# Gender dimensions of adaptive capacity and adaptation responses to climate shocks in rural households: Ghana, Mali, Kenya, and Ethiopia

Sarah Redicker, Maria Franco Gavonel, W Neil Adger, Ricardo Safra de Campos, Mumuni Abu, Sidy Boly, Samuel Nii Ardey Codjoe, Dula Etana, Eshetu Gurmu, Hervé Nicolle, et al.

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Sarah Redicker, Maria Franco Gavonel, W Neil Adger, Ricardo Safra de Campos, Mumuni Abu, et al. Gender dimensions of adaptive capacity and adaptation responses to climate shocks in rural households: Ghana, Mali, Kenya, and Ethiopia. Environmental Research Letters, 2025, 20 (9), pp.094017. 10.1088/1748-9326/adf23b hal-05526183

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# Gender dimensions of adaptive capacity and adaptation responses to climate shocks in rural households: Ghana, Mali, Kenya, and Ethiopia

To cite this article: Sarah Redicker et al 2025 *Environ. Res. Lett.* 20 094017

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LETTER

# Gender dimensions of adaptive capacity and adaptation responses to climate shocks in rural households: Ghana, Mali, Kenya, and Ethiopia

Sarah Redicker<fnref n="1" />,*, Maria Franco Gavonel<fnref n="2" /> ID, W Neil Adger<fnref n="1" /> ID, Ricardo Safra De Campos<fnref n="1" /> ID, Mumuni Abu<fnref n="1" /> ID, Sidy Boly<fnref n="4" />,<fnref n="5" />, Samuel Nii Ardey Codjoe<fnref n="3" /> ID, Dula Etana<fnref n="6" /> ID, Eshetu Gurmu<fnref n="7" /> ID, Hervé Nicolle<fnref n="8" /> and Jared Owuor<fnref n="8" />

E-mail: s.redicker@exeter.ac.uk

**Keywords:** adaptive capacity, climate adaptation, gender, migration, sub-Saharan Africa

Supplementary material for this article is available [online](#)

## Abstract

In agrarian economies, household gender roles and demographic structure influence how families respond to climate shocks, affecting investment decisions, risk-spreading strategies, and all choices of adaptation options and adaptive capacity investments. Here we show that differential adaptive capacities, as a set of capabilities as well as assets, are distributed unevenly by gender leading to divergent choices between adaptation options in places and communities at risk. We demonstrate how gendered adaptive capacities impacts household *in-situ* adaptation and migration as adaptation to climate shocks. We use data from surveys of adults within rural households across localities in Mali, Ghana, Kenya and Ethiopia in 2022 and 2023 (6656 respondents from 3328 households) and employ negative binomial regression analysis to investigate how gendered adaptive capacity impacts household adaptation to climate shocks. Results indicate that adaptive capacity significantly enhances household adaptation, with a pronounced gender gap observed across most capacity domains. This disparity limits household's ability to respond effectively to climate shocks and poses a critical challenge for inclusive adaptation planning. Mobility also emerges as a critical strategy for increasing *in-situ* adaptation uptake. The findings imply that policies will be more effective if they address gender-specific challenges and support both migrants and immobile rural populations equitably. Recognising women's roles in managing remittances and livelihoods is crucial for resilience, highlighting the importance of integrating a gender perspective into adaptation policies. This includes gender-equal access to education, professional organisations, legal protection, and the empowerment of women in adaptation initiatives.

## 1. Introduction

In climate-sensitive areas, structural inequalities, particularly gendered disparities, play a central role in shaping how rural communities that rely on natural resources experience and respond to climate risks.

These communities are highly vulnerable to weather shocks due to their exposure to climate-related hazards, the sensitivity of their livelihoods, and their limited capacity to adapt (Adger 2006, Morton 2007). Vulnerability is further deepened by dependence on climate-sensitive sectors such as rainfed agriculture,

<footnote n="1">¹ Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom</footnote>

<footnote n="2">² School for Business and Society, University of York, York, United Kingdom</footnote>

<footnote n="3">³ Regional Institute for Population Studies, University of Ghana, Accra, Ghana</footnote>

<footnote n="4">⁴ National Statistical Institute of Mali, INSTAT, Bamako, Mali</footnote>

<footnote n="5">⁵ Integrated Development Authority of the Liptako-Gourma Member States (ALG), Ouagadougou, Burkina Faso</footnote>

<footnote n="6">⁶ College of Development Studies, Addis Ababa University, Addis Ababa, Ethiopia</footnote>

<footnote n="7">⁷ Center for Population Studies and Institute of Development and Policy Research, Addis Ababa University, Addis Ababa, Ethiopia</footnote>

<footnote n="8">⁸ Samuel Hall Research, Nairobi, Kenya</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
inadequate infrastructure, and restricted access to ser-
vices that limit households' ability to adapt. Among
these structural factors, gendered norms and roles
shape access to resources, decision-making power,
and livelihood responsibilities, and are central in
shaping adaptive strategies (Ngigi et al 2017, Assan
et al 2018). Women, for instance, often lack secure
land tenure, are underrepresented in household and
community decision-making, and are more likely to
engage in lower-value agricultural activities or be
excluded from agricultural extension and support
programmes (Carr and Thompson 2014). These dis-
parities affect not only the strategies available to dif-
ferent individuals but also determine who benefits
from adaptation interventions and whose capacities
are supported or neglected.

As such, an individuals' adaptive capacity is
shaped by access to resources, information, and
agency, all of which are structured by gender norms
and roles. Drawing on scholarship on gendered
adaptation (e.g. Carr and Thompson 2014, Call
and Sellers 2019), we understand adaptation not
as neutral or purely rational but as embedded in
social structures and power relations. Women and
men may experience different climate impacts and
unequal access to capital, networks, and institu-
tions. Adaptive capacity is therefore multidimen-
sional, encompassing assets, flexibility, knowledge,
social ties, and perceptions. These dimensions influ-
ence adaptation strategies through both material (e.g.
remittances, assets) and social (e.g. networks, con-
fidence) channels (Cinner and Barnes 2019, Barnes
et al 2020). Together, these intersecting factors shape
the adaptation space: the scope of viable and access-
ible responses available to individuals and house-
holds, influenced not only by resources but also by
social positioning, institutional inclusion, and per-
ceived agency.

