# Moving without cattle: migration patterns of Maasai from the Amboseli region (Southern Kenya)

François Mialhe, Vivian Varlet, Arthur Bostvironnois

► To cite this version:

François Mialhe, Vivian Varlet, Arthur Bostvironnois. Moving without cattle: migration patterns of Maasai from the Amboseli region (Southern Kenya). GeoJournal, 2025, 90, 10.1007/s10708-025-11442-1 . hal-05302957

HAL Id: hal-05302957

https://hal.umontpellier.fr/hal-05302957v1

Submitted on 8 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Moving without cattle: migration patterns of Maasai from the Amboseli region (Southern Kenya)

François Mialhe* · Vivian Varlet · Arthur Bostvironnois

Accepted: 26 June 2025 / Published online: 11 July 2025
© The Author(s) 2025

**Abstract** Maasai pastoralists face numerous con-
straints that compel them to diversify their liveli-
hoods and, at times, migrate without their livestock.
This paper focuses on such migrations undertaken
by young Maasai men living in the Amboseli region
of Kenya. The primary objective is to identify key
migration patterns—namely, destinations, activities
conducted, and the sequences of migration events.
Fieldwork conducted in 2023 gathered information
from 162 individuals who collectively undertook 445
migrations. Sequence and graph-based analyses were
employed to explore these patterns and revealed key
geographic hubs (e.g., Nairobi region and coastal
areas), functional roles of locations (transit points ver-
sus places of residence), and typical migration trajec-
tories. The analysis of graphs revealed three distinct
forms: (i) one-way migrations, (ii) direct return trips,
and (iii) circular migration patterns. A particularly
noteworthy finding is the high frequency of return
migrations among these individuals. Another impor-
tant finding is the enduring attachment to the place of
origin and pastoralism, maintained throughout migra-
tion trajectories and reinforced through remittances.
This continued connection helps explain the high rate
of return. Therefore, migration—far from alienating

individuals from pastoralism—appears to serve as
an instrumental means of sustaining it and eventually
resuming it upon return.

**Keywords** Migration · Maasai · Amboseli · Agro-pastoralism · Sequences · Graphs

Introduction

Migrations are among the strategies used by pastoral-
ists around the world to diversify their livelihoods,
much like the adoption or intensification of agricul-
ture or the commercialization of livestock products
(Fratkin, 1997; McCabe et al., 2014). Such diversifi-
cation strategies are a response to the multiple pres-
sures faced by mobile pastoralists globally, including
lack of political support, shrinking grazing lands,
the spread of market-based economies, demographic
growth, climate change, reduced access to critical
resources (e.g., water, forage), and security chal-
lenges (Fratkin, 1997; Homewood et al., 2004; Rut-
ten, 1992).

Migration, whether permanent or circular, may
either distance individuals from pastoralism or rein-
force the resilience of herding through comple-
mentary income (Fratkin, 1997). Initially, the act of
migrating and abandoning herding was viewed by
the Maasai as an "act of desperation and destitution"
(Archambault, 2013, p. 3666). More recently, how-
ever, migration has been celebrated in the migrants’

<footnote>F. Mialhe (✉) · V. Varlet · A. Bostvironnois
UMR 5600 Environnement Ville Société, Université
Lumière Lyon 2, CNRS, Rue Chevreul Lyon 7, Lyon,
France</footnote>

<footnote>e-mail: francois.mialhe@univ-lyon2.fr</footnote>
places of origin for the benefits it brings to fami-
lies left behind and to other community members.
In some cases, migration enables the expansion or
maintenance of livestock herds via remittances (May
& Ikayo, 2007; Munishi, 2019). There is therefore a
potential connection between migration and herding,
especially when migrants aspire to continue pasto-
ralism over the long term (Homewood et al., 2009).
Success can be evaluated based on the number of
livestock (cattle, sheep, or goats) purchased through
remittances (McCabe et al., 2014). Migration can
thereby have an environmental impact on the place
of origin by sustaining or even increasing the live-
stock load (Greiner & Sakdapolrak, 2013). For the
Maasai, herd growth is significant not only economi-
cally but also culturally, facilitating milestones such
as marriage and childbearing through the payment of
the bride price (May & Ikayo, 2007; McCabe et al.,
2014).

The Maasai of Kenya and Tanzania began migrat-
ing without livestock during the 1980s (McCabe &
Woodhouse, 2022). This delay, compared to other
pastoralist groups, is attributed to the central role of
pastoralism in Maasai cultural identity and the diffi-
culty of separating from it (Archambault, 2013; Mun-
ishi, 2019; Spear, 1993). Pastoralism-based mobility
remains essential for accessing pastoral resources
(mainly water and forage), whose spatial distribution
is unpredictable due to the semi-arid climate (Galaty,
2018, 2021). Migration, by contrast, provides access
to different resources such as social services, educa-
tion, or economic opportunities (Galaty, 2021). While
there is limited census data on migration, it is note-
worthy that in Elangata Wuas (Kenya), around one
in four households already had at least one child who
had migrated for educational or professional reasons
by the early 2000s (Archambault, 2013).

Though poverty is a major driver of migration
among the Maasai, it typically results from a com-
bination of factors: demographic dynamics, land
tenure policies, droughts, epizootics, commodifica-
tion of services (e.g., education, healthcare), and
loss of access to key resources due to the expansion
of protected areas and agriculture (Archambault,
2013; Homewood et al., 2004; McCabe et al., 2014;
Munishi, 2019). In Kenya, land subdivision has
promoted individual ownership at the expense of
communal tenure, often disadvantaging individuals
either during land allocation or through underpriced

land sales, which in some cases has led to migration
(Homewood et al., 2009).

