The following is the established format for referencing this article:Schürmann, A., J. Kleemann, M. Teucher, C. Fürst, and C. Conrad. 2022. Migration in West Africa: a visual analysis of motivation, causes, and routes. Ecology and Society 27(3):16.
ABSTRACTMigration in West Africa has been taking place for centuries for different reasons. Many dimensions of migration remain insufficiently documented and poorly understood. In particular, factors of migration in destination areas and areas of origin are still lacking comprehensive analysis. In this paper, we bring a new perspective to the model of push and pull factors of migration in West Africa by reviewing and analyzing interview-based case studies of migration related to Ghana, Burkina Faso, and Nigeria, as well as to the associated migration routes. The overall aim of this study was to determine the areas that individuals historically chose as destinations for migration and what they perceived to be the distinctive conditions in those areas. Hence, characteristic features about destination areas and areas of origin were identified and located in maps, whereas interrelationships among push and pull factors were illustrated by means of Sankey diagrams. With these tools, we provide a novel combination for visualizing the reasons for migration. The literature review emphasizes the complex relationships between different drivers of migration, with environmental and economic factors emerging as the most important drivers of migration in the focus countries. Moreover, the identified and mapped migration patterns suggest that individuals migrate mainly from the northern part of a particular country to its center or southern regions. This scientific approach shows that the spatial allocation of migratory movements can facilitate assessments on how to meet specific Sustainable Development Goals and to improve regional policies.
The first objective of the UN’s Sustainable Development Goals (SDG), namely to end poverty in all its forms everywhere, is merely one of many SDGs indirectly or directly related to forced and voluntary migration (UN 2015, IOM 2018). Although the goal is formulated globally, it is notably relevant to West Africa. In fact, this region is particularly vulnerable to multiple pressures such as climate change, low soil fertility, conflicts, and limited access to economic resources, all of which can lead to poverty and food insecurity (Mertz et al. 2011, Sissoko et al. 2011, Hollinger and Staatz 2015, Partey et al. 2018, Adaawen et al. 2019). Globally, migration has been a strategy for escaping poverty, food insecurity, or other adverse circumstances for centuries (Black et al. 2011b, Adger et al. 2018, Wiederkehr et al. 2018, Kumasi et al. 2019). Hence, migration can be seen as an adaptation strategy that assists households to diversify their income and decrease their exposure to climate change impacts, contributing indirectly to the achievement of SDG 13 (climate action; ODI 2018). However, voluntary migration can also entail insecure living conditions and accelerate vulnerability for migrants and their dependents (Warner and Afifi 2014, Vinke et al. 2020). Collecting data on migration-related issues corresponding to SDG 17.18, such as migration status or migration movements to and from rural areas, is essential for decision makers to create local, migration-sensitive policies (IOM 2018).
According to the United Nations Department of Economic and Social Affairs (UNDESA), an estimated 7.5 million migrants originated from West African countries in the year 2020. Approximately 89% of international migrants from West Africa stay in other West African countries (author calculations based on UNDESA 2020), indicating internal and cross-border migration patterns as the predominant phenomenon and characterizing the region as a hot spot for migration movements. The population in West Africa, consisting of a variety of ethnic groups, has migrated for many generations (Zachariah and Conde 1980, Bassett and Turner 2007). Ethnic groups like Fulani (Tonah 2002, Bassett and Turner 2007, Bukari et al. 2020), and Mossi (Skinner 1960, Henry et al. 2004, Kress 2006) are observed to be highly mobile throughout West Africa. When referring to human mobility in this region, it is important to differentiate various types of migration. Forced migration or displacement driven by severe droughts, conflicts, or terrorist attacks must be distinguished from seasonal (labor) migration (Adaawen et al. 2019). Other types of migration are outlined in the literature as long-term, short-term, and permanent migration (Guilmoto 1998, Bilsborrow and Henry 2012). As reported by the International Organization for Migration (IOM 2019), seasonal migration refers to migrant workers who depend on certain seasonal conditions and migrate for only a specific part of the year. Short-term migrants migrate for more than three months but less than 12 months, detached from seasonal conditions. Migrants who change their residence but intend to return after a limited period of time are termed as temporary migration. Long-term migration (also referred to as permanent migration in certain studies) is described as a change of residence of more than one year. Migration patterns in West Africa are sensitive to changing conditions (Dick and Schraven 2021), and usually occur in corridors from the more arid north to the more humid south of West Africa (Flahaux and de Haas 2016, van der Land et al. 2018).
The theoretical model of five drivers of migration, which include environmental, economic, demographic, political, and social forces (Black et al. 2011a), is used in a variety of literature (Parnell and Walawege 2011, Neumann et al. 2015, Neumann and Hermans 2017, de Longueville et al. 2020). Food security is considered as a sixth driver of migration in this study because it has been cited as an important factor of migration in a large number of studies that deal with migration in West Africa (Doevenspeck 2011, Pearson and Niaufre 2013, Sow et al. 2014, Neumann et al. 2015, van der Land et al. 2018, Morales-Muñoz et al. 2020). Moreover, given that food security is mostly a combination of several (negative) factors, such as armed conflict, low agricultural production, poor infrastructure, etc., assigning food security to one of the five drivers does not adequately and sufficiently address its importance.
