The following is the established format for referencing this article:Nixon, R., Z. Ma, T. Birkenholtz, B. Khan, L. Zanotti, L. S. Lee, and I. A. Mian. 2023. The relationship between household structures and everyday adaptation and livelihood strategies in northwestern Pakistan. Ecology and Society 28(2):31.
The interactions between household size, capital, and adaptation to social-ecological change has been widely studied; however, little is known about the differences in everyday adaptation to social-ecological change across household structures. Joint family households are increasing in contexts where the nuclear family had previously been the norm, and remains a prevalent structure throughout the world. Thus, it is important to understand how these structures influence adaptation decision-making processes and outcomes. We draw on a survey of 448 self-identified household heads in three communities in northwestern Pakistan to assess everyday adaptation to social-ecological change. We demonstrate that livelihood and adaptation strategies vary across joint and nuclear family household structures because, in part, of joint family households’ greater access to natural and human capital in comparison to nuclear family households. Finally, household livelihood decision makers often include other family members to expand beyond the often-assumed husband-wife dyad. Our work highlights how everyday adaptations are expressions of existing opportunities in the space in which households are located, access to capital and resources that differ across household structures, and of various dynamics associated with household decision making. This points to the need for a nuanced understanding of how household structure influences everyday adaptation to social-ecological change and thereby shows the ways in which adaptive capacity is embedded within existing social systems and relationships, such as household structures.
Social-ecological change drives rural households throughout the world to employ a diverse array of adaptation strategies in response to compounding stress and complex variability. Adaptation is driven by both climatic and non-climatic changes and these interacting conditions create specific contexts in which adaptation takes place (Hageback et al. 2005, Carr 2008, Forsyth and Evans 2013, Manuel-Navarrete and Pelling 2015, Burnham and Ma 2016). Therefore, although large-scale planned adaptations are important and do occur, the vast majority of adaptations are autonomous, incremental, and employed at the household scale, drawing largely on “everyday” practices and processes to respond to local, context-specific changes (Agrawal and Perrin 2009, Artur and Hilhorst 2012, Gentle and Maraseni 2012, Adger et al. 2013, Lemos et al. 2013, Castro and Sen 2022). Thus, scholars are increasingly assessing the cultural dimensions of climate change adaptation to understand the ways shared norms, customs, behaviors, and values shape household adaptation processes and outcomes (Nielsen and Reenberg 2010, Adger et al. 2013, Few et al. 2021).
At the household level, adaptation has been studied through the lens of capital (Yohe and Tole 2002, Panda et al. 2013) in which capital is traditionally conceptualized as human, natural, physical, social, political, and financial assets that households utilize to support their well-being and resilience (Scoones 1998, Bebbington 1999, Rakodi 1999). Literature on household adaptation often includes demographic factors such as education level, age of household head, household size, income, and land ownership to examine how these factors contribute to differentiated access to capital and adaptive capacity (Adger 2003, Croppenstedt et al. 2003, Deressa et al. 2009, Below et al. 2012, Asfaw et al. 2019). Adaptation strategies and adaptive capacity are also intricately linked to household livelihoods, defined as “the assets (natural, physical, human, financial and social capital), the activities, and the access to these (mediated by institutions and social relations) that together determine the living gained by an individual or household” (Ellis 2000:10). That is, households may employ adaptation strategies to support their existing livelihoods; alternatively, they may engage in new livelihood strategies to distribute risks and cope with uncertainty associated with social-ecological change (Eakin 2005, Paavola 2008, Agrawal and Perrin 2009, Burnham and Ma 2016). In addition, a household’s livelihood strategies may also influence their access to capital and possible adaptation strategies, and in turn, shape their adaptive capacity (Lemos et al. 2013).
Scholars have also begun to study the role of household structure, or “the generational contours and the extent of nucleation in the household” (Madhavan et al. 2017:1895), as an influential factor in shaping household adaptation and related livelihood strategies. Household structures are often referred to as nuclear (i.e., single unit of married couple with or without their children, or one parent with their children) or multi-generational (i.e., households with three or more generations; Lofquist 2012). More specifically, joint families are a type of multi-generational households that include two or more partnered adults, two or more of the couple’s adult children, the children’s partner(s), and their children (Ruggles 2010), which is distinct from multi-generational stem families that include just one of the couple’s children (United Nations 2008). Joint family structures have been found to be positively associated with adoption of adaptation practices to social-ecological change in Pakistan (Ali and Erenstein 2017), Nepal (Regmi et al. 2017), and India (Senapati and Gupta 2017) because, in part, of surplus labor and increased knowledge sharing in comparison to nuclear families. Yaro et al. (2015) similarly argue that pooling resources increases adaptive capacity in multi-generational families in Ghana; however, they illustrate that this capacity is hindered if members are dissatisfied with the perceived equality in resource distribution. Zhang et al. (2017) show how household structure shapes livelihood strategies more broadly, illustrating that households with more members between the ages 40 and 59 are more likely to have diversified livelihood strategies in comparison to households with a greater number of younger or older members. Relatedly, scholars have shown that cultural norms around kinship influence adaptation and related livelihood strategies and mediate vulnerability to social-ecological change (Shaffril et al. 2020). For example, Kuruppu (2009) demonstrated that the traditional kinship system of kainga in Kiribati promoted resource sharing and collective well-being. In addition, Curry et al. (2015) found that adaptation and related livelihood strategies were shaped not only by economic values, but also by the value of supporting and building kinship relationships.
