Social protection and climate resilience among informal workers in Pakistan: a qualitative study from Khyber Pakhtunkhwa

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In Pakistan, the Benazir Income Support Programme (BISP) and one-time cash grants are the primary safety nets, yet their contribution to climate resilience remains underexplored. This study examined how socio-economic incentives influence resilience among informal workers in Khyber Pakhtunkhwa. Methods A qualitative phenomenological design was applied between April and June 2025 across three climatic zones of Khyber Pakhtunkhwa (north, central, south). A multistage purposive sampling strategy was used to ensure diversity across geography, occupation, gender, and urban-rural residence. Thirty in-depth interviews were conducted with informal workers (≥ 18 years, directly affected by a climate shock in the past five years), and six focus group discussions with 36 institutional stakeholders from social protection, disaster management, health, and local government sectors. Data were collected in Pashto and Urdu, transcribed, translated into English, and analysed using reflexive thematic analysis. Results Thematic analysis identified five themes and ten sub-themes: ( 1 ) Inadequate and inflexible financial support (static assistance amid dynamic crises; debt, instability, and recovery fatigue); ( 2 ) Barriers to accessing social protection (digital gaps and bureaucratic complexity; cultural and logistical barriers); ( 3 ) Gendered constraints and local power structures (aid without autonomy; gatekeeping and informal exploitation); ( 4 ) Everyday adaptation and informal resilience (social networks and mutual aid; flexible routines and low-tech innovations); and ( 5 ) Towards responsive and climate-sensitive aid (disaster-specific, localised relief; early warning and infrastructure gaps). Conclusions Pakistan’s social protection systems provide critical but limited relief against climate shocks for informal workers. To build long-term resilience, reforms are required to make these systems climate-sensitive, adaptive, and gender-transformative. The findings offer policy lessons for other LMICs seeking to strengthen social protection as a pathway to climate resilience. Climate resilience Social protection Informal workers Benazir Income Support Programme Cash transfers Pakistan Introduction Climate change constitutes a paramount challenge of the 21st century, threatening livelihoods, health, and socio-economic stability worldwide, with particularly severe consequences for low- and middle-income countries (LMICs) (1–3). Altered weather patterns and the rise in extreme events undermine agricultural production, cause crop failures, and increase food insecurity for millions (4). Vulnerable populations are disproportionately affected, with climate impacts exacerbating poverty and inequality (5). Evidence suggests heightened risks faced by developing countries, driven by financial constraints, reliance on climate-sensitive industries such as agriculture, and insufficient infrastructure to withstand severe shocks (5–7) Pakistan exemplifies these vulnerabilities, experiencing frequent floods, droughts, and heatwaves that compound existing socio-economic challenges (8). The 2010 floods affected 20 million people, caused 1,800 deaths, destroyed over 400 health facilities, and inflicted damages worth US$10 billion (9). Food insecurity was simultaneously aggravated, affecting nearly 30% of the population and leaving an additional five million people undernourished (9). More recently, the 2022 floods impacted more than 33 million people, caused 1,700 deaths, led to US$2.3 billion in agricultural losses, and generated total damages of nearly US$30 billion (10,11). Such shocks have been shown to generate long-term implications for health and nutrition, with water-related diarrhoeal diseases persisting as a leading cause of infant mortality and prolonged droughts leaving more than half of the country food and water insecure(12,13). Khyber Pakhtunkhwa (KP), a province in north-western Pakistan with a population of over 30.5 million and an annual growth rate of 2.9%, is particularly vulnerable to climate change owing to its diverse geography (14,15). The northern districts are exposed to glacier lake outburst floods, the central plains to heatwaves and urban flooding, and the southern areas to prolonged drought and water scarcity (16–18). These risks undermine agriculture and rural livelihoods, aggravate poverty, displace communities, and place additional strain on limited adaptation resources (19). The concept of resilience has become central within climate and development discourse. Resilience is defined as the capacity of individuals, households, and systems to absorb, adapt, and transform in response to shocks (20,21). Absorptive capacity is understood as immediate coping, adaptive capacity as incremental adjustment, and transformative capacity as systemic change that addresses structural drivers of vulnerability (20). It has been emphasised that resilience must be linked to equity, justice, and redistribution; otherwise, marginalised groups may be further disadvantaged under adaptation agendas (22,23). Globally, social protection has been positioned as a key instrument for strengthening resilience to climate shocks (24,25). Social assistance in particular has demonstrated effectiveness in stabilising consumption, improving nutrition, and supporting recovery during shocks. In Ethiopia, the Productive Safety Net Programme (PSNP) improved food security and reduced reliance on negative coping strategies during droughts (26,27). In Kenya, the Hunger Safety Net Programme (HSNP) raised food consumption scores by 2.6-3.4% during droughts, while the Inua Jamii programme improved dietary diversity and nutrient intake (28,29). In India, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) provided seasonal income while supporting water conservation and soil fertility, thereby strengthening adaptive capacity (30,31). In Latin America, cash transfers such as Ecuador’s Bono de Desarrollo Humano and Brazil’s Bolsa Família have demonstrated transformative effects on social inclusion and women’s agency (32–34). In Pakistan, the Benazir Income Support Programme (BISP) represents South Asia’s largest cash transfer programme, reaching 5.6 million families by 2021, equivalent to around 40% of those living below the poverty line (35). Evaluations have documented measurable impacts on women’s empowerment, household food security, and child nutrition (36–38) During crises, BISP and Ehsaas programme have been scaled up, including one-time disbursements of PKR 25,000 to over 250,000 households during the 2022 floods (10,39). Despite these contributions, such schemes have remained primarily designed for poverty alleviation and maternal and child health rather than climate resilience (40). The informal sector accounts for more than 70% of Pakistan’s workforce (41,42), and is particularly exposed to climate-related risks. Employment is concentrated in agriculture, daily wage labour, construction, and small-scale enterprises, with livelihoods characterised by instability, lack of savings, and exclusion from formal protection schemes (43,44). Women in the informal economy experience compounded disadvantages, including wage disparities, restricted mobility, and limited decision-making authority (45). Moreover, the informal sector forms the backbone of KP’s economy, absorbing nearly three-quarters of the working-age population through subsistence farming, daily wage labour, construction, and small-scale enterprises, yet it remains highly exposed to climate shocks and largely excluded from formal social protection systems (46). Therefore, this gap in the literature was addressed in the current study through an exploration of how socio-economic incentives, particularly the BISP and one-time cash grants, shape resilience among informal sector workers in KP, Pakistan. By foregrounding the lived experiences of workers and incorporating institutional perspectives, the study generates policy-relevant insights into the adequacy, accessibility, and gendered dimensions of social protection systems in contexts of climate vulnerability, with wider relevance for LMICs confronting similar challenges. Methods Study design This study employed a qualitative phenomenological design to explore how socio-economic incentives, particularly the BISP and one-time cash grants, influence the climate resilience of informal sector workers in KP, Pakistan. A phenomenological orientation was adopted to enable an in-depth exploration of participants lived experiences and the meanings they attributed to support mechanisms in the context of climate-related shocks. This approach is especially well suited to contexts where subjective realities and personal interpretations are central to understanding social phenomena (47,48). This study adopted a relativist ontology, acknowledging realities as shaped by socio-economic and environmental contexts (49,50), and a constructivist epistemology, viewing knowledge as co-produced between researchers and participants (51). Participant recruitment A multistage purposive sampling strategy was used in this study to capture variation across geography, occupation, gender, and age. This approach has previously been applied in qualitative research to ensure maximum variability and representation across system levels and settings (52,53). In the first stage, three climatic zones of Khyber Pakhtunkhwa were identified: northern, central, and southern. In the second stage, one district was purposively selected from each zone: Swat (north), Peshawar (central), and Dera Ismail Khan (south). In the third stage, two local government areas within each district were chosen to represent both urban and peri urban or rural contexts. This approach reflects guidance on purposeful sampling that recommends beginning with maximum variation to capture diverse contexts and then narrowing through criterion-based selection to achieve depth of understanding (54). Eligibility for informal sector workers required participants to be at least eighteen years of age, currently engaged in informal employment within one of the selected districts, and to have directly experienced at least one extreme weather event such as flooding, drought, or a heatwave within the past five years. Individuals were excluded if they had previously served as paid community mobilisers, volunteers, or informants for aid distribution agencies, in order to minimise institutional bias. Those with cognitive, speech, or language impairments that prevented meaningful participation in an interview-based study were also excluded. For institutional stakeholders, eligibility was defined as holding an active professional role in social protection, disaster management, health, or community development within the study districts. Those occupying purely administrative or symbolic positions without direct engagement with communities were excluded. Recruitment strategies were adapted to the two participant groups. For informal sector workers, community elders, peer groups, and representatives of non-governmental organisations acted as intermediaries, introducing the research team and fostering trust. To reduce external visibility and enhance accessibility, local researchers from the northern, central, and southern zones were recruited. Potential participants were approached in homes, markets, streets, and other workplaces, and were provided with a study information leaflet in the local language alongside a verbal explanation of the study aims, procedures, potential risks and benefits, and assurances of confidentiality. Recruitment and scheduling were adapted to minimise barriers for women and marginalised groups participation: female interviewers were available, interviews were arranged flexibly around work and household responsibilities, and family accompaniment was permitted when requested. Participation was voluntary, with individuals given adequate time to consider their involvement. In total, forty-five informal sector workers were approached (fifteen per climatic zone) for the in-depth interviews (IDIs), of whom thirty consented to participate in the IDIs (ten per zone). Institutional stakeholders were identified through purposive sampling of relevant sectors, including social protection, disaster management, health, and community development. Initial introductions were facilitated by district offices and organisational representatives, after which formal letters and follow-up calls were used to extend invitations. Interested stakeholders received a study information leaflet and a verbal briefing outlining the study’s aims, procedures, potential risks and benefits, and assurances of confidentiality. Participation was voluntary, and informed consent was obtained before involvement. In total, thirty-six stakeholders were recruited (twelve per zone) and organised into six focus group discussions (FGDs) (two per zone, six participants each). Data collection Based on the sampling strategy, data were collected from six purposively selected local government areas within three districts: Mingora and Kabal in Swat (northern zone), Peshawar City and Charsadda (central zone), and Dera Ismail Khan and Tank (southern zone). This selection encompassed both urban and peri-urban/rural contexts, enhancing the diversity and contextual depth of the dataset. Data were collected between April and June 2025 through thirty IDIs with informal sector workers (ten per climatic zone) and six FGDs with institutional stakeholders (two FGDs per zone, each with six participants). Semi-structured topic guides were developed by a multidisciplinary team at Khyber Medical University… Separate versions were prepared for IDIs and FGDs to capture the lived experiences of informal workers and the institutional perspectives of programme implementers and policymakers. Semi-structured topic guides were developed by a multidisciplinary team at Khyber Medical University, bringing together expertise in qualitative research, global health, epidemiology and biostatistics, climate change, climate finance, and public health data science. Efforts were made to draw on this diversity of expertise, together with relevant literature on climate resilience and social protection in LMICs, to develop guides that were methodologically robust and contextually relevant. The team comprised specialists in public health, qualitative research, epidemiology, climate science, and health data science, ensuring that the guides were informed by both conceptual knowledge and applied expertise. Separate versions were prepared for IDIs and FGDs to capture both the lived experiences of informal workers and the institutional perspectives of programme implementers and policymakers. IDIs were conducted at participants’ worksites or in nearby safe settings by bilingual, trained research associates (one male and one female), each lasting approximately 30 minutes. FGDs were facilitated by the female principal investigator in private community venues, such as school facilities, with each session lasting between 30 and 45 minutes. All IDIs and FGDs were conducted in the local language (Pashto or Urdu). Reflective notes were made by the researchers after each session to enhance reflexivity (55). Informed consent was documented in accordance with participant preference and literacy level, either by signed form for those with sufficient education or by thumb impression for those with limited formal education. All participants provided consent for both participation and audio recording. The first IDI and FGD were piloted to assess the appropriateness of the topic guides; as no substantive revisions were required, both were retained in the dataset. In qualitative research, data saturation refers to the point at which further data collection no longer generates new codes, themes, or insights, and additional data collection are unlikely to add substantive value to the analysis (56). Data saturation in this study was reached after thirty IDIs and six FGDs, at which point no new themes emerged and participant accounts became repetitive. Data analysis All IDIs and FGDs were conducted in local language (Pashto or Urdu) and transcribed verbatim into English by trained bilingual members of the research team. For