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Published in final edited form as: Environ Res Lett. 2015 Nov 23;10(11):114023. doi: 10.1088/1748-9326/10/11/114023

Climate Change as Migration Driver from Rural and Urban Mexico

Raphael J Nawrotzki a,✉, Lori M Hunter b, Daniel M Runfola c, Fernando Riosmena d
PMCID: PMC4674075  NIHMSID: NIHMS740939  PMID: 26692890

Abstract

Studies investigating migration as a response to climate variability have largely focused on rural locations to the exclusion of urban areas. This lack of urban focus is unfortunate given the sheer numbers of urban residents and continuing high levels of urbanization. To begin filling this empirical gap, this study investigates climate change impacts on U.S.-bound migration from rural and urban Mexico, 1986–1999. We employ geostatistical interpolation methods to construct two climate change indices, capturing warm and wet spell duration, based on daily temperature and precipitation readings for 214 weather stations across Mexico. In combination with detailed migration histories obtained from the Mexican Migration Project, we model the influence of climate change on household-level migration from 68 rural and 49 urban municipalities. Results from multilevel event-history models reveal that a temperature warming and excessive precipitation significantly increased international migration during the study period. However, climate change impacts on international migration is only observed for rural areas. Interactions reveal a causal pathway in which temperature (but not precipitation) influences migration patterns through employment in the agricultural sector. As such, climate-related international migration may decline with continued urbanization and the resulting reductions in direct dependence of households on rural agriculture.

Keywords: Climate change, climate migration, environment, international migration, rural livelihoods, urban livelihoods, Mexico

1. Introduction

Climate change is an issue of global magnitude that will impact human populations through, for example, increases in frequency and intensity of storms, floods, and heat waves, as well as more gradual changes involving sea-level rise and desertification (IPCC 2013, 2014). Particularly poor, less developed countries (LDCs) will likely suffer most from climate change due to a less adequate infrastructure such as irrigation, combined with limited financial capital to develop technological protection including sea walls (cf., Gutmann and Field 2010).

When climate change negatively impacts livelihoods, households may employ in situ (in place) adaptation strategies to meet basic needs (Bardsley and Hugo 2010; Davis and Lopez-Carr 2010). In situ strategies include selling assets, using formal and informal credit, reducing nonessential expenditures, and/or drawing on social networks and public programs for assistance (Gray and Mueller 2012). However, households may also send a member elsewhere as an ex situ strategy to diversify income sources through remittances. As such, migration functions as an informal insurance mechanism. This mechanism is most effective when the household member is sent to a destination where environmental and market conditions are largely uncorrelated with those at the origin (Massey et al. 1993; Stark and Bloom 1985). This diversification strategy may be useful in times of climate stress (Black et al. 2011). In addition, remittances not only benefit the migrant family but positively impact community development through various multiplier effects (Taylor et al. 1996).

Evidence from around the world suggests that environmental factors influence migration patterns (e.g., Bohra-Mishra et al. 2014; Gray and Bilsborrow 2013; Hunter et al. 2013, 2015; Mueller et al. 2014). However, studies frequently find that the climate-migration association varies by location characteristics (e.g., Nawrotzki et al. 2013), access to social networks (Hunter et al. 2013), and demographic factors such as gender (Gray 2010). Yet, this body of research, and the generalizations emerging from collective results, have a decidedly rural focus. In fact, there is no published study available (to our knowledge) that contrasts climate migration from rural and urban areas. This lack of research is unfortunate, given continuing high levels of urbanization (Ravallion et al. 2007). About 54% of the world's population currently lives in urban areas (UNPD 2014) and in future decades the population of LDCs will be predominantly urban (Seto et al. 2012).

Understanding the implications of climate variability and change for urban migration is of tremendous policy importance given the high levels of global population in urban regions (c.f., Adamo 2010). In this letter, we report research results contrasting climate migration responses from rural and urban areas in Mexico, with a focus on international migration. Bringing physical climate science into social science, we employ two measures of climate extremes, the warm spell duration index (WSDI) and the wet spell duration index (CWD) formalized by the Expert Team on Climate Change Detection and Indices (ETCCDI) (Peterson and Manton 2008).

2. Mexico as a Setting for Research on Climate and Migration

Mexico provides an excellent case for the study of international climate migration dynamics due to a long history of migration. Longstanding international labor migration between Mexico and the U.S. has led to the establishment of strong transnational migrant networks (see supplement S1 for a detailed account of the history of Mexico-U.S. migration). Such networks operate as migrant corridors and may strongly facilitate climate-related migration (Bardsley and Hugo 2010).

We conceptually consider migration a household-based strategy for income diversification to guard against the adverse influence of environmental and economic impacts (Massey et al. 1993; Stark and Bloom 1985). Yet, we argue that climate variability and change influence livelihoods differently in rural and urban areas. In rural areas of Mexico, households depend heavily on agricultural production for sustenance and income generation (Conde et al. 2006; de Janvry and Sadoulet 2001; Wiggins et al. 2002). This reliance on agriculture combined with a lack of technological infrastructure (e.g., limited irrigation) makes rural Mexican households highly vulnerable to climatic shifts (Eakin and Appendini 2008; Endfield 2007; Schroth et al. 2009).