Gendered differences in access and opportunity
are further amplified by the uneven impacts of cli-
mate change. Climate shocks are not experienced uni-
formly but reflect existing social hierarchies and the
roles that individuals occupy within households and
communities (MacGregor 2009, Morchain et al 2015,
Call and Sellers 2019, Rao et al 2019). As a result,
vulnerability and adaptive capacity are closely inter-
twined. This dynamic interaction underscores the
importance to examine not only the scope of adaptive
capacity but also how it is distributed within house-
holds. Understanding these dynamics is essential for
explaining differentiated responses to climate shocks,
whether through *in-situ* adaptation or mobility-based
strategies.

In rural economies, adaptation entails multiple
actions that seek to spread risk, leverage networks,
and build resilience (Agrawal and Perrin 2009,
Goulden et al 2013). Adaptation is understood here

as a targeted response to specific climate shocks, not
a general livelihood strategy. These responses include
adjustments to local livelihood systems, such as crop
diversification, input use, and labour shifts, as well as
migration. Following Castells-Quintana et al (2018),
we define adaptation to include both in-situ actions
that bolster farming and other livelihood systems and
‘transformational adaptations that reduce vulnerabil-
ity or exposure to climate change through the move-
ment of people and economic activity across sectors
and across space’ (p 184). Migration, in this context,
is often a strategic and proactive adaptation, facilit-
ating risk redistribution, access to remittances, and
knowledge transfer, making households trans-local
units of resilience (Sakdapolrak et al 2024, Andersson
Djurfeldt 2021). However, outmigration may also
affect labour availability and limit the range of other
adaptation options (Tuholske et al 2024).

Yet, the adaptive potential of migration is
not equally accessible. Because migration requires
resources, it is itself an expression of adaptive capa-
city. Households with fewer resources may remain
in place not by choice, but due to structural con-
straints, resulting in resource-constrained immob-
ility (Benveniste et al 2022, Thalheimer et al 2025).
These constraints are deeply gendered. Social norms
and responsibilities often limit women's mobility and
access to land, credit, and networks (Serraglio and
Thornton 2024, Upadhyay et al 2024). At the same
time, when men migrate, women often assume new
agricultural and decision-making roles, yet without
a shift in authority or access to resources, increas-
ing their workload and vulnerability (Rao et al 2019,
Lama et al 2021). Gendered norms thus shape both
who can migrate and who bears the costs of staying.
Understanding how migration and in-situ adaptation
intersect, and how gender mediates both, is essential
for designing equitable adaptation policies.

While adaptive capacity, social norms, and mobil-
ity are recognised as central to household climate
responses, the interactions between these factors
remain poorly understood. Climate adaptation inter-
ventions aim to enhance climate resilience through
support for consumption smoothing, storage, mar-
ket access, and innovation (Aberman et al 2015, Perez
et al 2015, Assan et al 2018). Yet, social and gender
differences in adaptive capacity are less well under-
stood: hence the need for a more comprehensive
approach that considers their interplay is needed to
inform effective and equitable adaptation policies and
practices.

Therefore, this study examines how adaptive
capacity is distributed by gender and how it shapes
household-level adaptation strategies. While often
linked to economic development adaptive capacity
also depends on experience, knowledge, and reliance
on weather-sensitive resources (Lemos et al 2013). We
explore how gendered differences in adaptive capacity influence the uptake of both *in-situ* adaptation strategies and migration in rural African regions, viewing them as interconnected within a broader adaptation space shaped by access to capital, networks, and decision-making power. This research contributes to understanding how gender and mobility mediate adaptation in vulnerable communities in east and west Africa. Using quantitative data, we ask: (1) does adaptive capacity translate into concrete adaptation measures at the household level? (2) How does gender moderate these responses? and (3) How does migration influence the overall portfolio of household-level adaptation strategies?

## 2. Methods

### 2.1. Study areas and data collection

To explore the relationship between adaptive capacity, gender, and adaptation in rural, climate-vulnerable households across sub-Saharan Africa, this study focuses on rural communities in Ghana, Mali, Kenya, and Ethiopia (see figure 1), using cross sectional household survey data collected in 2022–2023 (Franco Gavonel et al 2025). Field sites were selected based on: (a) exposure to environmental stressors (e.g. droughts, erratic rainfall, heat stress, and floods), (b) socioeconomic vulnerability, and (c) integration into migration systems, reflecting the dynamic interplay between environmental stress and human mobility. Using these criteria, two regions were chosen per country (see table 1).

Households were sampled using a multi-stage strategy incorporating historical climate data, expert selection, and randomisation to ensure regional representativeness. Full details are provided in supplementary material 1. The survey targeted household heads and spouses, or a knowledgeable substitute if needed. Only households with complete responses from both primary participants were included for this analysis, yielding 3328 households (6656 respondents) (see table 1). This design enabled an intra-household perspective on adaptation and decision-making, offering richer insights beyond individual responses. In this analysis, gender is understood as socially constructed roles and identities shaped by historical and cultural contexts (UN Women n.d.), influencing social interactions and power dynamics. Given contextual alignment between social roles and biological sex, self-reported sex was used as a proxy.

### 2.2. Household adaptation to climate change

This study captured household-level adaptation responses to climate shocks experienced between 2022 and 2023 (see figure 2), including droughts, floods, heat stress, and erratic rainfall. The focus is on targeted actions taken in direct response to these events, not general livelihood practices. Respondents

first identified which climate shocks they experienced and when, based on their own perceptions, and then reported any coping or adaptation measures. This framing captured context-specific and time-bound adaptation responses rather than routine household strategies.