The scientific discourse in recent years has focused on the influence of environmental change on migration patterns on account of the climate change debate (Brown 2008, Black et al. 2011c, McLeman 2013, Gautier et al. 2016, de Longueville et al. 2019, de Longueville et al. 2020, Rigaud et al. 2021). However, environmental factors must be integrated into a complex network of factors and processes and cannot be seen as a stand-alone determinant of migration (Bilsborrow and Henry 2012, Cattaneo and Massetti 2019, Adger et al. 2021). In particular, recent literature highlights the combination and interplay of several factors that influence the decision to migrate (Ackah and Medvedev 2010, Black et al. 2011a, Abu et al. 2014, Neumann et al. 2015, Sanfo et al. 2017, van Hear et al. 2018, Bukari et al. 2020). Economic and social factors play an important role when it comes to deciding whether to migrate or not (Carr 2005, Bassett and Turner 2007, Doevenspeck 2011, Sow et al. 2014). However, beyond a combination of factors that would be conducive to migration, the process also requires financial means. In other words, households that do not have the necessary resources may send only one household member or none to migrate, and thus remain trapped in their situation (Foresight 2011, Black et al. 2013, Cattaneo and Massetti 2019).
To further specify the reasons for migration, the model of push and pull factors (based on Lee 1966) is an approach that has been widely discussed in the literature (de Haas 2011, Parnell and Walawege 2011, Flahaux and de Haas 2016, Castelli 2018). Push and pull factors are seen as determinants of migration, with push factors being forces that pressure individuals to leave their place of origin, whereas pull factors induce people to move to a specific new place (Ackah and Medvedev 2010, Black et al. 2011c, Garcia et al. 2015, Sanfo et al. 2017, FAO et al. 2018). In this study the model of push and pull factors was used to retrieve information on destination areas and areas of origin, as these are essential for understanding migration patterns. Studies agree that migration in the region occurs mainly within the country or to neighboring countries (Adepoju 2003, Mercandalli and Losch 2017, van der Land et al. 2018, Adaawen et al. 2019). Ghana, Burkina Faso, and Nigeria were selected as focus regions in this study because they are of central importance for West African and North—South migration patterns (UNDESA 2019). Considering only international migration routes, according to estimations made by UNDESA, the main destination countries in 2019 for migrants from Burkina Faso were Côte d’Ivore, Ghana, and Mali. Migrants from Ghana moved mainly to Nigeria, Côte d’Ivoire, or Togo, and individuals from Nigeria especially migrated to Niger, Benin, or Ghana (UNDESA 2019). Although broad interregional and international migration corridors have been characterized in the literature (Mercandalli and Losch 2017, UNCTAD 2018, McAuliffe et al. 2019), the exact locations affected by out-migration or in-migration, especially in terms of within-country migration, still lack in-depth documentation.
Although several literature reviews or meta-analyses exist on the environmental influence on human mobility in West Africa (Jónsson 2010, Obokata et al. 2014, Gautier et al. 2016, Thober et al. 2018, Borderon et al. 2019), to date there is no scientific literature that specifically address reasons for migration in destination areas and areas of origins, nor scientific reviews that include a spatially explicit analysis of all possible driving forces in West Africa. In the studies published so far, the reasons for migration have mostly been presented in the form of text, tables, or bar charts (Ango et al. 2014, Olaniyan and Okeke-Uzodike 2015, Sanfo et al. 2016, Goldbach 2017, Neumann and Hermans 2017). The majority of studies have illustrated migration routes separately from the underlying factors (Henry et al. 2003, Rademacher-Schulz et al. 2014, Warner and Afifi 2014, Goldbach 2017). Paone and Richmond (2017) visualize both routes and reasons of migration, but focus exclusively on environmental factors.
