UNDERSTANDING THE SOCIOECONOMIC DIFFERENCES OF URBAN AND CAMP-BASED REFUGEES IN KENYA Comparative analysis brief 2018 Kalobeyei settlement, 2019 Kakuma camp, and 2020-21 Urban Socioeconomic Surveys1 2021 Summary findings and recommendations The comparative analysis on the socioeconomic conditions of urban and camp-based refugees in Kenya builds upon the findings of the Kalobeyei, Kakuma and Urban Socioeconomic Surveys (SES). It offers an analytical understanding about key differences between refugees while providing explanations, and policy recommendations. 2 Finding Recommendations Refugees in Kenya are not systematically Systematic inclusion of refugees in national household surveys included in national surveys, which results in a complemented by specific refugee and host community lack of comparable socioeconomic data of surveys can provide evidence for policy planning and refugees and their hosts. programming. Panel surveys can offer a better understanding of changes over time to inform durable solutions. Urban and camp-based refugees Camp-based refugees are more likely to live in Short-term priorities: Scaling up permanent shelters in unimproved houses, to suffer from Kalobeyei with extension to Kakuma through ongoing cash- overcrowding and to use biomass fuel s for based interventions as well as subsidies and vouchers can be cooking than those in urban areas (65, 17 and crucial to improve refugees’ living conditions. 3 Increasing 65 percentage points respectively). Urban funding for national housing programs such as the informal non-protracted households are more likely settlements upgrade schemes, to address host’s needs while than protracted ones to live in unimproved including refugees can also reduce overcrowding. Increasing houses, with protracted households being access to clean cooking fuels is key to enhance health more likely to suffer from overcrowding. outcomes mainly for women and children under age 5. Although less often than in camps, urban Expanding energy access, particularly moving host and refugee households also use biomass fuels for cooking. households up the energy ladder to non-biomass fuels is key to enhance health outcomes specifically for cooks (primarily women) and their accompanying children. Bank account ownership is low in both Medium-term priorities: Expanding access to bank accounts locations (10 percent). Camp-based refugees and mobile money, especially among urban refugees, is key to are 40 percentage points more likely to have increase access to formal loans, improve savings, and access to bank accounts while urban refugees are more credit. This can help start and grow businesses as well as likely to use mobile banking. Access to loans in smooth consumption shocks. Collaborations with the private both areas is mostly informal with camp- sector, simplification of requirements for SIM card registration based refugees being 22 percentage points as well as by embedding refugees in government led social less likely to have access to loans. protection safety nets can support these efforts. * This brief was prepared by a team led by Utz Pape (World Bank) and Theresa Beltramo (UNHCR). The team consisted, Jedediah Fix (UNHCR), Florence Nimoh (UNHCR), Ibrahima Sarr (UNHCR) and Laura Abril Ríos Rivera (World Bank). The team would like to thank the peer reviewers Christina Wieser (World Bank) and Nga Thi Viet Nguyen (World Bank). This work is part of the Prospects partnership program funded through the Multi Donor Trust Fund for Forced Displacement (FDTF) administered by the World Bank. 1 To ease readability, the brief refers to Kalobeyei settlement and Kakuma camp as ‘camps’ while acknowledging that they are di fferent. 2 Comparability between camp- and urban refugees may be affected by the timing of the data collection and the COVID’19 outbreak. 3 UNHCR, “KISEDP. Kalobeyei Integrated Socio-Economic Development Plan in Turkana West.” i Camp-based refugees are 19 percentage Medium-term priorities: Accelerating area-based points less likely to have positive perceptions interventions providing integrated service delivery for about trust in the host community than urban- refugees and hosts while fostering socioeconomic interactions based refugees. However, perceptions of and expanding similar programs in urban areas will be crucial security and participation in decision-making to improve social cohesion. Collaborating with governments to are higher in camps than in urban areas. enhance security in urban areas is important to improve perceptions of safety. Strengthening communication mechanisms between refugees, organizations, and the government could be instrumental to raise concerns of refugees and improve participation in decision making. Kakuma and Kalobeyei-based refugees Refugees in Kalobeyei spend around 50 Short term priorities: Synchronizing cash transfer between percent more than those in Kakuma on food agencies is essential to improve food assistance and support and non-food items which can be partly households’ capacity to allocate resources and prioritize cash explained by the difference in the type of food utilization. Shifting from in-kind to cash transfers will be crucial assistance. 4 60 percent of camp-based to improve food security among camp-based refugees. Cash refugees are highly food insecure without transfers for refugees can be a more cost-efficient way significant differences between camps. forward and can increase food consumption.5 Employment rates are very low with refugees Short-term priorities: Increasing employment opportunities, in Kakuma being 21 percentage points less through improving pathways for refugees to legally access likely to be employed than those in Kalobeyei. work can be further enhanced. Strategies may include the Literate refugees are 11 percentage points engagement of the private sector to enable the creation of job more likely to be employed than those who markets, easing access to credit markets, strengthening are illiterate. 52 percent of youth (15-29) in business skills coupled with cash grants, second-chance camps are not in employment, education, or education programs for adults and children out of school training (NEET). People NEET are more likely linked to financial support and competency-based training or to be in their 20s, to have no education and apprenticeships. Kiswahili and English literacy programs can are not proficient in Kenya’s official languages. help increase participation in the paid labor market. Attendance rates, especially at the secondary Short-term priorities: The transition to secondary school can level, are low and not significantly different (5 be enhanced by investing in scholarship programs, conditional percent in Kalobeyei and 14 percent in cash transfers, and strengthening the Free Day Secondary Kakuma). Education program and recognition of prior learning can be key to support transition Medium-term priorities: Constructing new facilities and classrooms in existing schools and inclusion of refugees into the National Education Management Information System (NEMIS) can also increase transition to secondary school. Refugee women specific vulnerabilities Women refugees are more likely to live in Short-term priorities: Women and girls’ empowerment overcrowded rooms, are less likely to receive programs in camp and urban areas can help alleviate barriers remittances and have lower access to loans to access socioeconomic opportunities as well as to build and and mobile banking. Women headed maintain human capital. Financial inclusion programs coupled households have worse perceptions of safety with entrepreneurship skills, business training and cash grants than those headed by men. Camp-based targeting women, especially those with young dependents, women who head households with at least can be a starting point to unlock refugee women’s one child under 5 years of age are less likely to socioeconomic potential. be employed. Youth who are NEET are more Medium-term priorities: Further research is crucial to provide likely to be women. a deeper understanding regarding such barriers and how to overcome them through gender-responsive solutions. 4 While refugees in Kakuma receive 70 percent of food aid in kind and 30 percent in cash, refugees in Kalobeyei receive 100 percent of food aid in cash through the Bamba Chakula program. Bamba Chakula (“get your food”) is a monthly transfer on SIM-cards that beneficiaries use to purchase food items from registered traders. 5 Delius and Sterck. 