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Direct and indirect impacts of environmental factors on migration in Burkina Faso: application of structural equation modelling

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Abstract

In the prolific literature on the impact of environment on migration, direct and indirect effects are often mentioned but rarely estimated separately. We use structural equation modelling to estimate how the drivers of migration (socio-economic, environmental and individual) interact with each other and jointly contribute to individuals’ migration decision in rural Burkina Faso (1970–1998). Facing a worsening environmental situation, people’s direct response tends to be short-term migrations to rural and urban areas, but the indirect effect differs: poor rainfall conditions push down socio-economic situation in communities, which in turn discourages migration to rural areas or to abroad. In total, an adverse environmental situation tends to increase the likelihood of short-term migrations to rural and urban areas and to decrease that of long-term migrations to rural areas and to abroad. These findings contribute to a clearer understanding of the migration response to poor environmental conditions.

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  1. The odds ratio can be computed as exp(loading).

References

  • Afifi, T., Milan, A., Etzold, B., Schraven, B., Rademacher-Schulz, C., Sakdapolrak, P., et al. (2015). Human mobility in response to rainfall variability: Opportunities for migration as a successful adaptation strategy in eight case studies. Migration and Development, 5(2), 254–274.

    Article  Google Scholar 

  • Allison, P. (2002). Missing data. Thousand Oaks: Sage.

    Book  Google Scholar 

  • Barbieri, A. F., Domingues, E., Queiroz, B. L., Ruiz, R. M., Rigotti, J. I., Carvalho, J. A., et al. (2010). Climate change and population migration in Brazil’s northeast: scenarios for 2025–2050. Population and Environment, 31(5), 344–370.

    Article  Google Scholar 

  • Beauchemin, C., & Schoumaker, B. (2009). Are migrant associations actors in local development? A national event-history analysis in rural Burkina Faso. World Development, 37(12), 1897–1913.

    Article  Google Scholar 

  • Beauchemin, C., Beauchemin, E., & Le Jeune, G. (2002). TABVILLES BF: rapport de présentation, Technical report n°2002-1. Department of Demography, University of Montreal. Available at http://cris.beauchemin.free.fr/pdf/tabvilles_bf.pdf. Accessed on January 15 2019.

  • Beine, M., & Jeusette, L. (2018). A meta-analysis of the literature on climate change and migration, CREA Discussion Paper Series 18-05. Centre for Research in Economic Analysis, University of Luxembourg. Available at https://knowledge.unccd.int/sites/default/files/inline-files/2018_05%20A%20Meta-Analysis%20of%20the%20Literature%20on%20Climate%20Change%20and%20Migration.pdf. Accessed on January 20 2019.

  • Black, R., & Collyer, M. (2014). Populations ‘trapped’ at time of crisis. Forced Migration Review, 45.

  • Black, R., Adger, W. N., Arnell, N. W., Dercon, S., Geddes, A., & Thomas, D. (2011). The effect of environmental change on human migration. Global Environmental Change, 21, S3–S11.

    Article  Google Scholar 

  • Burkina Faso NAPA. (2007). Ouagadougou: Ministère de l'environnement et du cadre de vie, Secrétariat permanent du conseil national pour l'environnement et le développement durable. 76 p. Available at http://unfccc.int/resource/docs/napa/bfa01f.pdf. Accessed on October 15 2018.

  • Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304.

    Article  Google Scholar 

  • Cadwallader, M. (1985). Structural-equation models of migration: an example from the Upper Midwest USA. Environment and Planning A, 17(1), 101–113.

    Article  Google Scholar 

  • Cai, R., Feng, S., Oppenheimer, M., & Pytlikova, M. (2016). Climate variability and international migration: the importance of the agricultural linkage. Journal of Environmental Economics and Management, 79, 135–151.

    Article  Google Scholar 

  • Cattaneo, C., & Peri, G. (2016). The migration response to increasing temperatures. Journal of Development Economics, 122, 127–146.

    Article  Google Scholar 

  • D’haen, S. A. L., Nielsen, J. Ø., & Lambin, E. F. (2014). Beyond local climate: rainfall variability as a determinant of household nonfarm activities in contemporary rural Burkina Faso. Clim Dev, 6(2), 144–165.

    Article  Google Scholar 

  • De Brauw, A., Mueller, V., & Lee, H. L. (2014). The role of rural–urban migration in the structural transformation of sub-Saharan Africa. World Development, 63, 33–42.