In view of the above, our objectives in this paper are as follows:
- to ascertain and spatially allocate reasons for migration by analyzing survey-based case studies in the context of the previously described six drivers;
- to characterize destination areas and areas of origin by assigning respective push and pull factors in order to supplement the traditional push–pull model;
- to locate migration routes based on the conducted literature review; and
- to visualize the outcomes of the aforementioned objectives for a better understanding of migration patterns in the West African countries Ghana, Burkina Faso, and Nigeria
This study focuses on the three West African countries Ghana, Burkina Faso, and Nigeria (hereafter “focus countries”) as important countries of an international collaboration to tackle challenges related to climate change and poverty (see West African Science Service Centre on Climate Change and Adapted Land Use [WASCAL], https://wascal.org/). For this study, emphasis is placed on English-speaking countries where UNDESA (2019) reports high migration rates (Sierra Leone, Liberia, and the Gambia report rather lower migration numbers). The selected countries are amongst the five most densely populated countries in West Africa (World Bank 2021). Given the substantial migration flows between Ghana and Burkina Faso and the availability of extensive literature on migration patterns in Burkina Faso, we have additionally included this country in our analysis. In addition, studies related to migration routes to or from the focus countries, such as Benin or Côte d’Ivoire, were analyzed. These countries differ not only in their economic situation, but also in their migration rates and population density, as illustrated in Figure 1. Nigeria and Ghana are anglophone countries and are similar in their gross domestic product (GDP) per capita, but total GDP in Nigeria is considerably higher (World Bank 2021). Although francophone Burkina Faso is the least densely populated country among the focus countries, it experiences the highest rate of out-migration (World Bank 2021; WorldPop, https://www.worldpop.org/project/categories?id=18). The focus countries cover several bioclimatic regions, ranging from the arid Sahel subregion in northern Burkina Faso to the humid Guinea-Congo subregion in southern Nigeria (Herrmann et al. 2020). The three focus countries are analyzed separately because of their different geopolitical and socio-economic backgrounds, but cross-border migration among them is analyzed together.
Selection of literature and location of case studies
With the aim of a comprehensive literature research, multiple keywords were selected, which are indicated in Figure 2. We used the search terms “migra*” or “human mobility” in combination with a keyword from the second and third box together with the respective country name or the term “West Africa.” The definition of keywords is based on a previous literature review on the topic of migration in West Africa. Therefore, only keywords that have been identified in numerous studies as being associated with the term “migration” were applied. The search was conducted between March 2021 and June 2021 using the search engines Web of Science (https://apps.webofknowledge.com) and Google Scholar (https://scholar.google.com).
We additionally formulated several criteria for the selection of case studies in order to maintain quality standards and achieve our research objectives. To be included, a study had to fulfill the following criteria:
- qualitative or quantitative surveys carried out by the authors of the case studies (literature reviews or studies that only processed census data were excluded);
- published in a journal with peer-review process;
- published in the English language;
- published in the last 20 years;
- defined destination areas and areas of origin of migrants; and
- defined push and pull factors.
The terms “push” or “pull” did not necessarily have to be used in the studies but rather the reasons related to the destination area or area of origin had to be mentioned. In the end, 24 scientific papers were included. Of these, 14 pertain to Ghana, six to Burkina Faso, and four to Nigeria. In two of the studies, multiple sites were evaluated. These were counted separately because all the above-mentioned criteria apply, resulting in a total of 26 case studies for the analysis. Certain studies that did not meet all criteria have been excluded from the analysis but serve as supporting literature for the discussion. An overview of all case studies is provided in Appendix 1 (Table A1.1). The respective location of case studies in West Africa can be found in Figure 1. Table A2.1 in Appendix 2 lists the references that were found on Web of Science prior to applying the criteria for case study selection, but were not included in the underlying analysis.
Analysis of literature according to push and pull factors of migration
For the most part, factors were included in the analysis that were reported in the methods or results section of the respective study, in other words, factors that were mentioned by the respondents. Some of the factors, however, came from third sources, but were supported by statements from the respondents. We analyzed the literature according to environmental, economic, demographic, social, and political drivers (based on Black et al. 2011a), as well as in terms of food security, which has been described as a driver of migration in arid regions (Neumann and Hermans 2017). The drivers of migration were divided into push and pull factors to retrieve information on the characteristics of destination areas or areas of origin and to address the question regarding which factors are perceived to make a region attractive and which are considered repulsive. The respective factors are shown in Figure 3. For the exact wording of the factors, we refer to Appendix 3 (Table A3.1 and Table A3.2).
A classification of the factors to the drivers is complex because certain factors can be associated with several drivers. However, for our analysis or the visualization of the results, one driver had to be selected. Currently, no standard classification of factors is reported in the literature, thus a classification based on the relevant references was designed in this study. The assignment of environmental factors is based on Black et al. (2011a), describing that weather conditions and land productivity are related to the environment. Black et al. (2011a) and Neumann et al. (2015) described employment opportunities as an economic driver. Lack of available land or access to land are assigned to the category of economic drivers, in line with Parrish et al. (2020), whereas “scarcity of land” is also considered a demographic push factor when it is linked to population pressure. The category of social drivers is subdivided into “social conflicts” (Parrish et al. 2020) as a push factor; we refer to Neumann and Hermans (2017) who describe “escape from family problems” and “escape from assault and violence’ as social drivers. “Social network” as well as “educational opportunities” are defined as social pull factors as described in Black et al. (2011a). Political push factors are “political conflicts”, including ethnic conflicts, (Black et al. 2011a, Neumann et al. 2015) and “poor infrastructure” (Parrish et al. 2020), whereas “better infrastructure” and “safety” are defined as political pull factors. Economic and political drivers are closely interrelated, as Neumann et al. (2015) emphasize. The factor “infrastructure” needs to be disentangled to differentiate economic infrastructure and infrastructure in the context of political aspects. For this reason, we classify “access to market” as an economic driver (Deen-Swarray et al. 2014). In case studies where “infrastructure” refers to the development of infrastructure, roads and transportation, or access to certain facilities, we consider “infrastructure” as a political factor that depends on regional development policies (Czaika and Reinprecht 2020). Food security as a driver of migration is divided into “food insecurity” as a push factor and “food security” as a pull factor (Neumann et al. 2015). Multiple citations of a factor in the same study were only counted once. However, it was not possible to weight the factors, given that in most case studies quantitative information was missing.
