Climate shocks and the timing of migration from Mexico

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.

Appendices

Appendix 1: Definition of climate measures

Warm spell duration index (wsdi): The warm spell duration index is defined as the annual count of days when at least six consecutive days surpassed the 90th percentile of the maximum temperature of the baseline period (1961–1990). Let TX _ij be the daily maximum temperature on day i in period j and let TX _in 90 be the calendar day 90th percentile centered on a 5-day window for the base period 1961–1990. The warm spell duration can then be computed as the period specific count of days N _j with at least 6 consecutive days where TX _ij  > TX _in 90 (Eq. 2).

$$ wsdi_{j} = N_{{j(TX_{ij} > TX_{in} 90,N \ge 6)}} $$

(2)

No. days heavy precip (r10mm ): The no. of days of heavy precipitation is defined as the annual count of days with more than 10 mm of precipitation. Let RR _ij be the daily precipitation amount on day i in period j. The number of days with heavy precipitation is then computed as the count of days N where RR _ij  ≥ 10 mm (Eq. 3).

$$ \text{r} 1 0\,{\text{mm}}_{j} = N_{{(RR_{ij} \ge 10\,{\text{mm}})}} $$

(3)

For a full list of ETCCDI indices and their technical definitions, see http://etccdi.pacificclimate.org/list_27_indices.shtml.

Appendix 2: Correlation matrix and parameter estimates of control variables

Table 4 provides a matrix of correlations of outcome and substantive predictor variables employed in the investigation of the timing of international migration in response to climate shocks from rural Mexico during 1986–1999.

Table 4 Correlation matrix

The decision to migrate is influenced by various socio-demographic factors (Brown and Bean 2006). Table 5 shows multilevel event-history models, including only household-level variables (Model 1), and then adding municipality-level predictors (Model 2). In line with much prior work on Mexican migration, the models suggest that the typical migrant household is male-headed (Lindstrom and Lauster 2001), has few young children (Massey and Riosmena 2010; Nawrotzki et al. 2013), is employed in a blue-collar occupation with limited work experience (Fussell 2004; Massey et al. 1987), and does not own property or a business (cf., Massey and Parrado 1998). Only a few municipality characteristics influence the probability to migrate. The probability to migrate is strongly elevated for communities with large proportions of adults with prior international migration experience, testifying to the importance of social networks (Fussell 2004; Massey and Espinosa 1997; Massey et al. 1994). In addition, households are less likely to migrate from areas with historically warm temperatures, which likely reflects that most migrants come from the cooler west-central parts of Mexico, instead of the hot arid northern border states (Hamilton and Villarreal 2011).

Table 5 Parameter estimates derived from multilevel event-history models for household and municipality control variables to predict international migration from rural Mexico, 1986–1999