Undocumented migration in response to climate change

Abstract

In the face of climate change-induced economic uncertainties, households may employ migration as an adaptation strategy to diversify their livelihood portfolio through remittances. However, it is unclear whether such climate-related migration will be documented or undocumented. In this study we combined detailed migration histories with daily temperature and precipitation information from 214 weather stations to investigate whether climate change more strongly impacted undocumented or documented migrations from 68 rural Mexican municipalities to the U.S. from 1986−1999. We employed two measures of climate change, the warm spell duration index (WSDI) and precipitation during extremely wet days (R99PTOT). Results from multi-level event-history models demonstrated that climate-related international migration from rural Mexico was predominantly undocumented. We conclude that programs to facilitate climate change adaptations in rural Mexico may be more effective in reducing undocumented border crossings than increasing border fortification.

Full text

International Journal of Population Studies
International Journal of Population Studies | 2015, Volume 1, Issue 1 60
RESEARCH PAPER
Undocumented migration in response to
climate change
Raphael J. Nawrotzki1*, Fernando Riosmena2, Lori M. Hunter2, and Daniel M.
Runfola3
1 Minnesota Population Center, University of Minnesota, 225 19th Avenue South, 50 Willey
Hall, Minneapolis, MN 55455, USA
2 Institute of Behavioral Science, CU Population Center, University of Colorado Boulder, 1440
15th St. Boulder, CO 80309, USA
3 The College of William and Mary, 200 Stadium Drive, Williamsburg, VA 23185, USA
Abstract: In the face of climate change-induced economic uncertainties, households may em-
ploy migration as an adaptation strategy to diversify their livelihood portfolio through remit-
tances. However, it is unclear whether such climate-related migration will be documented or
undocumented. In this study we combined detailed migration histories with daily temperature
and precipitation information from 214 weather stations to investigate whether climate change
more strongly impacted undocumented or documented migrations from 68 rural Mexican mu-
nicipalities to the U.S. from 1986−1999. We employed two measures of climate change, the
warm spell duration index (WSDI) and precipitation during extremely wet days (R99PTOT).
Results from multi-level event-history models demonstrated that climate-related international
migration from rural Mexico was predominantly undocumented. We conclude that programs to
facilitate climate change adaptations in rural Mexico may be more effective in reducing undo-
cumented border crossings than increasing border fortification.
Keywords: climate change, environment, climate change adaptation, international migration,
undocumented migration, documentation status, rural Mexico
*Correspondence to:
Raphael J. Nawrotzki, Minnesota Population Center, University of Minnesota, 225 19th
Avenue South, 50 Willey Hall, Minneapolis, MN 55455, USA; Email:
r.nawrotzki@gmail.com
Received: September 15, 2015; Accepted: November 24, 2015; Published Online: December 31, 2015
Citation: Nawrotzi R J, Riosmena F, Hunter L M, et al. (2015). Undocumented migration in response to
climate change. International Journal of Population Studies, vol.1(1): 60–74.
http://dx.doi.org/10.18063/IJPS.2015.01.004.
1. Introduction
Climate change has the potential to strongly influence economic conditions through the
agricultural sector (Boyd and Ibarraran, 2009). For instance, in Mexico, about 80% of
economic losses between 1980 and 2000 have been attributed to climatic shocks
(Saldana-Zorrilla and Sandberg, 2009). In rural areas of Mexico, households heavily de-
pend on agricultural production for income and sustenance (de Janvry and Sadoulet, 2001;
Winters, Davis and Corral, 2002). Similar to many households in various developing
countries, rural Mexican households often lack the technological infrastructure to guard
against adverse climate impacts (Gutmann and Field, 2010) as only about 23% of arable
Copyright: © 2015 Raphael J. Na-
wrotzki, et al.
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Raphael J. Nawrotzki, Fernando Riosmena, Lori M. Hunter, and Daniel M. Runfola
International Journal of Population Studies | 2015, Volume 1, Issue 1 61
land in Mexico was irrigated in 2000 (Carr, Lopez and Bilsborrow, 2009). As such, we
assume an agricultural pathway in which climate change impacts agricultural production,
leading to livelihood instabilities (Black, Adger, Arnell et al., 2011a).
In response to livelihood uncertainties, households may employ migration as a house-
hold-level risk management strategy (Massey, Arango, Hugo et al., 1993). A household
may send a migrant to an international destination to access a stable income stream
through remittances, which is independent of the local climate and market conditions
(Stark and Bloom, 1985). A number of studies have explored the relationship between
climate and migration from Mexico and found a significant relationship between rainfall
decline and international outmigration, largely from rural areas with established transna-
tional networks (Feng and Oppenheimer, 2012; Hunter, Murray and Riosmena, 2013;
Nawrotzki, Riosmena and Hunter, 2013). However, no studies have been done to investi-
gate whether climate change is associated with undocumented versus documented/legal
migrations.
Insights from related literatures suggest that climate change may influence undocu-
mented migrations in different ways than documented migrations. If climatic shocks such
as droughts impair the livelihoods of rural farmers, households may not have sufficient
time for visa applications, a process which could take years to complete (Papademetrious
and Terrazas, 2009) and would instead choose the more rapid path of undocumented bor-
der crossing. This assumption is in line with the literature on migratory responses to the
impact of economic recessions. Historical evidences suggest that economic crises in Mex-
ico have resulted in surges of undocumented migration to the U.S. (Hanson and Spili-
mbergo, 1999). Likewise, unauthorized movement is also much more responsive to eco-
nomic crises in the U.S. than movement through legal immigration channels (Papade-
metrious and Terrazas, 2009). In a similar way, climatic shocks may indirectly influence
migration dynamics through its impacts on various economic sectors (Boyd and Ibarraran,
2009) and therefore disproportionately drive undocumented migrations. Shedding some
light on this unsolved puzzle, this paper investigated whether climate change and variabil-
ity more strongly influences undocumented versus documented migrations from rural
Mexico to the U.S.
2. Data and Methods
2.1 Data
We combined detailed migration histories from the Mexican Migration Project (MMP)
(Massey, 1987) with daily temperature and precipitation information obtained from the
Global Historical Climate Network (GHCN) (Menne, Durre, Vose et al., 2012) from 214
weather stations across Mexico. Both data sets undergo rigorous quality checks and have
been used in a wide range of published research (Alexander, Zhang, Petersen et al., 2006;
Hunter, Murray and Riosmena, 2013; Massey, Durand and Pren, 2015; Wu, 2015). The
MMP started collecting data in 1982 and selects between two and five communities each
year, interviewing a random sample of 200 households in each community (Massey, 1987).
For this study, we employed data from MMP waves 1987–2013, resulting in an analytical
sample of 7,062 households located in 68 rural municipalities. Although not strictly na-
tionally representative, validation exercises have demonstrated that the MMP very accu-
rately reflects the characteristics and behavior of international migrants (Massey and
Capoferro, 2004).
The MMP data contains a wealth of sociodemographic information on all household
members and most importantly for this study, about the year of the first move to the U.S.
and the documentation status during that particular trip. This retrospective information on