2020. “Cash Transfers and Micro-Enterprise Performance: Theory and Quasi-Experimental Evidence from Kenya.” ii Table 1: Refugees’ and hosts’ living conditions summary CAMPS URBAN AREAS Kalobeyei Kakuma Turkana Refugees (SES Hosts (KIHBS Refugees (SES Refugees (SES Hosts (KIHBS 2020/21) 2015/16) 2018) 2019) 2015/16) Gender Men (50%) Men (54%) Men (52%) Men (51%) Men (52%) Women (50%) Women (46%) Women (48%) Women (49%) Women (48%) Age Below 18: 71% Below 18: 61% Below 18: 60% Below 18: 45% Below 18: 32% Above 64: 0.6% Above 64: 0.4% Above 64: 0.4% Above 64: 1.8% Above 64: 0.7% Dependency 1.9 1.2 1.4 0.6 0.4 ratio Women- 66% 56% 47% 41% 32% headed households Improved 5% 3% 8% 82% 78% housing Improved 100% 100% 71% 91% 92% drinking Improved 52% 78% 19% 84% 99% sanitation6 Sharing: 66% Sharing: 37% Sharing: -- Sharing: 68% Sharing: -- Biomass Fuels -- 100% 98% 26% 10% as main source of energy for cooking Primary Net 77% 82% 59% 69% 90% Enrolment rate* Secondary Net 5% 14% 23% 28% 61% Enrolment rate* Employment 39% 20% 42% 42% 66% Rate* LSCI Food 61% 58% -- 61% -- Insecurity Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21); KCHS (2019) Note: Urban Estimates may be affected by the COVID-19 pandemic. 6 Sharing of toilet imply the household share the toilet facility with others who are not members of the household. iii Context A. CONTEXT Kenya hosts over half a million refugees, who along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities.7 Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru.8 Refugees in Kenya have become an integral part of the social, cultural, and economic fabric of the country and the local communities that host them. Socioeconomic interactions between refugees and hosts, especially in camp areas, have helped to boost the overall economic landscape and improve nutritional outcomes and well- being for both communities.9 Nevertheless, refugees and hosts communities continue to face poor living conditions, restricted access to socioeconomic opportunities and specific vulnerabilities which need to be understood through socioeconomic data to inform the design and implementation of solutions.10 Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data of camp-based and urban refugees, and their hosts. Even though preceding research provides useful information on the living conditions of urban and camp-based refugees and their hosts, data gaps persist.11 Limitations include a lack of comparable socioeconomic data for both communities as well as scarce and/or outdated data on the living conditions of refugees, especially in urban areas, which limits comparisons between urban and camp-based communities. The present analysis focuses on the latter data limitation. Understanding the socioeconomic needs of urban and camp-based refugees in Kenya is crucial, especially in face of ongoing conflicts, environmental hazards and others shocks, as well as the recent government announcement to close Kenya’s refugee camps which highlights the potential move of refugees from camps into urban settings. 12 A deeper understanding of refugees’ socioeconomic needs can help inform targeted interventions to enable self- reliance while uncovering under-researched dynamics adding to the growing body of evidence on the socioeconomic differences between urban and camp-based refugees in Sub-Saharan Africa. This comparative examination on the socioeconomic conditions of urban and camp-based refugees helps close data gaps by offering an analytical understanding about key differences between refugees while providing explanations, and policy recommendations. The Kalobeyei 2018, Kakuma 2019 and Urban 2020-21 Socioeconomic Surveys (SES), initiated by the World Bank and the UNHCR, were used to select key findings which can help understand factors driving socioeconomic differences between urban and camp-based refugees.13 The comparative analysis presents differences between urban and camp-based refugees with regards to housing, energy, sanitation, access to finance, and social cohesion while covering specific differences on education and livelihoods for camp-based refugees in Kalobeyei settlement and Kakuma camp (Box A-A-1). Box A-A-1: Survey Design and Methodology The SESs are representative of urban refugees and camp-based refugees in Turkana county. For the Kalobeyei 2018 and Urban 2020-21 SES, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise was used to draw 7 UNHCR, “Africa.” 8 UNHCR, “Kenya: Registered Refugees and Asylum-Seekers. July 2020.” 9 Betts, Omata, and Sterck, “Refugee Economies in Kenya”; World Bank, “‘Yes’ In My Backyard? The Economics of Refugees and Thei r Social Dynamics in Kakuma, Kenya.” 10 Verwimp and Maystadt, “Forced Displacement and Refugees in Sub‐Saharan Africa: An Economic Inquiry”; United Nations, “Global Compact on Refugees.” 11 See Annex 11 UNHCR and World Bank, “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume B: Kakuma Camp.” 12 The Guardian, “UN Outlines Plan to Close Camps Housing 430,000 Refugees in Kenya.” 13 To ease readability, the brief refers to Kalobeyei settlement and Kakuma camp as ‘camps’ while acknowledging that they are di fferent. 4 Context 14 the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru and Mombasa. Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES include modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with Kenya Integrated Household Budget (KIHBS) Survey 2015/16 and the recent Kenya Continuous Household Survey (KCHS) 2019. Additional modules on access to services, vulnerabilities, social cohesion, coping mechanisms to lack of food, displacement trajectories and durable solutions are administered to capture refugee-specific challenges.15 Box A-2: Limitations The mode of data collection limits comparability between urban and camp-based refugees. As the Urban SES was collected through CATI, the representativeness of the sample and external validity can be limited due to telephone coverage, low participation, and response rates.16 These limitations are a possible source of bias, which can be partially addressed by adjusting the survey weights using information from the population data. While the sampling weights for the Urban SES accounts for differences that might exist between households that have phones and all households, it does not account for differences in responses that may arise as a result of collecting data through CATI and CAPI. In addition, the training of enumerators and fieldworks may differ between phone surveys and face-to-face surveys which can affect the comparison between urban and camp-based refugees. Comparisons between urban and camp-based refugees are also limited by the timing of the data collection. Since camp-based refugee data were collected before the COVID-19 outbreak while that of urban refugees were collected after the outbreak, some socioeconomic dimensions are expected to have changed as a result of the pandemic impacts. Socioeconomic dimensions that are assumed to not have significantly changed due to the pandemic are compared between urban and camp-based refugees, these are, housing, energy, sanitation, access to finance, and social cohesion. As it is likely that education, livelihoods, and food insecurity fluctuated due to the COVID-19 outbreak, differences on these are presented only for camp-based refugees. Furthermore, comparability between camp-based and urban refugees is limited by a gap of one to two years between the Urban SES and camp- 14 The difference in sampling schemes was driven by the timing of the UNHCR Registration Verification Exercise (VRX) in each location. For the Kalobeyei SES, the survey data collection coincided with the VRX and thus, households were selected during the VRX interviews with a fixed probability. All household were administered the VRX questionnaire while only a random subset completed the Kalobeyei SES questionnaire. Since the Kakuma SES was completed after the VRX data collection was finalized, a complete list of dwellings was used to select the survey sample. In turn, the Urban SES used as a sampling frame the urban VRX which was updated before the data collection. 15 A Linear Probability Model (LPM) is used to examine the differences between urban-based and camp-based refugees: = 0 + 1 ∗ + 2 ∗ + 3 ∗ + 4 ∗ ∗ + 5 ∗ + 6 ∗ ∗ + 7 ∗ + ∗ + Where is the dependent variable, is a dummy indicating whether the household resides in camp (Kakuma, Kalobeyei) or not, is a dummy for Kakuma, is a dummy for women-headed households, ∗ is a dummy for woman-headed households in camp, is a dummy indicating if the household is protracted or not, ∗ is a dummy indicating if the household is protracted and resides in camp, and is a categorical variable for country of origin of the head. is a vector of household and head characteristics and is the error term. 1 is the main variable of interest that measures the impact of residing in camp. 2 is the effect for Kakuma households compared to Kalobeyei households. 3 is the effect for woman-headed households compared to man-headed households in urban areas. 