As with other ethnic groups, Maasai migra-
tion is highly gendered (Greiner & Sakdapolrak,
2013). Women migrate primarily upon marriage to
join their husband’s household or to support fam-
ily needs when the husband cannot provide (Jowell
et al., 2018). Some women also migrate for educa-
tion (Jowell et al., 2018). However, men—espe-
cially young men belonging to the warrior age-set
(ilmurran)—comprise the majority of migrants
(Homewood et al., 2009). Common occupations
include security and manual labor, which are often
precarious, poorly paid, unskilled, and temporary
(Homewood et al., 2009; May & Ikayo, 2007). For
these young men, migration is also motivated by a
desire for independence from their fathers (McCabe
et al., 2014). Migration may even be viewed as a
rite of passage, akin to leading herds during the dry
season (McCabe & Woodhouse, 2022).

In Tanzania, young Maasai men began migrat-
ing to cities in the 1980s to earn money to buy live-
stock, often working as night guards (McCabe &
Woodhouse, 2022). After encountering challenges,
many moved to Mererani for the gemstone trade,
where some achieved significant financial suc-
cess (McCabe et al., 2014). Migration intensified
in the 1990s following livestock losses due to dis-
ease, drought, and land loss, with migrants moving
to cities like Arusha and Karatu, the Lake Victoria
region, or border areas near Kenya (May & Ikayo,
2007; McCabe et al., 2014). In Kenya, migrations
to rangeland towns and urban centers are the most
common according to Pollini and Galaty (2021).

This paper seeks to expand the current under-
standing of migration by examining the migration
trajectories of young Maasai men living in or origi-
nating from Amboseli. The research addresses the
following questions:

Where have young Maasai men from the
Amboseli region migrated since the mid-1990s?

What activities have they engaged in at their des-
tinations?

What were their migration trajectories between
the ages of 15 and 35?

What connections do they maintain with their
places of origin?
Study area

The fieldwork was carried out in several villages located near Amboseli National Park in southern Kenya (Fig. 1). This area is classified as semi-arid and experiences two rainy seasons per year (Altmann et al., 2002). The presence of water sources has supported the development of significant biodiversity, which explains the high number of protected areas, including Amboseli National Park, established in 1974. This hydro-climatic context has also supported mobile pastoralism as a dominant livelihood for generations. More recently, water has been redirected through hydraulic infrastructure to supply nearby towns with drinking water and to promote irrigated agriculture in the second half of the twentieth century (Bostvironnois et al., 2025; Hemingway et al., 2022). The expansion of protected areas, agricultural lands, and urban spaces

have progressively fragmented the savannah (Burnsilver et al., 2008).

The area forms part of Maasiland, which extends across northern Tanzania and southern Kenya (Spear & Waller, 1993). Originally inhabited predominantly by the Maasai, migration trends have reduced their proportion in Kajiado County from 79% in 1962 to 47% in 1989 (Homewood et al., 2004; Spear, 1993). After Kenya's independence in 1963, significant migration occurred from densely populated agricultural areas toward frontier lands such as the Amboseli region to engage in rainfed agriculture. These flows were particularly strong toward the foothills of Mount Kilimanjaro, near the town of Loitokitok, which is wetter than the surrounding plains (Campbell, 1984). Farmers, primarily Kikuyu, initially settled in these upper areas before lower zones, such as around Kimana, were also cultivated thanks to rural development projects.

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Map of the study area showing the investigated villages and group ranches, protected areas, cultivated zones, and densely inhabited areas, which fragment the remaining pastoral</figcaption></figure>

space. Note: Conservancies currently being established as part of the land reform are not shown
Agriculture now coexists with livestock keeping for those who have access to water (Hemingway et al., 2022). Mobile pastoralism, initially oriented toward milk production and increasingly toward meat, requires pastoralists to migrate during the dry season in search of water and forage (Grandin, 1988). The appropriation of these resources by conservation and agriculture has significantly altered migration routes. Major droughts, notably those in 1972–1976, 1983, and 2009, have also marked critical periods when herding systems evolved, either through the adoption of agriculture or a shift toward smaller livestock species. Conversely, extreme floods such as those of 2019 (Wainwright et al., 2021) have led to high animal mortality. More recently, land subdivision in the early 2020s, following the Community Land Act (Alden Wily, 2018), has reshaped land tenure by granting individual land titles and increasing the areas designated for conservation and irrigated agriculture, thereby reducing the land available for pastoralism. As a result, herders must navigate a complex array of constraints and continuously adapt their strategies within a local context that also offers opportunities for diversification.

## Material and method

Fieldwork was conducted during the first half of 2023. Information was collected through a structured questionnaire written in English and composed of three main sections: (i) personal information (e.g., age, education level, size of individually owned herd), (ii) migration history, and (iii) remittance practices. Preliminary discussions with key informants were used to compile a list of migrants in each surveyed village. Here, a "village" is defined as a cluster of Maasai dwellings that generally includes a central paddock and one or more households, enclosed by a barrier made of acacia branches, sometimes reinforced with wire fencing. Respondents were also asked to identify other migrants within their communities. The fieldwork spanned several months, allowing researchers to meet with return migrants visiting their families during holidays. However, this approach may have led to the underrepresentation of individuals who rarely return, or conversely, those engaged in shorter, potentially frequent forms of migration—such as those involving commercial transactions, for example.

Interviews were conducted in Maa with the assistance of two Maasai research assistants native to the Amboseli region. All participants provided informed consent to participate in this study. The migration history section employed a visual mapping technique following the method described by Mialhe et al. (2024). Interviewees were asked to draw their migration trajectories, representing places of residence with circles and migrations with arrows. They were also asked to indicate the main activity they undertook upon arrival at each destination, as well as the year in which each migration occurred. Migration was defined as a change in primary residence lasting at least one year.

A total of 162 male participants were interviewed, reporting a combined total of 445 migration events (Table 1). This male-only sample reflects the pronounced gender imbalance in Maasai migration patterns, where men dominate migratory movements as defined in this study. Although this was not an initial objective of the research, it became evident during the early stages of fieldwork that the vast majority of identified migrants were men. Consequently, a decision was made early on to focus exclusively on collecting data from male respondents, given both the observed reality in the field and the logistical constraints of the study.