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International Journal of Population Studies | 2015, Volume 1, Issue 1 62
the date of the first move enabled us to construct an event-history file, indicating the
household migration status for each observational year during the study period of
1986–1999. This period was chosen as a time of relatively stable migration policies fol-
lowing the enactment of the Immigration Reform and Control Act (IRCA) in 1986
(LoBreglio, 2004) and because Mexico experienced conditions of increased temperature
and drought during the 1990s (Stahle, Cook and Villanueva Diaz et al., 2009) that resem-
ble conditions expected under climate change (Collins, Knutti, Arblaster et al., 2013;
Wehner, Easterling, Lawrimore et al., 2011). A reduction in the weather stations available
through GHCN after 1999 prevented the construction of the climate measures for later
years.
2.2 Outcome Variable
In the cultural context of Mexico, migration needs to be considered as a household-level
strategy (Cohen, 2004). A household sends a migrant to an international destination as a
self-insurance mechanism against local market failure, expecting the migrant to remit
money to support the household in Mexico (Massey, Arango, Hugo et al., 1993; Taylor,
1999). We therefore focused on the household as the unit of analysis, in line with prior
work (de Janvry, Sadoulet, Davis et al., 1997; Hunter, Murray and Riosmena, 2013;
Kanaiaupuni, 2000). We constructed an event history file (risk set) in which house-
hold-years are assigned a value of 0 when the household was at risk for international mi-
gration but no move occurred, a value of 1 if an undocumented international move oc-
curred, or a value of 2 if a documented international move occurred. Households were at
risk of migration if they did not send a member to the U.S. before 1986. Households were
included in the data set for the years after 1986 as long as the household heads were at
least 15 years of age, and after the date of their first union formation (household heads can
get divorced, widowed, and remarry in later years). These criteria ensured that households
were truly formed during the years when they were exposed to the risk of migration.
Households were removed from the data set following the year of the first move, when the
household head turns 65, when the household is censored at the survey year, or at the end
of the study period in 1999. Households may move in and out of the study community and
are only exposed to the risk of migration if at least one core household member (head or
spouse) was present during a given year.
Although other pathways are possible (Burke, Miguel, Satyanath et al., 2009; Naw-
rotzki, Diaconu and Pittman, 2009), we assumed that climatic effects lead to migration
through negative impacts on the agricultural sector (Mueller, Gray and Kosec, 2014). Ru-
ral households in Mexico heavily depend on agricultural production for income and sus-
tenance (Conde, Ferrer and Orozco, 2006; Wiggins, Keilbach, Preibisch et al., 2002;
Winters, Davis and Corral, 2002). As such, we focus our analysis on 68 municipalities that
contain rural MMP communities (population < 10,000) dispersed across the country. Fig-
ure 1 illustrated the location of the rural municipalities as well as the 214 weather stations
from which daily temperature and precipitation data were available.
2.3 Primary Predictors
Previous research has shown that temperature and precipitation above and below certain
thresholds have the strongest impact on agricultural production (Lobell, Hammer, McLean
et al., 2013; Schlenker and Roberts, 2009). As such, we employed two climate change in-
dices that reflect percentile-based threshold effects, namely the warm spell duration index
(WSDI) and precipitation during extremely wet days (R99PTOT). The warm spell dura-
tion index was computed as the annual count of days when at least six consecutive days of