FGDs, institutional stakeholders were offered the opportunity to review their transcripts, though none requested changes. As some informal sector participants had limited literacy, transcripts were not returned for review. Instead, interviewers provided verbal summaries at the end of each session, inviting participants to clarify or expand on their views. A thematic analysis was undertaken, guided by a descriptive phenomenological orientation. We followed Sundler et al. (2019), who outline an approach to thematic analysis suited to phenomenological research, emphasising openness, reflexivity, and questioning presuppositions (57). ATLAS.ti (version 1.8) was used to support the organisation and management of data (58). Two researchers (SSB, SIK) independently read the transcripts several times to immerse themselves in the text and develop a deeper understanding of participants lived experiences. From these readings, they identified key statements and short sections of text that reflected important aspects of participants’ accounts. These were grouped into patterns, which formed the basis for preliminary themes. The preliminary themes were then reviewed, refined, and discussed within the wider research team (MIK, ZUH, SIK, SSB, KR) to ensure consistency and strengthen interpretation. Finally, the themes were integrated into a descriptive narrative that captured both shared experiences and variations across participants. To enhance rigour, presuppositions were bracketed as far as possible and regular team discussions were held to reflect on assumptions and strengthen the credibility of interpretation. Trustworthiness was further considered in line with Lincoln and Guba’s (1985) criteria: credibility, dependability, confirmability and transferability (59), which are elaborated in the Discussion. The study was conducted and reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) (60). Ethics This study received approval from the Khyber Medical University Ethics Board (Reference: Dir/Ethics/KMU-EB/EC/00106). Before taking part, individuals were informed about the aims of the research, the voluntary nature of their involvement, and their freedom to stop at any time without any consequences. To protect privacy, each participant was assigned a code, and any information that could reveal their identity was removed from all written records. All files were kept on secure, password-protected devices that could only be accessed by the research team. Following university guidelines, the data will be kept for five years and then permanently deleted. The study was carried out in line with internationally accepted ethical standards, including those outlined in the Declaration of Helsinki. Results A total of 30 informal sector workers participated in the study, with equal representation from the northern (n = 10), central (n = 10), and southern (n = 10) climatic zones summarised in Table 1 . The sample comprised more women (n = 16, 56.7%) than men (n = 14, 43.3%). Participants ranged in age from 25 to 55 years, with a mean age of 38.0 years (SD = 9.0). Educational attainment was generally low: two-thirds (n = 20, 66.7%) had completed only primary-level education, while the remainder (n = 10, 33.3%) had reached secondary level. Housing conditions reflected socio-economic precarity, with one-third (n = 10, 33.3%) residing in mud houses, 23.3% (n = 7) in semi-permanent structures, and 20% (n = 6) in concrete houses, while others lived in rented rooms, flats, or informal shelters. Occupations varied, highlighting the heterogeneity of informal work. The most common livelihoods were domestic work (16.7%), farming (16.7%), and home-based businesses (16.7%). Other activities included casual labour (10%), street vending (6.7%), tailoring (6.7%), shopkeeping (6.7%), and low-wage work such as waste picking, gardening, and service provision. Household dependency levels were high. Nearly half of participants (46.7%) reported supporting four or more dependents, while 36.7% supported two to three, and 16.6% had only one dependent. Table 1 Socio-demographic and Household Characteristics of Informal Sector Worker Participants (n = 30) ID Age Gender Occupation Region Location Education Level Type of Housing Dependents Level ID-1 42 Male Street Vendor North Rural Primary Semi-permanent High (6 dependents) ID-2 44 Female Home-based Business North Urban Secondary Concrete house Medium (3 dependents) ID-3 38 Female Farmer North Rural Primary Mud house High (5 dependents) ID-4 25 Male Gardener North Urban Primary Rented room Low (1 dependent) ID-5 45 Female Home Tutor North Rural Secondary Brick house Medium (3 dependents) ID-6 32 Male Street Vendor North Urban Primary Rented flat High (4 dependents) ID-7 55 Male Electrician North Rural Secondary Semi-permanent Medium (2 dependents) ID-8 28 Male Tailor North Urban Secondary Concrete house Low (1 dependent) ID-9 51 Male Farmer North Rural Primary Mud house High (5 dependents) ID-10 37 Male Farmer North Urban Primary Mud house Medium (3 dependents) ID-11 51 Female Service Worker South Urban Secondary Rented flat Medium (2 dependents) ID-12 37 Female Casual Laborer South Rural Primary Mud house High (6 dependents) ID-13 43 Female Domestic Worker South Urban Primary Rented room High (5 dependents) ID-14 55 Female Construction Worker South Rural Primary Semi-permanent Medium (3 dependents) ID-15 26 Female Waste Picker South Urban Primary Informal shelter Medium (2 dependents) ID-16 34 Female Home-based Business South Rural Secondary Brick house Low (1 dependent) ID-17 34 Female Home-based Business South Urban Secondary Concrete house Medium (3 dependents) ID-18 29 Male Shopkeeper South Rural Secondary Semi-permanent Medium (3 dependents) ID-19 32 Female Seasonal Farmer South Urban Primary Mud house High (4 dependents) ID-20 53 Male Shopkeeper South Rural Secondary Concrete house High (6 dependents) ID-21 34 Female Home-based Business Central Urban Secondary Concrete house Medium (2 dependents) ID-22 29 Female Tailor Central Rural Secondary Semi-permanent Low (1 dependent) ID-23 37 Female Domestic Worker Central Urban Primary Rented room High (4 dependents) ID-24 28 Female Domestic Worker Central Rural Primary Mud house Medium (3 dependents) ID-25 25 Female Domestic Worker Central Urban Primary Informal shelter Medium (2 dependents) ID-26 42 Male Domestic Worker Central Rural Primary Semi-permanent Medium (3 dependents) ID-27 38 Male Casual Laborer Central Urban Primary Rented flat High (4 dependents) ID-28 42 Male Casual Laborer Central Rural Primary Mud house High (5 dependents) ID-29 44 Male Farmer Central Urban Primary Mud house Medium (3 dependents) ID-30 30 Male Farmer Central Rural Primary Mud house Medium (2 dependents) A total of 36 institutional stakeholders participated in the study, summarised in Table 2 . The majority were male (n = 25, 69.4%), with females comprising (n = 11, 30.6%). Regional representation was evenly distributed, with 12 participants drawn from each of the Central, North, and South climatic zones. Participants’ ages ranged from 26 to 59 years, with a mean age of 42.8 years (SD = 9.3); the mean age for males was 44.0 years and for females 40.3 years. With respect to education, most stakeholders (n = 26, 72.2%) had attained tertiary-level qualifications, while the remainder (n = 10, 27.8%) had completed education up to the secondary level. Years of professional experience varied considerably, ranging from 4 to 25 years, with an overall mean of 11.1 years. Stakeholders from the South zone reported the highest mean work experience (13.3 years), followed by those in the Central (10.3 years) and North (9.8 years) zones. Table 2 Socio-demographic and Professional Characteristics of Institutional Stakeholder Participants (n = 36) ID Age Gender Occupation Region Location Education Level Work Experience P1 45 Male Provincial Disaster Management Dept Central Urban Tertiary 9 years P2 39 Male Rescue Department Central Urban Secondary 7 years P3 37 Male Environment Department Central Urban Tertiary 5 years P4 48 Male Social Safety Net (BISP) Dept Central Urban Tertiary 12 years P5 35 Female Health Department Central Urban Tertiary 8 years P6 32 Female Environment Department Central Urban Tertiary 6 years P7 57 Male Provincial Disaster Management Dept Central Urban Tertiary 18 years P8 39 Male Rescue Department Central Urban Secondary 8 years P9 33 Male Environment Department Central Urban Tertiary 7 years P10 51 Male Social Safety Net (BISP) Dept Central Urban Tertiary 17 years P11 49 Female Health Department Central Urban Tertiary 15 years P12 47 Female Environment Department Central Urban Tertiary 12 years P13 58 Male Rescue Department North Rural Secondary 19 years P14 40 Male Provincial Disaster Management Dept North Rural Tertiary 10 years P15 29 Male Environment Department North Rural Tertiary 4 years P16 47 Male Social Safety Net (BISP) Dept North Rural Tertiary 12 years P17 42 Female Health Department North Rural Tertiary 9 years P18 33 Female Emergency Medicine North Rural Tertiary 6 years P19 52 Female Environment Department North Rural Tertiary 12 years P20 33 Male Climate Finance North Urban Tertiary 6 years P21 44 Male Forestry North Urban Secondary 8 years P22 53 Male Environmental Protection Agency North Urban Secondary 14 years P23 49 Male Social Safety Net (BISP) Dept North Urban Secondary 12 years P24 40 Male Healthcare Commission North Urban Tertiary 6 years P25 27 Male Social Safety Net (BISP) Dept South Rural Tertiary 15 years P26 52 Female Health Department South Rural Tertiary 20 years P27 47 Male Environmental Protection Agency South Rural Secondary 10 years P28 57 Male Provincial Disaster Management Dept South Rural Tertiary 23 years P29 35 Male Rescue Department South Rural Tertiary 7 years P30 29 Female Climate Finance South Rural Tertiary 4 years P31 38 Male Emergency Medicine South Rural Tertiary 8 years P32 46 Female Social Welfare South Rural Secondary 16 years P33 59 Male Environment Department South Rural Secondary 25 years P34 26 Female Health Department South Rural Secondary 4 years P35 41 Male Agriculture South Rural Tertiary 16 years P36 53 Male Rescue Department South Rural Tertiary 11 years Analysis of the interviews and focus group discussions generated five overarching themes and ten related sub-themes, these are summarised in Table 3 , which presents the themes and associated sub-themes. Table 3 Overview of themes and sub-themes Themes Sub-themes 1. Inadequate and inflexible financial support 1.1 Static assistance amid dynamic crises 1.2 Debt, instability, and recovery fatigue 2. Barriers to accessing social protection 2.1 Digital gaps and bureaucratic complexity 2.2 Cultural and logistical barriers 3. Gendered constraints and local power structures 3.1 Aid without autonomy 3.2 Gatekeeping and informal exploitation 4. Everyday adaptation and informal resilience 4.1 Social networks and mutual aid 4.2 Flexible routines and low-cost innovations 5. Towards responsive and climate-sensitive aid 5.1 Disaster-specific, localised relief 5.2 Early warning and infrastructure gaps Theme 1. Inadequate and inflexible financial support Subtheme 1.1. Static assistance amid dynamic crises Participants across rural and urban settings emphasised that although social safety nets and one-time grants provided short-term relief, the fixed amounts and quarterly disbursements were inadequate to meet the scale and duration of climate-related crises. Rural households, particularly those dependent on farming and livestock, described a mismatch between seasonal vulnerabilities and rigid support structures. “ The cash from the social programme and one-time grants provided immediate help, which we used for food and some crop medicines. However, the funds were quickly depleted since support comes only every three months. As we depend on farming for income, I invested part of it in hopes of a good harvest. Unfortunately, the limited and infrequent support does not address larger issues, especially during floods or bad weather, and it doesn't help us prepare for the long term or recover from major losses .” (ID-9, Farmer) Urban informal workers similarly reported that support was mainly used to cover immediate needs such as rent, utilities, and food, but did not compensate for lost income or broader economic disruption. A few participants attempted to invest the assistance in small-scale livelihood activities, though these efforts were frequently undermined by subsequent climate shocks. “I can't really say that the support has boosted my income since I still rely on selling flowers, seeds, and plants from my garden to help my family. However, it did provide me with some extra cash to grow my business. I used some of that money to buy essential tools and cover transport costs to local markets. The weather, though, is a real challenge. Heavy rains and heatwaves frequently ruin my plants or hinder my ability to reach customers. Sometimes, roads get blocked, making it tough to deliver orders or bring in supplies on time. The assistance is helpful to some extent, but it doesn't fully address the actual costs of these setbacks. As a gardener, I really need steady support that can safeguard my work and income when the weather doesn't cooperate.” (ID-4, Gardener) Stakeholders echoed these views, noting that BISP is primarily oriented towards poverty alleviation and maternal health rather than climate resilience. While it has the capacity to offer one-time cash grants during climate events or emergencies, it was regarded as insufficiently flexible or responsive to the acute and evolving challenges posed by climate-induced shocks. “In the floods of 2022, the government handed out an extra Rs. 12,000, but honestly, that was just a tiny fraction of the massive losses. Whole crops were destroyed, livestock died, and families had to start over from scratch. The truth is, programme like BISP and unconditional cash transfers weren’t really set up with climate resilience in mind. They overlook areas and people that are frequently hit by floods, droughts, and other disasters. These payments aim to help with women’s education, health, and reducing poverty but they really don’t cut it when it comes to ongoing climate crises.” (FGD South, P25) Subtheme 1.2. Debt, instability, and recovery fatigue The fragile nature of informal work made participants particularly vulnerable during climate events, often pushing them into debt. Rural workers described being caught in cycles of borrowing to offset agricultural and livestock losses, while urban workers reported taking loans to survive when floods or storms disrupted customer flows and reduced their earnings. “However, when heavy rains flooded the streets, customers stopped coming. That’s when things got really tough. We receive Rs. 12,500 every three months, which is helpful, but it’s not sufficient especially during emergencies. If the money came in monthly, it would really make a difference. Even during normal times, we find it hard to make ends meet, and when a flood or heatwave strikes, everything goes downhill. I had to borrow from family just to keep afloat. Once you get into debt like that, it’s really difficult to bounce back.” (ID-12, Casual Labourer) Stakeholders observed that successive disasters had created a sense of “recovery fatigue,” as households struggled to rebuild before the next crisis. They noted that the increasing frequency and intensity of climate events, together with limited support systems, hindered households’ ability to recover. “In Khyber Pakhtunkhwa, communities are stuck in a constant loop of rebuilding. Every flood or heatwave brings new losses, whether it’s crops, livestock, homes, or health. Recovery rarely finishes before another disaster strikes, and we find ourselves scrambling to tackle the next problem. There’s no solid prevention strategy in place, neither financially nor in terms of climate.” (FGD Central, P-2) Theme 2. Barriers to accessing social protection Subtheme 2.1. Digital gaps and bureaucratic complexity Participants reported that limited literacy and lack of digital access posed major barriers to engaging with social protection services, especially in rural areas and among women. Older adults, in particular, described challenges with reading, writing, and using mobile technologies. The absence of smartphones, unfamiliarity with SMS-based systems, and confusion about