    Article  Google Scholar 

  • De Longueville, F., Hountondji, Y. C., Kindo, I., Gemenne, F., & Ozer, P. (2016). Long-term analysis of rainfall and temperature data in Burkina Faso (1950–2013). International Journal of Climatology, 36(3), 4393–4405.

    Article  Google Scholar 

  • Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430–457.

    Article  Google Scholar 

  • Findley, S. (1992). Circulation as a drought coping strategy in rural Mali. In C. Goldscheider (Ed.), Migration, population structure, and redistribution policies (pp. 61–89). Boulder: Westview Press.

    Google Scholar 

  • Findley, S. E. (1994). Does drought increase migration? A study of migration from rural Mali during the 1983-1985 drought. The International Migration Review, 28(3), 539–553.

    Google Scholar 

  • Gautier, D., Denis, D., & Locatelli, B. (2016). Impacts of drought and responses of rural populations in West Africa: a systematic review. WIREs Climate Change, 7(5), 666–681.

    Article  Google Scholar 

  • Gemenne, F. (2010). Migration, a possible adaptation strategy? The Institute for Sustainable Development and International Relations, 3, 1–4.

  • Gemenne, F. (2013). Migration doesn’t have to be a failure to adapt. In J. Palutikof, S. L. Boulter, A. J. Ash, M. S. Smith, M. Parry, M. Waschka, & D. Guitart (Eds.), Climate adaptation futures (pp. 235–421). Oxford: John Wiley & Sons.

    Chapter  Google Scholar 

  • Gray, C. L. (2009). Environment, land, and rural out-migration in the southern Ecuadorian Andes. World Development, 37(2), 457–468.

    Article  Google Scholar 

  • Gray, C., & Wise, E. (2016). Country-specific effects of climate variability on human migration. Climatic Change, 135, 555–568.

    Article  Google Scholar 

  • Hamilton, E. R., & Savinar, R. (2015). Two sources of error in data on migration from Mexico to the United States in Mexican household-based surveys. Demography, 52(4), 1345–1355.

    Article  Google Scholar 

  • Henry, S., Boyle, P., & Lambin, E. F. (2003). Modeling inter-provincial migration in Burkina Faso, West Africa: the role of socio-demographic and environmental factors. Applied Geography, 23(2), 115–136.

    Article  Google Scholar 

  • Henry, S., Schoumaker, B., & Beauchemin, C. (2004). The impact of rainfall on the first out-migration: a multi-level event-history analysis in Burkina Faso. Population and Environment, 25(5), 423–460.

    Article  Google Scholar 

  • Hugo, G. (1998). Migration as a survival strategy: the family dimension of migration. In U. N. P. Division (Ed.), Population distribution and migration (pp. 139–149). New York: United Nations.

    Google Scholar 

  • Jha, C. K., Gupta, V., Chattopadhyay, U., & Sreeraman, B. A. (2017). Migration as adaptation strategy to cope with climate change: a study of farmers’ migration in rural India. International Journal of Climate Change Strategies, 10(1), 121–141.

  • Kniveton, D., Smith, C., & Wood, S. (2011). Agent-based model simulations of future changes in migration flows for Burkina Faso. Global Environmental Change, 21, S34–S40.

    Article  Google Scholar 

  • Laczko, F., & Aghazarm, C. (Eds.). (2009). Migration, environment and climate change: assessing the evidence. Geneva: International Organization for Migration.

    Google Scholar 

  • Lay, J., Narloch, U., & Mahmoud, T. O. (2009). Shocks structural change and the patterns of income diversification in Burkina Faso. African Development Review, 21, 36–58.

    Article  Google Scholar 

  • Marchiori, L., Maystadt, J.-F., & Schumaher, I. (2012). The impact of weather anomalies on migration in sub-Saharan africa. Journal of Environmental Economics and Management, 63(3), 355–374.

    Article  Google Scholar 

  • McCullagh, P. (1984). Generalized linear models. European Journal of Operational Research, 16(3), 285–292.

    Article  Google Scholar 

  • McLeman, R. (2013). Developments in modeling of climate change-related migration. Climatic Change, 117(3), 599–611.

    Article  Google Scholar 

  • McLeman, R., & Smit, B. (2006). Migration as an adaptation to climate change. Climatic Change, 76, 31–53.

    Article  Google Scholar 

  • Mortreux, C., & Barnett, J. (2009). Climate change, migration and adaptation in Funafuti, Tuvalu. Global Environmental Change, 19(1), 105–112.