For each study, we determined which pull factors and which push factors were mentioned to better understand the meaning and characteristics of the destination areas and areas of origin. With this information, a matrix was created for each focus country, which served as the basis for the Sankey diagram visualization. The Sankey diagram reflects a specific flow by the width of the lines between two connections and is commonly used to analyze energy or material flows (Schmidt 2008). In this study, the number in the boxes on the outgoing flow of the Sankey diagram show how many pull factors are named in the context of the respective push factors (see Fig. 4). The number on the box of the incoming flow indicates how many push factors are mentioned in the context of the respective pull factors. The width of lines was determined by how frequently a push factor was cited (counting only once per case study) in combination with a pull factor (multiple counting possible). For a detailed methodological overview of Sankey diagram preprocessing, please refer to Appendix 4 (Fig. A4.1). To generate the diagrams, the Sankey Diagram Generator provided by Acquire Procurement Services was used (http://sankey-diagram-generator.acquireprocure.com/) and subsequently adapted by the authors for better readability.
Migration routes and characterization of destination areas and areas of origin
Migration routes were identified by means of reported destination areas and respective areas of origin. Weighting of the arrows was included in our maps when respective information was provided. Dashed arrows were used for minor migration routes. The reported and categorized push and pull factors of migration were spatially assigned to the mentioned destination and areas of origin (see Fig. 5 and Fig. 6). For the spatial representation, ArcGIS Pro version 2.4.1 was used. Furthermore, infographics in the respective map show the push factors in red circles and pull factors in green circles.
Overview of case studies
As mentioned, all selected studies included in-situ surveys. However, the number of respondents and the type of interview or focus group discussion vary, ranging from 20 respondents (West and Nébié 2019) to 8834 (Hampshire 2002). In six studies, the questions focused directly on climate or environmental issues. The remaining studies asked about land use practices or reasons for migration in general, among other topics. In all studies, the migration movement had already taken place. Although most studies related to Burkina Faso link reported migration movements to the main migration waves associated with the droughts of the 1970s and 1980s (Ruf et al. 2015, Jahel et al. 2018), migration patterns in Ghana were affiliated with other events or lacked a temporal classification. Migration patterns after the 1990s to 2000s were mentioned for example in Braimoh (2004), whereas migration during the 2010s was reported in Rademacher-Schulz et al. (2014) and in Owusu-Ansah and Addai (2014). Migration patterns in northeastern Nigeria are mostly linked to the presence of the Islamist group Boko Haram starting in 2009 (Kamta et al. 2020). In Olaniyan and Okeke-Uzodike (2015), migration in Nigeria was described in the context of the 1960s and from 1990 onward. In 12 studies, a quantification of drivers was provided (Dreier and Sow 2015, Goldbach 2017) and in six studies, the number of migrants was specified (Hampshire 2002, Ango et al. 2014). The majority of studies (16) deal with rural to rural migration, although 13 studies address rural to urban migration and one study addresses urban to rural migration. Migration types cited in the case studies are long-term and permanent migration (21), seasonal migration (seven), short-term migration (four), and temporary migration (one). More than half of the selected case studies (17) focused only on internal migration (Ouedraogo et al. 2009, van der Geest 2011, Sward 2017). Fulani and Mossi as migrants were the most frequently cited ethnic groups (Barbier et al. 2009, Olaniyan and Okeke-Uzodike 2015, West and Nébié 2019).
Frequency of push and pull factors
A first overview indicated that economic drivers featured in 22 studies, environmental drivers in 18 studies, political drivers in 12 studies, social drivers in nine studies, food security as a driver in six studies, and demographic drivers in two studies. In total, 10 sub-categories for push factors and eleven sub-categories for pull factors were defined. Figure A5.1 in Appendix 5 shows the summarized push and pull factors by number of case studies, categorized by drivers of migration and by country. Overall, we identified 124 individual factors, of which 66 are counted as push factors and 58 as pull factors (see Appendix 3, Table A3.1 and Table A3.2). The majority of the push factors are associated with the environmental category (25). In contrast, the pull factors are mainly of economic character (30). Most factors are identified for Ghana (38 push and 29 pull factors), whereas for Burkina Faso (16 push and 15 pull factors) and Nigeria (12 push and 13 pull factors), fewer factors were specified, reflecting the smaller number of studies.
Interconnection of drivers
In 19 studies, a combination of at least two push factors was counted, with the same number applying to pull factors. The interrelation between push and pull factors becomes visible in the Sankey diagrams provided for each focus country (Fig. 4).