In urban areas, however, the livelihood impacts of climate are more complex and often related to extreme events (Revi et al. 2014). For example, flooding may damage wastewater systems (Revi 2008; Romero-Lankao 2010; Willems et al. 2012), transportation infrastructure (Gasper et al. 2011; Koetse and Rietveld 2009), and buildings. These impacts negatively affect job availability and housing quality (CEPAL 2008). In addition, heat waves may result in negative health outcomes (Burkart et al. 2011; Klinenberg 2015; WHO and WMO 2012) and may adversely impact the effectiveness of workers in manual occupations (cf., Kovats and Akhtar 2008). In urban areas, heat waves are often intensified by the urban heat island effect which is due, in part, to daytime absorption and nighttime release of heat by concrete, pavement, and buildings (Adachi et al. 2012; Wilby 2007). In short, while climate variability and change influence rural livelihoods through impacts on the agricultural sector, climate change may affect urban livelihoods through a broad spectrum of impacts to city functions, infrastructure, services, and employment opportunities.

Also important to consider is the connection between rural and urban regions. Adverse climatic conditions in rural areas may influence urban industries and livelihoods in various indirect ways (Boyd and Ibarraran 2009; Wackernagel et al. 2006). For example, urban residents may be employed in the agricultural sector near cities or work in factories processing agricultural outputs (Satterthwaite et al. 2007). In addition, climate variability and change related to agricultural failure in rural areas may decrease food supply and increase urban food prices (Satterthwaite et al. 2007). As such, adverse climatic conditions may directly impact rural livelihoods and at the same time indirectly impact urban livelihoods. Consequently, both rural and urban households may employ migration as a livelihood diversification strategy in the face of climate variability and change (cf., Adamo 2010).

3. Data and Methods

To investigate the climate change–migration association for both urban and rural Mexico, we combined sociodemographic data from the Mexican Migration Project (MMP) with climate information from the Global Historical Climatology Network Daily (GHCN-D) data set (Menne et al. 2012). While the MMP provides detailed migration histories and sociodemographic information, the GHCN-D provides daily temperature and precipitation readings for 214 weather stations across Mexico (see S2 for additional detail on the data).

In Mexico, the decision to migrate -- particularly international migration -- is often a household decision and we, therefore, focused our analysis on household-level outmigration (Cohen 2004; Kanaiaupuni 2000). Our sample comprised 14,239 households located in 111 municipalities dispersed across Mexico. We employed geo-statistical interpolation to link spatial climate information to sociodemographic MMP data at the municipality level (see S3 for variable construction). Figure 1 shows the location of the weather stations as well as the 111 MMP municipalities, which are comprised of communities that can be considered either urban or rural. Communities located in a metropolitan area (a state’s capital city or other large city) or a city (smaller urban area of 10,000–100,000 inhabitants) are considered urban, while communities located in a town (2,500–10,000 inhabitants) or a village (< 2,500 inhabitants) are considered rural.

Figure 1.

Figure 1

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Geographic location of MMP municipalities and spatial distribution of weather stations across Mexico

Notes: Total municipalities: n=111; rural municipalities: n=62; urban municipalities: n=43; municipalities containing both rural and urban municipalities n=6

The period under investigation is 1986–1999, an excellent timeframe for examination of the potential migratory impacts of future climate change because much of Mexico experienced above normal temperature during the 1990s (Stahle et al. 2009). This climatic pattern resembles projected temperature increases under future climate change scenarios (IPCC 2013; Collins et al. 2013). And on a practical note, the number of Mexican weather stations available within the GHCN-D dropped substantially for more recent years, rendering the spatial interpolation of climate data after 1999 unstable.

For the study of climate-migration dynamics, we used multi-level event history models that account for household clustering within municipalities (see S4 for details on statistical methodology). Our outcome variable was international migration at the household level and we included a suite of important control variables such as indicators of access to migrant networks, occupation status, and socio-economic characteristics such as marital status and assets (Brown and Bean 2006). Community-level factors were also considered such as the level of agricultural employment and access to migrant networks (see supplement S3). We first developed a reliable multivariate base model with these control variables to predict international migration from urban and rural Mexican communities (see S5).

4. Results and Discussion

The results of the base model are in line with prior work on Mexican migration, suggesting that international migration from Mexico is influenced by a set of well-known socio-demographic factors. For example, households are more likely to send a migrant when they have good access to migrant networks (Fussell and Massey 2004) or when the household head is an uneducated young male (Fussell 2004). In contrast, the likelihood of sending a migrant is lower when young children are present (Nawrotzki et al. 2013) or when the household owns a business (Massey and Parrado 1998). After testing the robustness of this base model, we added the warm spell duration index (WSDI) and the wet spell duration index (CWD) jointly, allowing for simultaneous control of temperature and precipitation effects (Auffhammer et al. 2013). Table 1 provides separate estimates of the climate-migration association for rural and urban areas in Mexico, 1986–1999.

Table 1.

