3M4 ln_wtemperature_origin *0.265** se (0.085)(0.210)(0.148)(0.134) ln_wprecipitation_origin **-0.051**-0.052** se (0.050)(0.030)(0.024)(0.022)

Main Results Dynamic Model dep var: ln_migration_flow LSDV with time and country dummies LSDV with time dummies and bilateral FE Instrume ntal Variables First Diff. M2M3M4 Independent variables: ln_stocks (t-1)0.171***0.139***0.039 (0.013)(0.034)(0.079) ln_inflows (t-1)0.739***0.413***-0.161** (0.015)(0.022)(0.070) ln_wtem_origin se (0.180)(0.168)(0.201) ln_wpre_origin ***-0.085***-0.067*** se (0.033)(0.029)(0.024)

Main Results Dynamic Model (cont) Dep varln_inflowsln_emig_rate ols, time dummies time and i,j, fe time and pair fe ols, time dummies time and i,j, fe time and pair fe Indep. Variables:b/se L.ln_inflows0.873***0.740***0.409*** (0.010)(0.015)(0.023) L.ln_emig_rate0.927***0.740***0.409*** (0.006)(0.015)(0.023) L.ln_stocks0.078***0.172***0.148***0.026***0.172***0.147*** (0.009)(0.014)(0.034)(0.005)(0.014)(0.034) ln_wtem_or * (0.015)(0.181)(0.169)(0.013)(0.181)(0.169) ln_wpre_or ***-0.085*** ***-0.085*** (0.008)(0.033)(0.030)(0.007)(0.033)(0.030)

Main Results Instrumental Variables Dep variables: Instrumental Variables Inflows Instrumental Variables Ln_emig_rate Indep. Var:b/se LD.ln_inflows-0.661* (0.358) LD.ln_emig_rate-0.670* (0.362) LD.ln_stocks (0.271)(0.274) LD.ln_wtem_or (0.180)(0.181) LD.ln_wpre_or-0.080***-0.081*** (0.029)

Conclusions Increasing temperatures in origin increases migration stocks in the short term, whereas the migration rate is affected by both temperatures and precipitation changes (increase of 10 percent temp, increases mig rate by 2.6 percent ) Dynamics are important and only precipitation affect migration stock in the long term

Conclusion (cont) See relative importance in terms of beta coefficients Lower precipitation levels will lead to population displacements in the future

Further research Simulations for different countries according to different scenarios

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