4 and 6 measure the additional effects for women-headed households and protracted households in camps respectively. The parameter combination 3 + 4 measures the effect for woman-headed households compared to man-headed households in camps. Similarly, the parameter combination 5 + 6 measures the effect for protracted households compared to non-protracted households in camps. The LPM would provide consistent and unbiased results for binary response if no or very few predicted probabilities lie outside the unit interval. In our estimation, very few of the observations fall outside the unit interval (Horrace and Oaxaca 2005). As robustness check, we exclude these observations from the estimation and obtained very similar results (See Annex). We also use robust standard errors to control for possible heteroskedasticity that Ordinary Least Square (OLS) may impose. As another robustness check, we use logit to estimate the models and the results are very similar to the LPM. Horrace and Oaxaca, “Results on the Bias and Inconsistency of Ordinary Least Squares for the Linear Probability Mode.” 16 Ambel, McGee, and Tsegay, “Reducing Bias in Phone Survey Samples.” 5 Demographic profiles of refugees in Kenya based SES, during which camp averages might have changed. While comparisons with hosts are not included due to time differences in the data collection, the individual SES reports, provide comprehensive refugee-host comparisons.17 B. DEMOGRAPHIC PROFILES OF REFUGEES IN KENYA Since the 1990s, Kenya has been hosting refugees mainly from South Sudan, DR Congo, and Somalia. Most refugees were displaced after 2007 with a peak in 2016 and a subsequent fall in 2017 (Figure B-1). 74 percent of refugees in Kalobeyei and 52 percent of those in Kakuma are from South Sudan (Figure B-2). 23 percent of Kakuma refugees are from Somalia while Kalobeyei settlement hosts ethnic Somalis displaced mainly from Ethiopia’s Ogaden region (13 percent). About 89 percent of urban refugees live in Nairobi, 4 percent live in Nakuru and 7 percent in Mombasa. Most refugees in Nakuru are South Sudanese (73 percent) while in Mombasa Somalis are the majority (84 percent). In Nairobi 44 percent are from DR Congo and 22 percent from Somalia. Camp-based refugees are younger, their households are mostly headed by women and have higher dependency ratios than urban households. Compared to 45 percent of urban refugees, 71 percent of refugees in Kalobeyei and 61 percent in Kakuma are 18 years and below. Unlike urban households, most camp-households are headed by women. Dependency ratios are also higher in camps (Table 1). Figure B-1: Year of arrival of household head by location Figure B-2: Main countries of origin 50 100 5 2 5 4 9 8 7 6 8 40 80 13 5 % of population 9 0.1 4 44 % of households 11 23 30 60 5 20 40 13 74 61 52 10 20 22 0 11 0 1991 Pre 1990 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Overall Kalobeyei Kakuma Urban South Sudan Somalia Ethiopia Kalobeyei Kakuma Urban Burundi DR Congo Other Source: UNHCR (2021). UNHCR proGres Registration Database Sub- Source: Kalobeyei SES (2018); Kakuma SES (2019); Sample. Data not publicly available Urban SES (2020-21) Living conditions in Turkana County compared to those in Nairobi, Nakuru and Mombasa counties are difficult and often involve more socioeconomic limitations. Turkana County, where refugees in Kakuma and Kalobeyei reside, is among the poorest and remotest counties in Kenya with limited employment opportunities, access to basic services, and infrastructure. In Turkana County, 72 percent of Kenyans live below the international poverty line of USD 1.9 per day, versus 4 percent for Nairobi County, 18 percent for urban Nakuru and 10 percent for urban Mombasa where urban refugees reside.18 In Turkana County access to basic services is very limited compared to urban areas in Nairobi, Nakuru and Mombasa (Table A-1). While 95 percent of urban households have access to electricity, only 9 percent do so in Turkana. Similarly, access to improved sanitation in Turkana County is very low compared to urban areas (19 percent ‘vs.’ 99 percent respectively). Education and employment rates are 17 For detailed comparisons between refugees and hosts: UNHCR and World Bank, “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume A: Kalobeyei Settlement”; UNHCR and World Bank, “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume B: Kakuma Camp”; UNHCR and World Bank, “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume C: Urban Areas.” 18 Kenya National Bureau of Statistics, “Basic Report 2015/16 KIHBS.” 6 Urban and camp-based refugees’ comparative patterns also lower in Turkana County compared to urban areas (Table 1). These factors in Turkana County might exacerbate the difficult living conditions of camp-based refugees. C. URBAN AND CAMP-BASED REFUGEES ’ COMPARATIVE PATTERNS i. Housing, energy, and sanitation Camp-based refugees are less likely to live in improved houses, more likely to live in overcrowded rooms and more likely to use biomass fuels for cooking. Access to sanitation varies within urban and camp areas. Camp-based refugees are less likely to live in improved houses than those living in urban areas with significant variations by the date in which the head of household arrived in Kenya. Most houses in camps, especially those in Kakuma, are constructed with unimproved materials such as mud, iron sheets and tent materials (temporary shelters), while urban refugees mostly live in houses constructed with cement blocks and stones.19,20 The type of housing depends on the date in which the household head arrived in Kenya, with variations by location (Table A-1 Column 1). In urban areas, overall, protracted households (those whose head arrived in Kenya 5 or more years ago) are 6 percentage points more likely to live in improved houses than non-protracted households. In camps, there is no difference among protracted and non-protracted households in access to improved housing. Importantly, living in improved housing has been shown to be effective to controlling malaria, while having positive implications on educational outcomes.21 Overcrowded rooms are more common among camp-based refugees, urban women-headed households, and protracted urban households.22 Camp-based refugees are 17 percentage points more likely to live in crowded rooms than urban-based households (Table A-1 column 4). This may partly be explained by their larger household sizes compared to urban-based households (6.2 vs. 3.2, p<0.01). In urban areas, women-headed households are 7 percentage points more likely to face overcrowding in rooms than their male counterparts (Table A-1 Error! Reference source not found. column 6). Differences by gender are not significant in camps. Protracted households in urban areas are 4 percentage points more likely to be crowded in rooms than non-protracted ones, with no such difference in camps. Protracted households tend to have larger household sizes than non-protracted ones. Thus, the higher incidence of overcrowding among urban protracted households, could be linked to household sizes increasing according to the length of displacement and partly to difficulties in access affordable housing. Overcrowding is linked to stress, poor health and educational outcomes, and intergenerational transmission of social inequality.23 The use of biomass as the main fuel for cooking is more prevalent in camps, mainly among woman-headed households and protracted households in urban areas. 24 Camp-based refugees in Kakuma are 65 percentage points more likely to use biomass fuels (firewood and charcoal) for cooking 19 Improved housing is defined as having improved floor, wall and roof construction. Improved floor consists of floor constructed with tablets/wood planks, palm/bamboo/mat/adobe/polished wood, vinyl/asphalt, ceramic tiles, cement, carpet, stone and bricks. Improved wall materials consist of cement, stone with lime/cement, bricks, cement blocks, covered adobe, wood planks/shingles and burnt bricks with cement. Improved roof types are made with metal, wood, ceramic tiles, cement, asbestos. IFC, “DHS Analytical Studies. Using Household Survey Data to Explore the Effects of Improved Housing Conditions on Malaria Infection in Children in Sub-Saharan Africa.” 20 According to UNHCR-Kenya 5,378 permanent houses were built in Kalobeyei settlement after the SES was conducted in 2018. 21 Cunningham and MacDonald, “Housing as a Platform for Improving Education Outcomes among Low -Income Children”; IFC, “DHS Analytical Studies. Using Household Survey Data to Explore the Effects of Improved Housing Conditions on Malaria Infection in Children in Sub-Saharan Africa.” 22 Living in overcrowded room is defined as having three or more people occupying a room. 23 Solari and Mare, “Housing Crowding Effects on Children’s Wellbeing.” 24 We define the energy for cooking indicator as whether the household uses biomass fuel (firewood, coal/lignite, charcoal, straw/shrub/grass, animal dung) or modern fuel (electricity, LPG, natural gas, biogas, kerosene) for cooking. 