Migrants mentioned 102 different destinations, some identified as counties and others as villages. To facilitate analysis, destinations were first harmonized and simplified. This process aimed to associate each destination with a broader territorial unit. Accordingly, each location was matched to its corresponding county (e.g., Diani was assigned to Kwale County, and Irtika to Kiambu County). Given the high number of reported destinations in Kajiado County, these were further grouped into five categories: the surveyed villages (aggregated under the category "Study area" without distinguishing between group ranches), Loitokitok (the main town near the study area), the Amboseli National Park and its immediate surroundings, Kajiado Town (the county capital), and a residual "Kajiado" category for the remaining sites. This resulted in 25 spatial units used for the initial analyses, comprising the 20 counties and five sub-regions within Kajiado (Fig. 2, left). In some instances, it proved useful to disaggregate the surveyed villages according to their respective group ranches, thereby
<table><caption>**Table 1** Socio-economic characteristics of the 162 interviewed individuals</caption><thead><tr><th>Variable</th><th>Proportion (% of sample)</th></tr></thead><tbody><tr><td><strong>Place of birth</strong></td><td></td></tr><tr><td>■ Olgulului Olalarashi GR</td><td>60</td></tr><tr><td>■ Imbirikani GR</td><td>23</td></tr><tr><td>■ Kimana</td><td>14</td></tr><tr><td>■ Kuku GR</td><td>2</td></tr><tr><td><strong>Year of birth</strong></td><td></td></tr><tr><td>■ 1975–1979</td><td>&lt;1</td></tr><tr><td>■ 1980–1984</td><td>29</td></tr><tr><td>■ 1985–1989</td><td>51</td></tr><tr><td>■ 1990–1994</td><td>18</td></tr><tr><td>■ 1995–1999</td><td>1</td></tr><tr><td><strong>Education level</strong></td><td></td></tr><tr><td>■ None</td><td>30</td></tr><tr><td>■ Primary</td><td>17</td></tr><tr><td>■ Secondary</td><td>23</td></tr><tr><td>■ University</td><td>30</td></tr><tr><td><strong>Main livelihood in 2023*</strong></td><td></td></tr><tr><td>■ Agropastoralism</td><td>26</td></tr><tr><td>■ Professional**</td><td>16</td></tr><tr><td>■ Security</td><td>16</td></tr><tr><td>■ Herding</td><td>9</td></tr><tr><td>■ Tourism</td><td>9</td></tr><tr><td><strong>Land ownership (hectares)</strong></td><td></td></tr><tr><td>■ &lt;20</td><td>16</td></tr><tr><td>■ 20–39</td><td>43</td></tr><tr><td>■ 40–59</td><td>22</td></tr><tr><td>■ 60–79</td><td>17</td></tr><tr><td>■ &gt;79</td><td>2</td></tr><tr><td><strong>Tropical livestock units</strong></td><td></td></tr><tr><td>■ &lt;25</td><td>49</td></tr><tr><td>■ 25–49</td><td>32</td></tr><tr><td>■ 50–74</td><td>11</td></tr><tr><td>■ 75–99</td><td>4</td></tr><tr><td>■ &gt;99</td><td>4</td></tr></tbody></table>

grouping them at this jurisdictional level for more refined analysis.

To perform sequence analysis using the R package TraMineR (Bernard & Vidal, 2023; Gabadinho et al., 2011; Liao & Gan, 2020; Mialhe et al., 2024), it was necessary to reduce the number of states within

each sequence. Therefore, destination counties were aggregated into six broader geographic zones aligned with Kenya's former provincial boundaries (Coast, Eastern, Central, Rift Valley, Nairobi, and Nyanza). Additionally, areas near the study site were grouped into three categories: the study area itself, the vicinity —including notably Loitokitok—, and a residual Kajiado category, which includes locations such as Kajiado Town. This aggregation produced nine spatial categories for the purposes of sequence analysis (Fig. 2, right).

The collected data also supported graph-based analyses (Mialhe et al., 2024). These graphs represent each individual's migration trajectory, and various network metrics were extracted, including the number of nodes and edges, circularity, density, diameter, and betweenness centrality. These metrics were used to cluster migration trajectories into distinct typologies.

## Results

### Migration flows

Figure 3 presents the total migration flows between destinations, regardless of direction. Five flow intensity levels are distinguished based on the number of migrations recorded. The two most prominent flows (between 50 and 68 migrations) connect the study area to Nairobi and Mombasa. The third most significant flow (30–50 migrations) links the study area with Loitokitok. Two intermediate-level flows (10–30 migrations) connect Nairobi and Mombasa and, separately, the study area and Kajiado. The Nairobi-Mombasa flow is thus the primary migration route that bypasses the study area. Lower-level flows (5–10 migrations) include routes bypassing the study area (e.g., Mombasa to Kilifi, Nairobi to Kajiado Town, Loitokitok to Mombasa) as well as others that connect the study area with inland destinations (Nakuru, Machakos, Makueni, Simba Cement) or coastal areas (Lamu). These flows indicate three main migration poles: (1) Nairobi and its surrounding areas, (2) Mombasa and the coastal region, and (3) the vicinity of the study area. Radial migrations centered on the study area are more prevalent than transverse migrations.

Table 2 distinguishes between incoming and outgoing migrations for the main destinations.

<footnote>*Only the main activity was recorded, total < 100%</footnote>

<footnote>**include medical and skilled professions</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 2** Maps showing the destinations of migrants. The left map (A) displays 25 locations aggregated at the county level. The right map (B) shows nine destination areas following a second level of aggregation, used for sequence analysis</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 3** Flow map of migrations recorded during the investigation. The left map shows movements at the national scale (Kenya), while the right map provides a detailed view of the area highlighted by the red rectangle</figcaption></figure>

Mombasa, with 73 migrants, slightly surpasses Nairobi (66 migrants), although Nairobi had a larger population in 2019 (4.4 million vs. Mombasa's 1.2 million, KNBS 2019). These cities differ significantly in their out/in migration ratios. Nairobi's higher ratio (0.89 vs. 0.7 for Mombasa) suggests

a greater proportion of migrants leaving the city. The nature of activities also differs: Nairobi attracts individuals pursuing education, especially university studies, while Mombasa primarily draws those seeking low-skilled jobs in security and tourism.