In Ghana, economic pull factors were found to play the most important role, as each push factor was reported in combination with an economic pull factor. The second most frequently cited pull factor “available land” was named in combination with push factors from all driver categories. “Better climatic conditions” is mostly cited together with environmental or economic pull factors. It is notable that each pull factor was named together with push factors from multiple driver categories. This observation also applies to the majority of pull factors in the other focus countries.
As in Ghana, the most frequently cited push factors in Burkina Faso include environmental drivers, but economic drivers are dominant for pull factors. Although “food insecurity” occurs together with “better soils or fertile land” or “increase of income or better opportunities,” food security was not reported as a pull factor in Burkina Faso. Moreover, the pull factors “access to market” and “better climatic conditions” were not quoted. “Available land” and “better soils or fertile land” were cited alongside “unfavorable climatic conditions” and “land scarcity due to population pressure.”
Although Nigeria was only represented in four cases, a similar trend can be observed. In fact, environmental and economic push and pull factors seem to be the most important factors here as well. The most frequently reported push factor, as in the other focus countries, is “lack of economic opportunities.” The pull factor “safety” was only cited in the context of Nigeria, alongside the push factor “political conflicts.”
Migration flows identified in studies
Given that the selected case studies report on areas of destination and origin, we were able to depict migration paths, directions and allocate the respective push and pull factors, as illustrated in Figures 5 and 6 for Ghana, Burkina Faso, and Nigeria. In northern Ghana, areas of out-migration were situated in the Upper West Region (Nadowli District and Nandom), in the Upper East Region (Bongo District as well as Bawku West, Kassena Nankana East, and Talensi) and the Northern Region (Tamale, Yendi), where unfavorable climatic conditions like insufficient rainfall or droughts as well as poor soil fertility, food insecurity, and the lack of employment opportunities were named as push factors (van der Geest 2011, Rademacher-Schulz et al. 2014, Adamtey et al. 2015, Tufuor and Sato 2017, Aniah et al. 2019, Antwi-Agyei and Nyantakyi-Frimpong 2021). Migrants from these regions mainly migrate to southern parts of Ghana such as Kumasi, Techiman, or Accra in order to find work or more fertile land.
Out-migration from the Greater Accra Region (Dangbe East), Volta Region (Keta), and Central Region (Moree) occurred for multiple reasons such as poor economic situations, the destruction of landing sites for canoes, or the impact of storms (Marquette et al. 2002, Codjoe et al. 2017, Goldbach 2017). In the respective destination areas, migrants wanted to find better educational opportunities, better markets, or safe landing sites (Marquette et al. 2002, Codjoe et al. 2017, Goldbach 2017). In-migration took place in Savannah Region (Wuripe), Bono Region (Asuoano), Bono East Region (Pru District, Nkoranza South Municipal District), Ashanti Region (Kumasi), and Accra. These regions attracted individuals primarily on account of improved economic conditions and access to farmland (Adjei-Nsiah et al. 2004, Braimoh 2004, Owusu-Ansah and Addai 2014, Sward 2017). Migrants left their home regions, located particularly in the northern regions of Ghana, because of scarcity of land, erratic precipitation, or the desire to find better jobs (Adjei-Nsiah et al. 2004, Braimoh 2004, Owusu-Ansah and Addai 2014, Sward 2017).
The literature review identified three case studies in northern Burkina Faso (namely in the districts Nord, Centre-Nord, and Sahel), where out-migration occurred (Fig. 6a; Hampshire 2002, Barbier et al. 2009, West and Nébié 2019). People migrated from these regions to southern Burkina Faso, to Ghana, or to Côte d’Ivore. Environmental factors like frequent droughts, saturation of land, or lack of drinking water for animals, as well as economic factors such as limited off-farm income opportunities, were the main reasons for migration (Hampshire 2002, Barbier et al. 2009, West and Nébié 2019). Three case studies involved in-migration to locations in Burkina Faso (Ouedraogo et al. 2009, Jahel et al. 2018, West and Nébié 2019) in the districts Centre-Ouest (Neboun and Sissili) and Hauts-Bassins (Tuy Province). Fertile lands or the opportunity to make a better income were pull factors (Ouedraogo et al. 2009, Jahel et al. 2018, West and Nébié 2019). In-migration from Burkina Faso to Bayota in Côte d’Ivoire was reported in Ruf et al. (2015). According to this study, migrants were looking for land for cocoa plantations and better future opportunities given that they were affected by climate change in their areas of origin.