In addition to adaptive capacity, household structure may also influence who makes household adaptation decisions and thus, the related adaptation outcomes. Many scholars have studied intra-household adaptation decisions (Eriksen et al. 2010, Hung and Wang 2022) with particular attention on gender and decision making between a husband and wife (Hung 2017, Ngigi et al. 2017, Al-Amin et al. 2019, Rao et al. 2020). For example, Van Aelst and Holvoet (2018) show that adaptation decisions often differ between husbands and wives in households in Tanzania. To a lesser extent, this gendered focus has extended to the study of joint families with the presence of multiple adults in these families (Singh 2019, Rao et al. 2020). However, little has been documented about livelihood and adaptation decision making with decision makers other than the husband and wife, even within studies on multi-generational households or households with multiple decision makers. Indeed, “the literature often implicitly assumes that within multimember households, the only bargaining is between the husband and wife; other members are assumed to be passive or unimportant to the bargaining process” (Doss 2013:57). The presence of multi-generational households is increasing (Rao et al. 2020), and the literature has shown that this household structure shapes decision making in multiple sectors (e.g., education, caregiving; VanWey and Cebulko 2007, Cheng 2019). Thus, a more nuanced identification of decision makers across household structures is needed to better understand who influences the decision to adapt to social-ecological change.
Adaptation decisions are embedded in everyday household practices (Head et al. 2013) and reflective of the “complex social assemblages” that make up a household (Toole et al. 2016:203). Policies that do not acknowledge the intricacies of the everyday household practices can inadvertently limit adaptive capacity (Strengers and Maller 2012, Toole et al. 2016). Indeed, Rao (2020) argues that a recognition of the heterogeneity in household relationships is necessary to support sustainable and equitable household adaptations. A part of that heterogeneity is reflected in household structures. That is, despite the growth of the nuclear family (Wamsler et al. 2013), the multi-generational household remains a prevalent structure and is increasing in contexts where the nuclear family had previously been the norm (Easthope et al. 2017, Keene and Batson 2010, Rao et al. 2020). Thus, with this paper we seek to contribute to understanding how household structures shape livelihood strategies, everyday adaptation to social-ecological change, and the associated decision-making processes by analyzing household survey data from northwestern Pakistan. Specifically, we aim to address the following research questions: (1) How do adaptation and related livelihood strategies differ across locations in our study area? (2) How do livelihood strategies and associated adaptive capacity differ across household structures in our study area? (3) Who is involved in household decisions about livelihood strategies across household structures?
Research area and site description
This research was conducted in three communities (Madyan, Jehangira, Landakay) along the Swat and Kabul Rivers in the Khyber Pakhtunkhwa (KP) province in northwestern Pakistan (Fig. 1). Multiple social-ecological changes drive the need to adapt in this area and thus create a relevant context for studying everyday adaptation. Specifically, climate change is projected to increase the frequency of extreme events including flood and droughts in this region of Pakistan because of changes in precipitation and glacier melt (Hussain and Mumtaz 2014). Urbanization, industrialization, and agricultural intensification has also increased the presence of waste and pollutants in the water system (Ullah et al. 2013). Assessments of the Kabul and Swat Rivers reveal chemical waste, untreated domestic wastewater, and hotel waste in the waterways (Porter and Fuller 1994, Ullah et al. 2013). Although fishing has been a common livelihood strategy in the area, there are reports of fish kills and diminishing fish numbers in the Swat and Kabul rivers (Yousafzai et al. 2008). Furthermore, recent armed conflicts displaced an estimated two million people around our study area in 2008 and in 2010 a massive flood resulted in widespread destruction of homes, businesses, roads, and water systems, which further disrupted livelihoods in the KP province (Provincial Disaster Management Authority 2015). Since 2010, however, much infrastructure development and rehabilitation has taken place (Hye and Khan 2013). The Swat Valley, which had previously been a tourism destination for visitors seeking the mountain vistas, has begun to see tourists return (World Bank 2018) and hydropower development has been increasing in the area (Umar and Hussain 2015). Nevertheless, agriculture remains the predominant livelihood strategy for many households in area (World Bank 2018, Young et al. 2019). Additionally, joint family households are a prevalent household structure in this area, in part because it is a traditional structure for Pashtuns, who make up a majority of this region in Pakistan, and because the largely rural setting allows for this structure to persist (Lindholm 1982, Amin et al. 2010). Thus, it serves as a relevant case study to examine household structure and adaptation to social-ecological change.