Raphael J. Nawrotzki, Fernando Riosmena, Lori M. Hunter, and Daniel M. Runfola
International Journal of Population Studies | 2015, Volume 1, Issue 1 63
Figure 1. Location map of rural MMP municipalities and weather stations.
maximum temperature were above the 90th percentile of the 30-year reference period
(1961–1990). The 30-year period from 1961–1990 is known as “climate normal” and
recommended by the World Meteorological Organization (WMO) as reference period for
the study of climatological trends (Arguez and Vose, 2011). Precipitation during extremely
wet days was computed as the annual total precipitation from days when precipitation was
greater than the 99th percentile of the 30-year reference period (1961–1990). These climate
change indices have been formalized by the Expert Team on Climate Change Detection
and Indices (ETCCDI), sponsored by the World Meteorological Organization and the
United Nations, to increase the comparability of climate change studies across time and
space (Peterson and Manton, 2008).
Although the GHCN undergoes rigorous quality checks (Menne, Durre, Vose et al.,
2012), about 21% of the records were missing, largely due to instrumentation errors. As
recommended by Auffhammer et al. (2013), we imputed the missing data to generate a
balanced panel of complete weather station records. We employed Multiple Imputation
(MI) (Allison, 2002) using the R package Amelia (Honaker, King and Blackwell, 2011),
which was designed for the imputation of time-series data by explicitly accounting for
temporal trends. The complete time series of daily temperature and precipitation records
were then used as input to construct the two climate change indices for each weather sta-
tion for the years 1961–1999 using the R package climdex.pcic, maintained by the Pacific
Climate Impact Consortium (Bronaugh, 2014).
We then employed CoKriging as a geostatistical method of interpolation (Bolstad, 2012;
Hevesi, Istok and Flint, 1992) to generate a surface of climate change index values across
Mexico. CoKriging is a method frequently employed to interpolate climate measures and
indices (Aznar, Gloaguen, Tapsoba et al., 2013; Rogelis & Werner, 2013) and it allowed us