7 Urban and camp-based refugees’ comparative patterns 25 than those in urban areas (Table A-1 column 7). This may partly be explained by the cost of non-biomass fuels as well as by the limited access to electricity in camps. In Kakuma, refugees are provided with 10kg of firewood every two months with many of them supplementing their needs by purchasing firewood sold by Turkana hosts (often in exchange for food rations) or collecting it outside camps.26 In urban areas, households headed by women are 5 percentage points more likely to use biomass fuel for cooking than those headed by men. In addition, urban protracted households are 7 percentage points more likely to use this type of fuel than non-protracted households. Variations by gender and by protracted situation are not significant in camps. Collecting firewood and cooking with it has negative implications, including diseases and increased risk of physical abuse and sexual assault. 27 The combustion of solid fuels emits airborne pollutants which can generate acute respiratory diseases, especially for women and girls who are usually the main household cooks, as well as for children under age 5 who normally remain in the proximity of the cooking area. 28 Furthermore, the collection and cooking process can take several hours, limiting women’s and girls' time to pursue education or engage in paid work. The rising demand for biomass fuels, especially among refugees in camps, if left unmanaged can lead to conflicts with hosts as a result of increased competition for resources.29 Moreover, firewood collection degrades land which has serious long-term implications. Refugees in Kakuma and women-headed households in urban areas are more likely to have access to improved private toilets than Kalobeyei refugees and urban households headed by men, with no differences between camp and urban settings.30 Overall, there is no difference in access to private toilets between camp-based and urban-based refugees. However, differences within locations are significant. Refugees in Kakuma are 18 percentage points more likely to have access to private toilets than those living in Kalobeyei.31 Even though the settlement planning in Kalobeyei accommodates for household toilets, the refugee influx in 2016/17 affected the capacity to construct private toilets and thus, community toilets were built instead. However, during the time of comparative analysis, a cash-based intervention for latrines has been implemented which considerably increases household private latrines with an actual coverage of 78 percent and 42 percent of households in Kalobeyei and Kakuma, respectively . 32 Women-headed households in urban areas are 5 percentage points more likely to have access to private toilets than those headed by men, while no gender-based difference in camps is noted (Table A-1 column 10). Sharing of toilets is linked to Sexual and Gender-Based Violence (SGBV) and psychosocial stress among users, especially when using the toilet late at night.33 25 The source of energy for cooking is only available in Kakuma SES and Urban SES. 26 Since firewood collection is reserved for Turkana hosts, collecting firewood is dangerous for refugees as it can generate conflicts with hosts for whom selling firewood constitutes a main source of income. Betts, Omata, and Sterck. 2018. “Refugee Economies in Kenya.” 27 Global Alliance for Clean Cookstoves. 2016. “Gender-Based Violence in Humanitarian Settings: Cookstoves and Fuels”; UN Women . 2019. “Gender Assessment of Kalobeyei Settlement and Kakuma Camp. Determining the Level of Gender Mainstreaming in Key Coordination Structures.” 28 Smith, Mehta, and Feuz. 2004. “Indoor Air Pollution from Household Use of Solid Fuels”; Kurmi et al. 2012. “Lung Cancer Risk and Solid Fuel Smoke Exposure: A Systematic Review and Meta-Analysis”; Dasgupta et al. 2004. “Who Suffers from Indoor Air Pollution? Evidence from Bangladesh.” 29 Thulstrup et al., “Assessing Woodfuel Supply and Demand in Displacement Settings. A Technical Handbook.” 30 Improved private toilet is defined as having access to improved toilet facility that is not shared with other household members. 31 Sanitation coverage has increased in 2020/21 in Kalobeyei Settlement as part of conditional cash-based interventions for toilet construction. 32 UNHCR Kenya operation. 33 Sommer et al., “Violence, Gender and WASH.” 8 Urban and camp-based refugees’ comparative patterns ii. Access to finance Camp-based refugees have higher access to bank accounts but lower access to mobile banking and loans than urban refugees. Access to remittances varies within urban and camp areas. Ownership of bank accounts is higher in camps while mobile banking is higher in urban areas. Ownership of bank accounts is low in both areas (10 percent). However, camp-based refugees are 40 percentage points more likely to have bank accounts than urban refugees.34 The higher incidence of bank accounts ownership among refugees in camps can be explained by the cash-based intervention for shelter in Kalobeyei settlement which requires refugees to receive cash through regular bank accounts. Refugee beneficiaries of such intervention are supported to open bank accounts, enhancing their financial inclusion.35 Furthermore, after the SESs were conducted in 2018-2019, access to bank account has since increased as new bank accounts for refugees (60 percent women) were opened in Kakuma (34,958 accounts) and Kalobeyei (7,386).36 Despite the requirement of documentation to buy a SIM card—needed for mobile banking, most refugees own a mobile banking account, often by acquiring SIM cards registered in the name of a Kenyan. Urban refugees are 25 percentage points more likely to use mobile banking which is coincident with their higher ownership of mobile phones (69 percent urban vs. 41 percent camps, p<0.01). In terms of gender of the head of household, in urban areas, woman-headed households are 12 percentage points less likely to own mobile banking accounts than those headed by men with no such difference in camps (Table A-2). Protracted households in urban areas are 6 percentage points more likely to own bank accounts, whereas in camps, ownership of bank and mobile banking accounts do not vary among protracted and non-protracted households. Access to loans is higher in urban areas than in camps. 37 With very limited access to formal financial services, refugees, especially in low-income countries rely on informal services by borrowing primarily from relatives and friends.38 In Kenya a similar trend is noted. More than 90 percent of loans accessed by urban and camp-based refugees, were from friends and relatives, while only 2 percent were from formal sources. Key challenges to access loans through formal financial institutions are linked with their lack of assets and the perception that refugees may disappear at any time, and thus, will not pay the loan back. 39 Access to loans differs significantly between camps and urban areas. Camp-based refugees are 22 percentage points less likely to have access to loans than urban refugees (Table A-2 column 4). The low access of loan for these predominantly Muslim communities might partly be due to the preponderance of the non-shariah complaint loans, however Error! Reference source not found.new services providers through UNHCR leadership started providingError! Reference source not found. shariah compliant loans in camps. Women-headed households in camps are 6 percentage points more likely to borrow than those headed by men while in urban areas, they are 4 percentage points less likely to borrow than men. Low access to formal loans may partly be explained by a lack of information regarding the availability of loans and requirements.40 Lack of access to formal financial services affect savings practices, limit access to credit hindering opportunities to start businesses. Error! Reference source not found. Access to remittances is higher among urban households headed by men and urban protracted households, with no differences between camps and urban areas. The level of access to remittances 34 In Kenya, refugees can open bank accounts with their proof of registration document from UNHCR and RAS. 35 UNHCR, “Cash for Shelter in Kenya. A Field Experience.” 36 UNHCR Kenya operation. Some of these accounts include the Equitel service which facilitates access to emergency quick loans. 37 Access to loans includes borrowing from informal sources (family/friends/community saving groups) and formal sources such as banks. 38 UNHCR, GCAF, and Sida, “Assessing the Needs of Refugees for Financial and Non-Financial Services - Jordan.” 39 IFC, “Kakuma as a Marketplace. A Consumer and Market Study of a Refugee Camp and Town in Northwest Kenya”; Betts, Omata, and Sterck, “Refugee Economies in Kenya.” 40 For example, Equity Bank which is available in camp areas, has a program (Equitel) that allows small loans associated with bank accounts. 