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 2** Main activities conducted by migrants in the top nine destinations</caption><thead><tr><th>Destinations</th><th>In-migration</th><th>Out-migration</th><th>Out/in ratio</th><th>Occupation upon arrival (n)</th></tr></thead><tbody><tr><td>Mombasa</td><td>73</td><td>51</td><td>0.7</td><td>Security (27), street vendors and security (19), tourism (6)</td></tr><tr><td>Nairobi</td><td>66</td><td>59</td><td>0.89</td><td>Education (30), security (19)</td></tr><tr><td>Olgulului*</td><td>56</td><td>99</td><td>1.77</td><td>Crops and livestock farming (28)</td></tr><tr><td>Loitokitok</td><td>37</td><td>35</td><td>0.95</td><td>Education (32)</td></tr><tr><td>Kimana*</td><td>35</td><td>22</td><td>0.63</td><td>Education (19), crops and livestock farming (11)</td></tr><tr><td>Imbirikani*</td><td>24</td><td>37</td><td>1.54</td><td>Crops and livestock farming (15)</td></tr><tr><td>Kajiado Town</td><td>18</td><td>6</td><td>0.33</td><td>County government (8), professionals** (4)</td></tr><tr><td>Amboseli</td><td>16</td><td>11</td><td>0.69</td><td>Tourism (11)</td></tr><tr><td>Makueni</td><td>13</td><td>11</td><td>0.84</td><td>Education (9)</td></tr></tbody></table>

Loitokitok and Makueni also show high out/in migration ratios (0.95 and 0.84, respectively), both functioning as education destinations. Conversely, Kajiado Town has a low ratio (0.33), with skilled jobs being overrepresented, indicating that it attracts and retains qualified individuals. These differences suggest a gradient between transit locations (high out/ in ratios) and long-term residence destinations (low ratios), each associated with distinct occupational profiles.

The data also reveal differences among the surveyed villages. Each village is associated with the group ranch to which it belongs, although it does not

necessarily represent the group ranch in its entirety. For example, the villages of Olgulului and Imbirikani exhibit positive migration ratios, indicating more departures than arrivals, some of which are returnees engaging in agro-pastoralism. In contrast, Kimana has a lower ratio, with more arrivals than departures, due to the presence of secondary schools attracting young migrants from nearby areas.

Median lengths of stay range between 3 and 6 years across destinations. Migrants' ages upon arrival vary significantly (Fig. 4). A one-way ANOVA showed a significant effect of destination on age, $F(30, 409)=6.14, p<0.001$. The youngest migrants

<figure><img src="image_6.png" /><figcaption>**Fig. 4** Ages of migrants upon arrival in the thirteen most popular destinations (note: Destinations are ordered by median age)</figcaption></figure>

<footnote>*Located in the study area</footnote>

<footnote>**Include nurse, veterinary officer, teacher and accountant</footnote>
tended to move to Loitokitok, Kimana, and Makueni (ages 17–20), which are nearby and often related to educational pursuits. In contrast, older migrants (ages 28.5–30) were more likely to settle in Olgulului, Lamu, and Kajiado Town. In these locations, migrants either return home to engage in pastoral or agro-pastoral activities (Olgulului) or find skilled employment (Kajiado Town).

Remittances were sent in 75% of reported migrations and, in 83% of those cases, were used for agro-pastoral purposes. Transfers aimed at livestock production were more common than those for agriculture: 198 versus 86 migrations. For livestock-related investments, funds were mainly used to acquire new animals (especially cattle, followed by small ruminants), purchase inputs (e.g., tick sprays, medications), and hire herders. The average annual amount spent on livestock acquisition was 45,000 KSH (standard deviation = 63,543), a sum sufficient

in 2023 to purchase two cows or about ten small ruminants. In comparison, the mean amount spent annually on agricultural activities was 18,400 KSH (standard deviation = 30,000). Payments for herders, mostly Tanzanian, were also considerable for those hiring them year-round, with monthly salaries around 3000 KSH.

## Migratory sequences

The most frequently observed migration sequence was a move to the coastal region (Table 3). Just over 10% of all migrants followed this path. Nearly another 10% went to the coast and eventually returned. The third most common sequence involved individuals who migrated to the coast, returned, and also engaged in a secondary, shorter-range migration. In total, 47.5% of the sample (n=77) had migrated to the coast at least once.

The fourth most frequent sequence involves migration to Nairobi, followed by a return. The next two sequences combine an earlier migration with a subsequent move to Nairobi and then a return. All frequently observed sequences involving Nairobi include a return migration, underscoring the capital's role as a place of transit rather than long-term settlement. Overall, almost half of the migrants (49%, n=79) eventually returned to their place of origin.

Figure 5 aggregates all observed sequences along a timeline from 1997 to 2023. The contraction of the grey zone (representing the study area) followed by its expansion over time indicates phases of departure and return. The year 2010, following a severe drought in 2009, shows a marked decrease in residency in the study area, whereas return migrations began to increase in 2018 and continued through 2022. Migration flows in 2010 mainly favored Nairobi, which saw