Impact of climate change on the odds of international migration from rural and urban Mexico, 1986–1999

All Rural Urban
b sig. b sig. b sig.
  Warm spell duration 1.15 *** 1.22 *** 1.05
  Wet spell duration 1.05 * 1.06 * 1.02
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Note: Coefficients reflect odds ratios; climate effects were estimated using the fully adjusted multi-level event history models (Table S2); Municipality N: All=111, Rural=68, Urban=49; a jackknife procedure, omitting one municipality at a time from the sample (Nawrotzki 2012; Ruiter and de Graaf 2006), demonstrated that the estimates were highly robust;

*

p<0.05;

**

p<0.01;

***

p<0.001

The results suggest that longer warm spell durations and wet spell durations lead to higher odds of international migration in the combined model of rural and urban areas (“All”). But the temperature effect is three times as strong as the precipitation effect -- a one standard deviation unit increase in warm spell duration leads to an increase of the odds of international migration by 15% (Odds Ratio [OR]=1.15) while a one standard deviation unit increase in wet spell duration leads to an increase in the odds of an international move by 5% (OR=1.05). This observation is in line with prior work that consistently finds stronger temperature than precipitation effects on migration patterns (Bohra-Mishra et al. 2014; Mueller et al. 2014). An increase in temperature may increase evapotranspiration and lead to drought conditions even if precipitation patterns do not change (Diffenbaugh et al. 2015). In addition, technological responses to precipitation anomalies, such as irrigation, are more readily implemented than technologies to guard against adverse impacts of temperature extremes.

But our central finding is that disaggregating the sample into rural and urban areas reveals that climate change effects only emerge for rural areas. With regard to urban areas, non-significant coefficients suggest that climate effects do not translate into international migration among urban populations. Reasons for the lack of association could include: 1) the indirect effects on urban livelihoods through rural production deficits (e.g., job losses, increased food prices) are not sufficiently strong, 2) urban households have better access to alternative in situ (in place) adaptation strategies, or 3) migrant networks that facilitate climate migration (i.e., Bardsley and Hugo 2010) are underdeveloped in urban areas (Fussell and Massey 2004).

On the other hand, climate change measures exhibit statistically significant effects for migration from rural Mexican households. As noted above, rural populations in Mexico depend heavily on subsistence farming and agricultural employment, intensifying vulnerability to climate variability and change (Mueller et al. 2014). Both heat waves and flooding have negative impacts on agricultural productivity. Corn, an important staple in Mexico, is particularly sensitive to heat stress (Sanchez et al. 2014; Schoper et al. 1987; Tollenaar and Bruuslema 1988), and a warming in temperature has been shown to reduce crop yields (Bassu et al. 2014; Lobell and Field 2007). Similarly, flooding and excess soil moisture (saturation and waterlogging) impair plant growth (Ashraf and Habib-ur-Rehman 1999; Zaidi et al. 2003), increase the risk of plant disease and insect infestation (cf., Kozdroj and van Elsas 2000), and may delay planting or harvesting due to inability to operate machinery. As a result, precipitation and harvest yields are non-linearly related and when cumulative precipitation exceeds certain thresholds, a substantial decline in crop yields can occur (Rosenzweig et al. 2002). Overall, we argue that through such adverse agricultural impacts, heat waves and flooding both increase livelihood insecurity and can lead to elevated migration probabilities from rural areas.

To further test this agricultural pathway for climate change effects on rural migration, we estimated a model of international migration with an interaction term, reflecting a combination of the climate change indices and a municipality-level measure of percentage males employed in the agricultural sector (Table 2).

Table 2.

Interaction between climate change indices and the male labor force employed in the agricultural sector predicting the odds of international migration from rural and urban Mexico, 1986–1999

All Rural Urban
b sig. b sig. b sig.
Temperature interaction model
  Warm spell duration 1.13 *** 1.15 *** 1.05
  Male labor in agriculture 1.09 ** 0.98 1.19 ***
    Warm spell duration×Male labor in agriculture 1.01 * 1.03 ** 1.01
Precipitation interaction model
  Wet spell duration 1.06 * 1.08 0.99
  Male labor in agriculture 1.09 ** 1.00 1.17 ***
    Wet spell duration×Male labor in agriculture 0.99 0.99 0.97
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Note: Coefficients reflect odd ratios; coefficients for male labor force employed in the agricultural sector relate to a 10% change; each row represents a fully adjusted interaction model (all controls of Table S2 included) of which only the coefficients for the terms involved in the interaction are shown; variables were grand mean centered;

*

p<0.05;

**

p<0.01;

***

p<0.001

We observe a statistically significant interaction in the combined model (“All”) and for the rural sub-sample, but only for the temperature effect (Table 2). Figure 2 provides a visual representation of this important interaction – the warm spell duration index has almost no effect on international migration when only a small proportion of local males are employed in the agricultural sector. However, with higher levels of male agricultural employment, warm spells are associated with greater likelihood of international migration. A similar pathway in which the temperature-migration association is moderated by agricultural income was initially suggested, but not empirically tested, by Mueller et al. for Pakistan (2014).

Figure 2.

Figure 2

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Interaction between warm spell duration index and the percentage of males employment in the agriculture sector on the probability of international migration across Mexico (combined rural and urban sample), 1986–1999

Even so, no significant interaction emerged for the wet spell duration index. Although longer wet spell durations and possible flooding increase the likelihood of an international move from rural areas in general, this effect is not associated specifically with agricultural employment. This may indicate that flooding is equally harmful for nonagricultural sectors. Indeed, flooding not only damages agricultural production but also may have detrimental impacts on local infrastructure and other economic activities (CEPAL 2008). In short, we find evidence that climate change impacts rural migration through the agricultural sector, but only as related to temperature extremes.

5. Conclusion

Evidence continues to emerge suggesting that climate variability and change shapes migration patterns (Gray and Bilsborrow 2013; Mueller et al. 2014). However, most existing research focuses exclusively on rural areas. Given trends of rapid urbanization (Seto et al. 2012), it is of increasing policy relevance to investigate whether climate variability and change similarly influence the probability of international moves from urban areas (cf. Adamo 2010).