Data collection and analysis
We surveyed 448 households in Madyan, Jehangira, and Landakay from March to July 2019. These communities were selected following a diverse case study design in which cases are included to represent a variety of contexts based on the focus of our research questions (George and Bennett 2005, Yin 2002). The survey was designed to collect data on household demographics, livelihood and adaptation strategies, and household decision makers using both closed- and open-ended questions. The full survey questionnaire is included in Appendix 1.
Household demographic variables (Table 1) were included to understand the sources of natural, economic, and social capital in the household following the previous literature on adaptive capacity and household demographics (i.e., Croppenstedt et al. 2003, Flint and Luloff 2007, Deressa et al. 2009, Below et al. 2012, Akamani and Hall 2015). Respondents were asked if they lived in a nuclear or joint family household. Responses were coded as “1” for a joint family household structure (i.e., household of multi-generational families with two or more married children and their children) and “0” otherwise. Respondents were asked who made decisions for each livelihood strategy in the household. Respondents could indicate individuals from both in and outside their household. For example, they could indicate that they make decisions about agriculture with their father even if he was not in their household (Table 2).
In terms of adaptations, we asked respondents to indicate if they had employed various adaptation strategies in the past 10 years (Table 3). Responses were coded as “1” for adaptation strategies employed and “0” otherwise. It is important to note that survey questions about livelihood and adaptation strategies were developed based on findings from previously conducted qualitative research in the research area (Nixon et al. 2022) and theoretical and empirical insights from a review of the literature, particularly research on the different types of social-ecological adaptation (Agrawal and Perrin 2009, Burnham and Ma 2016). We chose a 10-year time frame to include changes related to the 2010 flood in the area and because salient events are often used in surveys to support recall and increase data accuracy (Loftus and Marburger 1983, Bernard 2017), so using this time period also allows respondents to use the flood for a reference point for their answers. The survey was piloted in similar communities outside the study area and subsequently revised to increase clarity and relevance.
The final survey questionnaire was administered in person, to the self-identified household heads, by a team of trained surveyors from the University of Peshawar using a handheld tablet furbished with Survey Solutions, an application supported by the World Bank. This in-person survey administration is conducive to longer or more complex survey design (Neuman 2010). Based on the total population of the research areas (238,400) a sample of 384 was needed to represent the population with confidence level of 0.05 and a 5% margin of error (Government of Pakistan Statistics Division 2017a) and we surveyed a total of 448 households within our time in the field. Respondents were given the option of responding in Pashto or Urdu but all chose Pashto. We used a systematic household sample (also known as random walking sampling strategy) in which a starting point and direction to walk is randomly selected and a survey is conducted at every nth house (e.g., 4th, 5th, 6th; Bauer 2016, Himelein et al. 2016). In our study, the location was randomly selected from a predetermined list of residential areas created by enumerators in order to avoid selecting non-residential areas. Each enumerator was assigned a side of the street, and if a household was non-responsive, enumerators selected the house immediately to the right of the initially selected household. This is a common sampling strategy in rural areas in the Global South where household lists and postal records are often incomplete or nonexistent (Himelein et al. 2016) and the availability of internet and phone service varies (Hughes and Lin 2018). Selection bias is a concern in this sampling strategy as remote households or those that are otherwise difficult to access may be excluded (Bauer 2016, Himelein et al. 2016). However, it was the only feasible way to sample households in our study area because of the lack of household records and inaccessibility of phones or internet for using other modes of survey administration. To minimize potential selection bias, we clearly demarcated routes on enumerators’ maps to avoid “main route bias” and ensured that our possible locations covered the entire residential area (Bauer 2016). We also compared our data to secondary data to examine the potential limitations of this sampling strategy (Table 1). This comparison revealed that our data is similar to the available secondary sources; however, differences do reflect the largely rural research area and thus, our data is not entirely reflective of the broader KP region. Nevertheless, insights from this research area can inform further study of the relationship between household structure and social-ecological adaptation to better understand these connections in other contexts. This research was conducted in collaboration with the University of Peshawar. It was approved by Purdue University Institutional Review Board and leaders in each study location gave permission for data collection.