registration procedures further constrained their ability to access available support. “ My wife and I struggle to grasp mobile apps and point-of-sale systems, which complicates things for us. The SMS notifications we get are also puzzling and hard to make sense of. All of this just adds to the confusion and stress we deal with during the whole process.” (ID-7, Electrician) Stakeholders acknowledged that while digitalisation enhanced programme efficiency, it also created new barriers that excluded vulnerable groups. “Since our launch in 2008–2009, we’ve seen a big digital shift. Initially, we operated as a paper-based transfer programme, but by 2015, we teamed up with NADRA to transition to ATM cards. Then in 2018, we collaborated with various banks for mobile cash transfers, which has improved our data management. However, this shift has also left many people behind, particularly women in rural or low-literacy areas. They often don’t know where to go or how to sign up. If we don’t address this gap, even the most well-designed programme will fail to reach those who need assistance the most.” (FGD Central, P6) By contrast, urban workers with smartphones and workplace connectivity reported smoother access to services. However, in both urban and rural settings, misinformation, missed announcements, and bureaucratic hurdles continued to restrict engagement. Rural residents were especially disadvantaged by the long travel required to reach distant service centres. “Whether you’re in the village or the city, many of us face the same problems. At first, I didn’t know how to apply and had to ask around. Some shops turned me away, saying only certain ones handled applications. When I finally found the right place, they said my wife wasn’t eligible, so we had to go to NADRA to prove otherwise, which caused further delays. In cities, people may also deal with paperwork issues, but they can usually reach offices more easily. In rural areas, it means long trips on broken roads and multiple visits, especially when documents are missing. Many people, like us, miss out on aid or deadlines simply because the process isn’t clearly explained. The system may be simple, but the confusion makes it much harder than it should be.” (ID-19, Seasonal Farmer) Subtheme 2.2. Cultural and logistical barriers Participants explained that in rural areas, cultural norms surrounding female modesty and family honour constrained women’s mobility. Women were often required to travel with male relatives, leading to delays or missed appointments when escorts were unavailable. This dependence on male family members limited timely access to services, particularly during emergencies. “For women in our Pashtoon culture, things are even harder. They’re not allowed to travel alone and always need a male family member to go with them for safety and respect. But we're often busy or not around, so they end up missing appointments or deadlines just because they can’t go on their own. These restrictions keep them dependent and make it hard for them to speak up or do things for themselves. In the end, it’s not just the women who are affected, this holds back progress for the whole community.” (ID30, Farmer) In urban contexts, women reported comparatively greater autonomy, facilitated by access to transport and digital technologies. By contrast, travel infrastructure posed a substantial barrier in rural areas, where poor road conditions and distant service centres made aid collection highly burdensome. Stakeholders further noted that cultural constraints, compounded by infrastructure breakdowns, disproportionately disadvantaged women, children, and persons with disabilities. “During disasters, people often struggle to access any kind of facilities, whether it's healthcare or social funds, especially in rural areas. Women are particularly affected due to cultural and religious practices. On top of these cultural barriers, groups like pregnant women, children, and individuals with congenital disabilities face even greater challenges because of physical limitations. For them, the impact is intensified by both physical and cultural obstacles.” (FGD North, P23) Theme 3. Gendered constraints and local power structures Subtheme 3.1. Aid without autonomy Participants reported that gendered constraints shaped women’s access to support in both rural and urban settings, with rural women facing stricter enforcement of patriarchal norms and fewer opportunities to contest them. Although women were frequently registered as beneficiaries of programme such as BISP and one-time cash grants, many described having little control over how these funds were used. In rural households, financial decisions were typically managed by men, with husbands or other male relatives determining spending priorities. In urban areas, some women noted slightly more space to exercise decision-making, particularly those with independent incomes or digital literacy, yet patriarchal expectations and family oversight continued to limit their agency. “The money is in my name, I go and collect it, and I get to decide how to use it. But sometimes my husband asks for it, claiming he’s low on cash for his work, and I feel like I have no choice but to hand it over. I have to live with him and we have kids together. There have even been times when I’ve had to use this social support to help pay off his debts.” (ID-5, Home Tutor) Stakeholders further emphasised that social protection programme should extend beyond cash disbursement to actively promote women’s empowerment and address restrictive gender norms. “But the programme needs to go beyond just giving out cash every few months. Real empowerment for women requires teamwork across different sectors like health, education, disaster management, and gender equality. We really need to move from just keeping an eye on cash distributions to looking at real empowerment results. Are women seeing significant changes in their homes, communities, and lives? Just cash isn't sufficient; true impact comes from changing norms, creating more opportunities, and making sure that support leads to lasting independence and respect.” (FGD South, P32) Subtheme 3.2. Gatekeeping and informal exploitation Participants in both rural and urban areas recounted experiences of gatekeeping, corruption, and informal exploitation when attempting to access social safety nets. Rural women, constrained by limited mobility and weaker social networks, were particularly disadvantaged. Urban participants described managing these obstacles through persistence, personal connections, or digital literacy; however, demands for informal payments and unresponsive officials remained widespread. “My family had gone through the application process before, so they filled me in on what to expect, which really helped. But the officials I dealt with for registration were totally unhelpful; they kept asking for money to process my application. They didn't even bother to say hello properly and wanted to know if I was ready to hand over a part of the money I’d get from the Benazir programme or any other social aid, whether it was regular or for disasters. When I said no, they claimed I wasn't eligible. Luckily, a relative stepped in and guided me through the application at a different centre, where the staff were way more supportive thanks to our social ties.” (ID-15, Waste picker) Stakeholders acknowledged these challenges, noting persistent issues of leakage and corruption, but also highlighted recent reforms introduced to strengthen transparency and accountability. “Back in 2020, we saw investigations into fund skimming, with reports of people who weren't beneficiaries receiving funds, including some government officials. Most of the issues reported involved local retail outlets taking money from beneficiaries. Even a World Bank impact evaluation pointed out that women beneficiaries were less satisfied because of fund leakage through human agents. Some solutions are already in the works; the programme is focusing on third-party monitoring, audits, grievance support, strict penalties for those caught embezzling, and increasing the number of female staff in the programme.” (FGD North, P4) Theme 4. Everyday adaptation and informal resilience Subtheme 4.1. Social networks and mutual aid Participants described resilience in rural areas as being rooted in kinship systems and informal social networks that provided crucial support during crises such as floods, heatwaves, and prolonged power outages. In the absence of robust formal safety nets, family ties, neighbourly relations, and shared experiences offered shelter, food, labour, and emotional reassurance. These community-based mechanisms were viewed as critical in enabling households to cope, adapt, and recover from recurring climate shocks. In urban settings, participants noted that social networks were present but weaker, often taking the form of short-term, charity-like assistance. While such support offered immediate relief during emergencies, it seldom contributed to sustained recovery. “In our neighbourhood, during climate-related emergencies, people occasionally help each other out, but it's usually just a one-time gesture. In big cities, the assistance during such events often follows a predictable pattern. Wealthier individuals tend to provide clothing, food, or shelter, but it often feels more like charity rather than genuine support for long-term recovery. In times of crisis, affluent families may help those in need, but they typically don't invest the time or effort to help rebuild lives; they simply lack the capacity for that.” (ID-11, Service Worker) Stakeholders reinforced these perspectives, emphasising the importance of informal networks in providing food, shelter, rescue, and communication during disasters, and recognising them as a vital complement to formal support systems. “Communities often rely on informal networks, like neighbours helping each other out during bad weather. In Khyber Pakhtunkhwa, this culture of helping one another is well-rooted, covering shelter, transport, rescue, food sharing, financial aid, and even local early warning systems. Many families welcome those who have lost their homes, while others use local resources like tractors or boats to transport at-risk families to safer areas. Community members frequently share basic food items or organise group meals funded by mosques. Plus, word-of-mouth alerts and announcements from mosques serve as common early warning systems, emphasizing the crucial role of community unity, which is built on social ties and mutual support in times of crisis.” (FGD Central, P3) Subtheme 4.2. Flexible routines and low-tech innovation Both rural and urban participants described adopting locally driven strategies to cope with climate extremes. In rural areas, adaptations included adjusting work schedules and introducing low-cost innovations to support daily survival. Rural participants stressed that these practices were grounded in practical knowledge, traditional methods, and a deep familiarity with their environment. “When flooding is expected, we design makeshift boats to transport our livestock and families to safer areas. We're familiar with local rainfall patterns and try to align our planting cycles with the weather to improve crop yields. Rainwater harvesting is important to us; we store water in barrels and underground tanks because our region experiences unpredictable shifts from droughts to heavy rainfall. We’ve seen it all.” (ID-19, Seasonal Farmer) Urban participants, by contrast, reported relying more on market-based solutions, including the use of solar energy, refrigeration, and adjustments to work schedules. Stakeholders confirmed that communities in both rural and urban settings adapted their routines, tools, and practices to manage risks during extreme weather events, drawing on a combination of indigenous knowledge and emerging technologies. “As summer’s heat up, they’re moving their work to the cooler early mornings or evenings. Weather changes are affecting school attendance, leading to more home-based schooling, and altering how people collect water for cooking. There’s a noticeable shift towards using solar-powered batteries, fans, lights, and direct current systems. Whether you’re in the village or the city, you can see this change. Through traditional practices, people are relying on old methods like using woven beds outside or on rooftops, and mud walls provide better natural ventilation compared to concrete houses. Farmers are quite skilled at observing river flows and animal behaviour to develop early warning systems.” (FGD South, P 36) Theme 5. Towards responsive and climate-sensitive aid Subtheme 5.1. Disaster-specific, localised relief Participants emphasised the limitations of static, poverty-focused initiatives such as BISP in addressing disaster-related needs. One-time cash grants were regarded as insufficient and poorly aligned with local priorities. Urban participants, in particular, called for reforms to make aid more responsive, suggesting mechanisms such as mobile cash transfers, tailored recovery packages, and support for livelihood diversification. “People who live in areas prone to floods, droughts, or heatwaves should definitely be on a list to receive assistance, and this list needs to be reviewed and updated at least once a year. When a disaster strikes, they should receive emergency funds immediately through mobile apps like Jazz Cash or Easypaisa, no waiting around. However, this support shouldn’t just last a few days. It should also help people rebuild their lives. For instance, if someone lost their livestock in a flood, they should be provided with new goats or chickens. If their crops were ruined, they should receive seeds and farming equipment. If heatwaves damaged their sewing machine or handcart, they should get help to replace those items. The programme should also focus on teaching people new skills and assisting them in starting small businesses such as mobile repair, sewing, or farming that can adapt to changing weather so they can earn money again and truly recover.” (ID-5, Home Tutor) Stakeholders similarly acknowledged the structural limitations of existing systems, particularly the lack of climate-specific indicators to guide programme design and implementation. They proposed the introduction of adaptive mechanisms, such as pre-disaster cash transfers and microinsurance schemes, to strengthen anticipatory capacity and enhance resilience. “BISP provides some relief... but it wasn't specifically created for disasters related to climate change. Genuine recovery calls for a comprehensive approach. We need to test an adaptive social protection system within BISP operations, targeting only those individuals who are at verified climate risk. Vulnerability mapping is crucial for pre-disaster payments, where high-risk households are identified in advance. Payments should be made through easy mobile cash transfers instead of just offering reactive support when a climate crisis hits. We also need microinsurance for small-scale farmers and climate resilience funds that link disaster-related public financing from government or international donor sources. We need a shock-responsive and forward-thinking adaptive system for BISP that not only focuses on reducing poverty but also on building climate resilience.” (FGD Central, P4) Subtheme 5.2. Early warning and infrastructure gaps Across both rural and urban settings, participants expressed dissatisfaction with weak early warning systems and inadequate infrastructure. Rural residents particularly highlighted delays in receiving alerts and challenges in accessing evacuation routes. Urban participants reported receiving warnings through multiple channels, yet noted inconsistencies and concerns regarding their reliability. “I remember a time when we received a flood alert via an announcement at the mosque it had a significant impact. We require that type of local alert system in all communities. Sometimes we receive text messages or automated calls, and occasionally we find updates on social media, TV, or radio. However, these alerts can be unreliable. There is a pressing need to enhance the accessibility, frequency, and clarity of early warnings so that everyone, no matter their location, can prepare in a timely manner.” (ID-21, Home-based Business) Stakeholders underscored