This letter reports results from the first empirical study to contrast climate migration from rural and urban areas in Mexico. As a strength of this research project, we employ two ETCCDI climate change indices constructed based on daily temperature and precipitation information at a high spatial resolution. We find that heat waves and wet spells significantly increased international migration from Mexico during 1986–1999. However, our results show that adverse climate variability and change drive international outmigration only from rural areas. In addition, we find evidence that the influence of climate change on migration operates primarily through employment in the agricultural sector. However, this causal pathway emerges only for temperature but not for precipitation effects, suggesting the particular importance of temperature extremes for international migration from rural areas.

These findings have important policy implications. First, the results suggest that attempts to project international climate migration (e.g., Christian Aid 2007; Myers 2002, 2005; Stern 2007) must account for urban-rural differentials in the influence of climate on migration. Second, our study suggests that adverse climate variability and change will continue to increase international migration from rural areas. Adaptation programs should therefore target rural Mexican communities, potentially as a means of lessening the need for migration as an ex situ strategy and to improve livelihoods and wellbeing of local populations.

Supplementary Material

01

Acknowledgements

This article is based on Chapter IV of Raphael Nawrotzki’s dissertation titled “Climate Change as a Migration Driver in Mexico, 1986–99”. The authors gratefully acknowledge support from the Minnesota Population Center (5R24HD041023) and the University of Colorado Population Center (R24 HD066613), funded through grants from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD). In addition, this work received support from the National Science Foundation funded Terra Populus project (NSF Award ACI-0940818). We thank Richard G. Rogers and Fred C. Pampel for insightful comments on the substantive discussion and helpful advice on the statistical model development. We express our gratitude to Gina Rumore for her careful editing. Thanks also to two anonymous reviewers and the journal editors for helpful comments and suggestions on earlier versions of this manuscript.