We conducted descriptive and multivariate analyses to address our research questions. Descriptive analyses were well suited for this study because scant empirical research exists on livelihood and adaptation decision making in joint families, particularly in northwestern Pakistan. Therefore, descriptive analyses are important for providing the necessary foundation for further inquiry and hypothesis generation (Grimes and Schulz 2002). Specifically, we used univariate statistics to determine if any outliers existed and to provide an overview of respondents’ socio-demographics and livelihood and adaptation strategies. We assessed bivariate relationships using a variety of tests to examine how adaptation and related livelihood strategies differ across locations in our study area and who is involved in household decisions about livelihood strategies across household structures. Specifically, we used (1) Pearson Chi-square test with pairwise comparisons for examining the relationship between categorical variables; (2) Fisher’s exact test when one or more assumptions for the Pearson Chi-square test was violated; and (3) Kruskal-Wallis H test as a nonparametric alternative to a one-way ANOVA. We used a Bonferroni correction to control the family-wise error rate because of multiple comparisons conducted to identify differences in livelihood and adaptation strategies across family types and locations (Gelman et al. 2012; Tables 1–3). Finally, to examine livelihood and adaptation strategies across household structures we used a binomial logistic regression model to examine the relationship between household structure as a dichotomous outcome (Comoé and Siegrist 2013, Mase et al. 2017) and a set of independent binary or continuous variables (Table 4). We chose to use household structure as the outcome variable to examine the relationship between household structure and adaptation and related livelihood strategies instead of using household structure to explain or predict adaptation and related livelihood strategies. This is because in this analysis our interest is to understand the association between household structure with each of the eight adaptation and related livelihood strategies examined in our study while holding the other strategies constant and controlling for other factors such as family size. Because our data is entirely observational, by using household structure as the left-hand variable we were able to run one regression to simultaneously incorporate several variables measuring adaptation and related livelihood strategies instead of running eight regressions. Doing so does not imply any directional effect of one variable on another nor infer that we used one to predict another. In brief, because we were interested in the association between two variables, which is symmetrical, the distinction between explanatory and outcome variables was not key to our analysis. The model is represented as follows:
The odds ratio (OR) is represented as
where P is the probability of a household reporting a joint family structure and 1-P is the probability of a household not reporting a joint family structure. β0 is the intercept, β1, β2 … βk are regression coefficients of the independent variables of X1, X2, X3, ..., Xk. If the value of the odds ratio is greater than 1, the odds of a household reporting a joint family structure increases as the independent variable increases (i.e., a positive relationship), a value less than one indicates a negative relationship, and a value of one indicates no relationship (Hosmer et al. 2013). The empirical models include a set of independent variables (Table 4), measuring household socio-demographic characteristics and livelihood strategies. Nagelkerke’s R² is used to evaluate the goodness of fit of the model and is defined as one minus the proportion of variance not explained by the independent variables (Nagelkerke 1991). To check for multicollinearity, we ran a variance inflation factor (VIF) test and the score was below 4, the rule of thumb for detecting multicollinearity (Hair et al. 2010), suggesting minimal concern about multicollinearity (Table 5). All statistical analyses were conducted in STATA 16 and 17.
Overview of survey respondents and their households
An overview of the socio-demographic characteristics of survey respondents (i.e., self-identified household heads) and their households is presented in Table 1. No statistically significant difference was observed in the respondents’ average age (39 years) or educational attainment (8 years) across our three study communities. This is slightly higher than the average educational attainment of men in KP (6.6. years), most likely because of our sample of household heads (Government of Pakistan Statistics Division 2017a). Neither household size nor percentage of joint family structure varied across the three study communities; however, the mean household size (10 persons; range: 2–35; SD = 5.3) was slightly larger than that of the KP province (7.3 persons; Government of Pakistan Statistics Division 2017a). This is likely due in part to our focus on rural communities where households are often larger. Fifty-eight percent of respondents reported living in a joint family. This is congruent with the UN report that 55% of households in Pakistan are multi-generational (United Nations 2019). All of our survey respondents were men. Although this sample reflects the context of the region in which 87% households are headed by men (Pakistan Bureau of Statistics 2017), we also acknowledge the importance of including female-headed households in assessments of adaptation to social-ecological change and decision making to understand their specific vulnerabilities and capacities (Aregu et al. 2016, Flatø et al. 2017, Ahmed and Kiester 2021). However, in order to align our data collection methods with the specific cultural context of northwestern Pakistan (Besio 2006, Grünenfelder 2013) and to follow guidance from local collaborators, all survey enumerators in our study were men and were unable to survey women. Thus, based on local gender norms, our data collection was limited to male-headed households.
The average farm size owned and rented by respondent households (3 ha) was larger than that of the region (1.5 ha; Government of Pakistan Statistics Division 2010). There was a difference in households’ total land owned and rented across the three study communities (χ² = 21.029; p < 0.001). Specifically, respondents in Madyan reported more land (6.1 ha) than did respondents in Landakay (2.4 ha) and Jehangira (1.0 ha). This is consistent with other studies in the region that show the variation in land ownership across the province (Ullah et al. 2015, 2020).
Although we did not collect the exact household income information for cultural sensitivity reasons, the distribution of our income data is reasonably consistent with the average monthly income of 35,391 Pakistani Rupees (PKRs; equivalent of US$221.12) in rural KP (Government of Pakistan Statistics Division 2017b). In our survey, 24% of respondents reported a monthly income of 20,000 PKR or less, 38% reported a monthly income between 20,001 PKR and 40,000 PKR, and 38% earned more than 40,001 PKR per month. Respondents in Madyan and Landakay reported higher incomes than respondents in Jehangira (χ² = 89.7851; p < 0.001).