    Article  Google Scholar 

  • Murali, J., & Afifi, T. (2014). Rainfall variability, food security and human mobility in the Janjgir-Champa District of Chhattisgarh State, India. Climate and Developlment, 6(1), 28–37.

  • Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical and continuous latent variable indicators. Psychometrika, 49(1), 115–132.

    Article  Google Scholar 

  • Muthén, B. O. (1998). Mplus technical appendices (p. 2004). Los Angeles: Muthén & Muthén.

    Google Scholar 

  • Muthén, B., & Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: multiple-group and growth modeling in Mplus. Mplus Web Notes, 4(5), 1–22.

    Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (2001). Statistical analysis with latent variables. User’s guide, Version, 4.

  • Nawrotzki, R. J., & DeWaard, J. (2018). Putting trapped populations into place: climate change and inter-district migration flows in Zambia. Regional Environmental Change, 18(2), 533–546.

    Article  Google Scholar 

  • Nielsen, J. Ø., & D’haen, S. A. L. (2015). Discussing rural-to-urban migration reversal in contemporary sub Saharan Africa: the case of Ouagadougou, Burkina Faso. THESys Discussion Paper No. 2015-1 (pp. 1–21). Humboldt-Universität zu Berlin. Available at https://edoc.hu-berlin.de/bitstream/handle/18452/3781/1.pdf. Accessed on August 25 2018.

  • Nielsen, J. Ø., & Reenberg, A. (2010). Temporality and the problem with singling out climate as a current driver of change in a small West African village. Journal of Arid Environments, 74, 464–474.

    Article  Google Scholar 

  • Obokata, R., Veronis, L., & McLeman, R. (2014). Empirical research on international environmental migration: a systematic review. Population and Environment, 36(1), 111–135.

    Article  Google Scholar 

  • Oliver-Smith, A. (2012). Debating environmental migration: society, nature and population displacement in climate change. Journal of International Development, 24(8), 1058–1070.

    Article  Google Scholar 

  • Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 411–420). San Francisco: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Perch-Nielsen, S. L., Bättig, M. B., & Imboden, D. (2008). Exploring the link between climate change and migration. Climatic Change, 91, 375.

    Article  Google Scholar 

  • Pietrzak, M. B., Żurek, M., Matusik, S., & Wilk, J. (2012). Application of structural equation modeling for analysing internal migration phenomena in Poland. Przegląd Statystyczny, 59(4), 487–503.

    Google Scholar 

  • Piguet, E. (2010). Linking climate change, environmental degradation, and migration: a methodological overview. WIREs Climate Change, 1(4), 517–524.

    Article  Google Scholar 

  • Poirier, J., Piché, V., Le Jeune, G., Dabire, B., & Wane, H. R. (2001). Projet d’étude des stratégies de reproduction des populations sahéliennes à partir de l’enquête ‘Dynamique migratoire, insertion urbaine et environnement au Burkina Faso. Cahiers Québécois de démographie, 30(2), 289–309.

    Article  Google Scholar 

  • Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research (pp. 251–294). Mahwah: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Roncoli, C., Ingram, K., & Kirshen, P. (2001). The costs and risks of coping with drought: livelihood impacts and farmers responses in Burkina Faso. Climate Research, 19(2), 119–132.

    Article  Google Scholar 

  • Schoumaker, B., Dabire, H., & Gnoumou-Thiombiano, B. (2006). Collecting community histories to study the determinants of demographic behaviour: a survey in Burkina Faso. Population, 61(1–2), 77–106.

    Article  Google Scholar 

  • Sivakumar, M. V. K. (1988). Predicting rainy season potential from the onset of rains in Southern Sahelian and Sudanian climatic zones of West Africa. Agricultural and Forest Meteorology, 42, 295–305.

    Article  Google Scholar 

  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.

    Article  Google Scholar 

  • Sward, J., & Codjoe, S. N. A. (2012). Human mobility and climate change adaptation policy: a review of migration in national adaptation programmes of action (NAPAs). Research Program Consortium Working Paper 6, Migrating Out of Poverty. Brighton: University of Sussex.

    Google Scholar 

  • Tacoli, C. (2009). Crisis or adaptation? Migration and climate change in a context of high mobility. Environment and Urbanization, 21(2), 513–525.

    Article  Google Scholar 

  • Thompson, B. (2004). Exploratory and confirmatory factor analysis: understanding concepts and applications. Washington, DC, US: American Psychological Association.