Undocumented migration in response to climate change
International Journal of Population Studies | 2015, Volume 1, Issue 1 64
to account for the correlation between climate and elevation using a Digital Elevation
Model (DEM) (Danielson and Gesch, 2011) as a covariate in the interpolation model. We
employed a bootstrap resampling procedure to cross-validate the interpolation results and
found the local estimates to be robust. Using a lattice of 700 × 700 m, we then extracted
climate change values from the interpolation surface and assigned the respective area av-
erage to each MMP municipality for which migration histories were available.
Finally, we computed relative change measures as the standardized difference between
the climate index value during the 3-year window leading up to each observational year
and a 30-year (1961–1990) long-term average. A 3-year window was chosen to minimize
the influence of short-term fluctuations and to account for lagged response patterns
(McLeman, 2011). Figure 2 shows the hazards of migration as well as the climate change
index values across the study period. Panel A shows a certain degree of similarity between
the trajectory of the hazard of documented and undocumented migrations with higher val-
ues in the late 80s and late 90s. During these years, Mexico experienced two economic
recessions (Lustig, 1990; McKenzie, 2006) that may have influenced the decision to mi-
grate with or without proper documentations. Panel B shows the change in the two climate
change measures relative to the baseline period (1961–1990). In line with climatological
reports (Stahle, Cook, Villanueva Diaz et al., 2009), the warm spell duration index showed
an increase in the consecutive number of hot days over the study period. However, no
clear trends could be discerned for precipitation during extremely wet days.
2.4 Control Variables
We included various control variables, reflecting social, human, physical, financial and
natural capitals. These variables have been shown to be important predictors of migration
in prior research (Brown and Bean, 2006; Massey, Axinn and Ghimire, 2010; Nawrotzki,
Riosmena and Hunter, 2013). Table 1 provides source information and summary statistics
on all control variables employed in the analysis. Variables were included as time varying
and time invariant and operated both at the household and municipality levels. When in-
formation was available at decadal time steps (e.g., census data), we employed linear in-
terpolation to derive semi time-varying measures, a common practice in event-history
analysis (Allison, 1984).
Measures of social capital include gender (female = 1) and marital status (married = 1)
of the household head. In a patriarchal society such as Mexico, social status and access to
social networks differ by gender and has been shown to significantly shape migration
Figure 2. Hazards of undocumented and documented international migrations from rural Mexico as well as climate change values across the study
period, 1986–1999.

Raphael J. Nawrotzki, Fernando Riosmena, Lori M. Hunter, and Daniel M. Runfola
International Journal of Population Studies | 2015, Volume 1, Issue 1 65
Table 1. Summarized statistical and source information of variables employed in the study of undocumented and documented migrations in response
to climate change from rural Mexico, 1986–1999
Unit TV Source Mean SD
Household level (head)
Social capital
Female 1|0 No MMP 0.14 0.35
Married 1|0 Yes MMP 0.80 0.40
Human capital
No. of children Count Yes MMP 0.85 1.04
Education Years Yes MMP 5.34 4.28
Working experience Years Yes MMP 24.94 12.34
Occupation: not in labor force 1|0 Yes MMP 0.09 0.29
Occupation: blue collar 1|0 Yes MMP 0.82 0.39
Occupation: white collar 1|0 Yes MMP 0.09 0.29
Physical capital
Owns property 1|0 Yes MMP 0.70 0.46
Owns business 1|0 Yes MMP 0.16 0.36
Community/municipality level
Social capital
Network density % Yes MMP-C 15.18 14.51
Financial capital
Wealth index z-values Yes IPUMS-I –0.79 0.39
Natural capital
Corn (area harvested) sqm/10ha No TerraPop 1.26 1.11
Farmland irrigated % No INEGI 23.67 25.74
Base period precip (1961-90) mm/day No GHCN-D 2.83 1.34
Base period temp (1961-90) deg. C No GHCN-D 21.07 2.93
Economic environment
Male labor in agriculture % Yes MMP-C 56.15 17.65
Climate change
Warm spell duration z-values Yes GHCN-D 1.79 2.22
Precip extremely wet days z-values Yes GHCN-D 0.34 1.05
Notes: TV = Time varying; Source information: MMP = Mexican Migration Project data available from http://mmp.opr.princeton.edu/; MMP-C = COMMUN
supplementary file of the MMP; IPUMS-I = Mexican census data (1% extract) obtained via Integrated Public Use Microdata Series – International (MPC, 2013a;
Ruggles et al., 2003); TerraPop = Cropland type data obtained via Terra Populus (Kugler et al., 2015; MPC, 2013b); INEGI = Data obtained from Instituto Nacional de
Estadística y Geografía (INEGI, 2012); GHCN-D = Data derived from the Global Historical Climate Network – Daily (Menne et al., 2012); ESRI = Spatial data library
ArcGIS Online (ESRI, 2012).
responses (Kanaiaupuni, 2000). Similarly, a marital union may expand a household’s fam-
ily and kin networks that may serve as an informal social security system in times of crisis
(Abu, Codjoe and Sward, 2014). In addition, we employed a measure of the percentage of
adults within the community with migration experiences as a proxy indicator of migrant
network density, which has been shown to strongly determine the likelihood of a future
move (Fussell and Massey, 2004).
We measured human capital by accounting for the number of young children (age < 5
years) in the household as well as the education (years of schooling), working experience
(years employed), and occupation (blue collar, white collar, not in labor force) of the
household head. The presence of young children ties human capital needed for nurturing