To quantify household adaptation, we constructed a count variable representing the number of distinct adaptation measures adopted between 2022 and 2023; approximately 61% of households reported taking at least one adaptation action during this period (see figure 3). While the count alone does not necessarily reflect effectiveness, it offers insight into the breadth of adaptive response. Adaptation is often conceptualised as a means to sustain the system over time, but it can also be transformative, expanding future options for change and flourishing (Hodbod and Eakin 2015, Castells-Quintana et al 2018). For example, in agrarian settings, livelihood diversification is a long-established risk management strategy, offering redundancy and creating buffers (Ellis 1998). Studies on household economies show that greater diversity in adaptation practices enhances flexibility to climate risks (Below et al 2012) and improves food security (Gebre et al 2023).

Although the count variable helps to quantify engagement across households, it has limitations. Measuring the bandwidth of adaptation does not capture variation in the quality, impact, or sustainability of individual strategies. Nevertheless, it remains a useful proxy for adaptive effort and has been widely applied in similar analysis, including as a count variable (Tambo 2016), a categorical variable (Gebrehiwot and van der Veen 2013), or an index (Below et al 2012).

### 2.3. Capturing adaptive capacity

Adaptive capacity is a critical indicator of how effectively people can respond to environmental changes, shaping both resilience and vulnerability (Adger and Vincent 2005, Adger 2006). It reflects the conditions that enable individuals to anticipate, respond to effectively, recover, and minimise the impacts of changes (Adger and Vincent 2005). Crucially, adaptive capacity is not an intrinsic or immutable quality of individuals or groups but a potential for action, deeply rooted in socio-economic, cultural, and environmental contexts.

In literature, measures of adaptive capacity often focus on the availability of key assets necessary for adaptation, such as wealth, income, and social capital (Phan et al 2019). However, the concept has evolved to include agency and action, grounded in psycho-social processes. Comprehensive approaches now consider factors like agency, competencies, and the willingness to act (Cinner et al 2015, 2018, Cohen et al 2016, Cinner and
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Sampled rural areas in Ethiopia, Kenya, Mali and Ghana.</figcaption></figure>

<table><caption>**Table 1.** Sample distribution by country.</caption><thead><tr><td>Country</td><td>Respondents</td><td>Households<sup>a</sup></td></tr></thead><tbody><tr><td>Ghana total</td><td>1450</td><td>725</td></tr><tr><td>Volta region</td><td>670</td><td>335</td></tr><tr><td>Northern region</td><td>780</td><td>390</td></tr><tr><td>Kenya total</td><td>1358</td><td>679</td></tr><tr><td>Makueni</td><td>702</td><td>351</td></tr><tr><td>Baringo</td><td>656</td><td>328</td></tr><tr><td>Mali total</td><td>1950</td><td>975</td></tr><tr><td>Sikasso</td><td>950</td><td>475</td></tr><tr><td>Kayes</td><td>1000</td><td>500</td></tr><tr><td>Ethiopia total</td><td>1898</td><td>949</td></tr><tr><td>Oromia</td><td>908</td><td>454</td></tr><tr><td>SNNP</td><td>990</td><td>495</td></tr><tr><td>Total across countries</td><td>6656</td><td>3328</td></tr></tbody></table>

Barnes 2019, Barnes et al 2020). This broader perspective highlights the capability of individuals to pursue desired options and is intertwined with broader socio-economic and cultural contexts, including gender roles and identities. The measure of adaptive capacity here follows the approach used by Barnes et al (2020) and incorporates six key domains: assets, flexibility, organisation, learning, socio-cognitive constructs, and agency, along with fifteen

specific indicators. Table 2 provides an overview of these variables. Full details of index construction and variable selection are available in supplementary material 2.

## 2.4. Empirical approach

The overall impact of adaptive capacity indicators on the number of household adaptations was assessed using negative binomial regression, selected due to

<footnote><sup>a</sup> In line with the United Nations (1989) definition, a household in this study is defined as a group of individuals who: (a) live under the same roof or within the same compound, or homestead, or stand; (b) share food from a common source at least once a day; and (c) contribute to, or share in, a common resource pool.</footnote>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

the count nature of the dependent variable and the presence of overdispersion in the data (Cameron and Trivedi 2022). The model includes regional-level fixed effects to control for unobserved heterogeneity across regions. Marginal effects were computed to interpret the influence of each adaptive capacity indicator on

the number of adaptations. The general model specification is as follows:

