Climate, migration, and the local food security context: introducing Terra Populus

Population and Environment volume 38, pages 164–184 (2016)Cite this article

Abstract

Studies investigating the connection between environmental factors and migration are difficult to execute because they require the integration of microdata and spatial information. In this article, we introduce the novel, publically available data extraction system Terra Populus (TerraPop), which was designed to facilitate population–environment studies. We showcase the use of TerraPop by exploring variations in the climate–migration association in Burkina Faso and Senegal based on differences in the local food security context. Food security was approximated using anthropometric indicators of child stunting and wasting derived from Demographic and Health Surveys and linked to the TerraPop extract of climate and migration information. We find that an increase in heat waves was associated with a decrease in international migration from Burkina Faso, while excessive precipitation increased international moves from Senegal. Significant interactions reveal that the adverse effects of heat waves and droughts are strongly amplified in highly food insecure Senegalese departments.

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Notes

  1. 1.

    Geographic unit boundaries are the key to TerraPop’s location-based integration, and the system provides several types of boundaries to serve the needs of various users. For use with microdata, geographic units are regionalized to ensure that the population of each unit is >20,000 people to maintain confidentiality. TerraPop also includes both harmonized and year-specific boundaries. In the harmonized boundaries, units that have changed over time are combined to provide consistent footprints facilitating the analysis of change over time (Kugler et al. 2015). For most countries, TerraPop provides first and second administrative level boundaries.

  2. 2.

    For Burkina Faso, the DHS survey year (2003) falls within the 5-year window (2001–2006) prior to the census round in 2006. However, for Senegal the DHS survey was conducted in year 2005, three years after the census round in 2002. Although another full DHS survey was conducted in Senegal in 1997, this earlier wave did not include the relevant anthropometric indicators. We use the Senegal DHS for 2005 based on the common assumption that food security within a population is relatively static (Saha et al. 2009).

  3. 3.

    For confidentiality purposes, DHS randomly displaces rural cluster centroids between 0 and 5 km and an additional random selection of 10 % of the cluster points between 0 and 10 km, resulting in a relatively small average displacement distance of 2.45 km (Burgert et al. 2013). The random displacement algorithm ensures that centroids fall within the correct first-level administrative unit (Burgert et al. 2013). Although we aggregate points to the second-level administrative unit, the introduced uncertainty is likely minimal due to the large size of provinces/departments (most clusters are more than 5 km away from the borders). In addition, the random nature of the displacement ensures that the resulting estimates are not systematically biased.

  4. 4.

    We use the years 1961–1990 as the standard “climate normal” period recommended by the World Meteorological Organization to be used as reference period for studies of climate change and climate variability (Arguez and Vose 2011).

  5. 5.

    The wealth index combined three measures of the quality of the housing unit (material of floor, wall, roof), three measures of the type and quality of services available at the residence (type of cooking fuel, toilet type, access to electricity), and three measures to capture the possession of appliances (car, refrigerator, TV).

  6. 6.

    The models were fitted using the package lme4 (Bates 2010; Bates et al. 2014) within the R statistical environment (RCoreTeam 2015). For improved speed and more robust convergence properties, we adjusted the model settings (integer scalar setting nAGQ = 0) so that the random and fixed effects were optimized (optimizer=“bobyqa”) in the penalized iteratively reweighted least squares step (Bates et al. 2014).

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Acknowledgments

The authors gratefully acknowledge support from the Minnesota Population Center (#R24 HD041023), 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). The authors wish to acknowledge the statistical offices that provided the underlying data making this research possible: National Institute of Statistics and Demography, Burkina Faso, and National Agency of Statistics and Demography, Senegal. We express our gratitude to Joshua Donato and David Haynes for help with the construction of the spatial variables. Special thanks to the journal editor and two anonymous reviewers for insightful comments and suggestions on earlier versions of this manuscript.

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  1. Minnesota Population Center, University of Minnesota, 225 19th Avenue South, 50 Willey Hall, Minneapolis, MN, 55455, USA

    Raphael J. Nawrotzki, Allison M. Schlak & Tracy A. Kugler

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  1. Raphael J. Nawrotzki
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Correspondence to Raphael J. Nawrotzki.