<table><caption>**Table 3** Most frequent migration sequences (representing 48% of the sample)</caption><thead><tr><th>Sequences</th><th>Frequency (%)</th><th>Cumulative (%)</th></tr></thead><tbody><tr><td>Study area (SA) → Coast</td><td>10.5</td><td>10.5</td></tr><tr><td>SA → Coast → SA</td><td>8.6</td><td>19.1</td></tr><tr><td>SA → Vicinity → Coast → SA</td><td>4.9</td><td>24</td></tr><tr><td>SA → Nairobi → SA</td><td>4.3</td><td>28.3</td></tr><tr><td>SA → Vicinity → Nairobi → SA</td><td>4.3</td><td>32.6</td></tr><tr><td>SA → Coast → Nairobi → SA</td><td>2.5</td><td>35.1</td></tr><tr><td>SA → Coast → Vicinity</td><td>2.5</td><td>37.6</td></tr><tr><td>SA → Kajiado</td><td>2.5</td><td>40.1</td></tr><tr><td>SA → Nairobi → Coast</td><td>2.5</td><td>42.6</td></tr><tr><td>SA → Nairobi → Coast → SA</td><td>2.5</td><td>45.1</td></tr><tr><td>SA → Vicinity</td><td>2.5</td><td>47.6</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5** Density plot showing the residential locations of the sampled population for each year from 1997 to 2023 (see color legend in Fig. 6)</figcaption></figure>
a decline in migrants beginning in 2016. The coast also emerged as an attractive destination that, while decreasing slightly over time, remained a residence for nearly 20% of the sample in 2023. A different pattern was observed in Kajiado Town, which became an increasingly common destination, suggesting longer-term settlement by older migrants. These trends reflect both lifecycle effects among individuals of the same generation and the probable influence of contextual economic, political, or environmental events.

A hierarchical cluster analysis grouped the 162 migration sequences into five clusters (Fig. 6), with sequences displayed along a horizontal axis representing migrants' ages.

- Cluster A (n=23), labeled “Nairobi”, includes migrants who lived for many years in Nairobi. The proportion residing in Nairobi increased substantially between ages 19 and 26, then declined sharply by age 35, with a corresponding increase

<figure><img src="image_8.png" /><figcaption>**Fig. 6** The five clusters resulting from the hierarchical clustering performed on the migration sequences</figcaption></figure>
in coastal, Kajiado Town, and home area residen-
cies. The main activities undertaken in each loca-
tion are detailed in Table 4.

- Cluster B (n=77), labeled “Home”, includes individuals who spent most of their time in their place of origin, only migrating for short periods. Around age 24, up to 40% were living elsewhere, and some migrated again around age 35.

- Cluster C (n=29), labeled “Coast”, consists of migrants who lived on the coast for extended periods between ages 15 and 35. Coastal residency increased steadily, peaking at nearly 100% by age 28, and then declined as migrants returned home.

- Cluster D (n=18), labeled “Other”, includes diverse paths to secondary destinations. A peak in external residency occurred between ages 26 and 28, mainly in Rift Valley, Eastern, and Central regions.

- Cluster E (n=15), labeled “Kajiado”, is characterized by long stays in Kajiado between ages 26 and 35 and notable periods spent in the area surrounding the study site.

Table 4 characterizes each cluster and highlights statistically significant differences using ANOVA and chi-square tests. Variables such as age, duration spent outside the study area, return rate, and education level showed strong differences (*p* < 0.001). The number of migrations also differed significantly but to a lesser extent (*p* < 0.05). Primary activities at each destination are listed, showing different profiles. Educational reasons (secondary and university combined) accounted for 37, 21, 16, 34, and 49% respectively across the five clusters, suggesting that members of the Home and Coast clusters were generally less educated and less likely to migrate for schooling.

This section provides a concise overview of the migration trajectories associated with the five clusters identified in the analysis: *Nairobi, Coast, Kajiado, Home, and Other*. These representative life histories serve to humanize and concretely illustrate each category.

In the *Nairobi* cluster, a typical case involves a male respondent born in 1995. He left his village in the Olgulului group ranch at the age of 15 to attend a high school in Kajiado, known for its academic rigor and discipline. At 19, he enrolled at Mount Kenya University near Nairobi, drawn by its relatively affordable tuition. Upon graduation, he secured

employment at a Huduma Centre in Nairobi—a gov-
ernment one-stop shop for public services—where
he continues to work. Despite living in the city,
he returns to his home village approximately once
a month to manage his livestock, which includes
around 30 cattle and 80 small ruminants.

In the *Home* cluster, a representative case is that of a man born in 1991 with no formal education. At the age of 21, he moved to Loitokitok to work as a security guard for two years before relocating to Mombasa, where his brother helped him find similar employment. While on the coast, he also engaged in the informal trade of Maasai cultural goods on the beaches and surrounding areas. After four years, he returned permanently to Namelok in Olgulului GR, where he now tends a small herd, focuses on farming, and occasionally supplements his income as a motor-cycle taxi driver (*boda boda*).

The *Coast* cluster is exemplified by a respondent born in 1984 who initially moved to Loitokitok for secondary school. He returned to his home village in Olgulului only during school holidays. Four years later, he migrated to Malindi (Kilifi County), where he found work through a friend. During his ten-year stay on the coast, he returned infrequently to the Amboseli region. Eventually, he settled back in the area to marry and now manages a herd of approximately 350 ruminants, built in part through remittances sent over the years.

In the *Other* cluster, a representative case is that of a man from Olgulului who moved at age 17 to Makueni County to attend secondary school. After four years, a friend helped him find work in a tea factory in Kiambu County, where he remained for 13 years, regularly sending money to his family in the Amboseli region. He later relocated to the coast (Kwale County) for employment in the hospitality industry. By 2022, his remittances had contributed to the maintenance of a herd comprising 21 cattle and 42 small ruminants.