$$ \log(E[Y_{ij}X_{ij}, \alpha_{ij}]) = \beta_0 + \beta_1 X_{1ij} + \beta_2 X_{2ij} + \dots + \beta_k X_{kij} + \alpha_j $$
<table><caption>**Table 2.** Elements of adaptive capacity, data type, and observation unit of analysis.</caption><thead><tr><th>Adaptive capacity domain</th><th>Indicators</th><th>Description</th><th>Data type</th><th>Level of observation</th></tr></thead><tbody><tr><td rowspan="3">Assets</td><td>1. Income</td><td>Income in USD</td><td>ln</td><td>individual</td></tr><tr><td>2. Remittances</td><td>Remittance payments (in USD) from family members living or working outside of the community</td><td>ln</td><td>individual</td></tr><tr><td>3. Wealth</td><td>Relative Wealth Index based on household structures and possessions</td><td>normalised index (0-1)</td><td>household</td></tr><tr><td rowspan="3">Flexibility</td><td>4. Occupational multiplicity</td><td>More than one income source</td><td>binary</td><td>individual</td></tr><tr><td>5. Adaptability and Learning Index (ALI)</td><td>Perceptions regarding livelihood adaptation in relations to CC</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td>6. Age</td><td>Age in years</td><td>continuous</td><td>individual</td></tr><tr><td rowspan="3">Organisation</td><td>7. Professional organisations</td><td>Membership in professional organisations (e.g. farmer group, business association, ...)</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td>8. Social organisations</td><td>Membership in social organisations (e.g. cultural group, festival society, ...)</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td>9. Networks</td><td>Number of households within a community that would support a person in case of shocks</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td rowspan="2">Learning</td><td>10. Education</td><td>Years of schooling</td><td>continuous</td><td>individual</td></tr><tr><td>11. Climate adaptation and preparedness index (CAPPI)</td><td>Perception of own preparedness to deal with future climate change impacts (Likert scale)</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td rowspan="3">Socio-cognitive constructs</td><td>12. Past experience</td><td>Perception of severity and frequency of exposure to climate shocks (flooding, heat waves, drought, and erratic rainfall) in the past</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td>13. Perceived loss</td><td>Perceived loss in USD due to past climate shocks</td><td>ln</td><td>individual</td></tr><tr><td>14. Place attachment</td><td>Place attachment and connectedness</td><td>normalised index (0-1)</td><td>individual</td></tr><tr><td>Agency</td><td>15. Active in decision-making</td><td>Actively involved in household decision-making</td><td>normalised index (0-1)</td><td>household</td></tr></tbody></table>
where $Y_{ij}$ represents the number of household adaptations for observation $i$ in region $j$ and $X_{1ij}, X_{2ij}, \dots, X_{kij}$ are the independent variables for observation $i$ in region $j$ (adaptive capacity indicators). $\beta$ coefficients represent the marginal effects of the predictors on the expected count of adaptations and $\alpha_j$ represents the regional fixed effect for region $j$, capturing unobserved heterogeneity across regions. Marginal effects were calculated to provide a more intuitive interpretation of the model results. Marginal effects represent the average change in the predicted number of adaptation strategies for a one-unit change in each predictor, holding other variables constant. ($E[X_{ij}, \alpha_{ij}]$) represents the expected count of events given the predictors and the regional fixed effect.

To explore gender differences in the relationship between adaptive capacity and the number of adaptations, separate negative binomial regressions were estimated for male and female sub-samples. Marginal effects were calculated for each to enable comparison. These gender-specific models allowed us to examine whether, and how, the influence of adaptive capacity indicators varies between men and women.

To ensure the robustness, we conducted diagnostic tests, including checks of alternative model specifications and multicollinearity among the independent variables. These confirmed that multicollinearity remained within acceptable thresholds and did not compromise the stability or reliability of our estimates. Full results and supporting tables are presented in supplementary material 3-6.

Finally, we acknowledge a potential source of bias in estimating coefficients for some choice variables due to a correlation between perceived exposure to weather shocks and unobserved traits such as the case of risk aversion. Although we cannot control for the latter, this bias is likely negative: more risk-averse individuals tend to report greater exposure to weather shocks (Holden and Quiggin 2017) but are less likely to adopt more adaptation measures (Feder 1980, Liu 2007). Thus, our estimates likely represent a lower bound of the true effect, which would remain positive and at least as large in magnitude.

## 3. Results

Table 3 presents key demographic, economic, and social variables across Ghana, Kenya, Mali, and Ethiopia. Demographic characteristics, including age and gender distribution, are similar across countries, while educational attainment varies considerably, with Kenya reporting the highest average years of schooling and Mali the lowest. Economic indicators also differ, with Mali showing the highest mean

income and remittances, though these are influenced by outliers. To address this, income and remittances are log-transformed in the regression analysis to stabilise variance and allow for cross-country comparison. Social and adaptive capacity measures, including the adaptability and learning index (ALI), are relatively consistent, suggesting comparable perceived resilience. Migration in response to climate shocks is more prevalent in Ghana and Kenya, highlighting regional differences in adaptation strategies.

Table 4 presents the results of the negative binomial regression analysis. Models 1–3 use the total number of adaptation measures adopted by a household as the dependent variable, examining how different dimensions of adaptive capacity influence adaptation uptake. To test our hypothesis that migration functions both as an adaptation strategy and as an indicator of adaptive capacity, Model 4 includes migration as a covariate and examines its association with *in-situ* adaptation measures as the dependent variable. This model helps to determine whether migrating households are also more likely to adopt a wider range of local adaptation strategies.

### 3.1. The effect of adaptive capacity on household adaptation

A central finding of this study is that individual's adaptive capacity significantly shapes the diversity of adaptation strategies adopted by the household in response to climate shocks. These includes both *in-situ* and mobility-based measures, often used in combination rather than as alternatives. Results from Model 1 (table 4) show that households with higher adaptive capacity, particularly in the domains of assets, organisational involvement, and socio-cognitive constructs, are more likely to adopt a wider range of adaptation strategies.

Within the assets' domain, a one-unit increase in relative household wealth, measured by household structures and possessions, is associated with an average increase of 1.83 adaptation measures. Notably, a 1% rise in income is linked to a small but significant decrease of 0.03 adaptation measures ($p < 0.05$), suggesting that increased income alone does not drive adaptation. By contrast, a 1% increase in remittances corresponds to a 0.13 increase in adaptation measures ($p < 0.01$), highlighting the value of external financial support.

Organisational involvement also plays a crucial role in facilitating adaptation. Membership in professional organisations and stronger community networks are both significantly associated with more adaptation measures (2.89 and 0.53, respectively, with $p < 0.01$). These findings underscore the importance of professional and community networks in strengthening adaptive capacity.