References

  1. Adachi SA, Kimura F, Kusaka H, Inoue T, Ueda H. Comparison of the impact of global climate changes and urbanization on summertime future climate in the Tokyo metropolitan area (vol 51, pg 1441, 2012) Journal of Applied Meteorology and Climatology. 2012;51(11):2074–2075. [Google Scholar]
  2. Adamo SB, de Sherbinin A. Population distribution, urbanization, internal migration and development: An international perspective. New York: Unite Nations, Department of Economic and Social Affairs, Population Division; 2011. The impact of climate change on the spatial distribution of populations and migration; pp. 161–195. [Google Scholar]
  3. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Tank A, Vazquez-Aguirre JL. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research-Atmospheres. 2006;111(D5):22. [Google Scholar]
  4. Allison P. Event history analysis. Thousand Oaks, CA: Sage Publications; 1984. [Google Scholar]
  5. Allison PD. Missing data. Thousand Oaks, CA: Sage Publications; 2002. [Google Scholar]
  6. Angelucci M. U.S. Border Enforcement and the Net Flow of Mexican Illegal Migration. Economic Development and Cultural Change. 2012;60(2):311–357. [Google Scholar]
  7. Ashraf M, Habib-ur-Rehman Interactive effects of nitrate and long-term waterlogging on growth, water relations, and gaseous exchange properties of maize (Zea mays L.) Plant Science. 1999;144(1):35–43. [Google Scholar]
  8. Auffhammer M, Hsiang SM, Schlenker W, Sobel A. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change. Review of Environmental Economics and Policy. 2013;7(2):181–198. [Google Scholar]
  9. Aznar JC, Gloaguen E, Tapsoba D, Hachem S, Caya D, Begin Y. Interpolation of monthly mean temperatures using cokriging in spherical coordinates. International Journal of Climatology. 2013;33(3):758–769. [Google Scholar]
  10. Barber JS, Murphy SA, Axinn WG, Maples J. Discrete-time multilevel hazard analysis. Sociological Methodology. 2000;30(1):201–235. [Google Scholar]
  11. Bardsley DK, Hugo GJ. Migration and climate change: examining thresholds of change to guide effective adaptation decision-making. Population and Environment. 2010;32(2 – 3):238–262. [Google Scholar]
  12. Bassu S, Brisson N, Durand J-L, Boote K, Lizaso J, Jones JW, Waha K. How do various maize crop models vary in their responses to climate change factors? Global Change Biology. 2014;20(7):2301–2320. doi: 10.1111/gcb.12520. [DOI] [PubMed] [Google Scholar]
  13. Bates D, Maechler M, Bolker BM, Walker S. lme4: Linear mixed-effects models using Eigen and S4. 2014 Retrieved from. [Google Scholar]
  14. Bates DM. lme4: Mixed-effects modeling with R. New York: Springer; 2010. [Google Scholar]
  15. Bindoff NL, Stott PA, Achutarao KM, Allen MR, Gillett N, Gutzler D, Zhang X. Detection and attribution of climate change: from global to regional. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate change 2013: The physical science basis. Contribution of working group 1 to the fifth assessment report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press; 2013. [Google Scholar]
  16. Black R, Bennett SRG, Thomas SM, Beddington JR. Migration as adaptation. Nature. 2011;478(7370):447–449. doi: 10.1038/478477a. [DOI] [PubMed] [Google Scholar]
  17. Bohra-Mishra P, Oppenheimer M, Hsiang SM. Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(27):9780–9785. doi: 10.1073/pnas.1317166111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Bolstad P. GIS Fundamentals: A first text on Geographic Information Systems. 4 ed. White Bear Lake, MN: Eider Press; 2012. [Google Scholar]
  19. Boyd R, Ibarraran ME. Extreme climate events and adaptation: an exploratory analysis of drought in Mexico. Environment and Development Economics. 2009;14:371–395. [Google Scholar]
  20. Bronaugh D. R package climdex.pcic: PCIC implementation of Climdex routines. Victoria, British Columbia, Canada: Pacific Climate Impact Consortium; 2014. [Google Scholar]
  21. Brown SK, Bean FD. International Migration. In: Posten D, Micklin M, editors. Handbook of population. New York: Springer Publishers; 2006. pp. 347–382. [Google Scholar]
  22. Burkart K, Schneider A, Breitner S, Khan MH, Kraemer A, Endlicher W. The effect of atmospheric thermal conditions and urban thermal pollution on all-cause and cardiovascular mortality in Bangladesh. Environmental Pollution. 2011;159(8 – 9):2035–2043. doi: 10.1016/j.envpol.2011.02.005. [DOI] [PubMed] [Google Scholar]
  23. Caesar J, Alexander L, Vose R. Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research-Atmospheres. 2006;111(D5) [Google Scholar]
  24. Calavita K. Inside the state: The bracero program, immigration, and the I.N.S. New York: Routledge; 1992. [Google Scholar]
  25. Cardoso LA. Mexican emigration to the United States, 1897 – 1931: Socio-economic patterns. Tucson, AZ: University of Arizona Press; 1980. [Google Scholar]
  26. Carney D, Drinkwater M, Rusinow T, Neefjes K, Wanmali S, Singh N. Livelihoods approaches compared. London: Department for International Development; 1999. [Google Scholar]
  27. CEPAL. Tabasco: Caracteristicas e impactos socioeconomico de las inundaciones provoadas a finales de octubre y a comienzos de noviembre de 2007 por el frente frio numero 4. Santiago de Chile, Chile: United Nations Comisión Económica para América Latina; 2008. [Google Scholar]
  28. ChristianAid . Human tide: The real migration crisis. London: Christian Aid; 2007. [Google Scholar]
  29. Cohen JH. The culture of migration in Southern Mexico. Austin, TX: University of Texas Press; 2004. [Google Scholar]
  30. Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Wehner M. Long-term climate change: Projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate change 2013: The physical science basis. Contribution of working group 1 to the fifth assessment report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press; 2013. [Google Scholar]
  31. Conde C, Ferrer R, Orozco S. Climate chage and climate variability impacts on rainfed agricultural activities and possible adaptation measures. A Mexico case study. Atmosfera. 2006;19(3):181–194. [Google Scholar]
  32. Cornelius WA. From sojourners to settlers: The changing profile of Mexican migration to the United States. In: Bustamante JA, Reynolds CW, Hinojosa Ojeda RA, editors. U.S.-Mexico relations: Labor market interdependence. Stanford, CA: Stanford University Press; 1992. pp. 155–195. [Google Scholar]