the absence of localised forecasting mechanisms and recommended enhanced inter-agency coordination, greater investment in technology, and the establishment of community-based warning networks. “It's not too tough to set up early warning systems in Pakistan. We could kick things off by bringing together platforms like NDMA and PDMA, along with real-time dashboards. We should invest in forecasting models and monitoring systems, especially in provinces that are prone to extreme weather. Plus, we can train local volunteers, like climate activists, to act as ambassadors for early warning systems and help build community-based initiatives. Improving communication is key, so we should use radio and SMS to send localized early alerts instead of the usual generic extreme weather warnings. For instance, we could notify people that a specific area is expected to have 4 feet of water in the next 24 hours. Right now, we just get a standard voice message or text. International donor agencies could also help with capacity building for community training and evacuation drills. These are all basic yet essential steps for enhancing early warning systems.” (FGD North, P20) Discussion This study explored how socio-economic incentives particularly one-time cash grants and the Benazir Income Support Programme (BISP) shape the climate resilience of informal workers in Khyber Pakhtunkhwa. Participants identified five interconnected areas: inadequate and inflexible financial support; barriers to accessing programmes; gendered constraints and power dynamics; everyday adaptation strategies; and the need for climate-sensitive and responsive aid. Overall, while cash transfers offered short-term relief, they did not support long-term resilience, leaving workers to depend heavily on their own adaptive practices. Our findings highlight a persistent misalignment between existing social protection schemes and the recurrent, long-term nature of climate risks. This reflects broader debates in resilience literature, where resilience is often framed as a short-term “bounce back” capacity ( 61 , 62 ). Cash transfers contributed to absorptive capacity, helping households manage immediate shocks consistent with evidence from Kenya’s HSNP, Ethiopia’s PSNP, and Mexico's programmes ( 62 , 63 ). However, as documented elsewhere, cash alone seldom enables sustained adaptation without complementary interventions such as credit, assets, or skills development ( 64 ). Barriers to access were central to participants’ experiences. Digital exclusion, low literacy, and bureaucratic procedures disproportionately affected rural and marginalised groups. Women faced compounded disadvantages due to mobility restrictions, low digital literacy, and dependence on male relatives, echoing evidence that digital inclusion requires simplified tools, non-digital options, and local training ( 65 ). Participants also reported corruption, gatekeeping, and informal deductions, consistent with independent evaluations documenting leakage and elite capture in BISP ( 33 ). Gendered and cultural constraints identified in our study mirror broader LMIC evidence that weak attention to gender norms limits social protection effectiveness and can perpetuate hidden forms of exploitation ( 66 , 67 ). Participants also described everyday adaptive strategies: informal social support, adjustments to work routines, rainwater harvesting, and small-scale solar use, reflecting adaptive capacity as described in resilience frameworks. Similar forms of community-based resilience have been reported in Nepal and Bangladesh ( 68 , 69 ), and across Asia and Africa, where indigenous knowledge and community agency underpin climate adaptation ( 70 ). Finally, participants stressed the need for flexible, rapid, and hazard-specific support, including mobile cash transfers and climate-sensitive programme design. These suggestions align with global calls for shock-responsive, anticipatory social protection that integrates climate triggers, vulnerability mapping, and microinsurance LMICs ( 71 , 72 ). Participants also noted weaknesses in Pakistan’s disaster governance, echoing wider critiques of fragmented early warning systems and limited local infrastructure ( 73 – 75 ). Evidence from South Asia shows that community-embedded early warning systems can substantially reduce disaster losses ( 76 , 77 ). Implications for practice, policy and research The findings of this study indicate several ways to strengthen the contribution of social safety nets, including BISP and one-off cash grants, to the climate resilience of informal sector workers in Pakistan. First, social protection programmes should incorporate climate sensitivity into their design. BISP needs to move beyond a static poverty focus and adopt climate responsive mechanisms such as mobile cash transfers, rapid disbursement during emergencies, and disaster contingent top ups. Strengthening the links between climate change, humanitarian response, and long-term development would allow BISP to operate as a more transformative safety net rather than a minimal source of temporary relief. Second, supporting long-term resilience requires a shift from short-term absorptive assistance toward interventions that build adaptive and transformative capacities. Access to credit, skills training, livelihood diversification, and asset building would enable households to invest in climate resilient housing, livelihoods, and small enterprises, reducing recurring reliance on emergency transfers. Third, improving accessibility and eligibility is essential. Regularly updating beneficiary lists through poverty and climate vulnerability assessments, particularly in high-risk districts, would enhance programme responsiveness. Aligning BISP with disaster risk reduction frameworks, alongside training female community facilitators to support women with low literacy and limited mobility, would reduce digital exclusion and dependence on male intermediaries. Fourth, governance and accountability mechanisms require strengthening to address corruption, elite capture, and informal deductions. A hybrid accountability model that combines technology enabled transparency with community-based monitoring, local grievance redress systems, and citizen oversight can promote greater trust and ensure that both institutions and communities have meaningful roles in shaping programme delivery. Finally, improving interagency coordination is critical for climate resilience. Collaboration between BISP, the National Disaster Management Authority, and Provincial Disaster Management Authorities can support the development of climate smart safety nets. Practical measures include the establishment of trained community early warning ambassadors and investment in localised, actionable early warning systems that directly link with social protection delivery. Strengths and weaknesses of the study This study, to our knowledge the first qualitative inquiry in Pakistan to explore social safety nets and climate resilience among informal workers, has several limitations. Qualitative findings are not statistically generalisable, though they offer transferable insights. The study focused on KP; experiences may differ across provinces. Cultural constraints limited women’s participation, particularly among stakeholders, reflecting broader gender imbalances in leadership across LMICs. Finally, reliance on self-reported experiences may involve recall bias, although contextual probes were used to improve accuracy. Despite these limitations, the study provides early evidence on how socio-economic incentives intersect with climate vulnerability in KP. The findings underscore the need for climate-sensitive, inclusive, and adaptive social protection systems capable of addressing both immediate shocks and long-term risks in Pakistan and comparable LMIC settings. Conclusion This study shows that while BISP and one-off cash grants provide essential short-term relief, they do not address the recurring and long-term nature of climate shocks faced by informal workers in Khyber Pakhtunkhwa. Limited amounts, inflexible delivery, digital and bureaucratic barriers, and gendered constraints restrict their effectiveness. As a result, households rely heavily on informal networks and low-cost adaptations to cope with floods, heatwaves, and droughts. To strengthen resilience, social protection in Pakistan must become climate-sensitive, gender-responsive, and easier to access. Priority actions include rapid and hazard-specific cash support, updated beneficiary targeting based on climate risk, improved outreach for women and low-literacy groups, and stronger accountability. Linking cash transfers with measures that build skills, assets, and diversified livelihoods is essential for longer-term adaptation. Although qualitative findings are not generalisable, they offer important insights for designing social protection systems that support both immediate recovery and sustained resilience in Pakistan and similar LMIC contexts. Declarations Competing Interests: The authors declare that they have no competing interests. Funding: This study was supported by a grant from the American Red Cross Global Disaster Preparedness Center (GDPC) Research Grants Program for Urban Climate Resilience. The funder had no involvement in the study’s design, data collection, analysis, interpretation of findings, decision to publish, or writing of the manuscript. Author Contribution MIK led the study, guided data collection, and developed the main manuscript. ZUH contributed to the study design, coordination, and interpretation of the findings. KR provided climate and health expertise and supported the analytical framing. SIK managed the data and assisted with analysis. SSB contributed to data collection, transcription, and preliminary coding. HMJ and AA provided methodological guidance, supported interpretation, and contributed to manuscript revisions. All authors reviewed and approved the final manuscript. Acknowledgement We thank all the participants who took part in this study, including community members and district-level stakeholders. Their time, insights, and cooperation were essential to this work. 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Social vulnerability, impacts and adaptations strategies in the face of natural hazards: insight from riverine islands of Bangladesh. BMC Public Health [Internet]. 2023 Dec 1 [cited 2025 Aug 26];23(1):1–15. Available from: https://bmcpublichealth.biomedcentral.com/articles/ 10.1186/s12889-023-16497-8 Dorji T, Rinchen K, Morrison-Saunders A, Blake D, Banham V, Pelden S. Understanding How Indigenous Knowledge Contributes to Climate Change Adaptation and Resilience: A Systematic Literature Review. Environ Manag 2024 746 [Internet]. 2024 Aug 31 [cited 2025 Aug 26];74(6):1101–23. Available from: https://link.springer.com/ article/10.1007/s00267-024-02032-x Huber J, Murray U. Turning climate justice into practice? Channeling loss and damage funding through national social protection systems in climate-vulnerable countries. Wiley Interdiscip Rev Clim Chang [Internet]. 2024 Mar 1 [cited 2025 Aug 26];15(2):e867. Available from: /doi/pdf/10.1002/wcc.867 Aleksandrova M, Kuhl L, Malerba D. Unlocking climate finance for social protection: an analysis of the Green Climate Fund. Clim Policy [Internet]. 2024 Aug 8 [cited 2025 Aug 26];24(7):878–93. Available from: https://www.tandfonline.com/doi/pdf/ 10.1080/14693062.2024.2338817 Anis F. Role of Digital Media in Disaster Management: A Case of Khyber Pakhtunkhwa Pakistan. J Dev Soc Sci. 2023;4(I). Batool DS, Zaffer A, Batool DF. Role of New Media in Good Governance: A Study of Disaster Management Authorities of Pakistan. J Prof Res Soc Sci [Internet]. 2024 Jun 30 [cited 2025 Aug 27];11(1):239–65. Available from: https://ojs.mul.edu.pk/index.php/JPRSS/article/view/425 Ishrat S, Hameed N. The Disaster Profile of Pakistan & Its Management Strategies. Res J Soc Issues [Internet]. 2024 Feb 17 [cited 2025 Aug 27];6(1):27–49. Available from: https://rjsi.org.pk/index.php/Research/article/view/174 Gumiran BAL, Moncada FM, Gasmen HJ, Boyles-Panting NR, Solidum RU. Participatory capacities and vulnerabilities assessment: Towards the realisation of community-based early warning system for deep-seated landslides. Jàmbá J Disaster Risk Stud [Internet]. 2019 [cited 2025 Aug 27];11(1):555. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6489152/ Al-mueed M, Chawdhery MRA, Harera E, Alhazmi RA, Mobrad AM, Althunayyan SM et al. Potential of community volunteers in flood early warning dissemination: A case study of Bangladesh. Int J Environ Res Public Health [Internet]. 2021 Dec 1 [cited 2025 Aug 27];18(24):13010. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8700901/ Additional Declarations No competing interests reported. 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Altered weather patterns and the rise in extreme events undermine agricultural production, cause crop failures, and increase food insecurity for millions (4). Vulnerable populations are disproportionately affected, with climate impacts exacerbating poverty and inequality (5). Evidence suggests heightened risks faced by developing countries, driven by financial constraints, reliance on climate-sensitive industries such as agriculture, and insufficient infrastructure to withstand severe shocks (5\u0026ndash;7)\u003c/p\u003e\n\u003cp\u003ePakistan exemplifies these vulnerabilities, experiencing frequent floods, droughts, and heatwaves that compound existing socio-economic challenges (8). The 2010 floods affected 20 million people, caused 1,800 deaths, destroyed over 400 health facilities, and inflicted damages worth US$10 billion (9). Food insecurity was simultaneously aggravated, affecting nearly 30% of the population and leaving an additional five million people undernourished (9). More recently, the 2022 floods impacted more than 33 million people, caused 1,700 deaths, led to US$2.3 billion in agricultural losses, and generated total damages of nearly US$30 billion (10,11). Such shocks have been shown to generate long-term implications for health and nutrition, with water-related diarrhoeal diseases persisting as a leading cause of infant mortality and prolonged droughts leaving more than half of the country food and water insecure(12,13).\u003c/p\u003e\n\u003cp\u003eKhyber Pakhtunkhwa (KP), a province in north-western Pakistan with a population of over 30.5 million and an annual growth rate of 2.9%, is particularly vulnerable to climate change owing to its diverse geography\u003cem\u003e\u0026nbsp;\u003c/em\u003e(14,15). The northern districts are exposed to glacier lake outburst floods, the central plains to heatwaves and urban flooding, and the southern areas to prolonged drought and water scarcity (16\u0026ndash;18). These risks undermine agriculture and rural livelihoods, aggravate poverty, displace communities, and place additional strain on limited adaptation resources (19).\u003c/p\u003e\n\u003cp\u003eThe concept of resilience has become central within climate and development discourse. Resilience is defined as the capacity of individuals, households, and systems to absorb, adapt, and transform in response to shocks (20,21). Absorptive capacity is understood as immediate coping, adaptive capacity as incremental adjustment, and transformative capacity as systemic change that addresses structural drivers of vulnerability (20). It has been emphasised that resilience must be linked to equity, justice, and redistribution; otherwise, marginalised groups may be further disadvantaged under adaptation agendas (22,23).\u003c/p\u003e\n\u003cp\u003eGlobally, social protection has been positioned as a key instrument for strengthening resilience to climate shocks (24,25). Social assistance in particular has demonstrated effectiveness in stabilising consumption, improving nutrition, and supporting recovery during shocks. In Ethiopia, the Productive Safety Net Programme (PSNP) improved food security and reduced reliance on negative coping strategies during droughts (26,27). \u0026nbsp;In Kenya, the Hunger Safety Net Programme (HSNP) raised food consumption scores by 2.6-3.4% during droughts, while the Inua Jamii programme improved dietary diversity and nutrient intake (28,29). In India, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) provided seasonal income while supporting water conservation and soil fertility, thereby strengthening adaptive capacity (30,31). In Latin America, cash transfers such as Ecuador\u0026rsquo;s Bono de Desarrollo Humano and Brazil\u0026rsquo;s Bolsa Fam\u0026iacute;lia have demonstrated transformative effects on social inclusion and women\u0026rsquo;s agency (32\u0026ndash;34).