  • Warner, K., & Afifi, T. (2014). Enhancing adaptation options and managing human mobility: the United Nations Framework Convention on Climate Change. Social Research: An International Quarterly, 81(2), 299–326.

    Google Scholar 

  • Warner, K., Hamza, M., Oliver-Smith, A., Renaud, F., & Julca, A. (2010). Climate change, environmental degradation and migration. Natural Hazards, 55(3), 689–715.

    Article  Google Scholar 

  • Zampaligré, N., Dossa, L. H., & Schlecht, E. (2014). Climate change and variability: perception and adaptation strategies of pastoralists and agro-pastoralists across different zones of Burkina Faso. Regional Environmental Change, 14(2), 769–783.

    Article  Google Scholar 

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Acknowledgements

We thank insightful comments from three anonymous reviewers that have led to improvements of this paper. We also thank Prof. Fiona Steele and Prof. Martin Bell for reading the first draft of the paper and the helpful feedback and Colin Starr for proofreading the entire final manuscript.

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Author notes

  1. Florence De Longueville and Yajing Zhu contributed equally to this work.

Authors and Affiliations

  1. Department of Geography & Institute of Life-Earth-Environment, University of Namur, Namur, Belgium

    Florence De Longueville & Sabine Henry

  2. MRC Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge, UK

    Yajing Zhu

  3. Department of Statistics, London School of Economics, London, UK

    Yajing Zhu

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  1. Florence De Longueville
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Appendix

Appendix

For simplicity and ease of interpretation, all variables except age at first migration are treated as categorical. As these are proxies (with measurement error) of the actual quantities of interest, this helps us to centre our interpretations of model on the structural part of the model.

Variables from the individual survey

Age: Age at first migration. Only those whose first migration was at the age of 15 or greater were included in the data. We use log(age) as a continuous variable.

Economic activity: Encoded as agriculture, cattle-raising or other, consistent with Henry et al. (2004).

Ethnicity: Mossi is coded as 1, Fulani 2 and others 3.

Education level: The level of schooling attained by the date of migration. 0 is for ‘no education’, 1 for ‘primary school’ and 2 for ‘secondary or higher education’.

Variables from the community survey

Collapsing large categories is employed because the categories are derived from multiple questions and some have big ranges.

Water provision: This variable collates results from three questions: Is there a permanent river in the village? Is there a permanent lake or reservoir? Are there flood plains/marshlands nearby? Villages that answer ‘No’ to all three questions are coded 0, villages that answer ‘Yes’ to one or two are coded 1, villages that answer ‘Yes’ to all three are coded 2.

Health provision: Taken from two questions in the survey: Are there health services such as a clinic in the village? If no, what is the distance to the nearest health service? Coded 0 if the nearest health service is greater than the median distance over the whole survey, coded 1 if the nearest service is less than the median distance and coded 2 if there is a health service in the village itself.

Transport provision: Taken from two questions: Is there a transport route through the village that is accessible to all vehicles year-round? Are there public transport vehicles that stop at the village regularly? Coded 0 if the answer is ‘No’ for both questions, 1 for one ‘Yes’ and 2 if the answer is ‘Yes’ to both.

Education provision: How many primary schools in this village? Encoded as 0 for no schools, 1 for one or two schools and 2 for three or more schools.

Association of villagers: This question asks about the connections that villagers have with those outside the village. Coded 0 for no connections, 1 for one to two connections and 2 for three or more.

Participation in activities insensitive to rainfalls: This variable measures the percentage of families participating in rainfall-insensitive activities in the village. These activities are those not related to agriculture or farming, i.e. handicrafts, trade, mining, gardening and other industries and services. The variable is coded 0, 1 or 2 for a low, medium or high level of involvement respectively.

Paid work opportunities: Taken from two questions: Is there salaried work available in the village? Was there salaried work available before 1960? Coded 0 for the answer ‘No’ to both questions, 1 for only one ‘Yes’ and 2 for ‘Yes’ to both questions.

Project development: This variable serves as an indicator of the future economic prospects of the village. It is a measure of the number of development projects that have taken place or are currently running in the village. Coded 0 for ‘no projects’, 1 for ‘one project now or previously’ and 2 for ‘two or more projects’.

Technology used in agriculture: This variable is a measure of the number of pieces of machinery used in agriculture, weighted by its prevalence. Coded 0 for less than six pieces in the village, 1 for six to 11 and 2 for 12 or more.