Finally, the *Kajiado* cluster is represented by a man from the Olgulului group ranch who left at age 17 to attend high school in Loitokitok. Four years later, he enrolled at Egerton University in Nakuru, where he married and had a child. After completing his studies, he moved to Kajiado Town to work as an accountant for the county government. Throughout his migration journey, he maintained a herd that, as of 2022, included 30 cattle and 27 small ruminants. He continues to return regularly to the Amboseli region.
<table><caption>**Table 4** Socioeconomic characteristics of individuals by cluster (N = 162)</caption><thead><tr><th>Cluster</th><th>Mean age at first migration (***)</th><th>Mean number of migrations (*)</th><th>Years spent outside (***)</th><th>Return migrants (%) (***)</th><th>Remittances for livestock (%)</th><th>Remittances for cropping (%)</th><th>Modal education level (***)</th><th>Mean land owned (ha)</th><th>Mean tropical livestock units (TLU)</th><th>Main activities (top proportions)</th></tr></thead><tbody><tr><td>Nairobi</td><td>19</td><td>2.8</td><td>12.6</td><td>0.39</td><td>83</td><td>39</td><td>University</td><td>32</td><td>40.5</td><td>High School (20%)<br>University (17%)<br>Security (17%)<br>Labourer (6%)</td></tr><tr><td>Home</td><td>23.8</td><td>2.6</td><td>5.2</td><td>0.73</td><td>62</td><td>39</td><td>None</td><td>33.5</td><td>29.2</td><td>Security (21%)<br>High School (11%)<br>Crop and livestock<br>Farming (10%)<br>University (10%)<br>Street vendor (8%)</td></tr><tr><td>Coast</td><td>20.5</td><td>2.5</td><td>12.1</td><td>0.45</td><td>86</td><td>31</td><td>None</td><td>25.5</td><td>35.7</td><td>Security (23%)<br>High School (16%)<br>Street Vendor (11%)<br>Tourism (8%)<br>Herding (7%)</td></tr><tr><td>Other</td><td>18.8</td><td>3.1</td><td>12</td><td>0.5</td><td>94</td><td>50</td><td>High school</td><td>38.2</td><td>26</td><td>High School (23%)<br>Security (14%)<br>University (11%)<br>Farming (7%)<br>Public office (7%)</td></tr><tr><td>Kajiado</td><td>17.5</td><td>3.2</td><td>15.2</td><td>0.27</td><td>100</td><td>40</td><td>University</td><td>35.8</td><td>31</td><td>University (26%)<br>High School (23%)<br>Tourism (13%)<br>Public Office (9%)</td></tr></tbody></table>

Significance levels from ANOVA and Chi-square tests: ***p < 0.001; **p < 0.01; *p < 0.05
## Shapes of migration trajectories

A K-means clustering algorithm was used to categorize the migration trajectories into three distinct types, based on six graph metrics: number of nodes, number of edges, circularity, density, diameter, and betweenness centrality (Fig. 7). The statistical characteristics of these metrics are summarized in Table 5.

The first type, representing 43% of the total migrations, is labeled “no return.” These trajectories are characterized by a linear structure and a minimum of one migration. Migrants following this type do not return to their place of origin.

The second type, comprising 36% of the migrations, is labeled “circular.” These trajectories include at least three migration events and feature a return to the original location.

The third type, “one-way round trip,” accounts for 21% of the migrations. This pattern involves a single move away from the place of origin followed by a return, representing a basic round-trip trajectory.

A chi-square test of independence was conducted to examine the relationship between the five sequence clusters and the three graph types. The results were statistically significant, $\chi^2(df=8)=21.359, p<0.01$, indicating an association between sequence profiles and graph structure (Table 6).

<figure><img src="image_9.png" /><figcaption>**Fig. 7** The three trajectory patterns identified: no return (top), circular (middle), and one-way round trip (bottom). Circles represent locations and arrows indicate migration flows (SA = Study Area). The metrics shown on the right correspond to the illustrated graphs</figcaption></figure>

<table><caption>**Table 5** Average values of graph metrics for each migration trajectory type</caption><thead><tr><td>Graph type</td><td>Nodes</td><td>Edges</td><td>Circular</td><td>Density</td><td>Diameter</td><td>Betweenness</td></tr></thead><tbody><tr><td>No return</td><td>3.26</td><td>2.36</td><td>False</td><td>0.34</td><td>NaN</td><td>0.14</td></tr><tr><td>Circular</td><td>3.19</td><td>3.34</td><td>True</td><td>0.49</td><td>2.19</td><td>0.49</td></tr><tr><td>One-way round trip</td><td>2</td><td>2</td><td>True</td><td>1</td><td>1</td><td>0.000000</td></tr></tbody></table>
Cluster A shows a higher-than-expected proportion of “no return” trajectories, highlighting Nairobi’s role in redistributing migrants across the country and the relatively low rate of returns among those who settle there.

Cluster B is characterized by a high proportion of return trajectories, both circular and one-way round trips, suggesting a strong continued attachment to the place of origin.

Cluster C, like Cluster A, includes more “no return” trajectories than expected, reflecting the tendency of coastal migrants to settle long-term in that region.

Cluster D displays a balance between returns and non-returns but shows a lower-than-expected proportion of one-way round trips, suggesting more complex migration paths involving multiple destinations.

Cluster E exhibits a very high proportion of “no return” trajectories, indicating a tendency toward more permanent relocation, particularly in or near Kajiado Town and the surrounding areas.

## Discussion

The identification of major migration flows in this study has highlighted the role of key urban centers as strong pull factors. These findings confirm the importance of metropolitan areas as attractive destinations for Maasai herders, a pattern previously observed in Tanzania. However, this study also underscores the differentiated functions of these urban centers depending on their specific characteristics: Nairobi serves primarily as a hub for education, particularly higher education, while Mombasa and its surrounding coastal towns offer employment opportunities, especially in the tourism sector. In the latter case, the sale of Maasai products within the tourist economy illustrates the cultural dimension embedded in these economic activities. This marks a notable shift from earlier findings—for

<table><caption>**Table 6** Distribution (%) of graph types across migration sequence clusters</caption><thead><tr><th>Cluster</th><th>No return</th><th>Circular</th><th>One-way round trip</th></tr></thead><tbody><tr><td>A</td><td>61</td><td>22</td><td>17</td></tr><tr><td>B</td><td>26</td><td>44</td><td>30</td></tr><tr><td>C</td><td>55</td><td>31</td><td>14</td></tr><tr><td>D</td><td>50</td><td>39</td><td>11</td></tr><tr><td>E</td><td>73</td><td>20</td><td>7</td></tr><tr><td>Total (N)</td><td>43% (n=70)</td><td>36% (n=58)</td><td>21% (n=34)</td></tr></tbody></table>

instance, Coast (2002) documented a minimal Maasai presence in the tourism sector at the time—indicating evolving patterns of economic integration.