\u003c/p\u003e\n\u003cp\u003eIn Pakistan, the Benazir Income Support Programme (BISP) represents South Asia\u0026rsquo;s largest cash transfer programme, reaching 5.6 million families by 2021, equivalent to around 40% of those living below the poverty line (35). Evaluations have documented measurable impacts on women\u0026rsquo;s empowerment, household food security, and child nutrition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(36\u0026ndash;38)\u003c/p\u003e\n\u003cp\u003eDuring crises, BISP and Ehsaas programme have been scaled up, including one-time disbursements of PKR 25,000 to over 250,000 households during the 2022 floods (10,39). Despite these contributions, such schemes have remained primarily designed for poverty alleviation and maternal and child health rather than climate resilience (40).\u003c/p\u003e\n\u003cp\u003eThe informal sector accounts for more than 70% of Pakistan\u0026rsquo;s workforce (41,42), and is particularly exposed to climate-related risks. Employment is concentrated in agriculture, daily wage labour, construction, and small-scale enterprises, with livelihoods characterised by instability, lack of savings, and exclusion from formal protection schemes (43,44). Women in the informal economy experience compounded disadvantages, including wage disparities, restricted mobility, and limited decision-making authority (45). Moreover, the informal sector forms the backbone of KP\u0026rsquo;s economy, absorbing nearly three-quarters of the working-age population through subsistence farming, daily wage labour, construction, and small-scale enterprises, yet it remains highly exposed to climate shocks and largely excluded from formal social protection systems (46).\u003c/p\u003e\n\u003cp\u003eTherefore, this gap in the literature was addressed in the current study through an exploration of how socio-economic incentives, particularly the BISP and one-time cash grants, shape resilience among informal sector workers in KP, Pakistan. By foregrounding the lived experiences of workers and incorporating institutional perspectives, the study generates policy-relevant insights into the adequacy, accessibility, and gendered dimensions of social protection systems in contexts of climate vulnerability, with wider relevance for LMICs confronting similar challenges.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a qualitative phenomenological design to explore how socio-economic incentives, particularly the BISP and one-time cash grants, influence the climate resilience of informal sector workers in KP, Pakistan. A phenomenological orientation was adopted to enable an in-depth exploration of participants lived experiences and the meanings they attributed to support mechanisms in the context of climate-related shocks. This approach is especially well suited to contexts where subjective realities and personal interpretations are central to understanding social phenomena (47,48). This study adopted a relativist ontology, acknowledging realities as shaped by socio-economic and environmental contexts (49,50), and a constructivist epistemology, viewing knowledge as co-produced between researchers and participants (51).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant recruitment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multistage purposive sampling strategy was used in this study to capture variation across geography, occupation, gender, and age. This approach has previously been applied in qualitative research to ensure maximum variability and representation across system levels and settings (52,53). In the first stage, three climatic zones of Khyber Pakhtunkhwa were identified: northern, central, and southern. In the second stage, one district was purposively selected from each zone: Swat (north), Peshawar (central), and Dera Ismail Khan (south). In the third stage, two local government areas within each district were chosen to represent both urban and peri urban or rural contexts. This approach reflects guidance on purposeful sampling that recommends beginning with maximum variation to capture diverse contexts and then narrowing through criterion-based selection to achieve depth of understanding (54).\u003c/p\u003e\n\u003cp\u003eEligibility for informal sector workers required participants to be at least eighteen years of age, currently engaged in informal employment within one of the selected districts, and to have directly experienced at least one extreme weather event such as flooding, drought, or a heatwave within the past five years. Individuals were excluded if they had previously served as paid community mobilisers, volunteers, or informants for aid distribution agencies, in order to minimise institutional bias. Those with cognitive, speech, or language impairments that prevented meaningful participation in an interview-based study were also excluded. For institutional stakeholders, eligibility was defined as holding an active professional role in social protection, disaster management, health, or community development within the study districts. Those occupying purely administrative or symbolic positions without direct engagement with communities were excluded.\u003c/p\u003e\n\u003cp\u003eRecruitment strategies were adapted to the two participant groups. For informal sector workers, community elders, peer groups, and representatives of non-governmental organisations acted as intermediaries, introducing the research team and fostering trust. To reduce external visibility and enhance accessibility, local researchers from the northern, central, and southern zones were recruited. Potential participants were approached in homes, markets, streets, and other workplaces, and were provided with a study information leaflet in the local language alongside a verbal explanation of the study aims, procedures, potential risks and benefits, and assurances of confidentiality. Recruitment and scheduling were adapted to minimise barriers for women and marginalised groups participation: female interviewers were available, interviews were arranged flexibly around work and household responsibilities, and family accompaniment was permitted when requested. Participation was voluntary, with individuals given adequate time to consider their involvement. In total, forty-five informal sector workers were approached (fifteen per climatic zone) for the in-depth interviews (IDIs), of whom thirty consented to participate in the IDIs (ten per zone).\u003c/p\u003e\n\u003cp\u003eInstitutional stakeholders were identified through purposive sampling of relevant sectors, including social protection, disaster management, health, and community development. Initial introductions were facilitated by district offices and organisational representatives, after which formal letters and follow-up calls were used to extend invitations. Interested stakeholders received a study information leaflet and a verbal briefing outlining the study\u0026rsquo;s aims, procedures, potential risks and benefits, and assurances of confidentiality. Participation was voluntary, and informed consent was obtained before involvement. In total, thirty-six stakeholders were recruited (twelve per zone) and organised into six focus group discussions (FGDs) (two per zone, six participants each).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the sampling strategy, data were collected from six purposively selected local government areas within three districts: Mingora and Kabal in Swat (northern zone), Peshawar City and Charsadda (central zone), and Dera Ismail Khan and Tank (southern zone). This selection encompassed both urban and peri-urban/rural contexts, enhancing the diversity and contextual depth of the dataset. Data were collected between April and June 2025 through thirty IDIs with informal sector workers (ten per climatic zone) and six FGDs with institutional stakeholders (two FGDs per zone, each with six participants). Semi-structured topic guides were developed by a multidisciplinary team at Khyber Medical University\u0026hellip; Separate versions were prepared for IDIs and FGDs to capture the lived experiences of informal workers and the institutional perspectives of programme implementers and policymakers.\u003c/p\u003e\n\u003cp\u003eSemi-structured topic guides were developed by a multidisciplinary team at Khyber Medical University, bringing together expertise in qualitative research, global health, epidemiology and biostatistics, climate change, climate finance, and public health data science. Efforts were made to draw on this diversity of expertise, together with relevant literature on climate resilience and social protection in LMICs, to develop guides that were methodologically robust and contextually relevant. The team comprised specialists in public health, qualitative research, epidemiology, climate science, and health data science, ensuring that the guides were informed by both conceptual knowledge and applied expertise. Separate versions were prepared for IDIs and FGDs to capture both the lived experiences of informal workers and the institutional perspectives of programme implementers and policymakers.\u003c/p\u003e\n\u003cp\u003eIDIs were conducted at participants\u0026rsquo; worksites or in nearby safe settings by bilingual, trained research associates (one male and one female), each lasting approximately 30 minutes. FGDs were facilitated by the female principal investigator in private community venues, such as school facilities, with each session lasting between 30 and 45 minutes. All IDIs and FGDs were conducted in the local language (Pashto or Urdu). Reflective notes were made by the researchers after each session to enhance reflexivity (55). Informed consent was documented in accordance with participant preference and literacy level, either by signed form for those with sufficient education or by thumb impression for those with limited formal education. All participants provided consent for both participation and audio recording. The first IDI and FGD were piloted to assess the appropriateness of the topic guides; as no substantive revisions were required, both were retained in the dataset. In qualitative research, data saturation refers to the point at which further data collection no longer generates new codes, themes, or insights, and additional data collection are unlikely to add substantive value to the analysis (56). Data saturation in this study was reached after thirty IDIs and six FGDs, at which point no new themes emerged and participant accounts became repetitive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll IDIs and FGDs were conducted in local language (Pashto or Urdu) and transcribed verbatim into English by trained bilingual members of the research team. For FGDs, institutional stakeholders were offered the opportunity to review their transcripts, though none requested changes. As some informal sector participants had limited literacy, transcripts were not returned for review. Instead, interviewers provided verbal summaries at the end of each session, inviting participants to clarify or expand on their views.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA thematic analysis was undertaken, guided by a descriptive phenomenological orientation. We followed Sundler et al. (2019), who outline an approach to thematic analysis suited to phenomenological research, emphasising openness, reflexivity, and questioning presuppositions (57). ATLAS.ti (version 1.8) was used to support the organisation and management of data (58).\u003c/p\u003e\n\u003cp\u003eTwo researchers (SSB, SIK) independently read the transcripts several times to immerse themselves in the text and develop a deeper understanding of participants lived experiences. From these readings, they identified key statements and short sections of text that reflected important aspects of participants\u0026rsquo; accounts. These were grouped into patterns, which formed the basis for preliminary themes. The preliminary themes were then reviewed, refined, and discussed within the wider research team (MIK, ZUH, SIK, SSB, KR) to ensure consistency and strengthen interpretation. Finally, the themes were integrated into a descriptive narrative that captured both shared experiences and variations across participants.\u003c/p\u003e\n\u003cp\u003eTo enhance rigour, presuppositions were bracketed as far as possible and regular team discussions were held to reflect on assumptions and strengthen the credibility of interpretation. Trustworthiness was further considered in line with Lincoln and Guba\u0026rsquo;s (1985) criteria: credibility, dependability, confirmability and transferability (59), which are elaborated in the Discussion. The study was conducted and reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) (60).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Khyber Medical University Ethics Board (Reference: Dir/Ethics/KMU-EB/EC/00106). Before taking part, individuals were informed about the aims of the research, the voluntary nature of their involvement, and their freedom to stop at any time without any consequences. To protect privacy, each participant was assigned a code, and any information that could reveal their identity was removed from all written records. All files were kept on secure, password-protected devices that could only be accessed by the research team. Following university guidelines, the data will be kept for five years and then permanently deleted. The study was carried out in line with internationally accepted ethical standards, including those outlined in the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 30 informal sector workers participated in the study, with equal representation from the northern (n\u0026thinsp;=\u0026thinsp;10), central (n\u0026thinsp;=\u0026thinsp;10), and southern (n\u0026thinsp;=\u0026thinsp;10) climatic zones summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sample comprised more women (n\u0026thinsp;=\u0026thinsp;16, 56.7%) than men (n\u0026thinsp;=\u0026thinsp;14, 43.3%). Participants ranged in age from 25 to 55 years, with a mean age of 38.0 years (SD\u0026thinsp;=\u0026thinsp;9.0). Educational attainment was generally low: two-thirds (n\u0026thinsp;=\u0026thinsp;20, 66.7%) had completed only primary-level education, while the remainder (n\u0026thinsp;=\u0026thinsp;10, 33.3%) had reached secondary level. Housing conditions reflected socio-economic precarity, with one-third (n\u0026thinsp;=\u0026thinsp;10, 33.3%) residing in mud houses, 23.3% (n\u0026thinsp;=\u0026thinsp;7) in semi-permanent structures, and 20% (n\u0026thinsp;=\u0026thinsp;6) in concrete houses, while others lived in rented rooms, flats, or informal shelters. Occupations varied, highlighting the heterogeneity of informal work. The most common livelihoods were domestic work (16.7%), farming (16.7%), and home-based businesses (16.7%). Other activities included casual labour (10%), street vending (6.7%), tailoring (6.7%), shopkeeping (6.7%), and low-wage work such as waste picking, gardening, and service provision. Household dependency levels were high. Nearly half of participants (46.7%) reported supporting four or more dependents, while 36.7% supported two to three, and 16.6% had only one dependent.