Socio-cognitive factors further influence household adaptation decisions. A one-unit increase in
<table><caption>**Table 3.** Summary statistics of key variables across study countries.</caption><thead><tr><th>Variable</th><th>Type</th><th>Ghana mean</th><th>Kenya mean</th><th>Mali mean</th><th>Ethiopia mean</th><th>Total mean</th><th>Total SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="10"><strong>Demographics</strong></td></tr><tr><td>Male respondents (Proportion)*</td><td>Binary</td><td>0.49</td><td>0.5</td><td>0.52</td><td>0.46</td><td>0.49</td><td></td><td>0</td><td>1</td></tr><tr><td>Age of respondent</td><td>Continuous</td><td>45.44</td><td>45.94</td><td>45.17</td><td>44.81</td><td>45.28</td><td>14.39</td><td>18</td><td>102</td></tr><tr><td>Years of schooling</td><td>Continuous</td><td>4.34</td><td>9.92</td><td>1.92</td><td>2.65</td><td>4.29</td><td>4.91</td><td>0</td><td>18</td></tr><tr><td colspan="10"><strong>Economic Indicators</strong></td></tr><tr><td>Occupational multiplicity*</td><td>Binary</td><td>0.34</td><td>0.57</td><td>0.23</td><td>0.41</td><td>0.37</td><td></td><td>0</td><td>1</td></tr><tr><td>Self-reported income USD</td><td>Continuous</td><td>540.30</td><td>640.98</td><td>854.27</td><td>578.66</td><td>663.76</td><td>1012.28</td><td>0.00</td><td>8495.85</td></tr><tr><td>Ln (Income USD)</td><td>Continuous</td><td>5.46</td><td>5.27</td><td>5.09</td><td>4.49</td><td>5.03</td><td>2.49</td><td>0.00</td><td>9.05</td></tr><tr><td>Received remittances USD</td><td>Continuous</td><td>18.03</td><td>22.54</td><td>68.41</td><td>21.38</td><td>34.66</td><td>230.81</td><td>0.00</td><td>9900.00</td></tr><tr><td>Ln (remittances USD)</td><td>Continuous</td><td>0.62</td><td>0.81</td><td>0.84</td><td>0.45</td><td>0.68</td><td>1.69</td><td>0.00</td><td>9.20</td></tr><tr><td>Relative household wealth</td><td>Continuous</td><td>0.47</td><td>0.49</td><td>0.37</td><td>0.25</td><td>0.38</td><td>0.17</td><td>0</td><td>0.89</td></tr><tr><td colspan="10"><strong>Social and adaptive capacity</strong></td></tr><tr><td>Adaptability and Learning Index</td><td>Continuous</td><td>0.78</td><td>0.78</td><td>0.69</td><td>0.63</td><td>0.71</td><td>0.20</td><td>0</td><td>1</td></tr><tr><td>Number of professional groups</td><td>Continuous</td><td>0.32</td><td>0.18</td><td>0.28</td><td>0.14</td><td>0.23</td><td>0.50</td><td>0</td><td>1</td></tr><tr><td>Number of social groups</td><td>Continuous</td><td>0.68</td><td>0.60</td><td>0.14</td><td>0.65</td><td>0.50</td><td>0.79</td><td>0</td><td>1</td></tr><tr><td>Networks index</td><td>Continuous</td><td>0.21</td><td>0.17</td><td>0.17</td><td>0.26</td><td>0.20</td><td>0.16</td><td>0</td><td>1</td></tr><tr><td>Climate adaptation and preparedness index</td><td>Continuous</td><td>0.54</td><td>0.56</td><td>0.54</td><td>0.57</td><td>0.55</td><td>0.11</td><td>0.04</td><td>1</td></tr><tr><td colspan="10"><strong>Migration and attachment</strong></td></tr><tr><td>Perceived exposure to shocks index</td><td>Continuous</td><td>0.22</td><td>0.23</td><td>0.26</td><td>0.16</td><td>0.22</td><td>0.23</td><td>0</td><td>1</td></tr><tr><td>Loss due to shocks USD</td><td>Continuous</td><td>332.18</td><td>728.82</td><td>840.55</td><td>500.95</td><td>610.17</td><td>1368.55</td><td>0.00</td><td>25 482.60</td></tr><tr><td>Ln (Loss due to shock USD)</td><td>Continuous</td><td>3.02</td><td>4.69</td><td>3.93</td><td>2.12</td><td>3.37</td><td>3.27</td><td>0.00</td><td>10.15</td></tr><tr><td>Place attachment index</td><td>Continuous</td><td>0.72</td><td>0.73</td><td>0.7</td><td>0.65</td><td>0.70</td><td>0.16</td><td>0</td><td>1</td></tr><tr><td>Household decision index</td><td>Continuous</td><td>0.42</td><td>0.42</td><td>0.2</td><td>0.26</td><td>0.31</td><td>0.23</td><td>0</td><td>1</td></tr><tr><td>Migrant themselves (last 12 years) *</td><td>Binary</td><td>0.26</td><td>0.31</td><td>0.11</td><td>0.05</td><td>0.17</td><td></td><td>0</td><td>1</td></tr><tr><td>Migration as climate shock response*</td><td>Binary</td><td>0.28</td><td>0.21</td><td>0.09</td><td>0.10</td><td>0.16</td><td></td><td>0</td><td>1</td></tr><tr><td>Observations</td><td></td><td>1450</td><td>1358</td><td>1950</td><td>1998</td><td>6656</td><td></td><td></td><td></td></tr></tbody></table>