As shown in Table 3, the average number of livelihood strategies employed by a household was 2.1 (range: 1–5, SD = 1.0). It should be noted that respondents in Jehangira reported fewer livelihood strategies (1.6 on average) than respondents in Madyan (2.3) or Landakay (2.3; χ² = 42.105; p < 0.001). The most frequently reported livelihood strategies employed were day labor (temporary employment, paid by day) (38%), crop production (38%), and animal husbandry (30%). The least frequently reported were owning and/or operating hotels (3%), fishing (9%), and salaried labor (permanent employment, paid by regular salary; 11%). Respondents in Jehangira were more likely to report engaging in day labor (55%) than respondents in Madyan (23%) and Landakay (38%; χ² = 32.394; p < 0.001). Commerce was most likely to be reported in Madyan (45%) followed by Landakay (29%) and Jehangira (11%; χ² = 41.819; p < 0.001).
We asked respondents how they had responded to these changes in the past 10 years (Table 3). On average, respondents reported adopting 5.6 adaptation strategies (range: 5–10, SD = 0.87). The most frequently reported strategies were increasing agricultural inputs (35%), having a household member migrate to another place (30%), or decreasing time fishing (21%). The least reported adaptation strategies were changing irrigation water supply (0.6%) or the type of animals raised (3%). In terms of differences, respondents in Madyan were more likely to have started a business (12%) in the past 10 years as they adapt to ongoing social-ecological changes than respondents in Landakay (4%) or Jehangira (1%; χ² = 16.847; p < 0.001).
Comparisons between two types of family structures
The results of the binomial regression indicate that having a joint family structure was associated with several household socio-demographic characteristics and their livelihood strategies (Table 5). Specifically, age of the household head (OR = 1.04; p < 0.001), household size (OR = 4.50; p < 0.001), land owned and rented (OR = 3.36; p = 0.02), and the number of livelihood strategies employed within a household (OR = 3.61; p < 0.001) were positively associated with a joint family structure. Of all livelihood strategies employed, day labor (OR = 0.54; p = 0.04) and agriculture (OR = 0.40; p = 0.01) were negatively associated with a joint family structure and migration was positively associated with the joint family structure (OR = 2.01; p = 0.04).
We also asked respondents to indicate who is involved in decision making about household livelihood strategies (Table 2). We found that the frequency at which heads of joint and nuclear families indicated that they made decisions with other family members were similar. The two most frequently reported family members involved in decision making in addition to the household head was the head’s father (joint families: 25%; nuclear families: 32%) and brother (joint families: 40%; nuclear families: 18%), but joint family heads reported making decisions with their brother more than nuclear family heads (χ² = 25.793; p < 0.001).
Our discussion is focused on three expressions of everyday adaptation. First, everyday adaptation is embedded in localized changes and resources that differ across space. Second, access to capital and associated adaptive capacity differs across household structures. Third, household livelihood and adaptation decision making expands beyond the often-assumed husband-wife dyad. As mentioned earlier, little is known about the differences in adaptive capacity and decision-making processes across household structures (Rao at el. 2020); thus, our results, together, add to the understanding of the ways everyday adaptation to social-ecological change is embedded in existing social systems and the culture in which these relationships are expressed.
Differentiated livelihood and adaptation strategies across space
Our results show distinct differences in how our respondents adapted in our study communities. Specifically, respondents in Madyan were more likely to start a business than respondents in Landakay and Jehangira while respondents in Jehangira were more likely to increase agricultural inputs in comparison to respondents in Madyan and Landakay. Further, our results show that the higher income, larger number of livelihood strategies employed, and more engagement in the hotel industry and commerce in Madyan and Landakay could be in part due to the spatial differences in livelihood opportunities and access to resources (e.g., natural scenery that attracts tourists). Although there was no difference in respondents reporting crop production as a livelihood strategy, respondents in Jehangira were more likely to report an increase in their agricultural inputs than those in Madyan and Landakay. The complexity of agricultural production makes it difficult to draw conclusions outside the context of this research; however, the difference in increasing agricultural inputs as an everyday adaptation strategy could be in part due to access. That is, Jehangira is closer to urban centers than Madyan and Landakay, thus respondents there may have easier access to inputs and extension services that often distribute these inputs (Waithaka et al. 2007, Alene et al. 2008). Proximity to urban centers and associated employment opportunities could have also contributed to the greater reliance on day labor among respondents in Jehangira than respondents in Madyan and Landakay. Day labor can provide important and flexible income sources; however, it is associated with low wages and unreliable work (Niehof 2004, Gautam and Andersen 2016), which could contribute to the lower income reported in Jehangira.
