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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Notes
The odds ratio can be computed as exp(loading).
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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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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:
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:
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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DOI: https://doi.org/10.1007/s11111-019-00320-x