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic and Household Characteristics of Informal Sector Worker Participants (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eType of Housing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDependents Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStreet Vendor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (6 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHome-based Business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConcrete house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (5 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGardener\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented room\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow (1 dependent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHome Tutor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBrick house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStreet Vendor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented flat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (4 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElectrician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTailor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConcrete house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow (1 dependent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (5 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eService Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented flat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCasual Laborer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (6 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDomestic Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented room\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (5 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConstruction Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaste Picker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInformal shelter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHome-based Business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBrick house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow (1 dependent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHome-based Business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConcrete house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShopkeeper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSeasonal Farmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (4 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShopkeeper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConcrete house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (6 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHome-based Business\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConcrete house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTailor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow (1 dependent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDomestic Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented room\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (4 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDomestic Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDomestic Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInformal shelter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDomestic Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSemi-permanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCasual Laborer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRented flat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (4 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCasual Laborer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh (5 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (3 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMud house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMedium (2 dependents)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of 36 institutional stakeholders participated in the study, summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The majority were male (n\u0026thinsp;=\u0026thinsp;25, 69.4%), with females comprising (n\u0026thinsp;=\u0026thinsp;11, 30.6%). Regional representation was evenly distributed, with 12 participants drawn from each of the Central, North, and South climatic zones. Participants\u0026rsquo; ages ranged from 26 to 59 years, with a mean age of 42.8 years (SD\u0026thinsp;=\u0026thinsp;9.3); the mean age for males was 44.0 years and for females 40.3 years. With respect to education, most stakeholders (n\u0026thinsp;=\u0026thinsp;26, 72.2%) had attained tertiary-level qualifications, while the remainder (n\u0026thinsp;=\u0026thinsp;10, 27.8%) had completed education up to the secondary level. Years of professional experience varied considerably, ranging from 4 to 25 years, with an overall mean of 11.1 years. Stakeholders from the South zone reported the highest mean work experience (13.3 years), followed by those in the Central (10.3 years) and North (9.8 years) zones.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic and Professional Characteristics of Institutional Stakeholder Participants (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWork Experience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProvincial Disaster Management Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRescue Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Safety Net (BISP) Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProvincial Disaster Management Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRescue Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Safety Net (BISP) Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRescue Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProvincial Disaster Management Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Safety Net (BISP) Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEmergency Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClimate Finance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eForestry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironmental Protection Agency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Safety Net (BISP) Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealthcare Commission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Safety Net (BISP) Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironmental Protection Agency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProvincial Disaster Management Dept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRescue Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClimate Finance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEmergency Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial Welfare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnvironment Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRescue Department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAnalysis of the interviews and focus group discussions generated five overarching themes and ten related sub-themes, these are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which presents the themes and associated sub-themes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of themes and sub-themes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThemes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSub-themes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Inadequate and inflexible financial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 Static assistance amid dynamic crises \u003c/p\u003e\u003cp\u003e1.2 Debt, instability, and recovery fatigue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Barriers to accessing social protection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1 Digital gaps and bureaucratic complexity \u003c/p\u003e\u003cp\u003e2.2 Cultural and logistical barriers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Gendered constraints and local power structures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1 Aid without autonomy \u003c/p\u003e\u003cp\u003e3.2 Gatekeeping and informal exploitation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Everyday adaptation and informal resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.1 Social networks and mutual aid \u003c/p\u003e\u003cp\u003e4.2 Flexible routines and low-cost innovations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Towards responsive and climate-sensitive aid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.1 Disaster-specific, localised relief\u003c/p\u003e\u003cp\u003e5.2 Early warning and infrastructure gaps\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eTheme 1. Inadequate and inflexible financial support\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 1.1. Static assistance amid dynamic crises\u003c/h2\u003e\u003cp\u003eParticipants across rural and urban settings emphasised that although social safety nets and one-time grants provided short-term relief, the fixed amounts and quarterly disbursements were inadequate to meet the scale and duration of climate-related crises. Rural households, particularly those dependent on farming and livestock, described a mismatch between seasonal vulnerabilities and rigid support structures.\u003c/p\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eThe cash from the social programme and one-time grants provided immediate help, which we used for food and some crop medicines. However, the funds were quickly depleted since support comes only every three months. As we depend on farming for income, I invested part of it in hopes of a good harvest. Unfortunately, the limited and infrequent support does not address larger issues, especially during floods or bad weather, and it doesn't help us prepare for the long term or recover from major losses\u003c/em\u003e.\u0026rdquo; (ID-9, Farmer)\u003c/p\u003e\u003cp\u003eUrban informal workers similarly reported that support was mainly used to cover immediate needs such as rent, utilities, and food, but did not compensate for lost income or broader economic disruption. A few participants attempted to invest the assistance in small-scale livelihood activities, though these efforts were frequently undermined by subsequent climate shocks.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I can't really say that the support has boosted my income since I still rely on selling flowers, seeds, and plants from my garden to help my family. However, it did provide me with some extra cash to grow my business. I used some of that money to buy essential tools and cover transport costs to local markets. The weather, though, is a real challenge. Heavy rains and heatwaves frequently ruin my plants or hinder my ability to reach customers. Sometimes, roads get blocked, making it tough to deliver orders or bring in supplies on time. The assistance is helpful to some extent, but it doesn't fully address the actual costs of these setbacks. As a gardener, I really need steady support that can safeguard my work and income when the weather doesn't cooperate.\u0026rdquo;\u003c/em\u003e (ID-4, Gardener)\u003c/p\u003e\u003cp\u003eStakeholders echoed these views, noting that BISP is primarily oriented towards poverty alleviation and maternal health rather than climate resilience. While it has the capacity to offer one-time cash grants during climate events or emergencies, it was regarded as insufficiently flexible or responsive to the acute and evolving challenges posed by climate-induced shocks.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In the floods of 2022, the government handed out an extra Rs. 12,000, but honestly, that was just a tiny fraction of the massive losses. Whole crops were destroyed, livestock died, and families had to start over from scratch. The truth is, programme like BISP and unconditional cash transfers weren\u0026rsquo;t really set up with climate resilience in mind. They overlook areas and people that are frequently hit by floods, droughts, and other disasters. These payments aim to help with women\u0026rsquo;s education, health, and reducing poverty but they really don\u0026rsquo;t cut it when it comes to ongoing climate crises.\u0026rdquo;\u003c/em\u003e (FGD South, P25)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 1.2. Debt, instability, and recovery fatigue\u003c/h2\u003e\u003cp\u003eThe fragile nature of informal work made participants particularly vulnerable during climate events, often pushing them into debt. Rural workers described being caught in cycles of borrowing to offset agricultural and livestock losses, while urban workers reported taking loans to survive when floods or storms disrupted customer flows and reduced their earnings.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;However, when heavy rains flooded the streets, customers stopped coming. That\u0026rsquo;s when things got really tough. We receive Rs. 12,500 every three months, which is helpful, but it\u0026rsquo;s not sufficient especially during emergencies. If the money came in monthly, it would really make a difference. Even during normal times, we find it hard to make ends meet, and when a flood or heatwave strikes, everything goes downhill. I had to borrow from family just to keep afloat. Once you get into debt like that, it\u0026rsquo;s really difficult to bounce back.\u0026rdquo;\u003c/em\u003e (ID-12, Casual Labourer)\u003c/p\u003e\u003cp\u003eStakeholders observed that successive disasters had created a sense of \u0026ldquo;recovery fatigue,\u0026rdquo; as households struggled to rebuild before the next crisis. They noted that the increasing frequency and intensity of climate events, together with limited support systems, hindered households\u0026rsquo; ability to recover.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In Khyber Pakhtunkhwa, communities are stuck in a constant loop of rebuilding. Every flood or heatwave brings new losses, whether it\u0026rsquo;s crops, livestock, homes, or health. Recovery rarely finishes before another disaster strikes, and we find ourselves scrambling to tackle the next problem. There\u0026rsquo;s no solid prevention strategy in place, neither financially nor in terms of climate.\u0026rdquo;\u003c/em\u003e (FGD Central, P-2)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTheme 2. Barriers to accessing social protection\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003eSubtheme 2.1. Digital gaps and bureaucratic complexity\u003c/h2\u003e\u003cp\u003eParticipants reported that limited literacy and lack of digital access posed major barriers to engaging with social protection services, especially in rural areas and among women. Older adults, in particular, described challenges with reading, writing, and using mobile technologies. The absence of smartphones, unfamiliarity with SMS-based systems, and confusion about registration procedures further constrained their ability to access available support.\u003c/p\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eMy wife and I struggle to grasp mobile apps and point-of-sale systems, which complicates things for us. The SMS notifications we get are also puzzling and hard to make sense of. All of this just adds to the confusion and stress we deal with during the whole process.\u0026rdquo;\u003c/em\u003e (ID-7, Electrician)\u003c/p\u003e\u003cp\u003eStakeholders acknowledged that while digitalisation enhanced programme efficiency, it also created new barriers that excluded vulnerable groups.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Since our launch in 2008\u0026ndash;2009, we\u0026rsquo;ve seen a big digital shift. Initially, we operated as a paper-based transfer programme, but by 2015, we teamed up with NADRA to transition to ATM cards. Then in 2018, we collaborated with various banks for mobile cash transfers, which has improved our data management. However, this shift has also left many people behind, particularly women in rural or low-literacy areas. They often don\u0026rsquo;t know where to go or how to sign up. If we don\u0026rsquo;t address this gap, even the most well-designed programme will fail to reach those who need assistance the most.\u0026rdquo;\u003c/em\u003e (FGD Central, P6)\u003c/p\u003e\u003cp\u003eBy contrast, urban workers with smartphones and workplace connectivity reported smoother access to services. However, in both urban and rural settings, misinformation, missed announcements, and bureaucratic hurdles continued to restrict engagement. Rural residents were especially disadvantaged by the long travel required to reach distant service centres.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Whether you\u0026rsquo;re in the village or the city, many of us face the same problems. At first, I didn\u0026rsquo;t know how to apply and had to ask around. Some shops turned me away, saying only certain ones handled applications. When I finally found the right place, they said my wife wasn\u0026rsquo;t eligible, so we had to go to NADRA to prove otherwise, which caused further delays. In cities, people may also deal with paperwork issues, but they can usually reach offices more easily. In rural areas, it means long trips on broken roads and multiple visits, especially when documents are missing. Many people, like us, miss out on aid or deadlines simply because the process isn\u0026rsquo;t clearly explained. The system may be simple, but the confusion makes it much harder than it should be.