9 Urban and camp-based refugees’ comparative patterns does not significantly differ between camp-based and urban refugees. However, differences exist within communities (Table A-2 column 1). Urban households headed by women are 5 percentage points less likely to receive remittances than those headed by men while there is no gender-based difference in camps. Access to remittances varies with the date of arrival of the household head. Urban protracted households, overall, are 3 percentage points more likely to receive remittances than non-protracted households. In camps, protracted households are 4 percentage points less likely to receive remittances than non-protracted ones. Remittances help maintain consumption during shocks and contribute to local economic activity. iii. Social cohesion Camp-based refugees are less likely to have positive perceptions about trust in the host community, however, their perceptions of security are better than for urban-based refugees. Perceptions on participation in decision-making are better in camps than in urban areas. Perceptions of trust, safety, and participation in decision making are used as proxies to measure social cohesion. Social cohesion is key to strengthen resilience among refugees. 41 Given the multi- dimensional and context specific nature of social cohesion, and the lack of a clear-cut definition, standard instruments to measure social cohesion are inexistent. 42 The most common proxy to measure social cohesion often includes generalized levels of trust, membership in associations or civic engagement. In the context of forced displacement, social cohesion focuses on intergroup perceptions and interactions.43 While camp-based refugees are less likely to agree that the host community is trustworthy, their perceptions of safety are more positive than for urban refugees. Camp-based refugees are about 19 percentage points less likely to agree that their hosts are trustworthy than urban refugees (Table A-3 column 2). This could be explained by the fewer interactions that refugees in camps may have with hosts compared to urban refugees (50 percent vs. 58 percent; p<0.01). While refugees in camps mainly interact with hosts through market transactions, urban refugees live mixed with the host community. In addition, differences in access to services has often created tension between the host community and camp refugees.44 Poor refugee-host relations can be a threat to local integration. On safety, refugees in camps feel safer in their neighborhoods than those in urban areas. However, those in Kakuma feel less safe at night than those in Kalobeyei (Table A-3). The camp-urban difference may be partly explained by a higher perception of crime in urban areas, where 60 percent of households agree that crimes are common in their neighborhood. Perceptions of safety are worse among women-headed households in camps. Refugee women are vulnerable to Sexual and Gender-Based Violence (SGBV) and often live in fear.45 Perceptions of participation in decision making are more positive in camps than in urban areas. Camp-based refugees are 15 percentage points more likely to agree they are able to express their opinions through the existing community leadership structure and 23 percentage points more likely to feel their opinions are being considered for decisions that affect their well-being than those in urban areas (Table A-4). In both areas, women-headed households are less likely to agree that their opinions are considered for decision making than those headed by men which could be linked to cultural differences and lower educational levels that would enable women to occupy decision-making positions. 46 The exclusion of the opinions of women in decision making could hinder the protection, economic and social empowerment opportunities they require. 41 3RP, “Regional Strategic Overview. Regional Refugee and Resilience Plan.” 42 Kuhnt et al., “Social Cohesion in Times of Forced Displacement – the Case of Young People in Jordan.” 43 De Berry and Roberts, “Social Cohesion and Forced Displacement.” 44 Rodgers. 2020. “What does ‘Social Cohesion’ Mean for Refugees and Hosts? A View from Kenya.” 45 SGBV Strategy, Kakuma Refugee Camp. 2017; The Impact of Sexual and Gender Based Violence in Kalobeyei Integrated Settlement and Host Community. 2019. 46 UNSW, “The World’s Biggest Minority? Refugee Women and Girls in the Global Compact on Refugees.” 10 Kakuma and Kalobeyei-based refugees’ comparative patterns Box C-1. Country of origin analyses Separate analyses are done to understand key differences by the two main countries of origin, South-Sudan and Somalia. The country of origin of the household head is used to explore variations in housing characteristics and access to finance between households headed by refugees from South-Sudan and Somalia. 50 percent of household heads in camps are from South Sudan, while 17 percent are from Somalia. In urban areas, 24 percent of heads of households are from Somalia (mainly living in Mombasa) while 7 percent are from South Sudan (mainly residing in Nakuru). Housing characteristics are generally poorer in camps than in urban areas with households headed by South- Sudanese facing worse housing conditions. Camp-based households headed by refugees from South-Sudan and Somalia are less likely to live in improved houses than their counterparts in urban areas (Table A-1 column 2Error! Reference source not found. column 3). Urban and camp-based households headed by Somali refugees are equally likely to live in overcrowded rooms and to have access to private toilets. In turn, camp-based households headed by refugees from South-Sudan are more likely to be crowded in rooms and less likely to have access to private toilets than those in urban areas (Table A-1). In addition, protracted households headed by South- Sudanese refugees are 11 percentage points less likely to live in improved houses than those who are not protracted (Table A-1 column 2Error! Reference source not found. column 1). The use of biomass varies by country of origin and area of residence. Camp-based households with Somali heads are 59 percentage points more likely to use biomass fuels than those in urban-areas (Table A-1 Error! Reference source not found.columns 8 and 9). Variations in the use of biomass fuels by area of residence are not significant for households with heads from South-Sudan. Ownership of bank accounts is higher for camp-based households with South Sudanese heads than for those in urban areas; however, South Sudanese-headed households in camps are less likely to have access to loans than those in urban areas. South Sudanese-headed households living in camps are 40 percentage points more likely to have bank accounts compared to those living in urban areas, while there is no such difference among Somali households (Table A-2Error! Reference source not found. column 8 and 9). The higher ownership of bank account among South-Sudanese headed households in camps is likely to be explained by the fact that most of them live in Kalobeyei and might have benefited from the cash-based intervention for shelter that required them to open a bank account. Even though South Sudanese-headed households in camps are more likely to have bank accounts than those in urban areas, they are 24 percentage points less likely to have access to loans. For Somali- headed households such difference is not significant (Table A-2Error! Reference source not found. columns 5 and 6). D. KAKUMA AND KALOBEYEI-BASED REFUGEES’ COMPARATIVE PATTERNS Refugees in Kalobeyei are more likely to be employed and to consume more food and nonfood items than those in Kakuma. However, refugees in Kalobeyei are less likely to own assets while there is no difference in school attendance and food insecurity. Even though refugees have the right to work in Kenya, they face practical restrictions. The 2006 Refugee Act stipulates that refugees can work in Kenya if they have a work permit. The migration section of the Ministry of Interior issues ‘Class M’ work permits that enable refugees to legally work in the country. Applications for permits need a recommendation from a prospective employer and must be accompanied by a letter from the RAS confirming refugee status.47 While refugees are legally allowed to work, in practice, it is reportedly much more difficult given that work permits for asylum seekers or 47 Zetter and Ruaudel. 2016. “KNOMAD Study Part-II Refugees’ Right to Work—An Assessment.” 