Amplification or suppression: Social networks and the climate change—migration association in rural Mexico

Abstract

Increasing rates of climate migration may be of economic and national concern to sending and destination countries. It has been argued that social networks – the ties connecting an origin and destination – may operate as “migration corridors” with the potential to strongly facilitate climate change-related migration. This study investigates whether social networks at the household and community levels amplify or suppress the impact of climate change on international migration from rural Mexico. A novel set of 15 climate change indices was generated based on daily temperature and precipitation data for 214 weather stations across Mexico. Employing geostatistical interpolation techniques, the climate change values were linked to 68 rural municipalities for which sociodemographic data and detailed migration histories were available from the Mexican Migration Project. Multi-level discrete-time event-history models were used to investigate the effect of climate change on international migration between 1986 and 1999. At the household level, the effect of social networks was approximated by comparing the first to the last move, assuming that through the first move a household establishes internal social capital. At the community level, the impact of social capital was explored through interactions with a measure of the proportion of adults with migration experience. The results show that rather than amplifying, social capital may suppress the sensitivity of migration to climate triggers, suggesting that social networks could facilitate climate change adaptation in place.

Might climate change the “healthy migrant” effect?

“…Connections with migration have been demonstrated in that rainfall shortage is associated with international migration, particularly from rural areas with strong migration networks (Hunter, Murray and Riosmena 2013; Nawrotzki, Hunter et al 2015; Nawrotzki, Riosmena and Hunter 2013; Nawrotzki, Riosmena et al 2015). In addition, migration from drought-stricken regions is highest 2–3 years following substantial decline in rainfall, suggesting migration as shorter-term adaptation to environmental stress (Nawrotzki and DeWaard 2016).…”

“…In some places, environmental scarcity is acting as a “push” factor – intensifying migration as a means of diversifying livelihoods or pursuing new ones (e.g., Nawrotzki, Riosmena et al 2015). In other places, increased availability of environmental resources provides the capital through which households are able to send migrants elsewhere to earn income (e.g., Gray 2010; Hunter et al 2013).…”

Climate shocks and the timing of migration from Mexico

“…We investigate the first move from within a household, based on the assumption that the first move is a livelihood strategy more directly related to environmental factors compared to later moves (Henry et al 2004; Nawrotzki et al 2015b). 3 For each household, we obtain all years during which any household member left for the U.S. and select the earliest year.…”

“…For the present analysis, we selected the warm spell duration index (wsdi) and the number of days of heavy precipitation (r10mm) from among the suite of core ETCCDI indices (for a definition of the climate change measures see Appendix A). We selected these indices because (1) they tap two climatic dimensions of temperature and precipitation, (2) measure climatic extremes rather than changes in average conditions, (3) were only moderately correlated and, so, permit joint inclusion in the models, and (4) have been shown to be associated with migration behavior in prior research (Nawrotzki et al 2015b). In order to compute the climate change measures, four steps were necessary: (1) missing data imputation, (2) computation of climate measures at each station, (3) spatial interpolation to obtain values for unmeasured municipalities, and (4) relating annual climate measures to a 30-year reference period to approximate change.…”

“…In many developing countries, income is received in monetary and non-monetary forms and often varies seasonally (Montgomery et al 2000). Hence, the possession of assets and access to various services constitute a more stable measure of household wealth and has been frequently used in prior research (Hunter et al 2014; Mberu 2006; Nawrotzki et al 2013; Nawrotzki et al 2015b). Accordingly, we constructed a standardized wealth index (Cronbach's alpha = 0.85) that combines 10 variables measuring housing quality (material of floor, wall, roof, number of rooms and bedrooms, and toilet type) and service and infrastructure access (water supply, electricity, sewage system, and cooking fuel type).…”