Out-migration in Nigeria took place in Sokoto State (Fig. 6b), from which migrants temporarily moved to Kano State or Kaduna State in search of better economic opportunities and educational facilities (Ango et al. 2014). Migrants left Sokoto State, especially the Local Government Areas Wamakko, Kware, and Bodinga, because of lack of social facilities and poor employment opportunities. In Benin (Dreier and Sow 2015), out-migration to the cities Saki, Adjuba, and Abeokuta (Oyo and Ogun State) in Nigeria was reported. The main reasons for migration were limited land and food insecurity (Dreier and Sow 2015). Migrants from Benin, who stay for a short or for a long time, stated they came for better access to land and to find better soil quality in the mentioned locations. Because of the Islamist group Boko Haram and the resulting conflicts, people in northeastern Nigeria had to move to the Bakassi internally displaced people’s (IDP) camp in Maiduguri, where they sought refuge (Kamta et al. 2020). In-migration was reported in a case study in Saki (Olaniyan and Okeke-Uzodike 2015), where migrants came from northern Nigeria because of erratic rainfall or decreasing grazing opportunities. They stated they moved to Saki because of climate-related and economic issues.
Interrelation of push and pull factors
When the reported reasons for migration are depicted in Sankey diagrams, it becomes apparent that the presence of factors that attract people to an area do not imply that these factors are absent on the sending side. Thus, our findings indicate that the counterpart of a pull factor is not necessarily identified as the push factor. For example, the push factor “unfavorable climatic conditions” is not inevitably accompanied by “favorable climatic conditions” as a pull factor. In this regard, it becomes clear that there is an interplay of different drivers of migration. This is highlighted in the overview maps as a result of the categorization and symbolization of the reasons for migration according to the respective drivers. The review of studies underscores that environmental factors are important in the context of migration in West Africa. Nevertheless, it also emerged that particularly economic, followed by social and political factors, have a significant impact in respect of migration decisions. This observation is in line with van der Land et al. (2018), who conclude that environmental drivers are strongly linked to additional factors, such as the economic or social situation of each individual, but also structural or political conditions. This finding is further supported by the Sankey diagrams which show that the majority of pull factors were cited in combination with push factors of multiple driver categories. Moreover, this result suggests that the decision to migrate depends on the concurrence of multiple unfavorable determinants.
Although unfavorable environmental conditions appear to be a pushing factor in Ghana and Nigeria, economic drivers have an equal importance. With regard to Ghana, better economic conditions and the availability of fertile land in the destination region are more likely to be the reasons for migration than unfavorable climatic conditions for agriculture in the place of origin (van der Geest 2011, van der Land et al. 2018). Given this set of observations, the relationship between environmental and economic drivers appears to be particularly complex within the context of migration research. Another result worth highlighting is the relevance of social factors in Ghana and Nigeria. Family ties in the destination area and the desire for better educational opportunities seem to pull individuals. In other words, individual characteristics of migrants substantially influence migration decisions (van der Land et al. 2018).
It becomes evident that in Burkina Faso environmental factors—especially droughts, erratic rainfall, or declining soil fertility—were frequently mentioned in combination with out-migration. Sanfo et al. (2016) confirmed this observation by arguing that dry spells and droughts are pushing people to migrate. However, the Sankey diagram revealed that these factors are closely related to economic drivers such as available land or increase of income. This assumption was also confirmed by Henry et al. (2004), whose results indicate that individuals in Burkina Faso do not migrate only because of unfavorable climatic conditions. Although environmental conditions are related to migration behavior, they are linked in a rather complex way, also depending on the different types of migration, particularly short- or long-term migration (Henry et al. 2004). In Burkina Faso, it is noticeable that factors connected to population density were mentioned more frequently when compared with the case studies in Ghana and Nigeria, even though the population density per district is comparatively lower. This may be attributed to the relatively high rate of population growth in Burkina Faso, which has been approximately 2.9% since the late 1990s (World Bank 2021). Survey data published by Sanfo et al. (2017) confirm the assumption that population pressure results in land degradation and land tenure insecurity.
Political drivers are related to conflicts in Côte d’Ivoire (Ouedraogo et al. 2009, Jahel et al. 2018), conflicts due to the presence of Boko Haram in northeastern Nigeria (Kamta et al. 2020), or violent conflicts with Fulani herdsmen in Nigeria (Olaniyan and Okeke-Uzodike 2015). However, the latter is not included as a factor of migration in the analysis, as it was not stated as a cause of migration itself, but as a consequence of migration (Lenshie et al. 2020). Meaning, as Olaniyan and Okeke-Uzodike (2015) described, climate change–induced migration of Fulani pastoralists may result in conflicts with the local residents due to economic competition or reluctance to assimilate and identify with local cultural values.
The case studies analyzed reveal a consistent picture, namely that northern regions of a country connect with its central or southern parts (see Fig. 5 and Fig. 6). This is true for all three focus countries and is also in line with other literature (Henry et al. 2003, Bassett and Turner 2007, Adaawen et al. 2019). Migration patterns are complex (Konseiga 2005), with some places serving as transit stations before migrants move on to their final destination (Owusu-Ansah and Addai 2014, Rademacher-Schulz et al. 2014).