This table presents the mean values for key demographic, economic, and social variables across Ghana, Kenya, Mali, and Ethiopia, as well as the mean, standard deviation, minimum, and maximum values for the combined sample (Total). Income and remittances are reported in both USD values and natural logarithmic (ln) form to facilitate interpretability in analyses. Binary variables are presented as proportions.
<table><caption>**Table 4.** Marginal effects of adaptive capacity on the uptake of household adaptations to climate shocks.</caption><thead><tr><th rowspan="2">Domain</th><th rowspan="2">Variables</th><th>(Model 1)</th><th>(Model 2)</th><th>(Model 3)</th><th>(Model 4)</th></tr><tr><th>Total sample</th><th>Female only</th><th>Male only</th><th>Total sample</th></tr></thead><tbody><tr><td></td><td>Migrant (within the last 12<sup>a</sup> years)</td><td></td><td></td><td></td><td>-0.14<sup>**</sup><br>(0.063)</td></tr><tr><td></td><td>Mobility as adaptation<sup>b</sup></td><td></td><td></td><td></td><td>1.08<sup>***</sup><br>(0.055)</td></tr><tr><td rowspan="4">Assets</td><td>Log income (USD)</td><td>-0.03<sup>**</sup><br>(0.014)</td><td>0.04<sup>**</sup><br>(0.019)</td><td>-0.04<br>(0.028)</td><td>-0.00<br>(0.012)</td></tr><tr><td>Wealth</td><td>1.83<sup>***</sup><br>(0.207)</td><td>1.88<sup>***</sup><br>(0.305)</td><td>1.66<sup>***</sup><br>(0.289)</td><td>1.84<sup>***</sup><br>(0.178)</td></tr><tr><td>Log remittances (USD)</td><td>0.13<sup>***</sup><br>(0.015)</td><td>0.13<sup>***</sup><br>(0.022)</td><td>0.12<sup>***</sup><br>(0.021)</td><td>0.07<sup>***</sup><br>(0.013)</td></tr><tr><td>Occupational multiplicity</td><td>0.03<br>(0.060)</td><td>-0.22<sup>**</sup><br>(0.089)</td><td>0.12<br>(0.084)</td><td>-0.03<br>(0.052)</td></tr><tr><td rowspan="3">Flexibility</td><td>Adaptability and learning</td><td>0.04<br>(0.196)</td><td>-0.09<br>(0.258)</td><td>0.10<br>(0.317)</td><td>0.06<br>(0.167)</td></tr><tr><td>Age (inverted years)</td><td>-0.01<sup>***</sup><br>(0.002)</td><td>-0.01<sup>*</sup><br>(0.003)</td><td>-0.01<sup>*</sup><br>(0.003)</td><td>-0.01<sup>***</sup><br>(0.002)</td></tr><tr><td>Professional organisations</td><td>2.89<sup>***</sup><br>(0.332)</td><td>4.74<sup>***</sup><br>(0.595)</td><td>2.07<sup>***</sup><br>(0.406)</td><td>2.66<sup>***</sup><br>(0.292)</td></tr><tr><td rowspan="3">Organisations</td><td>Social organisations</td><td>0.14<br>(0.244)</td><td>0.45<br>(0.372)</td><td>-0.10<br>(0.325)</td><td>-0.19<br>(0.208)</td></tr><tr><td>Community networks</td><td>0.53<sup>***</sup><br>(0.175)</td><td>0.36<br>(0.258)</td><td>0.78<sup>***</sup><br>(0.239)</td><td>0.47<sup>***</sup><br>(0.151)</td></tr><tr><td>Education</td><td>0.01<br>(0.007)</td><td>0.01<br>(0.012)</td><td>0.03<sup>***</sup><br>(0.010)</td><td>0.01<br>(0.006)</td></tr><tr><td rowspan="2">Learning</td><td>CAPPI<sup>c</sup></td><td>1.39<sup>***</sup><br>(0.240)</td><td>1.58<sup>***</sup><br>(0.333)</td><td>1.42<sup>***</sup><br>(0.342)</td><td>1.61<sup>***</sup><br>(0.212)</td></tr><tr><td>Perceived exposure</td><td>1.01<sup>***</sup><br>(0.154)</td><td>0.79<sup>***</sup><br>(0.245)</td><td>1.31<sup>***</sup><br>(0.202)</td><td>0.87<sup>***</sup><br>(0.137)</td></tr><tr><td rowspan="3">Socio-cognitive</td><td>Log past loss (USD)</td><td>0.07<sup>***</sup><br>(0.012)</td><td>0.11<sup>***</sup><br>(0.019)</td><td>0.06<sup>***</sup><br>(0.016)</td><td>0.04<sup>***</sup><br>(0.011)</td></tr><tr><td>Place attachment</td><td>-0.21<br>(0.192)</td><td>-0.29<br>(0.272)</td><td>-0.15<br>(0.272)</td><td>-0.02<br>(0.167)</td></tr><tr><td>Active in decision making</td><td>0.55<sup>***</sup><br>(0.138)</td><td>0.79<sup>***</sup><br>(0.210)</td><td>0.13<br>(0.254)</td><td>0.48<sup>***</sup><br>(0.120)</td></tr><tr><td>Agency</td><td>Observations</td><td>6656</td><td>3371</td><td>3291</td><td>6656</td></tr></tbody></table>

Table entries are the margins from negative binomial probit regression with regional level fixed effects.

Robust standard errors in parentheses: *** $p < 0.01$, ** $p < 0.05$, * $p < 0.1$.

The dependent variable in Model 1–3 is the total number of adaptation measures adopted by the household including migration and mobility.

The dependent variable in Model 4 is the number of in-situ adaptation measures adopted by the household.

<sup>a</sup> We control for whether the respondent has been a migrant in the past. Past migration experiences may influence respondents' views on its effectiveness as an adaptive strategy and shape their decision-making processes (Cassar *et al* 2017). Controlling for this factor allows us to isolate the effects of household-level migration in response to climate shocks from the potentially confounding influence of individual migration experiences.

<sup>b</sup> Migration as adaptation is represented as a binary variable, indicating whether any household member migrated within or outside the district in direct response to a climate shock between 2022 and 2023.

<sup>c</sup> CAPPI = climate adaptation and preparedness index.

perceived exposure is linked to 1.01 more adaptation measures ($p < 0.01$), and a 1% increase in past financial losses (logged) adds 0.07 measures ($p < 0.01$). Higher scores on the climate adaptation and preparedness index (CAPPI) correspond to 1.39 more adaptation measures

($p < 0.01$), reinforcing the role of perceived adaptability in promoting adaptive behaviour. Finally, greater decision-making power within households is associated with an additional 0.55 measures ($p < 0.01$) highlighting the role of agency in adaptation.
The analysis also reveals that households using migration as an adaptation strategy are significantly more likely to adopt a broader set of in-situ adaptations. Model 4 (table 4) shows that mobility is associated with an increase of 1.08 in-situ adaptation measures ($p < 0.01$), suggesting that migration complements rather than replaces local strategies. To further assess whether migration reflects higher capacity, we constructed a composite adaptive capacity index based on significant indicators and regressed migration uptake against it. The results (see supplementary material 7) indicate a positive association between adaptive capacity and migration, confirming that mobility requires resources, networks, and agency, and is thus more accessible to higher-capacity households. Overall, these findings highlight the critical role of adaptive capacity in driving both local and mobility-based responses to climate shocks. Households with stronger capacity are more likely to act and diversify their adaptation portfolios, potentially increasing future climate resilience.