11 Kakuma and Kalobeyei-based refugees’ comparative patterns 48 refugees are very rarely issued. Access to business permits and business registration is also difficult. Permits are issued only to enterprises with permanent facilities, while street vendors or traders with temporary stalls are charged daily fees that lack clear regulation.49 In addition, Kenya’s encampment policy restricts freedom of movement, and refugees in Kakuma and Kalobeyei are not allowed to travel beyond the town of Kakuma and adjacent areas unless a movement pass is granted.50 Passes are issued for a limited set of reasons, such as medical and educational requirements, or protection concerns. Movement restrictions and the obstacles faced in obtaining work permits fundamentally curtail refugees’ ability to work and generate income, undermining self-reliance. Refugees in Kalobeyei, men and those who are literate in English or Kiswahili are the most likely to be employed with self-employment, apprenticeship and volunteering being more common in Kalobeyei.51 Camp-based refugees’ employment rates are generally low, especially for those in Kakuma (Table 1) who are 21 percentage points less likely to be employed than those in Kalobeyei (Table A-5 Column 1). The larger employment rate in Kalobeyei is partly due to the larger number of volunteers and apprentices in Kalobeyei than in Kakuma (Table A-5 Columns 4 and 5). Due to regulatory frameworks that curtail refugees’ opportunities to move and work, many refugees take low paying jobs, usually in the informal sector.52 Formal jobs in Kakuma town are scarce and primarily filled by nationals. In the camp, jobs are mostly offered by partners of UNHCR and other UN agencies who employ approximately 2,400 refugee ‘incentive workers’ who must demonstrate literacy in English or Kiswahili in order to get an incentive job.53 Therefore, although most employed refugees are paid workers, they are not necessarily self-reliant. Women, especially heads of household that have at least a child under 5 in the household, are less likely to be employed. Due to traditional gender norms that refrain women from participating in the paid labor market, women with young children may drop out or not join the workforce to take care of dependents. In fact, 45 percent of Kakuma refugee women and 24 percent of Kalobeyei women did not look for work in the last 7 days prior to the data collection because of family responsibilities. In turn, women heads with older children (5-14 years), who may demand less care time from women, are more likely to be employed than those with younger children. Literacy in English or Kiswahili is positively correlated with being employed. Refugees in Kakuma are less likely to work on their own account, as an apprentice or volunteer than those in Kalobeyei (Table A-5 columns 2-5). About 52 percent of refugee youth (15-29 years) are not in employment, education, or training (NEET). Youth who are NEET are more likely to be in their 20s, more likely to have no education, lack skills in Kenya’s official languages and are more likely to be women (Table A-6). If measures are not adopted to increase refugee youth integration into the labor market and encourage their participation in education, their existing vulnerabilities will be exacerbated. NEET has severe consequences on mental health, social exclusion, welfare, and is linked with crime increase.54 While most refugee children attend primary school, transition into secondary is very low, with members of protracted households being more likely to attend secondary school than those who are members of non-protracted households. School attendance does not significantly differ between Kalobeyei and Kakuma (Table A-7). Secondary attendance rates are extremely low, only 5 percent of secondary school-age children in Kalobeyei and 14 percent in Kakuma attend secondary school (Table 1). 48 Refugee Consortium of Kenya. 2012. “Asylum Under Threat. Assessing the Protection of Somali Refugees in Dadaab Refugee Camps and along the Migration Corridor.” 49 UNHCR. 2017. “Kakuma Integrated Livelihoods Strategy 2017–2019. Towards Sustainable Solutions for Refugee and Host Communities in Kakuma and Kalobeyei, Turkana West, Kenya.” 50 O’Callaghan et al. 2019. “The Comprehensive Refugee Response Framework. Progress in Kenya.” 51 Employed is defined as having worked at least one hour either as a wage employee, own account/employer in a non-farm business enterprise, own account/employer in agriculture, contributing family worker, apprentice/ Intern or volunteer in the last 7 days preceding the interview. 52 Betts, Omata, and Sterck. 2018. “Refugee Economies in Kenya.” 53 IFC. 2018. “Kakuma as a Marketplace. A Consumer and Market Study of a Refugee Camp and Town in Northwest Kenya.” 54 OECD, “The NEET Challenge.” 12 Kakuma and Kalobeyei-based refugees’ comparative patterns Girls in Kalobeyei are 2 percentage points less likely to attend primary school than boys, while there is no such difference in Kakuma as well as no gender-based difference in secondary school attendance. Children living in protracted households (whose head arrived in Kenya 5 or more years ago) are more likely to attend secondary school than those living in non-protracted households. In addition, disabled children are less likely to attend school than those who are not disabled. Efforts need to be scaled up to meet disability needs and its mainstreaming in schools. While consumption expenditure is higher in Kalobeyei, asset ownership is higher in Kakuma, with food insecurity being alarmingly high in both camps. Refugees in Kalobeyei spend 57 percent and 53 percent more than those in Kakuma on food and non-food items, respectively (Table A-8 columns 1- 2). This may be explained by the difference in the type of food assistance as well as by the growth in farm activities. While refugees in Kakuma receive 70 percent of food aid in kind and 30 percent in cash, refugees in Kalobeyei receive 100 percent of food aid in cash through the Bamba Chakula program.5556 This program seems to have brought better socioeconomic outcomes than food rations, although food security rates have remained high.57 In contrast, refugees in Kakuma are more likely to own assets than those in Kalobeyei (Table A-8 column 4). 58 This may partly be inked to Kakuma refugees’ more protracted situation and their possibility to have accumulated more assets over time.59 High levels of food insecurity are widespread in both camps (Table 1) , with no significant differences between them.60 55 Bamba Chakula (“get your food”) is a monthly transfer on SIM-cards that beneficiaries use to purchase food items from registered traders. 56 The 70 percent of food aid received in-kind by refugees in Kakuma includes a mixture of dry grains, pulses, and cooking oil. 57 MacPherson and Sterck, “Empowering Refugees through Cash and Agriculture: A Regression Discontinuity Design”; Delius and Sterck, “Cash Transfers and Micro-Enterprise Performance: Theory and Quasi-Experimental Evidence from Kenya.” 58 Consumption expenditure is measured by using expenditure on food and nonfood items. The food consumption component consists of food items that were consumed over a 7-day period, with data collected by recall. The nonfood expenditure includes expenditure on energy, education, and other nonfood items such as clothing, footwear, transport, toiletries, etc. 59 Asset ownership is determined by a composite indicator constructed using the Principal Component Analysis (PCA) on the type of owned asset (radio, television, satellite, dish, smartphone, refrigerator, table, bed, mattress, mosquito net, fan, bicycle, motorcycle, car, generator, solar panels, kerosene stove, charcoal jiko, wheelbarrow, corrugated iron fencing, chickens/ducks or other animals). 60 Food Insecurity is measured using the Livelihood Coping Strategy Index (LSCI). The LSCI assesses the coping strategies used by households to address lack of food in the last 30 days. These can include selling assets or livestock, reducing spending on health and education, using savings and begging. A household is food secure if the household did not use any of the strategies in the last 30 days. 13 References E. REFERENCES 3RP, Regional Refugee and Resilience Plan. “Regional Strategic Overview. Regional Refugee and Resilience Plan.” 3RP, 2020. http://www.3rpsyriacrisis.org/. Ambel, Alemayehu, Kevin McGee, and Asmelash Tsegay. “Reducing Bias in Phone Survey Samples: Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames in Four African Countries.” Development Economics Development Research Group, Policy Research Working Papers, 2021. https://doi.org/10.1596/1813-9450-9676. Betts, Alexander, Naohiko Omata, and Olivier Sterck. “Refugee Economies in Kenya.” Oxford, UK, 2018. https://www.rsc.ox.ac.uk/publications/refugee-economies-in-kenya. 