The visual analysis indicates that the most common migration patterns within Ghana are from northern to southern regions, as discussed in several studies (van der Geest et al. 2010, Black et al. 2011c, Adaawen and Owusu 2013, Antwi Bosiakoh et al. 2014), but also between coastal regions of different countries or to the central part of Ghana (Marquette et al. 2002, Codjoe et al. 2017, Goldbach 2017). Figure 5 clearly shows that destination areas, which are predominantly located in the middle belt of Ghana, appear to be characterized primarily by more favorable economic opportunities and higher earnings, as well as better access to land. The capital Accra is a major destination area given its educational and economic opportunities. In contrast, areas of origin are mainly affected by unfavorable climatic conditions or the absence of economic opportunities and are particularly located in the northern Regions.
We identified migration routes both from Côte d’Ivoire to Burkina Faso and vice versa, which is also consistent with current estimations by UNDESA (2019). This migration route corresponds to the largest corridor when looking at migration patterns within Africa (UNCTAD 2018, McAuliffe et al. 2019). For internal migration, our study revealed that people in Burkina Faso mainly migrate from north to south, which is also supported by Adaawen et al. (2019) and Henry et al. (2003).
Internal migration movements in Nigeria do not appear to have been explored in depth in the existing literature. Likewise, given the criteria defined in the methods, pertinent literature may not have been part of this analysis, which of course cannot be all-encompassing. The fact that government and academic institutions have focused heavily on international migration in recent years (Oyeniyi 2013) may also explain why we found few case studies related to Nigeria compared to the other focus countries. Furthermore, we only found case studies describing internal and international in-migration or internal out-migration, whereas out-migration to other countries was not addressed.
When looking at the main corridors identified by UNDESA (2019), it is striking that this study did not identify Mali and Niger as destinations for migrants from Burkina Faso and Nigeria, respectively, although these countries are popular destinations. Also noticeable is the fact that migrants are willing to travel long distances. For example, migrants from the villages Séno and Oudalan in Burkina Faso travel a distance of 1200 km to their destination Abidjan in Côte d’Ivoire (Hampshire 2002). Likewise, migrants from Tougou or other regions in northern Burkina Faso travel long distances to Côte d’Ivoire (Barbier et al. 2009). In Ghana, this applies to migrants from Nandom, who migrate to Accra, a distance of about 800 km (Antwi Bosiakoh et al. 2014). This observation could indicate that migration is mainly performed by individuals who possess certain financial resources to travel these distances.
The identified studies of individuals either out-migrating because of lack of access to land or in-migrating for available land (Braimoh 2004, Barbier et al. 2009, Ouedraogo et al. 2009, Dreier and Sow 2015, Ruf et al. 2015, Sward 2017, Jahel et al. 2018) may contribute to more targeted use of land registration tools to strengthen land rights. Secure land rights are major development goals addressed in SDG 1 (no poverty), SDG 2 (zero hunger), SDG 5 (gender equality), SDG 11 (sustainable cities and communities), and SDG 15 (life on land), all of which directly affect migration issues (see the Land Portal SDG land tracker, https://landportal.org/book/sdgs). Our study could support the documentation and monitoring of the SDGs. In addition, the migration-related data obtained in this study, such as migration status, ethnicity, or geographic location, may support the fulfillment of SDG 17.18 (capacity-building for reliable data availability).
In our study, we were able to create an overview of reasons for migration and migration routes in West Africa analyzing studies from interdisciplinary social, economic, and natural sciences. We developed new approaches of visualization, tested new combinations of analysis and generated a new classification of migration. Destination areas and areas of origin can now be studied in a more targeted manner, and the individual indicators of migration defined in this literature review can be analyzed in more detail as they are already spatially allocated. Although similar trends of reasons for migration are evident in the three focus countries, the small number of case studies, the partly dated migration patterns, and the restriction to Ghana, Burkina Faso, and Nigeria preclude a generalization of our findings. Although this statement also applies to migration routes, they generally reflect today’s migration corridors, despite some of the data relating to past events. However, the reasons why people migrate along these routes may have changed over time.
The classic push–pull model can serve as a starting point for accumulating the reasons for migration and allocating factors to areas of destination and origin even though de Haas (2011), Castelli (2018), and Gemenne and McLeman (2018) perceive this model as too simplistic and deterministic. De Haas (2011) criticized this model for tending to characterize migrants as passive actors driven by macro-level drivers (i.e., environmental conditions or population growth) and not considering migration as a process. As this study only considers case studies in which the local population was interviewed, the individual motives for migration, i.e., the micro-level factors, are part of the analysis and thus represent the push and pull factors as direct perceptions of the respondents. Moreover, we argue that the model is intuitive and easy to visualize, allows the analysis of factors for migration in a structured way, and provides a first overview of causes, patterns, and interrelationships of migration (van Hear et al. 2018).