\u0026rdquo;\u003c/em\u003e (ID-19, Seasonal Farmer)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 2.2. Cultural and logistical barriers\u003c/h2\u003e\u003cp\u003eParticipants explained that in rural areas, cultural norms surrounding female modesty and family honour constrained women\u0026rsquo;s mobility. Women were often required to travel with male relatives, leading to delays or missed appointments when escorts were unavailable. This dependence on male family members limited timely access to services, particularly during emergencies.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;For women in our Pashtoon culture, things are even harder. They\u0026rsquo;re not allowed to travel alone and always need a male family member to go with them for safety and respect. But we're often busy or not around, so they end up missing appointments or deadlines just because they can\u0026rsquo;t go on their own. These restrictions keep them dependent and make it hard for them to speak up or do things for themselves. In the end, it\u0026rsquo;s not just the women who are affected, this holds back progress for the whole community.\u0026rdquo;\u003c/em\u003e (ID30, Farmer)\u003c/p\u003e\u003cp\u003eIn urban contexts, women reported comparatively greater autonomy, facilitated by access to transport and digital technologies. By contrast, travel infrastructure posed a substantial barrier in rural areas, where poor road conditions and distant service centres made aid collection highly burdensome. Stakeholders further noted that cultural constraints, compounded by infrastructure breakdowns, disproportionately disadvantaged women, children, and persons with disabilities.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;During disasters, people often struggle to access any kind of facilities, whether it's healthcare or social funds, especially in rural areas. Women are particularly affected due to cultural and religious practices. On top of these cultural barriers, groups like pregnant women, children, and individuals with congenital disabilities face even greater challenges because of physical limitations. For them, the impact is intensified by both physical and cultural obstacles.\u0026rdquo;\u003c/em\u003e (FGD North, P23)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTheme 3. Gendered constraints and local power structures\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003eSubtheme 3.1. Aid without autonomy\u003c/h2\u003e\u003cp\u003eParticipants reported that gendered constraints shaped women\u0026rsquo;s access to support in both rural and urban settings, with rural women facing stricter enforcement of patriarchal norms and fewer opportunities to contest them. Although women were frequently registered as beneficiaries of programme such as BISP and one-time cash grants, many described having little control over how these funds were used. In rural households, financial decisions were typically managed by men, with husbands or other male relatives determining spending priorities. In urban areas, some women noted slightly more space to exercise decision-making, particularly those with independent incomes or digital literacy, yet patriarchal expectations and family oversight continued to limit their agency.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;The money is in my name, I go and collect it, and I get to decide how to use it. But sometimes my husband asks for it, claiming he\u0026rsquo;s low on cash for his work, and I feel like I have no choice but to hand it over. I have to live with him and we have kids together. There have even been times when I\u0026rsquo;ve had to use this social support to help pay off his debts.\u0026rdquo;\u003c/em\u003e (ID-5, Home Tutor)\u003c/p\u003e\u003cp\u003eStakeholders further emphasised that social protection programme should extend beyond cash disbursement to actively promote women\u0026rsquo;s empowerment and address restrictive gender norms.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;But the programme needs to go beyond just giving out cash every few months. Real empowerment for women requires teamwork across different sectors like health, education, disaster management, and gender equality. We really need to move from just keeping an eye on cash distributions to looking at real empowerment results. Are women seeing significant changes in their homes, communities, and lives? Just cash isn't sufficient; true impact comes from changing norms, creating more opportunities, and making sure that support leads to lasting independence and respect.\u0026rdquo;\u003c/em\u003e (FGD South, P32)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 3.2. Gatekeeping and informal exploitation\u003c/h2\u003e\u003cp\u003eParticipants in both rural and urban areas recounted experiences of gatekeeping, corruption, and informal exploitation when attempting to access social safety nets. Rural women, constrained by limited mobility and weaker social networks, were particularly disadvantaged. Urban participants described managing these obstacles through persistence, personal connections, or digital literacy; however, demands for informal payments and unresponsive officials remained widespread.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;My family had gone through the application process before, so they filled me in on what to expect, which really helped. But the officials I dealt with for registration were totally unhelpful; they kept asking for money to process my application. They didn't even bother to say hello properly and wanted to know if I was ready to hand over a part of the money I\u0026rsquo;d get from the Benazir programme or any other social aid, whether it was regular or for disasters. When I said no, they claimed I wasn't eligible. Luckily, a relative stepped in and guided me through the application at a different centre, where the staff were way more supportive thanks to our social ties.\u0026rdquo;\u003c/em\u003e (ID-15, Waste picker)\u003c/p\u003e\u003cp\u003eStakeholders acknowledged these challenges, noting persistent issues of leakage and corruption, but also highlighted recent reforms introduced to strengthen transparency and accountability.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Back in 2020, we saw investigations into fund skimming, with reports of people who weren't beneficiaries receiving funds, including some government officials. Most of the issues reported involved local retail outlets taking money from beneficiaries. Even a World Bank impact evaluation pointed out that women beneficiaries were less satisfied because of fund leakage through human agents. Some solutions are already in the works; the programme is focusing on third-party monitoring, audits, grievance support, strict penalties for those caught embezzling, and increasing the number of female staff in the programme.\u0026rdquo;\u003c/em\u003e (FGD North, P4)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTheme 4. Everyday adaptation and informal resilience\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003eSubtheme 4.1. Social networks and mutual aid\u003c/h2\u003e\u003cp\u003eParticipants described resilience in rural areas as being rooted in kinship systems and informal social networks that provided crucial support during crises such as floods, heatwaves, and prolonged power outages. In the absence of robust formal safety nets, family ties, neighbourly relations, and shared experiences offered shelter, food, labour, and emotional reassurance. These community-based mechanisms were viewed as critical in enabling households to cope, adapt, and recover from recurring climate shocks. In urban settings, participants noted that social networks were present but weaker, often taking the form of short-term, charity-like assistance. While such support offered immediate relief during emergencies, it seldom contributed to sustained recovery.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In our neighbourhood, during climate-related emergencies, people occasionally help each other out, but it's usually just a one-time gesture. In big cities, the assistance during such events often follows a predictable pattern. Wealthier individuals tend to provide clothing, food, or shelter, but it often feels more like charity rather than genuine support for long-term recovery. In times of crisis, affluent families may help those in need, but they typically don't invest the time or effort to help rebuild lives; they simply lack the capacity for that.\u0026rdquo;\u003c/em\u003e (ID-11, Service Worker)\u003c/p\u003e\u003cp\u003eStakeholders reinforced these perspectives, emphasising the importance of informal networks in providing food, shelter, rescue, and communication during disasters, and recognising them as a vital complement to formal support systems.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Communities often rely on informal networks, like neighbours helping each other out during bad weather. In Khyber Pakhtunkhwa, this culture of helping one another is well-rooted, covering shelter, transport, rescue, food sharing, financial aid, and even local early warning systems. Many families welcome those who have lost their homes, while others use local resources like tractors or boats to transport at-risk families to safer areas. Community members frequently share basic food items or organise group meals funded by mosques. Plus, word-of-mouth alerts and announcements from mosques serve as common early warning systems, emphasizing the crucial role of community unity, which is built on social ties and mutual support in times of crisis.\u0026rdquo;\u003c/em\u003e (FGD Central, P3)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 4.2. Flexible routines and low-tech innovation\u003c/h2\u003e\u003cp\u003eBoth rural and urban participants described adopting locally driven strategies to cope with climate extremes. In rural areas, adaptations included adjusting work schedules and introducing low-cost innovations to support daily survival. Rural participants stressed that these practices were grounded in practical knowledge, traditional methods, and a deep familiarity with their environment.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;When flooding is expected, we design makeshift boats to transport our livestock and families to safer areas. We're familiar with local rainfall patterns and try to align our planting cycles with the weather to improve crop yields. Rainwater harvesting is important to us; we store water in barrels and underground tanks because our region experiences unpredictable shifts from droughts to heavy rainfall. We\u0026rsquo;ve seen it all.\u0026rdquo;\u003c/em\u003e (ID-19, Seasonal Farmer)\u003c/p\u003e\u003cp\u003eUrban participants, by contrast, reported relying more on market-based solutions, including the use of solar energy, refrigeration, and adjustments to work schedules. Stakeholders confirmed that communities in both rural and urban settings adapted their routines, tools, and practices to manage risks during extreme weather events, drawing on a combination of indigenous knowledge and emerging technologies.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;As summer\u0026rsquo;s heat up, they\u0026rsquo;re moving their work to the cooler early mornings or evenings. Weather changes are affecting school attendance, leading to more home-based schooling, and altering how people collect water for cooking. There\u0026rsquo;s a noticeable shift towards using solar-powered batteries, fans, lights, and direct current systems. Whether you\u0026rsquo;re in the village or the city, you can see this change. Through traditional practices, people are relying on old methods like using woven beds outside or on rooftops, and mud walls provide better natural ventilation compared to concrete houses. Farmers are quite skilled at observing river flows and animal behaviour to develop early warning systems.\u0026rdquo;\u003c/em\u003e (FGD South, P 36)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eTheme 5. Towards responsive and climate-sensitive aid\u003c/h2\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eSubtheme 5.1. Disaster-specific, localised relief\u003c/h2\u003e\u003cp\u003eParticipants emphasised the limitations of static, poverty-focused initiatives such as BISP in addressing disaster-related needs. One-time cash grants were regarded as insufficient and poorly aligned with local priorities. Urban participants, in particular, called for reforms to make aid more responsive, suggesting mechanisms such as mobile cash transfers, tailored recovery packages, and support for livelihood diversification.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;People who live in areas prone to floods, droughts, or heatwaves should definitely be on a list to receive assistance, and this list needs to be reviewed and updated at least once a year. When a disaster strikes, they should receive emergency funds immediately through mobile apps like Jazz Cash or Easypaisa, no waiting around. However, this support shouldn\u0026rsquo;t just last a few days. It should also help people rebuild their lives. For instance, if someone lost their livestock in a flood, they should be provided with new goats or chickens. If their crops were ruined, they should receive seeds and farming equipment. If heatwaves damaged their sewing machine or handcart, they should get help to replace those items. The programme should also focus on teaching people new skills and assisting them in starting small businesses such as mobile repair, sewing, or farming that can adapt to changing weather so they can earn money again and truly recover.\u0026rdquo;\u003c/em\u003e (ID-5, Home Tutor)\u003c/p\u003e\u003cp\u003eStakeholders similarly acknowledged the structural limitations of existing systems, particularly the lack of climate-specific indicators to guide programme design and implementation. They proposed the introduction of adaptive mechanisms, such as pre-disaster cash transfers and microinsurance schemes, to strengthen anticipatory capacity and enhance resilience.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;BISP provides some relief... but it wasn't specifically created for disasters related to climate change. Genuine recovery calls for a comprehensive approach. We need to test an adaptive social protection system within BISP operations, targeting only those individuals who are at verified climate risk. Vulnerability mapping is crucial for pre-disaster payments, where high-risk households are identified in advance. Payments should be made through easy mobile cash transfers instead of just offering reactive support when a climate crisis hits. We also need microinsurance for small-scale farmers and climate resilience funds that link disaster-related public financing from government or international donor sources. We need a shock-responsive and forward-thinking adaptive system for BISP that not only focuses on reducing poverty but also on building climate resilience.\u0026rdquo;\u003c/em\u003e (FGD Central, P4)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eSubtheme 5.2. Early warning and infrastructure gaps\u003c/h2\u003e\u003cp\u003eAcross both rural and urban settings, participants expressed dissatisfaction with weak early warning systems and inadequate infrastructure. Rural residents particularly highlighted delays in receiving alerts and challenges in accessing evacuation routes. Urban participants reported receiving warnings through multiple channels, yet noted inconsistencies and concerns regarding their reliability.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I remember a time when we received a flood alert via an announcement at the mosque it had a significant impact. We require that type of local alert system in all communities. Sometimes we receive text messages or automated calls, and occasionally we find updates on social media, TV, or radio. However, these alerts can be unreliable. There is a pressing need to enhance the accessibility, frequency, and clarity of early warnings so that everyone, no matter their location, can prepare in a timely manner.