Undocumented migration in response to climate change
International Journal of Population Studies | 2015, Volume 1, Issue 1 66
activities to the household and has been shown to reduce the odds of an international move
(Massey & Riosmena, 2010; Nawrotzki, Riosmena and Hunter, 2013). We were unable to
include a measure for age of the household head in the models due to high correlation with
working experience (r = 0.93) and resulting multi-collinearity.
Financial capital is measured by a standardized wealth index at the municipality level
that combines information from 10 variables on the quality of housing (floor material, wall,
roof, number of rooms, toilet type) as well as service and infrastructure access (water
supply, electricity, sewage system, cooking fuel type) (Cronbach’s alpha = 0.85). In the
developing world, migration is often used as a means to overcome liquidity constraints to
purchase a home or start a business (Massey and Parrado, 1998; Taylor, Arango, Hugo et
al., 1996). To account for this relationship, we measured the level of physical capital in
terms of business or property ownership (owner = 1) at the household level.
As a measure of natural capital we accounted for the general agricultural dependence
by using a measure of the corn area harvested. This measure was constructed by the Glob-
al Landscape Initiative (Monfreda, Ramakutty and Foley, 2008) for the year 2000 and is
available through the Terra Populus data extract system (Kugler, Van Riper, Manson et al.,
2015; MPC, 2013b). Since the impact of climate effects on livelihoods may depend on the
ability to employ technological infrastructure (Gutmann and Field, 2010), we accounted
for access to irrigation systems through a measure of the percentage of farmland irrigated.
This data was obtained from the Mexican agricultural census (INEGI, 2012) and averaged
across the years 2003–2005. In addition, prior research has shown that the effects of cli-
mate variability on migration differ based on the general climatic context (Nawrotzki,
Riosmena and Hunter, 2013). To account for the general climatic background, we included
measures of the average temperature and precipitation during the baseline years
(1961–1990). Finally, we captured employment in climate sensitive sectors through a
measure of the percentage of males in the labor force employed in agriculture.
2.5 Estimation Strategy
We employed event-history models for this analysis (Allison, 1984). The models were es-
timated within a competing risk framework, in which the household can either perform an
undocumented or documented move (Singer and Willett, 2003). Owing to the hierarchical
structure of our data, we employed a multi-level version of the event-history model that
accounted for the nesting of households within municipalities (Steele Diamond and Amin,
1996; Steele, Goldstein and Browne, 2004). To guard against endogeneity, all predictors
were lagged by one year (Gray, 2009; Gray, 2010).
12 3
log ( ) ( 99 ) ( )
y
ijk ik ik n nz k
ijk n
mWSDI R PTOT x u
s
αβ β β
=

=+ + ++



∑
(1)
In Equation 1, the multi-level event-history model is specified as the odds of experiencing
a migration event type m (undocumented or documented migrations) relative to no mobil-
ity (event type s) for each household j located in municipality k during year i. The pa-
rameter α captures the baseline hazard and was included as a set of year dummies for the
most flexible representation of time (Singer and Willett, 2003). This parameterization ac-
counts for differences in the overall migration levels in each year, which can be attributed
to various unmeasured factors such as changes in the macroeconomic conditions in the
origin and destination countries. The parameters β1 and β2 reflect the effect of the two cli-
mate change indices (WSDI and R99PTOT), which were jointly included in the model to
simultaneously account for temperature and precipitation changes (Auffhammer, Hsiang,
Schlenker et al., 2013). The climate change variables constitute time-varying municipal-