Appendices

Appendix A: Case

Burkina Faso and Senegal are among the poorest countries of the world, ranking 181 (Burkina Faso), and 163 (Senegal) out of 187 on the human development index (UNDP 2014). In rural areas, households depend heavily on agricultural production for sustenance and income generation (Davis et al. 2007). In Burkina Faso about 90 % and in Senegal about 78 % of the labor force is employed in the agricultural sector (CIA 2014). The low development level and associated constraints in financial resources hinder the use of technology to guard against adverse climatic impacts (Gutmann and Field 2010). For example, only 0.2 and 1.3 % of cropland is irrigated in Burkina Faso and Senegal, respectively (CIA 2014). The confluence of high agricultural dependence and low technological development renders households vulnerable to climate impacts.

Burkina Faso and Senegal are located in the semiarid Sahelian region of Western Africa. Both countries are characterized by a distinct North–South gradient of temperature and precipitation (Grouzis et al. 1998; Hampshire and Randall 1999). While the northern Sahelian areas are generally hot and arid, the southern regions are relatively cooler and more humid, making farm production more lucrative (Henry et al. 2003, 2004). Since the 1960s, West Africa has experienced a long-term reduction in rainfall and a warming in temperatures (Funk et al. 2012; Nicholson 2001). These historical trends are projected to continue in future decades as a result of global climate change (Niang et al. 2014), making this region an important geographical location for the study of climate impacts on rural livelihoods.

Burkina Faso and Senegal have a rich history of diverse migration patterns within and across national boundaries. International outmigration is generally employment related, and most migration is directed to neighboring countries on the African continent. For Burkina Faso, the primary destinations include Nigeria, Ghana, and Ivory Coast (Arthur 1991), while Senegalese labor migrants often seek employment in Mauritania and Gabon but also in Italy and Spain (Plaza and Ratha 2011; Sinatti 2011). Due to employment in the manual construction and agricultural sectors, these labor migrant streams are characterized by a distinct demographic profile, largely comprised of young males (Henry et al. 2003; Plaza and Ratha 2011; Sinatti 2014). Labor migration is often temporary and circular in nature and migrants usually return to their village of origin after a saving target has been reached (Sinatti 2009).

Appendix B: Base model

International migration is a process influenced by various sociodemographic determinants (Brown and Bean 2006), and our multivariate base model accounts for these factors (Table 4).

Table 4 Multilevel base model predicting the odds of international migration from rural households in Burkina Faso and Senegal
Full size table

The factors influencing international migration from Burkina Faso and Senegal show considerable similarity. In line with prior research from Ghana, South Africa, and Mexico, we find that the typical migrant household is relatively large (Abu et al. 2014), is relatively wealthy in terms of home ownership and physical assets (Hunter et al. 2014), and has good access to established migrant networks (Fussell and Massey 2004). In addition, an increase in the proportion of retirees was associated with higher odds of international migration, probably related to the added income from old-age pensions that may help finance an international move as demonstrated for rural South Africa (Schatz et al. 2012). Marital status and religious affiliation influence the odds of international migration from Burkina Faso but not from Senegal, with a lower probability of international migration from Muslim households in which the head was married, comparable to findings from rural Ethiopia and Mexico (Ezra and Kiros 2001; Riosmena 2009). In contrast, international migrants from Senegal are more likely to originate from regions with limited access to urban infrastructure and limited production of the primary crops. Finally, the directionality of the effect of age of the household head, child dependency ratio, and proportion of household members employed varied between countries as a result of the country-specific sociopolitical context.

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Nawrotzki, R.J., Schlak, A.M. & Kugler, T.A. Climate, migration, and the local food security context: introducing Terra Populus. Popul Environ 38, 164–184 (2016). https://doi.org/10.1007/s11111-016-0260-0

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Keywords

  • Climate
  • Environment
  • International migration
  • Burkina Faso
  • Senegal
  • Food security
  • Terra Populus
  • Demographic and Health Survey (DHS)