## 3.2. The impact of gender on adaptive capacity

Building on the finding that adaptive capacity significantly shapes adaptation uptake, the analysis highlights substantial gender disparities in access to and control over key adaptive capacity dimensions. Men consistently report higher adaptive capacity across most indicators, reflecting deep-rooted household inequalities in their climate shocks response ability. Detailed gender comparison shows men score significantly higher in nearly all domains, with women reporting higher social groups involvement as the only notable exception (see figure 4(a)). Country-specific analysis (figures 4(b)–(d)) reveals additional nuances, particularly in decision-making power. In Kenya and Ghana, gender differences in decision-making power are minimal, whereas in Mali and Ethiopia, the gap is more pronounced. Malian women reported limited decision-making power, while Ethiopian women expressed comparatively higher levels. Despite country-specific variations, the overall trend indicates men display higher adaptive capacity scores across studied countries and regions.

Model 2 and Model 3 in table 4 examine gendered adaptive capacity impacts on household adaptation. For most indicators, such as mobility, remittances, relative wealth, age, professional organisations, networks, the CAPPI, and perceived exposure to shocks, marginal effects are similar for men and women. This suggests that these adaptive capacity aspects have comparable impacts on household adaptation regardless of gender.

Professional organisation participation positively affects household adaptation for both men and women but shows considerable variation in effect size. Households with female members in professional organisations adopt an average of

4.74 additional adaptation measures compared to 2.07 male members, underscoring the importance of professional networks, particularly for women. Additionally, households where women score higher on decision-making indices show 0.79 more adaptation measures, suggesting active female participation in household decision-making links to broader adaptation strategies. No similar positive impact was found for higher male decision-making scores.

Distinct gender differences emerge for other indicators. Higher income for women shows small positive association with an increase in household adaptation measures ($p < 0.05$). Conversely diversified income sources are negatively associated with adaptation measures (0.22 fewer), suggesting non-agricultural income may reduce motivation or need for additional adaptation efforts. Men's income and occupational multiplicity show no impact on household adaptation.

## 4. Discussion: key drivers of household-level climate adaptation

This study finds that higher adaptive capacity, particularly in assets, organisational involvement, and socio-cognitive factors, significantly enhances households' likelihood of adopting adaptation measures. Financial support through remittances and relative household wealth positively correlates with adaptation, aligning with established literature linking greater financial resources to improved resilience (Béné et al 2014, Islam and Winkel 2017). This suggests financial resources enable households to invest in necessary climate risks adaptations. Our findings also point to the strong influence of past climate shock experiences on adaptation strategies, indicating that direct exposure drives households to adopt more comprehensive adaptation measures. This aligns with the understanding that lived experiences of climate impacts raise risk awareness and create urgency, motivating households to invest in adaptation (Di Falco and Veronesi 2013, Ndiritu 2021).

We find that significant gender disparities in adaptive capacity persist. In the study areas, men consistently report higher levels of adaptive capacity due to greater access to financial resources, education, and professional networks—advantages that are central to building and sustaining resilience to climate shocks. This finding reinforces established evidence that adaptation actions are shaped by social identities and power relations, including intersecting gender and demographic inequalities (Aberman et al 2015, Perez et al 2015, Cohen et al 2016, Assan et al 2018).

However, the analysis also reveals that household's adaptive potential is significantly enhanced when women have access to similar institutional and social resources. Women's participation in professional organisations stands out as one of the strongest
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Comparison of adaptive capacity indicators by gender and across countries.

Comparison of adaptive capacity indicators across sex (N = 6656 respondents). (a)–(d), Spider plots to look at the variation of adaptive capacity indicators among factors aggregated across all sites by (a) sex and (b)–(d) by country.

Significance level of t-test comparison among gender indicated as *0.1 **0.05 and ***0.01 level.</figcaption></figure>
predictors of adaptation uptake, underscoring the transformative role of organisational engagement in strengthening women's adaptive agency. This reinforces evidence that professional organisations facilitate access to resources, information, and collective support essential for effective climate adaptation (Carmen et al 2022). In addition, income shows complex gendered patterns with adaptation measures, as women's higher income leads to increased adaptation uptake while men's income has the opposite effect. These divergent impacts may reflect broader gendered income dynamics, where differences in access to and control over income between men and women influence household adaptation decisions in complex ways (Niemann et al 2024).

We further find that households incorporating migration into their adaptation strategies are more likely to adopt additional *in-situ* adaptation measures, suggesting migration serves as both a direct adaptation mechanism and as a means of strengthening households' capacity for further local adaptation. Migration facilitates access to remittances, information, and new skills while helping diversify income sources and spread risk across space, contributing to trans-local capacities wherein mobile household members maintain origin ties while transferring knowledge, financial resources, and social capital that strengthen local resilience (Gemenne and Blocher 2017, Vinke et al 2020, Sakdapolrak et al 2024). As such, recent research has reframed migration not only as a coping strategy but also as a driver of adaptive capacity formation (Scheffran et al 2012).

However, migration's impact on adaptive capacity is not universally beneficial, introducing trade-offs that reshape household structures, responsibilities, and priorities (Szaboova et al 2023). While mobility provides critical resources like remittances and knowledge that enhance adaptive capacity (Entzinger and Scholten 2022), it may also create dependencies on external income or alter household dynamics due to absent members (Niemann et al 2024). These shifts can affect adaptation priorities and potentially reduce the perceived need for local measures, adding unpredictability to long-term strategies. Nevertheless, this trans-locality can enrich households' adaptation options and improve their ability to respond to future shocks.