Raphael J. Nawrotzki, Fernando Riosmena, Lori M. Hunter, and Daniel M. Runfola
International Journal of Population Studies | 2015, Volume 1, Issue 1 67
ity-level predictors (indicated by subscript ik), and it has been shown that a two-level
model structure is appropriate for such variables (Barber, Murphy, Axinn et al., 2000). All
models control for the effect (βn) of various sociodemographic factors (xn) on the probabil-
ity to migrate. These controls can operate both at the household and municipality levels,
indicated by the generic subscript z.
Although tests have shown that recall bias is of little concern for the MMP data (Massey,
Alarcon, Durand et al., 1987), we included a measure for the survey year to account for
residual recall error. Finally, the parameter uk constitutes the municipality’s random effects
term that accounts for the nesting of households within municipalities. The multi-level
event history models were estimated using the package lme4 (Bates, 2010; Bates,
Maechler, Bolker et al., 2014) within the R statistical environment (RCoreTeam, 2015).
During the 1986–1999 study period, n = 819 households reported undocumented moves
while only n = 95 households reported documented moves. Although a documented move
constituted a rare event, discrete-time event history models are specifically designed for
small numbers. Simulation exercises have demonstrated that at least five events per pre-
dictor are necessary to produce unbiased and reliable estimates (Vittinghoff and
McCulloch, 2007). The fitted models (Table 2) contained 19 substantive predictors, yield-
ing an average of five events per predictor for the total of 95 documented migration events,
which constituted a sufficiently large number to produce valid and stable results.
3. Results
In line with prior work, results from the multi-level event-history models (Table 2) re-
vealed that undocumented migrations most likely occurred from male headed households
without young children in which the household head has little education and work experi-
ence, is employed in a blue collar occupation and does not own a business or property
(Fussell, 2004; Massey, Alarcon, Durand et al., 1987; Massey and Parrado, 1998;
Nawrotzki, Riosmena and Hunter, 2013; Woodruff and Zenteno, 2007). The presence of
migrant networks strongly facilitates both documented and undocumented migrations
(Fussell and Massey, 2004; Massey and Espinosa, 1997). In contrast, documented mi-
grants are usually better educated and come from areas less dependent on agricultural
production (Fussell, 2004). As the primary analytical focus, the models also included the
two climate change indices.
The results show that climate change significantly influenced international migration
from rural Mexico to the U.S. but that this relationship exclusively emerged for undocu-
mented moves. The significant temperature effect suggested that an increase in warm spell
duration by one standard deviation unit increased undocumented international
out-migrations by 19% (Odd Ratio [OR] = 1.19). In contrast, an increase in precipitation
during extremely wet days by one standard deviation reduced the odds of an undocu-
mented international move to the U.S. by 18% (OR = 0.82).
4. Discussion and Conclusions
Combining detailed migration histories with two climate change indices based on daily
temperature and precipitation information, this study provides evidence that rural Mexican
households employed migration as an adaptation strategy in the face of adverse climate
variability and change. However, while the results demonstrate that climate change sig-
nificantly influenced undocumented migrations, it had no impact on documented moves.
As it is often difficult to obtain a valid work visa given the quotas, backlogs and application
costs (Papademetrious and Terrazas, 2009), households may resort to undocumented border