  33. Danielson JJ, Gesch DB. Global multi-resolution terrain elevation data 2010 (GMTED 2010): Open-File Report 2011 – 1073. 2011. Retrieved from Reston, Virginia: [Google Scholar]
  34. Davis J, Lopez-Carr D. The effects of migrant remittances on population-environment dynamics in migrant origin areas: international migration, fertility, and consumption in highland Guatemala. Population and Environment. 2010;32(2 – 3):216–237. doi: 10.1007/s11111-010-0128-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. de Janvry A, Sadoulet E. Income strategies among rural households in Mexico: The role of off-farm activities. World Development. 2001;29(3):467–480. [Google Scholar]
  36. Diffenbaugh NS, Swain DL, Touma D. Anthropogenic warming has increased drought risk in California. Proceedings of the National Academy of Sciences of the United States of America. 2015;112(13):3931–3936. doi: 10.1073/pnas.1422385112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Durand J, Arias P. La experiencia migrante: Iconografia de la migracion Mexico-Estados Unidos. Xalapa, Mexico: Altexto; 2000. [Google Scholar]
  38. Eakin H, Appendini K. Livelihood change, farming, and managing flood risk in the Lerma Valley, Mexico. Agriculture and Human Values. 2008;25(4):555–566. [Google Scholar]
  39. Endfield GH. Archival explorations of climate variability and social vulnerability in colonial Mexico. Climatic Change. 2007;83(1–2):9–38. [Google Scholar]
  40. Feng S, Oppenheimer M. Applying statistical models to the climate-migration relationship. Proceedings of the National Academy of Science. 2012;109(43):E2915. doi: 10.1073/pnas.1212226109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Tank A, Peterson T. Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research. 2002;19(3):193–212. [Google Scholar]
  42. Fussell E. Sources of Mexico's migration stream: Rural, urban, and border migrants to the United States. Social Forces. 2004;82(3):937–967. [Google Scholar]
  43. Fussell E, Massey DS. The limits to cumulative causation: International migration from Mexican urban areas. Demography. 2004;41(1):151–171. doi: 10.1353/dem.2004.0003. [DOI] [PubMed] [Google Scholar]
  44. Garzon-Machado V, Otto R, Aguilar MJD. Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques. International Journal of Biometeorology. 2014;58(5):887–899. doi: 10.1007/s00484-013-0670-y. [DOI] [PubMed] [Google Scholar]
  45. Gasper R, Blohm A, Ruth M. Social and economic impacts of climate change on the urban environment. Current Opinion in Environmental Sustainability. 2011;3(3):150–157. [Google Scholar]
  46. Gray C, Bilsborrow R. Environmental Influences on Human Migration in Rural Ecuador. Demography. 2013;50:1217–1241. doi: 10.1007/s13524-012-0192-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Gray C, Mueller V. Drought and Population Mobility in Rural Ethiopia. World Development. 2012;40(1):134–145. doi: 10.1016/j.worlddev.2011.05.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Gray CL. Gender, natural capital, and migration in the southern Ecuadorian Andes. Environment and Planning. 2010;42:678–696. [Google Scholar]
  49. Gutmann MP, Field V. Katrina in historical context: environment and migration in the US. Population and Environment. 2010;31(1–3):3–19. doi: 10.1007/s11111-009-0088-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Henry S, Schoumaker B, Beauchemin C. The impact of rainfall on the first out-migration: A multi-level event-history analysis in Burkina Faso. Population and Environment. 2004;25(5):423–460. [Google Scholar]
  51. Hevesi JA, Istok JD, Flint AL. Precipitation estimation in mountainous terrain using multivariate geostatistics .1. Structural-analysis. Journal of Applied Meteorology. 1992;31(7):661–676. [Google Scholar]
  52. Hoffman A. Unwanted Mexican Americans in the Great Depression: Repatriation pressures 1929–1939. Tucson, AZ: University of Arizona Press; 1974. [Google Scholar]
  53. Honaker J, King G. What to Do about Missing Values in Time-Series Cross-Section Data. American Journal of Political Science. 2010;54(2):561–581. [Google Scholar]
  54. Honaker J, King G, Blackwell M. Amelia II: A Program for Missing Data. Journal of Statistical Software. 2011;45(7):1–47. [Google Scholar]
  55. Hunter LM, Luna JK, Norton RM. Environmental dimensions of migration. Annual Review of Sociology. 2015 doi: 10.1146/annurev-soc-073014-112223. XX(X), (in press). [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Hunter LM, Murray S, Riosmena F. Rainfall patterns and U.S. migration from rural Mexico. International Migration Review. 2013;47(4):874–909. doi: 10.1111/imre.12051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Hunter LM, O'Neill BC. Enhancing engagement between the population, environment, and climate research communities: the shared socio-economic pathway process. Population and Environment. 2014;35(3):231–242. doi: 10.1007/s11111-014-0202-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. INEGI. Sistema Estatal y Municipal de Bases de Datos. Aguascalientes, Mexico: Instituto Nacional de Estadística, Geografía e Informática; 2012. [Retrieved July 4, 2012]. from http://sc.inegi.org.mx/sistemas/cobdem/primeraentrada.do?w=25&Backidhecho=302&Backconstem=301&constembd=165. [Google Scholar]
  59. IPCC. Summary for Policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate Change 2013: The Physical Science Basis. Contribution of Working Group 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press; 2013. pp. 1–28. [Google Scholar]
  60. IPCC. Summary for policymakers. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, SKissel E, Levy AN, MacCracken S, Mastrandrea Pr, White LL, editors. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group 2 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press; 2014. pp. 1–32. [Google Scholar]
  61. Kanaiaupuni SM. Reframing the migration question: An analysis of men, women, and gender in Mexico. Social Forces. 2000;78(4):1311–1347. [Google Scholar]
  62. Klein Tank AMG, Peterson TC, Quadir DA, Dorji S, Zou X, Tang H, Spektorman T. Changes in daily temperature and precipitation extremes in central and south Asia. Journal of Geophysical Research-Atmospheres. 2006;111(D16) [Google Scholar]