Moreover, this research reveals a more prominent role for higher education as a driver of migration. Archambault (2013) had previously noted the role of education but primarily in terms of secondary schooling. While secondary education often initiates the first migration, this study shows that aspirations for university education now also play a central role. However, obtaining a university degree does not necessarily guarantee employment aligned with one’s qualifications. This mismatch suggests that migration, at least for some individuals, is not merely a response to vulnerability, but also a deliberate strategy for livelihood diversification and for enhancing resilience of agro-pastoral activities (Hoffmann et al., 2022).

Compared to other studies, such as Mialhe et al. (2024) on rural Filipino populations, the trajectories identified here appear less diverse and less complex. This may be due to more limited opportunities or to a deeper attachment to the place of origin among the Maasai. These factors could partly explain the relatively narrow range of migratory pathways observed.

In line with McCabe et al. (2014)'s findings for Tanzanian Maasai, this study shows that migration often aims to increase livestock holdings. The high proportion of migrants sending money for the purchase of animals, veterinary products, and the hiring of herders underscores their continued attachment to both the land they left and to livestock-based livelihoods. This suggests that pastoralism remains not only a strong cultural identity marker but also an economic activity that migrants actively seek to sustain. In this sense, migration may serve as a means to strengthen the resilience of pastoral systems and could be interpreted as a form of adaptation to local pressures (Gemenne & Blocher, 2017). Whether migration has become a new social norm, as suggested by McCabe et al. (2014), remains to be confirmed.

The diversity of migratory trajectories documented in this study indicates that a wide range of different profiles among Maasai men are affected by the phenomenon. If migration is increasingly acquiring the characteristics of a rite of passage—comparable to ritual age-set transitions—it is important to recognize that it also provides avenues for coping with future climatic or economic shocks through the development of networks and cultural capital that may, for instance, facilitate access to employment.
Remittances also serve to strengthen links between urban destinations and rural places of origin (Greiner & Sakdapolrak, 2013). Although less frequent, remittances directed toward agricultural investment further underscore the growing role of farming in Maasai livelihoods (Hemingway et al., 2022).

The severe 2009 drought was frequently cited during interviews as a driver of migration. Yet, the findings suggest that migration results from a combination of factors and cannot be solely explained by climatic conditions—a pattern also found among Fulani herders in Ghana (Bukari et al., 2020). As emphasized by Black et al. (2011), migration is inherently multicausal. Another important environmental factor is the gradual reduction and fragmentation of pastoral land over several decades (BurnSilver et al., 2008). The recent subdivision process affecting all group ranches around Amboseli National Park has further intensified pressure on herders by restricting access to communal resources and requiring land acquisition. Notably, some migrants have used remittances to secure land titles, thereby linking migration with the broader struggle to maintain access to critical pastoral resources (Unks et al., 2023).

Although the migratory trajectories of Maasai women were not examined in this study, this does not imply that women do not migrate. It is therefore essential that future research focuses on the migration experiences of Maasai women, investigating their patterns and sequences, and exploring how these may differ from those of men.

This study provides an updated perspective on migration dynamics among Maasai pastoralist communities that have already begun diversifying their livelihoods through agriculture. The diversity of migration patterns and associated activities highlights the multifunctional nature of migration, which appears less as a break from pastoralism and more as a means of sustaining it. This continuity is also shaped by the limited availability of stable and well-paid employment opportunities elsewhere in Kenya.

**Acknowledgements** We would like to warmly thank the reviewer for their constructive and thoughtful comments, which have significantly contributed to improving the clarity and quality of our manuscript.

**Funding** Open access funding provided by Université Lumière Lyon 2. This research was funded by the French National Research Agency (ANR) under the MAGNUM project—Maasai, Gnus and the Metropolis (ANR-16-CE03-0004).

## Declarations

**Conflict of interest** The authors have no relevant financial or non-financial competing interests.

**Ethical approval** This study was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

**Informed consent** All participants provided verbal informed consent to participate in this study.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Alden Wily, L. (2018). Collective land ownership in the 21st century: Overview of global trends. Land. https://doi.org/10.3390/land7020068

Altmann, J., Alberts, S. C., Altmann, S. A., & Roy, S. B. (2002). Dramatic change in local climate patterns in the Amboseli basin, Kenya. *African Journal of Ecology, 40*(3), 248–251.

Archambault, C. S. (2013). “I’ll Be Home for Christmas”: The role of international maasai migrants in rural sustainable community development. *Sustainability*. https://doi.org/10.3390/su5093665

Bernard, A., & Vidal, S. (2023). Linking internal and international migration over the life course: A sequence analysis of individual migration trajectories in Europe. *Population Studies, 77*(3), 515–537. https://doi.org/10.1080/00324728.2023.2231913

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. https://doi.org/10.1016/j.gloenvcha.2011.10.001

Bostvironnois, A., Mialhe, F., Gunnell, Y., & Navratil, O. (2025). Consequences for Maasai pastoralists of changing water access regimes in the Greater Amboseli Ecosystem, Kenya. *Water Alternatives, 18*(1), 125–152.

Bukari, K. N., Bukari, S., Sow, P., & Scheffran, J. (2020). Diversity and multiple drivers of pastoral Fulani migration to Ghana. *Nomadic Peoples, 24*(1), 4–31. https://doi.org/10.3197/np.2020.240102

BurnSilver, S. B., Worden, J., & Boone, R. B. (2008). Processes of fragmentation in the amboseli ecosystem,
Southern Kajiado District, Kenya. In K. A. Galvin, R. S. Reid, R. H. Behnke Jr, & N. T. Hobbs (Eds.), *Fragmentation in semi-arid and arid landscapes: Consequences for human and natural systems* (pp. 225–253). Springer.

Campbell, D. J. (1984). Response to drought among farmers and herders in southern Kajiado District, Kenya. *Human Ecology*, *12*(1), 35–64. https://doi.org/10.1007/BF01531283

Coast, E. (2002). Maasai socioeconomic conditions: A cross-border comparison. *Human Ecology*, *30*(1), 79–105. https://doi.org/10.1023/A:1014567029853

Fratkin, E. (1997). Pastoralism: Governance and development issues. *Annual Review of Anthropology*, *26*, 235–261. https://doi.org/10.1146/annurev.anthro.26.1.235

Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. *Journal of Statistical Software*, *40*, 1–37. https://doi.org/10.18637/jss.v040.i04

Galaty, J. (2018). Transhumance. In H. Callan (Ed.), *International encyclopedia of anthropology*. Wiley.