Tourism has long been a part of the conversation surrounding sustainable development (Butler 1999) and livelihood diversification for rural communities (Kull et al. 2007, Cinner and Bodin 2010). In our study area, the World Bank estimates that 4.45 million tourists visited sites around the Swat Valley in 2018 and created nearly 10,000 jobs in the KP province concentrated around the Swat River near Madyan and Landakay (World Bank 2018). This confirms the tourism associated livelihood strategies among our respondents in Madyan and Landakay in comparison to Jehangira. Although diversification through the tourism industry has been shown to increase financial security, it is also known to be highly vulnerable to social-ecological change (Forster et al. 2012), as seen in the ways the 2010 flood and armed conflicts decreased tourism in our study area. Nevertheless, the uneven spatial distribution of tourism reflects previous findings (Iorio and Corsale 2010) illustrating the ways this distribution can increase already existing spatial inequalities as some households can engage in this livelihood strategy while others cannot.
Overall, these results show the ways in which everyday adaptation is fundamentally tied to space (Johansson and Vinthagen 2016). That is, the localized changes and resources in a particular place will shape adaptation practices and access to resources. Even when communities experience similar climatic and other social-ecological changes these changes may manifest differentiated impacts on people’s livelihoods (Gentle and Maraseni 2012, Tschakert et al. 2019). Furthermore, our results show that livelihood options are location specific and reflect differences in available resources and broader economic factors even within a relatively small geographic region (Ellis 1998, Chamberlin et al. 2006, Yobe et al. 2019). Together, our results indicate the need to tailor external adaptation programs or policies to the specific challenges and opportunities present in a specific locale. That is, they must be flexible and cross-sectoral, and aligned with households’ and communities’ diverse livelihood strategies in order to support people’s needs for everyday adaptations.
Differentiated capital and adaptive capacity across household structures
By comparing joint and nuclear family household structures, our study shows that joint family households appear to own and rent more land, employ more livelihood strategies, and adapt in different ways in comparison to nuclear family households. Although livelihood diversification contributes to several measures of household well-being (Barrett et al. 2001, Liu et al. 2008, Babatunde and Qaim 2010), Gautam and Andersen (2016) specify that the improved well-being is more contingent on the type of diversification (i.e., trade, salaried labor, wage labor) rather than diversification alone. We similarly posit that the types of livelihood strategies that joint family households employ contribute to their adaptive capacity. For instance, in our study, day labor was negatively associated with the joint family household structure. Although day labor requires less immediate investment, it may also have a lower return (Loison 2015), and thus may be less advantageous for improving the well-being of nuclear family households.
Based on previous research suggesting that households draw on human, natural, physical, social, political, and financial capital (Scoones 1998, Bebbington 1999, Rakodi 1999), our study also shows that joint family households tend to have access to more natural (i.e., land) and human capital than do nuclear family households, which in turn contributes to their adaptive practices to social-ecological change. For instance, labor migration was positively associated with the joint family household structure. Labor migration requires the ability to absorb the loss of that family member’s labor at the original location, as well as a large initial investment to relocate a family member (Mendola 2008). We posit that in comparison to nuclear family households, joint family households’ larger size enables them to absorb the labor loss of migration. Additionally, joint family households own and rent more land on average than nuclear family households, which may contribute to their ability to come up with resources that are needed for relocating a migrated family member. Similarly, previous research has shown that land ownership supports households’ capacity to adapt to social-ecological change (Brown et al. 2019). As such, joint families face lower entry barriers to adopting certain adaptive practices (i.e., migration) in comparison to nuclear families (Mendola 2008, Huy 2009). Together, various sources of capital that may be available to joint family households but not nuclear family households (e.g., human and natural capital in this study) can further exacerbate existing inequalities as vulnerable households, particularly nuclear family households, with relative less adaptive capacity are further marginalized (Artur and Hilhorst 2012). At the same time, our study also shows the ways that adaptive capacity is supported by existing social systems such as household structures; that is, rather than drawing on external resources or support systems, everyday adaptations are embedded and supported by internal sources of capital associated with household structures.
Previous research has assessed the relationship between household characteristics and adaptive capacity with a particular focus on household size (Mendola 2008, Huy 2009, Dumenu and Obeng 2016, Ali and Erenstein 2017). Several scholars have argued that larger households tend to be more vulnerable to climate change (Dumenu and Obeng 2016) and food insecurity (Bashir et al. 2012). However, our study shows that it may not be sufficient to only consider household size when assessing adaptive capacity. We posit that joint families may be less vulnerable to climatic and other social-ecological changes despite their larger size, similar to the findings discussed in Senapati and Gupta (2017). Although it is already established that social structures and networks mediate vulnerability (Birkenholtz 2012, Scaggs et al. 2021) and that kinship networks can support adaptive capacity (Smit and Wandel 2006), little work has included household structure in the assessment of adaptation and capital. For instance, even if two households are of the same size, the joint family may include more adults who can contribute to various forms of capital in comparison to the nuclear family. As such, we highlight a great need for further research to understand the role of household structure in shaping households’ adaptive capacity and everyday adaptations. The prevalence of the joint family household structure is influenced by the cultural context of northwestern Pakistan (Lindholm 1982, Amin et al. 2010), thus, this work also responds to the need to better understand the way culture mediates climate change adaptation (Few et al. 2021) by expanding our understanding of how adaptation to social-ecological change is embedded in, and can be supported by, existing social systems and relationships including traditional household structures.