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A Consumer and Market Study of a Refugee Camp and Town in Northwest Kenya.” Washington DC: International Finance Corporation, 2018. https://www.ifc.org/wps/wcm/connect/0f3e93fb-35dc-4a80-a955- 6a7028d0f77f/20180427_Kakuma-as-a-Marketplace_v1.pdf?MOD=AJPERES&CVID=mc8eL2K. Kenya National Bureau of Statistics. “Basic Report 2015/16 Kenya Integrated Household Budget Survey (KIHBS).” Nairobi, Kenya: Kenya National Bureau of Statistics, 2018. http://statistics.knbs.or.ke/nada/index.php/catalog/88/related_materials. Kuhnt, Jana, Ramona Rischke, Anda David, and Tobias Lechtenfeld. “Social Cohesion in Times of Forced Displacement – the Case of Young People in Jordan,” 2017, 56. Kurmi, OP, PH Arya, KB Lam, T Sorahan, and JG Ayres. “Lung Cancer Risk and Solid Fuel Smoke Exposure: A Systematic Review and Meta-Analysis.” Eur Respir 40 (2012): 1228–37. MacPherson, Claire, and Olivier Sterck. “Empowering Refugees through Cash and Agriculture: A Regression Discontinuity Design.” Journal of Development Economics 149 (2021). https://www.sciencedirect.com/science/article/pii/S0304387820301899. O’Callaghan, Sorcha, Farah Manji, Kerrie Holloway, and Christina Lowe. “The Comprehensive Refugee Response Framework. Progress in Kenya.” London: Overseas Development Institute, 2019. https://www.odi.org/sites/odi.org.uk/files/resource-documents/12940.pdf. 14 References OECD. “The NEET Challenge: What Can Be Done for Jobless and Disengaged Youth?” In Society at a Glance 2016, by OECD, 13–68. Society at a Glance. OECD, 2016. https://doi.org/10.1787/soc_glance-2016- 4-en. Refugee Consortium of Kenya. “Asylum Under Threat. Assessing the Protection of Somali Refugees in Dadaab Refugee Camps and along the Migration Corridor.” Nairobi: Refugee Consortium of Kenya, 2012. https://reliefweb.int/sites/reliefweb.int/files/resources/Asylum_Under_Threat.pdf. Smith, Kirk, Sumi Mehta, and M Feuz. “Indoor Air Pollution from Household Use of Solid Fuels.” In Comparative Quantification of Health Risk: Global and Regional Burden of Disease Due to Selected Major Risk Factors. Geneva: WHO, 2004. Solari, Claudia, and Robert Mare. “Housing Crowding Effects on Children’s Wellbeing.” Soc Sci Res 41, no. 2 (2012): 464–76. Sommer, Marni, Suzanne Ferron, Sue Cavill, and Sarah House. “Violence, Gender and WASH: Spurring Action on a Complex, under-Documented and Sensitive Topic.” Environment and Urbanization 27, no. 1 (April 1, 2015): 105–16. https://doi.org/10.1177/0956247814564528. The Guardian. “UN Outlines Plan to Close Camps Housing 430,000 Refugees in Kenya.” The Guardian. 2021. https://www.theguardian.com/global-development/2021/apr/15/un-refugee-agency-plan-close- dadaab-kakuma-refugee-camps-kenya. Thulstrup, Andreas, Arturo Gianvenuti, Remi Dannunzio, and M Henry. “Assessing Woodfuel Supply and Demand in Displacement Settings. A Technical Handbook.” FAO, 2016. http://www.fao.org/publications/card/es/c/b113da0f-88f8-418c-9f7d-a42cdf505ee2/. UN Women. “Gender Assessment of Kalobeyei Settlement and Kakuma Camp. Determining the Level of Gender Mainstreaming in Key Coordination Structures.” Kenya: UN Women, 2019. https://www.genderinkenya.org/wp-content/uploads/2019/07/Kalobeyei-Gender-Assess-print-28- Feb.pdf. UNHCR, United Nations High Commissioner for Refugees. “Africa,” 2020. ———. “Cash for Shelter in Kenya. A Field Experience,” 2018. https://www.unhcr.org/5c487dde4.pdf. ———. “Kakuma Integrated Livelihoods Strategy 2017 - 2019. Towards Sustainable Solutions for Refugee and Host Communities in Kakuma and Kalobeyei, Turkana West, Kenya.” Kenya: UNHCR, 2017. ———. “Kenya: Registered Refugees and Asylum-Seekers. July 2020,” July 2020. https://www.unhcr.org/ke/wp-content/uploads/sites/2/2020/08/Kenya-Infographics-31-July- 2020.pdf. ———. “KISEDP. Kalobeyei Integrated Socio-Economic Development Plan in Turkana West.” Nairobi: UNHCR, 2018. https://www.unhcr.org/ke/kisedp-2. UNHCR, United Nations High Commissioner for Refugees, Grameen Crédit Agricole Foundation GCAF, and Swedish International Development Cooperation Agency Sida. “Assessing the Needs of Refugees for Financial and Non-Financial Services - Jordan,” 2019. https://reliefweb.int/sites/reliefweb.int/files/resources/66387.pdf. UNHCR, United Nations High Commissioner for Refugees, and World Bank. “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume A: Kalobeyei Settlement.” Nairobi, 2019. https://documents.worldbank.org/en/publication/documents- reports/documentdetail/982811613626800238/understanding-the-socioeconomic-conditions-of- refugees-in-kenya-volume-a-kalobeyei-settlement-results-from-the-2018-kalobeyei- socioeconomic-survey. ———. “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume B: Kakuma Camp.” Nairobi, 2021. https://documents.worldbank.org/en/publication/documents- reports/documentdetail/443431613628051180/socio-economic-profile-of-refugees-in-kakuma-in- kenya-volume-b-kakuma-camp-results-from-the-2019-kakuma-socioeconomic-survey. 15 References ———. “Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume C: Urban Areas.” Nairobi, Unpublished. United Nations. “Global Compact on Refugees.” New York: United Nations, 2018. https://www.unhcr.org/5c658aed4. UNSW, Australia’s Global University. “The World’s Biggest Minority? Refugee Women and Girls in the Global Compact on Refugees,” 2017. https://www.unhcr.org/59e5f4447.pdf. Verwimp, Philip, and Jean‐Francois Maystadt. “Forced Displacement and Refugees in Sub‐Saharan Africa: An Economic Inquiry,” Policy Research Working Paper 7517, 2015. https://openknowledge.worldbank.org/bitstream/handle/10986/23481/Forced0displac00an0econ omic0inquiry.pdf?sequence=1. World Bank. “‘Yes’ In My Backyard? The Economics of Refugees and Their Social Dynamics in Kakuma, Kenya.” Kenya: World Bank and UNHCR, 2016. Zetter, R, and H Ruaudel. “KNOMAD Study Part-II Refugees’ Right to Work - An Assessment,” 2016. 16 Annex: Regression Tables F. ANNEX: REGRESSION TABLES Standard errors are clustered at the enumeration area level. Regressions include other control variables such as head characteristics (age, education level, marital status, literacy in English and Kiswahili,) household size, asset quintiles, access to private toilet, insufficient drinking water, improved housing, source of lighting, remittances. The asset index is determined by a composite indicator constructed using the Principal Component Analysis (PCA) on the type of owned asset.61 i. Main results using linear probability model Table A-1: Impact of refugee characteristics on housing characteristics Improved Housing Overcrowded Rooms Biomass Fuel Private Toilet Full Sample South- Somali Full South- Somali Full Sample South- Somali Full Sample South- Somali Sudanese Sample Sudanese Sudanese Sudanese (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Camp -0.649*** -0.796*** -0.932*** 0.174*** 0.165** -0.078 0.057 -0.476** 0.345 (0.081) (0.064) (0.060) (0.029) (0.053) (0.161) (0.072) (0.148) (0.246) Kakuma -0.024 -0.053 -0.001 -0.031 0.653*** 0.212 0.586*** 0.180*** 0.123*** (0.051) (0.045) (0.033) (0.044) (0.080) (0.194) (0.147) (0.025) (0.026) Woman Head 0.005 0.080*** 0.003 0.073*** 0.113 0.154** 0.050* -0.015 0.030 0.049*** 0.094 0.063 (0.013) (0.024) (0.010) (0.012) (0.077) (0.045) (0.021) (0.088) (0.043) (0.011) (0.111) (0.049) Camp* -0.061* -0.004 -0.043 -0.159** 0.034 -0.029 -0.047 -0.017 Woman Head -0.009* -0.051* -0.090*** -0.024 (0.005) (0.029) (0.018) (0.023) (0.080) (0.052) (0.018) (0.123) (0.046) (0.025) (0.102) (0.033) Protracted 0.056*** -0.111*** -0.052* 0.039*** -0.100*** -0.075 0.071*** -0.062 0.087 -0.028* 0.120 -0.099* (0.014) (0.018) (0.025) (0.007) (0.026) (0.041) (0.012) (0.087) (0.052) (0.015) (0.086) (0.050) Camp* 0.119*** 0.079 0.083** 0.178 0.053 -0.047 -0.120 0.012 Protracted -0.084*** -0.085** -0.076** 0.046 (0.018) (0.016) (0.050) (0.031) (0.032) (0.148) (0.022) (0.081) (0.073) (0.050) (0.095) (0.273) R2 (%) 65.0 73.0 76.8 42.7 39.1 43.3 58.5 51.2 47.7 17.1 16.3 25.4 N 5326 2046 1251 5325 2045 1251 4177 1300 1180 5326 2046 1251 Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21) Note: Significance level: 1% (***), 5% (**), 10% (*). In column (3), data are only available for Kakuma and Urban. 61Assets: radio, television, satellite, dish, smartphone, refrigerator, table, bed, mattress, mosquito net, fan, bicycle, motorcycle, car, generator, solar panels, kerosene stove, charcoal jiko, wheelbarrow, corrugated iron fencing, chickens/ducks or other animals. 17 Annex: Regression Tables Table A-2: Impact of refugee characteristics on access to finance Remittances Access to loans Ownership of bank account Ownership of mobile banking account Full South- Somali Full South- Somali Full South- Somali Full South- Somali Sample Sudanese Sample Sudanese Sample Sudanese Sample Sudanese (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Camp -0.011 0.140 -0.021 -0.222*** -0.241** 0.213 0.400*** 0.403*** 0.057 (0.024) (0.129) (0.166) (0.030) (0.098) (0.141) (0.041) (0.076) (0.032) Kakuma 0.018 0.006 - 0.202*** 0.586*** -0.301* -0.150* 0.007 0.021* 0.206*** -0.276*** (0.011) (0.016) (0.010) (0.020) (0.024) (0.023) (0.147) (0.037) (0.146) (0.068) Woman 0.046 -0.062 -0.224 -0.013 -0.136*** 0.057*** -0.067 -0.186*** Head -0.048** -0.037** 0.028*** -0.119*** (0.017) (0.070) (0.041) (0.013) (0.162) (0.036) (0.005) (0.038) (0.010) (0.007) (0.054) (0.022) Camp* -0.082 0.040 0.245 0.095** 0.176*** -0.061** -0.067 0.149*** Woman Head 0.034 0.096*** -0.044** 0.085*** (0.024) (0.067) (0.045) (0.024) (0.159) (0.031) (0.017) (0.050) (0.021) (0.021) (0.052) (0.036) Protracted 0.030** 0.034 - 0.063 0.030 -0.088*** 0.061*** -0.090 -0.098 * 0.120*** -0.008* 0.058*** -0.031** (0.008) (0.030) (0.021) (0.004) (0.044) (0.027) (0.008) (0.020) (0.012) (0.012) (0.074) (0.053) Camp* -0.051 -0.019 -0.036 -0.381** 0.059** 0.026 0.167* -0.032 Protracted -0.062** -0.005 -0.060* 0.095* (0.021) (0.044) (0.166) (0.018) (0.055) (0.137) (0.030) (0.021) (0.040) (0.046) (0.081) (0.088) R2 (%) 23.5 18.9 26.4 10.5 19.5 9.8 10.6 13.5 4.3 24.3 32.2 14.7 N 5326 2046 1251 5326 2046 1251 5326 2046 1251 4277 1305 1251 Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21) Note: Significance level: 1% (***), 5% (**), 10% (*). In column (4), data are only available for Kakuma and Urban. 