Galaty, J. (2021). Modern mobility in East Africa: Pastoral responses to rangeland fragmentation, enclosure and settlement. In C. Gabbert, F. Gebresenbet, J. Galaty, & G. Schlee (Eds.), *Lands of the future: Anthropological perspectives on agro-pastoralism, land deals and tropes of modernity in Eastern Africa* (pp. 67–95). Berghahn Books.

Gemenne, F., & Blocher, J. (2017). How can migration serve adaptation to climate change? Challenges to fleshing out a policy ideal. *The Geographical Journal*. https://doi.org/10.1111/geoj.12205

Grandin, B. E. (1988). Wealth and pastoral dairy production: A case study from Maasiland. *Human Ecology*, *16*(1), 1–21. https://doi.org/10.1007/BF01262023

Greiner, C., & Sakdapolrak, P. (2013). Rural-urban migration, agrarian change, and the environment in Kenya: A critical review of the literature. *Population and Environment*, *34*(4), 524–553. https://doi.org/10.1007/s11111-012-0178-0

Hemingway, C., Cochet, H., Mialhe, F., & Gunnell, Y. (2022). Why pastoralists grow tomatoes: Maasai livelihood dynamics in Amboseli, southern Kenya. *Journal of Rural Studies*, *92*, 253–268. https://doi.org/10.1016/j.jrurstud.2022.03.027

Hoffmann, R., Wiederkehr, C., Dimitrova, A., & Hermans, K. (2022). Agricultural livelihoods, adaptation, and environmental migration in sub-Saharan drylands: A meta-analytical review. *Environmental Research Letters*, *17*(8), Article 083003. https://doi.org/10.1088/1748-9326/ac7d65

Homewood, K., Coast, E., & Thompson, M. (2004). In-migrants and exclusion in East African rangelands: Access, tenure and conflict. *Africa*, *74*(4), 567–610. https://doi.org/10.3366/afr.2004.74.4.567

Homewood, K., Kristjanson, P., & Trench, P. (2009). *Staying Maasai?: Livelihoods, conservation and development in East African Rangelands*. Springer.

Jowell, A., Wulfovich, S., Kuyan, S., & Heaney, C. (2018). Ethnic identity, resilience, and well-being: A study of female Maasai migrants. *International Journal of Public Health*, *63*(6), 703–711. https://doi.org/10.1007/s00038-018-1124-4

KNBS (Kenya National Bureau of Statistics). (2019). *Kenyan Population and Housing Census* (Vol. 2). KNBS.

Liao, T. F., & Gan, R. Y. (2020). Filipino and Indonesian migrant domestic workers in Hong Kong: Their life courses in

migration. *American Behavioral Scientist*, *64*(6), 740–764. https://doi.org/10.1177/0002764220910229

May, A., & Ikayo, F. N. O. (2007). Wearing Illkarash: Narratives of image, identity and change among Maasai labour migrants in Tanzania. *Development and Change*, *38*(2), 275–298. https://doi.org/10.1111/j.1467-7660.2007.00412.x

McCabe, J., Smith, N., Leslie, P., & Telligman, A. (2014). Livelihood diversification through migration among a pastoral people: Contrasting case studies of Maasai in northern Tanzania. *Human Organization*, *73*(4), 389–400. https://doi.org/10.17730/humo.73.4.vkr10nhr65g18400

McCabe, J. T., & Woodhouse, E. (2022). Maasai wellbeing and implications for wildlife migrating from Tarangire National Park. In C. Kiffner, M. L. Bond, & D. E. Lee (Eds.), *Tarangire: Human-wildlife coexistence in a fragmented ecosystem* (pp. 65–84). Springer. https://doi.org/10.1007/978-3-030-93604-4_4

Mialhe, F., De Longueville, F., & Henry, S. (2024). Explore the complexity of the migration for a rural population from the Philippines using sequence and graph analysis. *Population, Space and Place*, *30*(5), Article e2763. https://doi.org/10.1002/psp.2763

Munishi, E. J. (2019). Rural-urban migration and resilience implications on the Maasai Households' in North-Eastern Tanzania. *African Journal of Applied Research*, *5*(2), 24–44. https://doi.org/10.26437/ajar.05.11.2019.03

Pollini, J., & Galaty, J. G. (2021). Resilience through adaptation: Innovations in Maasai livelihood strategies. *Nomadic Peoples*, *25*(2), 278–311. https://doi.org/10.3197/np.2021.250206

Rutten, M. M. E. M. (1992). *Selling wealth to buy poverty: The process of the individualization of landownership among the Maasai pastoralists of Kajiado district, Kenya, 1890–1990*. Breitenbach. https://hdl.handle.net/1887/9046

Spear, T. (1993). Introduction. In T. Spear & R. Waller (Eds.), *Being Maasai: ethnicity and identity in East Africa* (pp. 1–18). Boydell & Brewer. https://doi.org/10.2307/j.ctv136c0gc.6

Spear, T., & Waller, R. (1993). Being Maasai: Ethnicity and identity in East Africa. *Boydell & Brewer*. https://doi.org/10.2307/j.ctv136c0gc.6

Unks, R. R., Goldman, M. J., Mialhe, F., Gunnell, Y., & Hemingway, C. (2023). Diffuse land control, shifting pastoralist institutions, and processes of accumulation in southern Kenya. *The Journal of Peasant Studies*, *50*(5), 1757–1790. https://doi.org/10.1080/03066150.2022.2160630

Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E., & Marsham, J. H. (2021). Extreme rainfall in East Africa, October 2019–January 2020 and context under future climate change. *Weather*, *76*(1), 26–31. https://doi.org/10.1002/wea.3824

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.