\u0026rdquo;\u003c/em\u003e (ID-21, Home-based Business)\u003c/p\u003e\u003cp\u003eStakeholders underscored the absence of localised forecasting mechanisms and recommended enhanced inter-agency coordination, greater investment in technology, and the establishment of community-based warning networks.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;It's not too tough to set up early warning systems in Pakistan. We could kick things off by bringing together platforms like NDMA and PDMA, along with real-time dashboards. We should invest in forecasting models and monitoring systems, especially in provinces that are prone to extreme weather. Plus, we can train local volunteers, like climate activists, to act as ambassadors for early warning systems and help build community-based initiatives. Improving communication is key, so we should use radio and SMS to send localized early alerts instead of the usual generic extreme weather warnings. For instance, we could notify people that a specific area is expected to have 4 feet of water in the next 24 hours. Right now, we just get a standard voice message or text. International donor agencies could also help with capacity building for community training and evacuation drills. These are all basic yet essential steps for enhancing early warning systems.\u0026rdquo;\u003c/em\u003e (FGD North, P20)\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored how socio-economic incentives particularly one-time cash grants and the Benazir Income Support Programme (BISP) shape the climate resilience of informal workers in Khyber Pakhtunkhwa. Participants identified five interconnected areas: inadequate and inflexible financial support; barriers to accessing programmes; gendered constraints and power dynamics; everyday adaptation strategies; and the need for climate-sensitive and responsive aid. Overall, while cash transfers offered short-term relief, they did not support long-term resilience, leaving workers to depend heavily on their own adaptive practices. Our findings highlight a persistent misalignment between existing social protection schemes and the recurrent, long-term nature of climate risks. This reflects broader debates in resilience literature, where resilience is often framed as a short-term \u0026ldquo;bounce back\u0026rdquo; capacity (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Cash transfers contributed to absorptive capacity, helping households manage immediate shocks consistent with evidence from Kenya\u0026rsquo;s HSNP, Ethiopia\u0026rsquo;s PSNP, and Mexico's programmes (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). However, as documented elsewhere, cash alone seldom enables sustained adaptation without complementary interventions such as credit, assets, or skills development (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Barriers to access were central to participants\u0026rsquo; experiences. Digital exclusion, low literacy, and bureaucratic procedures disproportionately affected rural and marginalised groups. Women faced compounded disadvantages due to mobility restrictions, low digital literacy, and dependence on male relatives, echoing evidence that digital inclusion requires simplified tools, non-digital options, and local training (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Participants also reported corruption, gatekeeping, and informal deductions, consistent with independent evaluations documenting leakage and elite capture in BISP (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Gendered and cultural constraints identified in our study mirror broader LMIC evidence that weak attention to gender norms limits social protection effectiveness and can perpetuate hidden forms of exploitation (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Participants also described everyday adaptive strategies: informal social support, adjustments to work routines, rainwater harvesting, and small-scale solar use, reflecting adaptive capacity as described in resilience frameworks. Similar forms of community-based resilience have been reported in Nepal and Bangladesh (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), and across Asia and Africa, where indigenous knowledge and community agency underpin climate adaptation (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Finally, participants stressed the need for flexible, rapid, and hazard-specific support, including mobile cash transfers and climate-sensitive programme design. These suggestions align with global calls for shock-responsive, anticipatory social protection that integrates climate triggers, vulnerability mapping, and microinsurance LMICs (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Participants also noted weaknesses in Pakistan\u0026rsquo;s disaster governance, echoing wider critiques of fragmented early warning systems and limited local infrastructure (\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Evidence from South Asia shows that community-embedded early warning systems can substantially reduce disaster losses (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eImplications for practice, policy and research\u003c/h2\u003e\u003cp\u003eThe findings of this study indicate several ways to strengthen the contribution of social safety nets, including BISP and one-off cash grants, to the climate resilience of informal sector workers in Pakistan. First, social protection programmes should incorporate climate sensitivity into their design. BISP needs to move beyond a static poverty focus and adopt climate responsive mechanisms such as mobile cash transfers, rapid disbursement during emergencies, and disaster contingent top ups. Strengthening the links between climate change, humanitarian response, and long-term development would allow BISP to operate as a more transformative safety net rather than a minimal source of temporary relief. Second, supporting long-term resilience requires a shift from short-term absorptive assistance toward interventions that build adaptive and transformative capacities. Access to credit, skills training, livelihood diversification, and asset building would enable households to invest in climate resilient housing, livelihoods, and small enterprises, reducing recurring reliance on emergency transfers. Third, improving accessibility and eligibility is essential. Regularly updating beneficiary lists through poverty and climate vulnerability assessments, particularly in high-risk districts, would enhance programme responsiveness. Aligning BISP with disaster risk reduction frameworks, alongside training female community facilitators to support women with low literacy and limited mobility, would reduce digital exclusion and dependence on male intermediaries. Fourth, governance and accountability mechanisms require strengthening to address corruption, elite capture, and informal deductions. A hybrid accountability model that combines technology enabled transparency with community-based monitoring, local grievance redress systems, and citizen oversight can promote greater trust and ensure that both institutions and communities have meaningful roles in shaping programme delivery. Finally, improving interagency coordination is critical for climate resilience. Collaboration between BISP, the National Disaster Management Authority, and Provincial Disaster Management Authorities can support the development of climate smart safety nets. Practical measures include the establishment of trained community early warning ambassadors and investment in localised, actionable early warning systems that directly link with social protection delivery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and weaknesses of the study\u003c/h2\u003e\u003cp\u003eThis study, to our knowledge the first qualitative inquiry in Pakistan to explore social safety nets and climate resilience among informal workers, has several limitations. Qualitative findings are not statistically generalisable, though they offer transferable insights. The study focused on KP; experiences may differ across provinces. Cultural constraints limited women\u0026rsquo;s participation, particularly among stakeholders, reflecting broader gender imbalances in leadership across LMICs. Finally, reliance on self-reported experiences may involve recall bias, although contextual probes were used to improve accuracy. Despite these limitations, the study provides early evidence on how socio-economic incentives intersect with climate vulnerability in KP. The findings underscore the need for climate-sensitive, inclusive, and adaptive social protection systems capable of addressing both immediate shocks and long-term risks in Pakistan and comparable LMIC settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that while BISP and one-off cash grants provide essential short-term relief, they do not address the recurring and long-term nature of climate shocks faced by informal workers in Khyber Pakhtunkhwa. Limited amounts, inflexible delivery, digital and bureaucratic barriers, and gendered constraints restrict their effectiveness. As a result, households rely heavily on informal networks and low-cost adaptations to cope with floods, heatwaves, and droughts. To strengthen resilience, social protection in Pakistan must become climate-sensitive, gender-responsive, and easier to access. Priority actions include rapid and hazard-specific cash support, updated beneficiary targeting based on climate risk, improved outreach for women and low-literacy groups, and stronger accountability. Linking cash transfers with measures that build skills, assets, and diversified livelihoods is essential for longer-term adaptation. Although qualitative findings are not generalisable, they offer important insights for designing social protection systems that support both immediate recovery and sustained resilience in Pakistan and similar LMIC contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study was supported by a grant from the American Red Cross Global Disaster Preparedness Center (GDPC) Research Grants Program for Urban Climate Resilience. The funder had no involvement in the study\u0026rsquo;s design, data collection, analysis, interpretation of findings, decision to publish, or writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMIK led the study, guided data collection, and developed the main manuscript. ZUH contributed to the study design, coordination, and interpretation of the findings. KR provided climate and health expertise and supported the analytical framing. SIK managed the data and assisted with analysis. SSB contributed to data collection, transcription, and preliminary coding. HMJ and AA provided methodological guidance, supported interpretation, and contributed to manuscript revisions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the participants who took part in this study, including community members and district-level stakeholders. Their time, insights, and cooperation were essential to this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to the confidential and sensitive nature of qualitative interview data. De-identified transcripts may be made available from the corresponding author, Dr Maria Ishaq Khattak ([email protected]), on reasonable request and subject to approval by the Ethics Committee of Khyber Medical University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMazumder H, Hossain MM. Climate change education for health-care professionals: crucial gaps in low-income and middle-income countries. Lancet Planet Heal [Internet]. 2024 Apr 1 [cited 2024 Oct 9];8(4):e216. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thelancet.com/article/S254251962400010X/fulltext\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com/article/S254251962400010X/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomanello M, Walawender M, Hsu SC, Moskeland A, Palmeiro-Silva Y, Scamman D et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC8700901/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8700901/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate resilience, Social protection, Informal workers, Benazir Income Support Programme, Cash transfers, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-8263407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8263407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eInformal sector workers in low- and middle-income countries are disproportionately vulnerable to climate shocks due to precarious livelihoods, limited social protection, and structural inequities. In Pakistan, the Benazir Income Support Programme (BISP) and one-time cash grants are the primary safety nets, yet their contribution to climate resilience remains underexplored. This study examined how socio-economic incentives influence resilience among informal workers in Khyber Pakhtunkhwa.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA qualitative phenomenological design was applied between April and June 2025 across three climatic zones of Khyber Pakhtunkhwa (north, central, south). A multistage purposive sampling strategy was used to ensure diversity across geography, occupation, gender, and urban-rural residence. Thirty in-depth interviews were conducted with informal workers (\u0026ge;\u0026thinsp;18 years, directly affected by a climate shock in the past five years), and six focus group discussions with 36 institutional stakeholders from social protection, disaster management, health, and local government sectors. Data were collected in Pashto and Urdu, transcribed, translated into English, and analysed using reflexive thematic analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThematic analysis identified five themes and ten sub-themes: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Inadequate and inflexible financial support (static assistance amid dynamic crises; debt, instability, and recovery fatigue); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Barriers to accessing social protection (digital gaps and bureaucratic complexity; cultural and logistical barriers); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Gendered constraints and local power structures (aid without autonomy; gatekeeping and informal exploitation); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Everyday adaptation and informal resilience (social networks and mutual aid; flexible routines and low-tech innovations); and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Towards responsive and climate-sensitive aid (disaster-specific, localised relief; early warning and infrastructure gaps).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePakistan\u0026rsquo;s social protection systems provide critical but limited relief against climate shocks for informal workers. To build long-term resilience, reforms are required to make these systems climate-sensitive, adaptive, and gender-transformative. The findings offer policy lessons for other LMICs seeking to strengthen social protection as a pathway to climate resilience.\u003c/p\u003e","manuscriptTitle":"Social protection and climate resilience among informal workers in Pakistan: a qualitative study from Khyber Pakhtunkhwa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 08:59:07","doi":"10.21203/rs.3.rs-8263407/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T15:32:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T18:18:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T14:24:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310864236455185407528412000782870672685","date":"2025-12-08T00:59:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299310437513136042005330259198615035973","date":"2025-12-08T00:03:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-07T15:23:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-07T15:17:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-04T06:15:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T17:54:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-03T17:43:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96aebd17-b3cf-4cd5-9c6a-f026a7be01f7","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T16:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 08:59:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8263407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8263407","identity":"rs-8263407","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}