Household decision making
Most research on household decision making in nuclear family households focuses on collaboration between a husband and a wife (Doss and Meinzen-Dick 2015, Acosta et al. 2020), whereas research on intergenerational households largely focuses on collaboration between adult children and their parents (Quisumbing 1997, Whyte et al. 2008, Evans et al. 2015). In our study, the two most frequently cited family members involved in collaborative decision making with the household head were the head’s father and brother, and joint family heads were more likely to collaborate with their brothers in making various livelihood decisions. Brothers are likely to live in the same joint family in the context of our study; thus, this reveals that household negotiations are connected to household structures.
Undoubtedly, our results are highly situated in the cultural context of our study in which patriarchal family structures and decision making are common (Qasim et al. 2015, Fahad and Wang 2018). However, as Rao et al. (2020:11) state, intra-household negotiations “are no longer restricted to couples. Households are increasingly multi-generational and multi-locational with new forms of cooperation and indeed conflict developing amongst them.” Therefore, our study confirms the need to expand examinations of intra-household negotiations beyond a husband-and-wife dyad to further understand the increasingly complex household decision-making dynamics. As rural households continue to face the need to adapt their livelihoods to ongoing social-ecological changes (through, for example, livelihood diversification), negotiation of resources including time, labor, finances, land, and water to support multiple livelihood strategies in one household will be commonplace, regardless of the household structure (Ellis 2000, Niehof 2004, Loison 2015). More broadly, negotiations about land use and natural resources among non-spousal family members have been documented in managing family-owned forestlands (Snyder and Kilgore 2018) and small-scale farms (Iles et al. 2021) in North America and in agricultural landscapes in India (Selvaraju et al. 2005) and Tanzania (McCabe et al. 2010). Therefore, insights from northwestern Pakistan shed light on broader negotiations in household livelihood and adaptation decision making. This not only increases our understanding of the ways adaptation is embedded in everyday decision-making processes, but also assists in targeting all relevant decision makers within a household for outreach, education, and the development and implementation of adaptation-support tools.
At the same time, examinations of household decision making also require the assessment of who is excluded from these processes. In our study, the gendered dynamic of decision making was clear in that the majority of household heads reported making decisions with their father or brother and that all self-identified household heads were men. Recently, scholars have drawn from Scott’s (1985) conceptualization of the entanglement of everyday resistance in everyday power to understand the ways in which everyday adaptations relate to power relations (Artur and Hilhorst 2012, Funder and Mweemba 2019). The trends in decision making in our study echoes the vast literature on gender and adaptation (e.g., Denton 2002, Arora-Jonsson 2011, Carr and Thompson 2014, Erwin et al. 2021) and further highlights the need to examine how everyday adaptations are not only reflective of household structures, but also enmeshed in existing intra-household exclusions.
This research drew from a survey of 448 households in northwestern Pakistan to examine livelihood and adaptation strategies, and the related decision-making processes across household structures (i.e., joint and nuclear families) and the three communities in our study area. Together, our data reveal that everyday adaptations and adaptive capacity are expressions of access to capital and resources that differ across household structures, of existing opportunities in the space in which households are located, and of various dynamics associated with household decision making. We found that joint family structures increase households’ capacity to diversify their livelihoods and more broadly, that they have greater access to various forms of capital than nuclear families. This counters the common assumption that large households have limited capital and instead, highlights the need for a nuanced understanding of how household structure, not just size, influences capital and adaptive capacity. Nevertheless, our research is focused on northwestern Pakistan; thus, further research is needed to explore the relationships between household structure and social-ecological adaptation in contexts outside of our research area. For example, research on household structures and adaptation in urban areas or other cultural contexts would provide necessary expansions of our understanding of these relationships. In addition, our study focuses on the joint family structure, and more research is needed to understand adaptive capacity in multi-generational households more broadly. Finally, household structure is in some ways a reflection of cultural norms and values around kinship and relationships; thus, more effort is needed to understand the ways culture influences household structures and the associated adaptive capacity.
Our results also indicate the need for a broader understanding of decision making and negotiation within a household. Much of the current examination of household decision making focuses on a husband and a wife as decision makers. However, we posit that assessments of household decision making should include other family members such as siblings or parents and incorporate analyses of power within the household. This will allow for a richer understanding of how households make the decision to adapt. It also informs outreach and extension strategies that ensure that climate-related information is available for the entire household, and in that way supports cooperative decision-making processes.
