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Population and Environment

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05-03-2016 | Original Paper

Climate shocks and the timing of migration from Mexico

Authors: Raphael J. Nawrotzki, Jack DeWaard

Published in: Population and Environment | Issue 1/2016

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Abstract

Although evidence is increasing that climate shocks influence human migration, it is unclear exactly when people migrate after a climate shock. A climate shock might be followed by an immediate migration response. Alternatively, migration, as an adaptive strategy of last resort, might be delayed and employed only after available in situ (in-place) adaptive strategies are exhausted. In this paper, we explore the temporally lagged association between a climate shock and future migration. Using multilevel event-history models, we analyze the risk of Mexico-US migration over a seven-year period after a climate shock. Consistent with a delayed response pattern, we find that the risk of migration is low immediately after a climate shock and increases as households pursue and cycle through in situ adaptive strategies available to them. However, about 3 years after the climate shock, the risk of migration decreases, suggesting that households are eventually successful in adapting in situ.

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The socioeconomic context will also shape the directionality of the migration response (Black et al. 2011a). In a Latin American context, adverse climatic conditions often lead to an increase in international out-migration (Feng and Oppenheimer 2012; Gray and Bilsborrow 2013; Hunter et al. 2013). A decline in international migration has been observed in a few case studies of the African continent (Gray and Mueller 2012a; Henry et al. 2004). Under conditions of extreme poverty, households may become “trapped” in place when adverse environmental conditions undermine the resource base to finance a move (Black et al. 2011b).

The Mexican Migration Project (MMP) is a collaborative research project based at Princeton University and the University of Guadalajara. The MMP data are available at http://mmp.opr.princeton.edu.

The first international migration can be considered a major event that is remembered with reasonable accuracy by most household members. As such, use of the first migration has the added benefit of guarding against recall bias.

The phenomenon that households leave the dataset after the year they are surveyed is known in the event-history literature as “right censoring.” We retain right censored cases in the analysis based on the assumption that the censoring is non-informative, meaning that the time of migration is independent of the time a particular community was surveyed (Allison 1984; Steele 2005).

While this omission could bias our estimates, the amount of error is likely to be small in rural areas where migrants are more likely to return (Cornelius 1992; Riosmena 2004). In addition, when the permanent relocation of the entire household was related to climate impacts, then the resulting sample of households will be less sensitive to climate shocks. In this way, the presented results can be considered conservative and likely underestimate the magnitude of the true climate–migration response.

The expert team is jointly sponsored by the World Meteorological Organization (WMO) Commission for Climatology (CCl), the World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR), and the Joint WMO-Intergovernmental Oceanographic Commission (IOC) of the United Nations Educational, Scientific and Cultural Organization (UNESCO) Technical Commission for Oceanography and Marine Meteorology (JCOMM).

Inspection of density, overimputation, and overdispersion plots suggested accurate performance of the imputation model (Honaker et al. 2011).

Cokriging is based on regionalized variable theory (Matheron 1971) and uses the spatial trend and local spatial autocorrelation to inform predictions (Bolstad 2012; Hevesi et al. 1992). Cokriging has been frequently used to interpolate climate measures (e.g., Aznar et al. 2013; Garzon-Machado et al. 2014).

With a 1-kilometer grid cell resolution, the DEM is based on remotely sensed images from the Shuttle Radar Topography Mission (SRTM), created and released by the US Geological Survey (USGS) and the National Geospatial-Intelligence Agency (NGA) (Danielson and Gesch 2011).

We tested the accuracy of the cokriging procedure by using a bootstrap split-sample method in which 10 % of the stations were omitted from the interpolation and error values were computed at known locations. The low magnitude of error values and random distribution across space suggests that the interpolations produced reliable results.

Unfortunately, measures of corn area harvested and percent irrigated farmland are only available for years after our study period. These variables were included to account for general differences in agricultural dependence and infrastructure availability. In our attempt to investigate changes in irrigation infrastructure, we were able to obtain a partial time series of the percent farmland irrigated for 25 of our 68 municipalities between 1994 and 2003. The average change in the proportion of farmland irrigated over this period was +0.003 % (SD = 7.27 %) and ranged from a minimum of −24.7 % to a maximum of +14.43 %. As such, the use of time-invariant measures to approximate historic conditions results in some uncertainty and the coefficient estimates should be interpreted with cautions.

Information on the percentage of adults with migration experience, the wealth index, and the percentage of male labor force employed in the agricultural sector was available at decadal time steps. For these measures, we employed linear interpolation to obtain semi-time-varying predictor, as recommended by the event-history literature (Allison 1984).

The year dummy variables account for unobserved changes, including policy changes, economic cycles, political events, technological advancements, and other climate shocks and natural disasters (Bohra-Mishra et al. 2014).

For increased speed and improved convergence properties, we used the integer scalar setting of nAGQ = 0 so that the random-effects and the fixed-effects coefficients were optimized (optimizer = “bobyqa”) in the penalized iteratively reweighted least squares step (Bates et al. 2014).

“Appendix 2” reports a correlation matrix (Table 4) as well as the parameter estimates for household and municipality control variables (Table 5) included in the fully adjusted multilevel event-history model.

We observed similar results for various other ETCCDI indices, including the % warm days (tx90p), the number of frost days (fd), the temperature during the coldest day (txn), the % cool nights (tn10p), and the total wet-day precipitation (prcptot). Results from different measures serve as a robustness test, suggesting that the reported functional form reflects a general pattern.

The coefficients for “No. days heavy precip” reflects the effect of a one standard deviation decrease in precipitation.

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Title: Climate shocks and the timing of migration from Mexico
Authors: Raphael J. Nawrotzki
Jack DeWaard
Publication date: 05-03-2016
Publisher: Springer Netherlands
Published in: Population and Environment / Issue 1/2016
Print ISSN: 0199-0039
Electronic ISSN: 1573-7810
DOI: https://doi.org/10.1007/s11111-016-0255-x

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