Existence of conflicts: Has there been any significant internal conflict since 1960? Coded 0 for ‘no’ and 1 for ‘yes’.

Degree of ethnic diversity before migration: The number of different ethnic groups present in the community. Coded 0 for only one ethnic group, 1 for two to six and 2 for seven or more.

In-migration history: Have there been any in-migrants since 1960? Coded as 1 if migrants arrived before 1980 and 0 otherwise.

Out-migration history: Have there been any out-migrants since 1960? Coded as 1 if the first out-migrants left before 1980 and 0 otherwise.

Environmental variables

Environmental variables are derived from continuous data but are converted to categorical variables.

Climatic zone (Climzone): average total annual rainfall (recorded between 1 January and 31 December) over the period 1970–1998. Calculated from a gridded database. Categorised as 1 for < 500 mm, 2 for ≥ 500 mm and < 700 mm, 3 for ≥ 700 mm and < 900 mm and 4 for 900 mm or above. Then a centroid value is calculated for each department, so every village in the department has the same value.

Interannual rainfall variability (IARV): The standard deviation (SD) of the total annual rainfall on the 1970–1998 period. Categorised as 1 if SD ≤ 100 mm and 2 if 100 mm < SD ≤ 130 mm, if SD > 130 mm.

Standardized Precipitation Index (SPI): calculated for each year y between 1970 and 1998 using the formula:

$$ \mathrm{SPI}(y)=\frac{\mathrm{Annual}\ \mathrm{rainfall}\ (y)-\mathrm{Average}\ \mathrm{annual}\ \mathrm{rainfall}\ \left(1970-1998\right)}{\mathrm{Standard}\ \mathrm{deviation}\ \mathrm{of}\ \mathrm{annual}\ \mathrm{rainfall}\ \left(1970-1998\right)} $$

Categorised as 0 if SPI(y) ≥ 0, 1 if − 1≤ SPI(y) < 0 and 2 if SPI(y) < − 1. As with the Climzone, one centroid value is calculated per department.

Cumulative SPI over 4 years (SPIcum): SPIcum(y) = SPI(y) + SPI(y − 1) + SPI(y − 2) + SPI(y − 3), where SPI(y) is the categorically encoded variable described previously (that is, it only takes the values 0, 1 or 2). Encoded categorically as 0 if SPIcum(y) = 0, 1 SPIcum(y) = 1 or 2 and 2 if SPIcum(y) = 3 or 4.

Rainfall deficiency (DEF30): calculated for each year of the 1970–1998 period with the range of 30 previous years of each year as reference:

$$ \mathrm{DEF}30(y)=\frac{\mathrm{Annual}\ \mathrm{rainfall}(y)-\mathrm{Average}\ \mathrm{annual}\ \mathrm{rainfall}\ \left(30\ \mathrm{previous}\ \mathrm{years}\right)}{\mathrm{Standard}\ \mathrm{deviation}\ \mathrm{of}\ \mathrm{annual}\ \mathrm{rainfall}\ \left(30\ \mathrm{previous}\ \mathrm{years}\right)} $$

Values of DEF30 are encoded categorically as 0 if DEF30(y) ≥ 0, 1 if – 1 ≤ DEF30(y) < 0 and 2 if DEF30(y) < − 1. Values are then attributed to each department by spatial extraction to the centroids.

Delaying starting date of rainy season (Onset): The mean starting date over the 1970–1998 period. The starting date of the rainy season is defined as the first day of rainfall after May 1 such that the accumulated rainfall over 3 consecutive days is ≥ 20 mm and where no dry spell within the next 30 days is longer than 7 days (Sivakumar 1988). This date is calculated for each measuring station and a mean start date determined for that station. The result is then converted to a categorical variable such that Onset = 0 if the start date is before the mean or less than 10 days after, 1 if the start date is between 10 and 29 days after the mean, 2 if the start date is between 30 and 99 days late and 4 if the start date is 100 days or more after the mean. A centroid value is then calculated for each department.

Cumulative delayed onset over 4 years (Onsetcum): The number of years in the past four with a delayed start to the rainy season of 30 days or more. Categorised as 0 if there have been no such delayed starts, 1 if there were one or two delayed starts and 2 if there were 3 or 4 years with a delayed start of 30 days or more.

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De Longueville, F., Zhu, Y. & Henry, S. Direct and indirect impacts of environmental factors on migration in Burkina Faso: application of structural equation modelling. Popul Environ 40, 456–479 (2019). https://doi.org/10.1007/s11111-019-00320-x

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