Critically, the interplay between migration and adaptive capacity is gendered. While men are often more mobile due to social norms and access to migration-enabling networks, their departure can create opportunities for women to take on more prominent economic and decision-making roles. This can enhance women's adaptive capacity, particularly when women gain control over remittances or assume leadership in managing household adaptation responses (Niemann et al 2024, Upadhyay et al 2024). Our findings suggest that when women are

supported through access to networks, training, and decision-making platforms, they are well positioned to drive household and community resilience.

However, these shifts do not automatically lead to empowerment, as structural constraints and normative expectations often limit women's ability to convert new responsibilities into lasting gains in agency and capacity (Serraglio and Thornton 2024). As such, migration introduces new complexities but also has potential to foster gender equality in adaptive capacity, allowing women to play a more significant role in household adaptation strategies.

More broadly, the findings suggest that women's marginalisation in adaptive capacity is sometimes offset by their involvement in social groups. This involvement may reflect their roles in managing household resources and sustaining social cohesion, areas where they have traditionally held influence (SOFA Team et al 2011). Yet, these gains often remain limited in scope. Systemic gender inequalities continue to restrict women's access to professional networks, assets, and decision-making roles, ultimately constraining their ability to contribute to or benefit from collective adaptation strategies (SOFA Team et al 2011, Glazebrook et al 2020, Sah et al 2022). Gender inequality is widely recognised to negatively impact development outcomes and well-being at individual, household, community, and national levels (Ferrant et al 2014, Meinzen-Dick et al 2014). Despite this understanding, women often remain underrepresented and underserved in many development efforts, limiting their contributions and potential impact (Meinzen-Dick et al 2014).

Importantly, our results show that when women do have access to key resources and platforms, particularly professional organisations and decision-making spaces, they make significant contributions to household adaptation portfolios. This underscores that the issue is not a lack of individual capacity, but unequal access to opportunities. Addressing these structural barriers through targeted interventions is essential for promoting gender-equitable adaptation and enhancing community adaptive capacity, especially in contexts where migration reshapes traditional gender roles, creating avenues for greater equality in resilience-building efforts.

## 5. Conclusion

This study demonstrates that higher adaptive capacity in households, encompassing both assets and capabilities, significantly enhances household adaptation. It further reveals substantial gender disparity in the agriculture-dominated areas across East and West Africa, with men exhibiting higher adaptive capacity to climate shocks. Women's participation in professional organisations and decision-making proves crucial in boosting household adaptation. Migration
emerges as both a direct adaptation strategy and a
mechanism for enhancing in-situ adaptations, high-
lighting its multifaceted role in building resilience.

Importantly, adaptive capacity is not an intrinsic
or fixed attribute of individuals or groups, such as
‘men’ or ‘women’, but is instead shaped by social,
economic, and cultural contexts, as well as struc-
tural inequalities. Gendered differences in adaptive
capacity therefore reflect unequal access to resources,
opportunities, and decision-making power that arise
from existing socio-economic and institutional sys-
tems. Gender also influences risk perceptions and
responses, with men and women often having differ-
ent perspectives based on their distinct social, eco-
nomic, and environmental contexts. For instance, in
agricultural labour allocation and household invest-
ment decisions, gendered risk perceptions can sig-
nificantly affect multiple practices strategies from
crops selection to migrates patterns (Cullen et al
2018). These differing perceptions reflect the com-
plex realities faced by individuals in gendered roles
rather than simply varying viewpoints (Andersson
Djurfeldt 2021). As such, adaptive capacity is fund-
damentally anchored in individual resilience within
socio-ecological systems.

The intersection of gender, migration, and adapt-
ive capacity critically shapes household decisions.
While higher adaptive capacity, rooted in access
to resources, social networks, and decision-making
power, enables better migration risks management
and informed adaptation choices, gender disparities
often limit women's access to these critical resources
and networks, leading to unequal outcomes. This
underscores the importance of increasing women's
access to professional organisations, remittances,
and decision-making platforms, especially given the
role of migration as both a traditional livelihood
strategy and an effective climate adaptation meas-
ure. Thus, empowering women in decision-making
roles significantly enhance household adaptive capa-
city, as evidenced by the positive impact of women's
involvement on the number of adaptation measures
adopted.

The findings stress the need for gender-inclusive
strategies. Empowering women through resources,
networks, and leadership roles is key to clos-
ing the adaptive capacity gap. Policies supporting
mobility must consider gender-specific challenges,
ensuring legal protections and inclusive decision-
making. Future research could explore how women's
empowerment relates to adaptation outcomes, such
as improved food security and wellbeing, in these
countries as well as in other regions. This could
be done through action research that supports
women's economic opportunities, education, and
social status. Addressing these gaps can advance tar-
geted, evidence-based interventions for equitable and
sustainable adaptation.

Data availability statement

The data that support the findings of this study are
openly available at the following URL/DOI: https://
10.5281/zenodo.14899641.

Conflict of interest

The authors declare that they have no known compet-
ing financial interests or personal relationships that
could have appeared to influence the work reported
in this paper.

Funding information

This research was conducted through the
HABITABLE project. The project received funding
from the European Union's Horizon 2020 research
and innovation programme under Grant Agreement
No. 869395.

Ethics statement

This project received ethical approval from the
University of Exeter Geography Ethics Committee
(Reference: eCLESGeo000820). Informed consent
was obtained from all participants prior to data col-
lection, ensuring voluntary participation, confiden-
tiality, and the right to withdraw at any time. The
research was conducted in accordance with estab-
lished ethical guidelines for studies involving human
participants.

ORCID iDs

Sarah Redicker ID 0000-0002-3677-2585

Maria Franco Gavonel ID 0000-0003-2377-5987

W Neil Adger ID 0000-0003-4244-2854

Ricardo Safra De Campos ID 0000-0003-2345-7524

Mumuni Abu ID 0000-0002-6455-0162

Samuel Nii Ardey Codjoe ID 0000-0002-6567-0262

Dula Etana ID 0000-0003-1553-1261

Eshetu Gurmu ID 0000-0001-7487-0058

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