18 Annex: Regression Tables Table A-3: Impact of refugee characteristics on social cohesion proxies Trust Safety Neighbor Host Safe to go to Safe to walk in Safe to walk in town by self neighborhood neighborhood during day at night (1) (2) (3) (4) (5) Camp 0.076 -0.189*** 0.121** 0.218*** 0.104* (0.060) (0.045) (0.043) (0.052) (0.046) Kakuma -0.001 0.085** 0.057 0.010 -0.142** (0.021) (0.029) (0.033) (0.018) (0.046) Woman Head 0.002 -0.009 0.050*** 0.027 0.039** (0.006) (0.023) (0.006) (0.016) (0.015) Camp*Woman Head -0.012 0.032 -0.060** -0.033* -0.068** (0.017) (0.034) (0.027) (0.015) (0.026) Protracted -0.012 -0.023 -0.021 0.008 0.019 (0.011) (0.021) (0.029) (0.011) (0.032) Country of origin (base: South Sudan) Somalia 0.002 0.030* 0.005 -0.021* 0.035 (0.029) (0.015) (0.019) (0.011) (0.032) Other -0.030 0.011 -0.078*** -0.024** -0.033 (0.025) (0.024) (0.014) (0.010) (0.025) R2 (%) 26.8 36.1 29.5 27.4 12.3 N 5007 5007 5007 5007 5007 Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21) Note: Significance level: 1% (***), 5% (**), 10% (*). Table A-4: Impact of refugee characteristics on decision making Express Opinions considered Opinions (1) (2) Camp 0.150*** 0.229*** (0.023) (0.046) Kakuma 0.005 -0.139*** (0.013) (0.023) Female Head -0.050** -0.031** (0.016) (0.012) Camp*Female Head 0.073** -0.001 (0.030) (0.024) Protracted 0.033*** -0.017 (0.006) (0.015) Country of origin (base: South Sudan) Somalia -0.055 -0.028 (0.035) (0.020) Other -0.019 -0.033 (0.023) (0.021) R2 (%) 37.9 36.0 N 4849 4849 Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21) Note: Significance level: 1% (***), 5% (**), 10% (*). 19 Annex: Regression Tables Table A-5: Impact of refugee characteristics on labor force participation Source of livelihood among the employed Employed Wage Business Apprenticeship Volunteer Employment (1) (2) (3) (4) (5) Kakuma -0.205*** 0.053 -0.188*** -0.089** -0.164* (0.049) (0.035) (0.026) (0.027) (0.072) Woman -0.163*** -0.178*** -0.133* 0.021 0.052 (0.024) (0.032) (0.054) (0.028) (0.063) Kakuma*Woman 0.040 0.020 0.071 -0.030 -0.020 (0.024) (0.031) (0.058) (0.032) (0.073) Woman head 0.039 0.012 0.079 -0.024 -0.099 (0.031) (0.055) (0.060) (0.028) (0.070) Has child in household (0-4 years) 0.049** -0.015 -0.033 0.010 0.049 (0.019) (0.020) (0.023) (0.023) (0.028) Woman head*child (0-4 years) -0.087** -0.029 0.008 -0.002 0.044 (0.028) (0.088) (0.075) (0.020) (0.040) Has child in household (5-14 years) -0.011 -0.043 -0.004 -0.016 -0.010 (0.018) (0.027) (0.024) (0.013) (0.018) Woman head*child (5- 14 years) 0.113** 0.048 -0.022 0.033 0.083 (0.034) (0.102) (0.024) (0.025) (0.056) Education Level (base: None) Primary 0.057** -0.033 -0.020 -0.010 0.032 (0.019) (0.030) (0.014) (0.026) (0.021) Higher 0.100*** -0.007 -0.089* 0.010 -0.020 (0.015) (0.036) (0.039) (0.025) (0.039) Technical/Vocational 0.162*** -0.022 -0.030 0.020 0.044 (0.040) (0.060) (0.038) (0.035) (0.055) Country of origin (base: South Sudan) Somalia 0.088*** 0.081*** 0.051 0.045 0.006 (0.012) (0.018) (0.042) (0.024) (0.040) Other 0.078** 0.091*** 0.067 -0.032 -0.058 (0.021) (0.016) (0.053) (0.019) (0.034) Literacy in Swahili/English 0.111*** 0.066 0.024 0.012 0.006 (0.018) (0.056) (0.058) (0.020) (0.026) Poor -0.101*** -0.035 -0.083*** -0.032 0.006 (0.021) (0.025) (0.015) (0.017) (0.029) Primary Employer (base: Other household) International Org/NGO -0.079 -0.046* -0.049 -0.079 (0.050) (0.020) (0.060) (0.050) Own household -0.104*** 0.002 0.549*** -0.104*** (0.025) (0.031) (0.094) (0.025) R2 (%) 21.2 39.7 14.7 7.4 27.6 N 5391 1868 1868 1868 1868 Source: Kalobeyei SES (2018); Kakuma SES (2019) Note: Significance level: 1% (***), 5% (**), 10% (*). 20 Annex: Regression Tables Table A-6: Impact of Refugee Characteristics on Youth Who are Neither Employed nor Enrolled in School (NEET) NEET Kakuma 0.031 (0.021) Woman 0.051** (0.015) Kakuma*Woman -0.007 (0.010) Has child in household -0.003 (0.010) Woman*Child 0.057* (0.024) Age (base:15-19) 20-24 0.145*** (0.015) 25-29 0.205*** (0.044) Education Level (base: None) Primary -0.260*** (0.032) Higher -0.322*** (0.024) Technical/Vocational -0.350*** (0.063) Country of origin (base: South Sudan) Somalia 0.064** (0.018) Other 0.047* (0.022) Literacy in Swahili/English -0.172** (0.052) Woman head -0.035** (0.013) Head working -0.035** (0.013) R2 (%) 32.1 N 5173 Source: Kalobeyei SES (2018); Kakuma SES (2019) Note: Significance level: 1% (***), 5% (**), 10% (*). Table A-7: Impact of Refugee Characteristics on School Attendance Rates Primary NAR Secondary NAR (1) (2) Kakuma 0.049 0.029 (0.034) (0.022) Woman -0.021*** 0.017 (0.001) (0.014) Kakuma*Woman 0.017 -0.036 (0.023) (0.019) Country of origin (base: South Sudan) Somalia -0.060 0.059** 21 Annex: Regression Tables (0.033) (0.022) Other 0.016 -0.003 (0.024) (0.022) Disabled -0.115** -0.049** (0.032) (0.017) Protracted 0.013 0.100*** (0.029) (0.024) Gender of head -0.002 0.043*** (0.030) (0.009) Education Level of head (base: None) Primary 0.015 -0.014 (0.023) (0.010) Higher -0.009 0.065*** (0.020) (0.015) Technical/Vocational 0.042 0.073 (0.055) (0.040) Head working 0.049 0.029 (0.034) (0.022) Poor 0.001 -0.006 (0.017) (0.031) R2 (%) 1.4 5.6 N 5591 2656 Source: Kalobeyei SES (2018); Kakuma SES (2019) Note: Significance level: 1% (***), 5% (**), 10% (*). Table A-8: Impact of Refugee Characteristics on Consumption Expenditure, Food Insecurity and Asset Index Food Nonfood LCS Food Asset Index Consumption Consumption Insecurity Expenditure Expenditure (1) (2) (3) (4) Kakuma -0.829*** -0.748*** -0.008 0.234*** (0.061) (0.067) (0.033) (0.061) Woman Head -0.127* -0.116** -0.071** 0.032 (0.053) (0.037) (0.027) (0.034) Kakuma*Woman Head 0.136** 0.130** 0.051* -0.119** (0.055) (0.053) (0.024) (0.037) Protracted -0.060** 0.103** 0.007 0.239*** (0.017) (0.037) (0.029) (0.043) Country of origin (base: South Sudan) Somalia 0.108*** -0.004 -0.077** 0.361*** (0.033) (0.043) (0.033) (0.049) Other 0.149*** 0.118** 0.004 0.478*** (0.037) (0.049) (0.037) (0.055) 2 R (%) 39.7 35.9 4.8 43.1 N 2935 2935 2978 2978 Source: Kalobeyei SES (2018); Kakuma SES (2019) Note: Significance level: 1% (***), 5% (**), 10% (*). 22 Annex: Regression Tables ii. Regression results using alternative estimation methods 62 Table A-9: Impact of Refugee Characteristics on Housing Characteristics Improved Housing Overcrowded Rooms Use of Biomass fuel LPM Logit LPM Logit LPM Logit (1) (2) (3) (4) (5) (6) Camp -0.649*** -0.521*** 0.122*** 0.084** (0.081) (0.072) (0.033) (0.036) Kakuma -0.024 -0.081** 0.013 0.012 0.650*** 0.464*** (0.051) (0.039) (0.029) (0.019) (0.072) (0.071) Female Head 0.005 -0.005 0.063*** 0.042** 0.045* 0.004 (0.013) (0.015) (0.012) (0.017) (0.020) (0.017) Camp*Female -0.008 0.008 -0.038** -0.038* -0.095*** 0.056 Head (0.005) (0.023) (0.017) (0.023) (0.020) (0.117) Protracted 0.056*** 0.023 0.032*** 0.015 0.068*** 0.040** (0.014) (0.014) (0.007) (0.018) (0.009) (0.016) Camp*Protracted -0.084*** -0.084* -0.089** -0.067*** -0.072** 0.165 (0.018) (0.046) (0.031) (0.023) (0.019) (0.125) Country of origin (base: South Sudan) Somalia -0.015 -0.013 -0.064 -0.032 0.036 -0.082* (0.009) (0.022) (0.042) (0.025) (0.059) (0.046) Other -0.049 -0.053** -0.012 -0.002 -0.073 -0.148*** (0.028) (0.021) (0.032) (0.019) (0.048) (0.045) 2 R (%) 64.9 45.7 57.5 N 5309 5043 3982 % of predicted 99.7 94.7 93.8 probabilities within unit interval Source: Kalobeyei SES (2018); Kakuma SES (2019); Urban SES (2020-21) Note: In columns (1), (3) and (5), the models are estimated by using observations whose predicted probabilities fall within the unit interval. In columns (2), (4) and (6), the models are estimated using logit and the marginal effects are shown. Significance level: 1% (***), 5% (**), 10% (*). 62We use the alternative methods to estimate the impact of refugee characteristics on housing characteristics, access to finance, social cohesion, labour force status, NEET, School Attendance Rates, Food Insecurity and Asset Index. Results are very similar to the estimates by LPM. Due to space limitation, we present only the results for housing characteristics. Other statistics are available upon request. 23