Finally, our study highlights the variation in livelihood and adaptation strategies that occurs within and across households and locations. That is, the same social-ecological changes can manifest differently in different communities and compound with varied available resources and broader economic factors to generate differentiated livelihood impacts. Further, the high entry barriers to certain types of livelihood diversification and differentiated opportunities can increase existing inequalities across space and household structures. Thus, adaptation policy must address these differences to provide targeted support across communities and households. In particular, given the ways the joint family structure appears to support adaptive capacity, policy makers should consider ways to further facilitate adaptation in this type of household structure while addressing the specific vulnerabilities of the nuclear family. Overall, these results add to our understanding of everyday adaptation to social-ecological change and household adaptive capacity, particularly illustrating how both are intricately linked to existing social systems and relationships such as household structures.
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The authors would like to thank the Sustainability and Development Initiative at the University of Michigan and the Center for the Environment at Purdue University for providing avenues to receive valuable feedback on this manuscript. We would also like to thank the survey enumerators for their remarkable efforts distributing the survey for this work.
The data for this research is available on request to maintain the privacy of study participants.
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Table 1. Socio-demographic characteristics of survey respondents and their households with Khyber Pakhtunkhwa region data.
(n = 150)
(n = 148)
(n = 150)
(n = 448)
|Mean age of household head (years)||37.6||41.9||38.7||39.4||46.9†|
|Mean education of household head (years)||9.9||6.1||9.1||8.4||6.6‡|
|Mean household (HH) size (persons)||11.0||10.3||8.9||10.1||7.3|
|% of HH that were joint families||62.7||58.8||51.3||57.6||55%|
|Mean land size (ha)*||6.1||1.0||2.4||3.2||1.5|
|% of HH with income over 30,000 PKRs*||67.6||23.4||74.8||55.7|
|† Country-wide data.|
‡ All KP men.
* p < 0.00142857 (.05/35 using Bonferroni correction Gelman et al. 2012).
Table 2. Comparisons of joint and nuclear families regarding livelihood strategies and decision-making processes.
(n = 258)
(n = 190)
|Decision making with other family members by livelihood strategy (% of respondents)|
|Decision making with other family members by member (% of respondents)|
|* p < 0.0033 (0.05/15 using Bonferroni correction Gelman et al. 2012).|
Table 3. An overview of household livelihood and adaptation strategies across the three study communities.
(n = 150)
(n = 148)
(n = 150)
(n = 448)
|Livelihood strategies (% of responding households)|
|Mean total livelihood strategies*||2.3||1.6||2.3||2.1|
|Adaptation strategies (% of responding households)|
|Changed animal type||4.1||2.9||1.9||2.9|
|Decreased time fishing||50.0||14.3||5.3||21.1|
|Increased agricultural input*||31.8||55.0||23.6||34.8|
|Changed irrigation water supply||0.0||2.4||0.0||0.6|
|Changed crop type||5.8||14.6||12.5||10.2|
|Changed domestic water supply||10.7||14.9||10.0||11.8|
|Applied for financial assistance||7.3||7.4||2.7||5.8|
|Started a business*||12.0||4.1||1.3||5.8|
|Family or family member moved for work||34.0||26.9||30.0||30.3|
|Mean total adaptation strategies||5.8||5.6||5.6||5.6|
|* p < 0.002 (0.05/25 using Bonferroni correction Gelman et al. 2012).|
Table 4. Variables including in binomial regression.
|Description||Mean (range; std. dev.) or % of respondents|
|Joint family structure||Binary-1, if multi-generational families with two or more married children; 0, if otherwise||57.6|
|Age of household head||Continuous- years||39.4 (19-77; 12.9)|
|Education of household head||Continuous- years||8.4 (0-18; 5.5)|
|Household size||Continuous- persons||10.1 (2-35; 5.3)|
|Size of land in agricultural production||Continuous- hectares of owned and rented land in agricultural production||3.2 (0-151.8; 15.0)|
|Household income over 30,000 PKR||Binary-1, if HH income over 30,000 PKR; 0, if otherwise||55.7|
|Livelihood strategies||Continuous-number of livelihood strategies||2.1 (1-5; 1.0)|
|Crop production||Binary - 1 if HH reports crop production; 0, if otherwise||37.9|
|Day labor||Binary - 1 if HH reports day labor; 0, if otherwise||38.4|
|Animal husbandry||Binary - 1 if HH reports animal husbandry; 0, if otherwise||29.7|
|Commerce||Binary - 1 if HH reports commerce; 0, if otherwise||28.6|
|Migration||Binary - 1 if HH reports migration; 0, if otherwise||21.9|
|Salaried labor||Binary - 1 if HH reports salaried labor; 0, if otherwise||10.5|
|Fishing||Binary - 1 if HH reports fishing; 0, if otherwise||8.7|
|Hotels||Binary - 1 if HH reports hotels; 0, if otherwise||3.1|
Table 5. Binary logistic regression results.
|Explanatory variable||Odds ratio||SE||Wald||P>|z|||95% conf. interval|
|* p < 0.05.|