Undocumented migration in response to climate change
International Journal of Population Studies | 2015, Volume 1, Issue 1 68
Table 2. Multi-level discrete-time event history models predicting the odds of undocumented and documented international migrations from rural
Mexico, 1986–1999
Undocumented Documented
b sig. b sig.
Household level (head)
Female 0.53 *** 0.68
Married 0.96 1.36
No. of children 0.90 ** 0.99
Education a 0.74 ** 3.29 ***
Working experience a 0.71 *** 1.00
Occupation: not in labor force 0.91 1.45
Occupation: white collar 0.50 *** 0.63
Owns property 0.83 * 1.14
Owns business 0.77 * 1.03
Community/municipality level
Network density a 1.56 *** 1.49 **
Wealth index 0.81 0.74
Corn (area harvested) 0.94 0.68 *
Farmland irrigated a 1.04 0.93
Base period precip (1961-90) 1.11 0.73
Base period temp (1961-90) 0.91 ** 0.98
Male labor in agriculture a 1.01 0.88
Climate change
Warm spell duration 1.19 *** 1.16
Precip extremely wet days 0.82 *** 0.98
Model statistics
Var. Intercept (Mun) 0.215 0.718
BIC 8451 1703
N (HH-year) 67511 67511
N (HH) 7062 7062
N (Mun) 68 68
Notes: Coefficients reflect odd ratios; a Coefficients relate to an incremental change of 10 units; baseline hazard of migration was included as a multi-part intercept
using year dummies (not shown); all models control for the survey year to account for recall bias (not shown); Occupation: Blue collar used as reference; all predictors
were lagged by one year; low values on the Variance Inflation Factor (VIF) demonstrated that multi-collinearity does not bias the estimates; a jack-knife type procedure
was performed, iteratively removing one municipality from the sample and re-estimating the model (Nawrotzki, 2012; Ruiter & De Graaf, 2006). The results showed
that the estimates for the climate change predictors are highly robust;
* p < 0.05; ** p < 0.01; *** p < 0.001
crossings to stabilize their livelihoods and access alternative income streams through re-
mittances.
The directionality of significant climate change effects suggests a rise in undocumented
international migrations in response to a warming in temperatures. Heat waves and tem-
perature increases are problematic for the agricultural sector and are associated with a de-
cline in crop yield (Lobell, Hammer, McLean et al., 2013). Adverse impacts on agricul-
tural productivity may lead to a decline in income and employment opportunities to which
households may respond with increased levels of migration (Bohra-Mishra, Oppenheimer
and Hsiang, 2014; Mueller, Gray and Kosec, 2014).
In contrast, increases in precipitation led to a decline in undocumented migrations. Only

Raphael J. Nawrotzki, Fernando Riosmena, Lori M. Hunter, and Daniel M. Runfola
International Journal of Population Studies | 2015, Volume 1, Issue 1 69
a small proportion (23%) of arable land in Mexico is irrigated (Carr, Lopez and Bilsborrow,
2009), making agricultural production highly dependent on rainfall. In addition, Mexico
experienced severe drought conditions during the study period (Stahle, Cook, Villanueva
Diaz et al., 2009). Under such conditions, an increase in rainfall was likely beneficial, re-
ducing households’ need to employ migration as an adaptation strategy (Feng &
Oppenheimer, 2012; Nawrotzki, Riosmena and Hunter, 2013).
Projections of future climate change suggest that, for Mexico, temperatures will in-
crease (Collins, Knutti, Arblaster et al., 2013) while precipitation will decline (Christensen,
Kanikicharla, Aldrian et al., 2013), potentially leading to an increase in frequency and se-
verity of droughts (Wehner, Easterling, Lawrimore et al., 2011). When livelihoods of ag-
riculturally-dependent households are impacted by adverse climate variability and change,
they may respond with an increase in migration rates (Black, Adger, Arnell et al., 2011a).
Our study suggests that such migrants will be predominantly undocumented. To reduce the
number of undocumented border crossings from Mexico, the U.S. government has sub-
stantially increased the budget for border control and fortification (Massey and Riosmena,
2010; Orrenius, 2004). However, an increase in border fortification has been shown to be
of limited success in deterring undocumented migrations (Massey and Riosmena, 2010).
Livelihood-based support programs to assist rural Mexicans in local climate change adap-
tation efforts may serve as a cost-efficient alternative to border control in decreasing the
number of climate related moves. Such programs may include agricultural extension ser-
vices to disseminate knowledge about the availability and use of drought resistant crop
varieties and alternative farming practices (Nawrotzki and Akeyo, 2009; Schroth,
Laderach, Dempewolf et al., 2009), subsidize the construction of irrigation systems
(Howden, Soussana, Tubiello et al., 2007), or assist households in finding non-agricultural
employment to reduce their dependency on climate-sensitive sectors (Macours, Premand
and Vakis, 2012).
Conflict of Interest and Funding
No conflict of interest was reported by the authors. 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).
Acknowledgements
We thank two anonymous reviewers and the journal editor for helpful comments and sug-
gestions on earlier versions of this manuscript. We also express our gratitude to Gina Ru-
more for her careful editing and suggestions.
Ethics Statement
The analyses described in this paper were performed using secondary data obtained from
various publicly available sources as outlined in the Data and Methods section.
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