We agree with van Hear et al. (2018), Castelli (2018), and de Haas (2011) that the drivers have to be considered under different dimensions. Although we assigned the factors to the respective driver categories in accordance with the literature, there is a problem of clear distinctive assignment, especially for the factors “poor infrastructure” and “better infrastructure.” We assigned them as political drivers following Czaika and Reinprecht (2020) on account of the higher actuality of reference, but according to Deen-Swarray et al. 2014, assignment as an economic driver is feasible as well. Therefore, we have included a Sankey diagram with these changes in Appendix 6 (Fig. A6.1), which shows a predominance of economic factors. With the spatial assignment of push and pull factors as well as the assignability of ethnic groups, a temporal scale or migration types, multiple dimensions were addressed in our study, even if only superficially. These dimensions, along with others, are proposed by van Hear et al. (2018) as part of their push-pull-plus model, which could not be implemented in our analysis because of a lack of information in some of the case studies. Nevertheless, in this study we extended the classic push–pull model by a visual analysis component and applied it to characterize destination areas and areas of origin. The reasons for migration were not considered in isolation; rather, the interplay of factors influencing the decision to migrate was elaborated using this model.
The Sankey diagrams show at first sight the interaction between the push and pull factors and thus show that the majority of the coupled factors do not belong to the same driver. However, these results depend directly on the research questions and objectives addressed in the individual studies. Given that the studies have a wide spread in the topics of the questionnaires, the results can be assumed to have a low level of bias. A limitation of Sankey diagrams could be the number of linkages to ensure traceability. Moreover, a higher number of connections between push and pull factors may not reflect that one factor is more relevant than another, but rather that the literature focuses on a particular group of factors (e.g., environmental factors of migration). Besides, we were unable to disaggregate the data by gender, which would be important to account for all dimensions of migration, because independent female migration patterns have become increasingly important as strategies for coping with poverty and social pressure in recent years (Adepoju 2003, Tufuor and Sato 2017, Lattof et al. 2018, Onyeneke et al. 2019).
For a holistic picture of migration patterns in West Africa, future studies should include francophone literature as well as gray literature (e.g., from the UN or World Bank), which were only considered as background information in this study. Moreover, comparing the occurrence of the factors over time is challenging, as only a few studies clearly document the implementation date of the surveys or the addressed migration movements. Surveys were often conducted with people who migrated at some point in the past. Consequently, our maps do not reflect current migration trends. Nevertheless, the findings allow us to draw conclusions about current migration patterns and serve as a basis for defining migration hot spots in the focus countries. Follow-up research of our analysis could focus on a finer distinction of drivers of individual migration factors (Fig. 3) by assigning a gradual weight to each relevant driver of the respective factor. The approach of ranking and weighting the most relevant drivers per factor could be combined with interviews and surveys with migration experts. Furthermore, by a Delphi approach with experts (Okoli and Pawlowski 2004), the relation between past, present, and future drivers of migration could identify how the circumstances or motives related to migration have changed or might change over time. In addition, documentation of the success of migration and how it has changed livelihoods would be of research interest. This would allow for inferences on how living conditions of individuals affect migration processes. Long-term information on migration patterns can thus contribute to the achievement of specific SDGs that address poverty alleviation (SDG 1), improved health and well-being (SDG 3), or combating and adapting to the impacts of climate change (SDG 13).
The purpose of this paper was to review and analyze survey-based case studies and migration routes, as well as the factors that drive migration, and to visualize their interplay. The evaluation of 26 case studies confirmed that environmental and economic drivers were the main forces affecting migration in the focus countries Ghana, Burkina Faso, and Nigeria. Although environmental factors were among the most frequently cited reasons for out-migration, economic factors appeared to be the most powerful factor attracting people to particular regions. Our visual analysis demonstrates that push and pull factors of the relevant drivers are closely interrelated, but that the counterpart of a push factor is not necessarily identified as the pull factor. The compilation of available information underlines the assumption that the decision to migrate depends on the coincidence of several unfavorable factors, based on the fact that in about 75% of the cases more than one push factor was mentioned. By means of the push–pull model, it was possible to spatially allocate and characterize destination areas and areas of origin with factors influencing migration and to illustrate these results in overview maps. In addition, Sankey diagrams appeared to be a useful tool to emphasize the outcomes of the overview maps, in particular with regard to disproving the assumption that a destination area is characterized by the very factors that are not present in the area of origin. This approach resulted in a novel enhancement of the classical push–pull model that can be easily adapted to other study areas. By identifying factors that motivate people to migrate and allocate them to locations where out- or in-migration took place, policy and decision makers can use these insights for the compliance and achievement of certain SDGs or the targeted registration of land.
RESPONSES TO THIS ARTICLEResponses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.
This work was conducted in the framework of the Project “WASCAL WRAP 2.0 - MIGRAWARE,” which is financed by the BMBF (German Federal Ministry of Research and Education), project number 02 - J02060825 / 6081 429810. This project is an integrated assessment framework for drivers, processes, and sustainable responses focusing on rural–urban and cross-border migration in West Africa. We acknowledge the financial support within the funding program Open Access Publishing by the German Research Foundation (DFG).
All data used to create figures and tables are from the reviewed articles which can be found in Appendix 1 (Table A1.1). Thus, the data used in this manuscript is freely accessible to everybody referring to the published articles.
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