  63. Klinenberg E. Heat wave: A social autopsy of disaster in Chicago. Chicago, IL: University of Chicago Press; 2015. [DOI] [PubMed] [Google Scholar]
  64. Koetse MJ, Rietveld P. The impact of climate change and weather on transport: An overview of empirical findings. Transportation Research Part D-Transport and Environment. 2009;14(3):205–221. [Google Scholar]
  65. Kovats S, Akhtar R. Climate, climate change and human health in Asian cities. Environment and Urbanization. 2008;20(1):165–175. [Google Scholar]
  66. Kozdroj J, van Elsas JD. Response of the bacterial community to root exudates in soil polluted with heavy metals assessed by molecular and cultural approaches. Soil Biology & Biochemistry. 2000;32(10):1405–1417. [Google Scholar]
  67. Kugler TA, Van Riper DC, Manson SM, Haynes DA, Donato J, Stinebaugh K. Terra Populus: Workflows for integrating and harmonizing geospatial population and environmental data. Journal of Map and Geography Libraries. 2015;11(2):80–206. [Google Scholar]
  68. Lindstrom DP, Lauster N. Local economic opportunity and the competing risks of internal and US migration in Zacatecas, Mexico. International Migration Review. 2001;35(4):1232–1256. [Google Scholar]
  69. Little RJA, Rubin DB. Statistical analysis with missing data. 2nd edition ed. New York: John Wiley and Sons; 2002. [Google Scholar]
  70. Lobell DB, Field CB. Global scale climate - crop yield relationships and the impacts of recent warming. Environmental Research Letters. 2007;2(1) [Google Scholar]
  71. LoBreglio K. The border security and immigration improvement act a modern solution to a historic problem. St. John's Law Review. 2004;78(3):933–963. [Google Scholar]
  72. Lustig N. Economic-crisis, adjustment and living standards in Mexico, 1982 – 85. World Development. 1990;18(10):1325–1342. [Google Scholar]
  73. Martin PL. Harvest of confusion - immigration reform and California agriculture. International Migration Review. 1990;24(1):69–95. [PubMed] [Google Scholar]
  74. Massey DS. The Ethnosurvey in theory and practice. International Migration Review. 1987a;21(4):1498–1522. [PubMed] [Google Scholar]
  75. Massey DS. Understanding Mexican migration to the United-States. American Journal of Sociology. 1987b;92(6):1372–1403. [Google Scholar]
  76. Massey DS, Alarcon R, Durand J, Gonzalez H. Return to Aztlan: The social process of international migration from Western Mexico. Berkely, CA: University of California Press; 1987. [Google Scholar]
  77. Massey DS, Arango J, Hugo G, Kouaouci A, Pellegrino A, Taylor JE. Theories of international migration - A review and appraisal. Population and Development Review. 1993;19(3):431–466. [Google Scholar]
  78. Massey DS, Axinn WG, Ghimire DJ. Environmental change and out-migration: evidence from Nepal. Population and Environment. 2010;32(2 – 3):109–136. doi: 10.1007/s11111-010-0119-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Massey DS, Capoferro C. Measuring undocumented migration. International Migration Review. 2004;38(3):1075–1102. [Google Scholar]
  80. Massey DS, Durand J, Malone NJ. Beyond smoke and mirrors: Mexican immigration in an era of economic integration. New York: Russell Sage Foundation Publications; 2002. [Google Scholar]
  81. Massey DS, Durand J, Pren KA. Border Enforcement and Return Migration by Documented and Undocumented Mexicans. Journal of Ethnic and Migration Studies. 2015;41(7):1015–1040. doi: 10.1080/1369183X.2014.986079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Massey DS, Parrado EA. International migration and business formation in Mexico. Social Science Quarterly. 1998;79(1):1–20. [Google Scholar]
  83. Massey DS, Riosmena F. Undocumented migration from Latin America in an era of rising U.S. enforcement. Annals of the American Academy of Political and Social Science. 2010;630:294–321. doi: 10.1177/0002716210368114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Massey DS, Zenteno R. A validation of the ethnosurvey: The case of Mexico-US migration. International Migration Review. 2000;34(3):766–793. [Google Scholar]
  85. McKenzie DJ. The consumer response to the Mexican peso crisis. Economic Development and Cultural Change. 2006;55(1):139–172. [Google Scholar]
  86. McLeman RA. Settlement abandonment in the context of global environmental change. Global Environmental Change. 2011;21:S108–S120. [Google Scholar]
  87. Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG. An Overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology. 2012;29(7):897–910. [Google Scholar]
  88. MPC. Integrated Public Use Microdata Series, International: Version 6.2 [Machine-readable database] Minneapolis, MN: University of Minnesota; 2013a. [Google Scholar]
  89. MPC. Terra Populus: Beta Version [Machine-readable database] Minneapolis, MN: University of Minnesota; 2013b. [Google Scholar]
  90. Mueller V, Gray C, Kosec K. Heat stress increases long-term human migration in rural Pakistan. Nature Climate Change. 2014;4(3):182–185. doi: 10.1038/nclimate2103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Myers N. Environmental refugees: a growing phenomenon of the 21st century. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences. 2002;357(1420):609–613. doi: 10.1098/rstb.2001.0953. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Myers N. Environmental refugees: An emergent security issue. 2005 Retrieved from Prague: [Google Scholar]
  93. Nawrotzki RJ. The politics of environmental concern: A cross-national analysis. Organization & Environment. 2012;25(3):286–307. doi: 10.1177/1086026612456535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Nawrotzki RJ, Hunter LM, Dickinson TW. Rural livelihoods and access to natural capital: Differences between migrants and non-migrants in Madagascar. Demographic Research. 2012;26:661–699. doi: 10.4054/DemRes.2012.26.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Nawrotzki RJ, Riosmena F, Hunter LM. Do rainfall deficits predict U.S.-bound migration from rural Mexico? Evidence from the Mexican census. Population Research and Policy Review. 2013;32(1):129–158. doi: 10.1007/s11113-012-9251-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Orrenius PM, Zavodny M. Do amnesty programs reduce undocumented immigration? Evidence from IRCA. Demography. 2003;40(3):437–450. doi: 10.1353/dem.2003.0028. [DOI] [PubMed] [Google Scholar]
  97. Peterson TC, Manton MJ. Monitoring changes in climate extremes - A tale of international collaboration. Bulletin of the American Meteorological Society. 2008;89(9):1266–1271. [Google Scholar]
  98. Ravallion M, Chen S, Sangraula P. New evidence on the urbanization of global poverty. Population and Development Review. 2007;33(4):667. [Google Scholar]
  99. RCoreTeam. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014. http://www.R-project.org/ [Google Scholar]
  100. Revi A. Climate change risk: an adaptation and mitigation agenda for Indian cities. Environment and Urbanization. 2008;20(1):207–229. [Google Scholar]
  101. Revi A, Satterthwaite D, Aragon-Durand F, Corfee-Morlot J, Kiunsi R, Pelling M, Solecki W. Urban Areas. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea Pr, White LL, editors. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group 2 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press; 2014. [Google Scholar]
  102. Riosmena F. Socioeconomic context and the association between marriage and Mexico-US migration. Social Science Research. 2009;38(2):324–337. doi: 10.1016/j.ssresearch.2008.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Romero-Lankao P. Water in Mexico City: what will climate change bring to its history of water-related hazards and vulnerabilities? Environment and Urbanization. 2010;22(1):157–178. [Google Scholar]
  104. Rosenzweig C, Tubiello FN, Goldberg R, Mills E, Bloomfield J. Increased crop damage in the US from excess precipitation under climate change. Global Environmental Change-Human and Policy Dimensions. 2002;12(3):197–202. [Google Scholar]
  105. Rubin D. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987. [Google Scholar]
  106. Ruggles S, King ML, Levison D, McCAA R, Sobek M. IPUMS-International. Historical Methods. 2003;36(2):60–65. [Google Scholar]
  107. Ruiter S, De Graaf ND. National context, religiosity, and volunteering: Results from 53 countries. American Sociological Review. 2006;71(2):191–210. [Google Scholar]
  108. Sanchez B, Rasmussen A, Porter JR. Temperatures and the growth and development of maize and rice: a review. Global Change Biology. 2014;20(2):408–417. doi: 10.1111/gcb.12389. [DOI] [PubMed] [Google Scholar]
  109. Sanchez Cohen I, Oswald Spring U, Diaz Padilla G, Cerano Paredes J, Inzunza Ibarra MA, Lopez Lopez R, Villanueva Diaz J. Forced migration, climate change, mitigation and adaptation policies in Mexico: Some functional relationships. International Migration. 2012 [Google Scholar]
  110. Satterthwaite D, Huq S, Pelling M, Raid H, Romero Lankao P. Adapting to climate change in urban areas. The possibilities and constraints in low- and middle-income nations. London: International Institute for Environment and Development; 2007. [Google Scholar]
  111. Schoper JB, Lambert RJ, Vasilas BL. Pollen viability, pollen shedding, and combining ability for tassel heat tolerance in maize. Crop Science. 1987;27(1):27–31. [Google Scholar]
  112. Schroth G, Laderach P, Dempewolf J, Philpott S, Haggar J, Eakin H, Ramirez-Villegas J. Towards a climate change adaptation strategy for coffee communities and ecosystems in the Sierra Madre de Chiapas, Mexico. Mitigation and Adaptation Strategies for Global Change. 2009;14(7):605–625. [Google Scholar]
  113. Scoones I. Sustainable rural livelihoods: A framework for analysis. Brighton UK: Institute of Development Studies; 1999. [Google Scholar]
  114. Seto KC, Gueneralp B, Hutyra LR. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(40):16083–16088. doi: 10.1073/pnas.1211658109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. Singer JD, Willett JB. Applied longitudinal data analysis. New York: Oxford University Press; 2003. [Google Scholar]
  116. Stahle DW, Cook ER, Villanueva Diaz J, Fye FK, Burnette DJ, Griffin RD, Heim RR. Early 21st-Century drought in Mexico. EOS Transactions of the American Geographical Union. 2009;90(11):89–100. [Google Scholar]
  117. Stark O, Bloom DE. The new economics of labor migration. American Economic Review. 1985;75(2):173–178. [Google Scholar]
  118. Steele F, Diamond I, Amin S. Immunization uptake in rural Bangladesh: A multilevel analysis. Journal of the Royal Statistical Society Series a-Statistics in Society. 1996;159:289–299. [Google Scholar]
  119. Steele F, Goldstein H, Browne W. A general multilevel multistate competing risks model for event history data, with an application to a study of contraceptive use dynamics. Statistical Modelling. 2004;4(2):145–159. [Google Scholar]
  120. Stern N. Economics of climate change: The Stern review. Cambridge: Cambridge University Press; 2007. [Google Scholar]
  121. Taylor JE, Arango J, Hugo G, Kouaouci A, Massey DS, Pellegrino A. International migration and community development. Population Index. 1996;62(3):397–418. [PubMed] [Google Scholar]
  122. Tollenaar M, Bruulsema TW. Efficiency of maize dry-matter production during periods of complete leaf-area expansion. Agronomy Journal. 1988;80(4):580–585. [Google Scholar]
  123. UNPD. World urbanization prospects: The 2014 Revision. New York, NY: United Nations Population Division; 2014. [Google Scholar]
  124. Wackernagel M, Kitzes J, Moran D, Goldfinger S, Thomas M. The Ecological Footprint of cities and regions: comparing resource availability with resource demand. Environment and Urbanization. 2006;18(1):103–112. [Google Scholar]
  125. WHO; WMO. Atlas of health and climate. Geneva, Switzerland: WHO Press; 2012. [Google Scholar]
  126. Wiggins S, Keilbach N, Preibisch K, Proctor S, Herrejon GR, Munoz GR. Discussion - Agricultural policy reform and rural livelihoods in central Mexico. Journal of Development Studies. 2002;38(4):179–202. [Google Scholar]
  127. Wilby R. A review of climate change impacts on the built environment. Build Environment. 2007;33(1):31–45. [Google Scholar]
  128. Willems P, Olsson J, Arnbjerg-Nielsen K, Beecham S, Pathirana A, Bulow Gregersen I, Nguyen VTV. Impacts of climate change on rainfall extremes and urban drainage systems. London: IWA Publishing; 2012. [DOI] [PubMed] [Google Scholar]
  129. Zaidi PH, Rafique S, Singh NN. Response of maize (Zea mays L.) genotypes to excess soil moisture stress: morpho-physiological effects and basis of tolerance. European Journal of Agronomy. 2003;19(3):383–399. [Google Scholar]

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