metadata: authors: 0: author: Hanmer,Lucia C.
1: author: Rubiano Matulevich,Eliana Carolina
2: author: Santamaria,Julieth
count: Somalia
docna: 0: docna: Differences in Household Composition :
            Hidden Dimensions of Poverty and Displacement in Somalia
docty: Policy Research Working Paper
subtopic: Inequality,Gender and Development,Economics and Gender,Gender and Economic Policy,Gender and Poverty,Gender and Economics,Voluntary and Involuntary Resettlement,Involuntary Resettlement Law,Educational Sciences
teratopic: Poverty Reduction,Macroeconomics and Economic Growth,Gender,Education,Law and Development,Social Development
abstracts: cdata!: Little is known about how gender
            inequality influences poverty rates of forcibly displaced
            people. This paper uses a nationally representative survey
            to analyze poverty among internally displaced people and
            non-displaced people in Somalia. More than half of
            internally displaced people’s households and 47 percent of
            non-displaced people’s households are female headed.
            Although poverty rates are higher among internally displaced
            people than non-displaced people (77 versus 66 percent),
            male-headed households are poorer than female-headed ones
            among both groups. Extending the analysis beyond headship to
            demographic characteristics and by the gender and number of
            earners provides a more nuanced picture. Demographic
            characteristics are strongly associated with poverty rates
            for internally displaced people but not for non-displaced
            people. Having more income earners reduces poverty risk for
            all households. For internally displaced people’s
            households, the largest decrease in poverty risk is
            associated with having more female earners, while having
            more male earners is associated with the lowest poverty for
            nondisplaced people’s households. The analysis highlights
            that poverty reduction policies and programs must cover all
            households and lift barriers to women’s economic
            opportunities. Programs that respond to women’s care
            responsibilities and address barriers to women’s economic
            opportunities are especially important for internally
            displaced people.
geo_reg_key: 517191
repnme: repnme: Differences in Household Composition :
            Hidden Dimensions of Poverty and Displacement in Somalia
display_title: Differences in Household Composition
            : Hidden Dimensions of Poverty and Displacement in Somalia
listing_relative_url: /research/2021/10/33555888/differences-household-composition-hidden-dimensions-poverty-displacement-somalia
topicv3: Female-Headed Households m1326228 916,Poverty m1326687 695,Education m1327048 470,Gender m1327621 615,Involuntary Resettlement m1327664 750
publishtoextweb_dt: 2021-10-28T00:00:00Z
dois: 10.1596/1813-9450-9818
historic_topic: Poverty Reduction,Macroeconomics and Economic Growth,Gender,Education,Law and Development,Social Development
origu: Gender Director (HGNDR)
no_of_pages: 40
author_keys: 0: author_key: 428158
1: author_key: 885272
model_extractions: Policy Research Working Paper 9818 Differences in Household Composition Hidden Dimensions of Poverty and Displacement in Somalia _Lucia C. Hanmer Eliana Rubiano-Matulevich Julieth Santamaria_ Gender Global Theme October 2021
Policy Research Working Paper 9818

# **Abstract**

Little is known about how gender inequality influences poverty rates of forcibly displaced people. This paper uses a nationally representative survey to analyze poverty among internally displaced people and non-displaced people in Somalia. More than half of internally displaced people’s households and 47 percent of non-displaced people’s households are female headed. Although poverty rates are higher among internally displaced people than non-displaced people (77 versus 66 percent), male-headed households are poorer than female-headed ones among both groups. Extending the analysis beyond headship to demographic characteristics and by the gender and number of earners provides a more nuanced picture. Demographic characteristics are strongly associated with poverty rates for internally displaced people but not for non-displaced people. Having more income earners reduces poverty risk for all households. For internally displaced people’s households, the largest decrease in poverty risk is associated with having more female earners, while having more male earners is associated with the lowest poverty for nondisplaced people’s households. The analysis highlights that poverty reduction policies and programs must cover all households and lift barriers to women’s economic opportunities. Programs that respond to women’s care responsibilities and address barriers to women’s economic opportunities are especially important for internally displaced people.

This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lhanmer@worldbank.org (corresponding author), erubiano@worldbank.org, and juliethsa@iadb.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Differences in Household Composition: Hidden Dimensions of Poverty and Displacement**

# **in Somalia***

Lucia C. Hanmer<sup>[†]</sup> , Eliana Rubiano- ~~M~~ atulevich<sup>[‡]</sup> , Julieth Santamaria<sup>[§]</sup> _Keywords:_ households, conflict, internally displaced people, host communities, gender.

_JEL codes:_ D10, O15, D74, J16.

The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

> * This work is part of the program ‘Building the Evidence on Protracted Forced Displacement: A MultiStakeholder

Partnership’. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.
# **1. Introduction**

Globally, the number of forcibly displaced people is unprecedented. At the end of 2020, the number of people forced to flee had reached 82 million, including 26 million refugees and 48 million internally displaced people (IDPs)<sup>1</sup> (UN High Commissioner for Refugees [UNHCR, 2021]).<sup>2</sup> Forcibly displaced women and men face specific barriers to taking up economic opportunities and accessing services compared to their host communities, which create higher risks of being trapped in poverty than many of their hosts (World Bank, 2017). Qualitative research underlines that women and men experience and respond differently to forced displacement (Forced Migration Review, 2018). However, little is known about how gender inequality influences poverty rates of forcibly displaced people, largely due to the scarcity of representative data adequate to investigate these differences.

We use the 2017-18 Somalia High-Frequency Survey to examine the role of gender-based disadvantage in poverty rates experienced by IDPs and non-IDPs. Three metrics of gender disadvantage are investigated: first, we use the traditional approach to measuring the impact of gender on poverty, via household headship; second, we classify households according to their demographic characteristics; and third, by the number and sex of earners in the household. To our knowledge, this is the first study to go beyond headship in analyzing the gender dimensions of poverty in situations of internal displacement.

> <u>[†]</u> Lead Economist, Gender Group, World Bank Group.

> <u>[‡]</u> Economist, Poverty and Equity Global Practice, World Bank Group.

> <u>[§]</u> Economist, International American Development Bank, Washington, D.C.

> 1 Internally displaced persons (IDPs), are “persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border” (UN 1998 Guiding Principles on Internal Displacement E/CN.4/1998/53/Add.2 11 February 1998)

> 2 Forcibly displaced people include refugees, internally displaced people, and asylum seekers (see <u>https://migrationdataportal.org/themes/forced-migration-or-displacement#definitions)</u>

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Our findings underline the importance of differentiating among different types of households using other metrics beyond simply male and female headship. Overall, 65 percent of female-headed households are poor compared with 70 percent of male-headed households. Poverty is higher among IDPs than non-IDP communities (77 versus 67 percent); however, male-headed IDP households are still poorer than female-headed ones (83 versus 71 percent). The relative disadvantage of male-headed households in both communities still holds when female heads are divided between _de jure_ and _de facto_ female heads. _De jure_ female household heads are those who are divorced, separated, or widows. _De facto_ household heads are married or in a civil union often with their husband or partner living away from the family home.

We find that, controlling for household and individual characteristics, IDP families with children, especially female single caregivers, experience much higher poverty rates than families without children. Interestingly, these demographic characteristics are not strongly associated with poverty rates for non-IDPs. For both IDPs and non-IDPs poverty risk is significantly lower for households with more income earners of either gender. An important finding for IDP households is that the largest decrease in poverty risk is associated with having more female earners. In contrast, for non-IDPs more male earners are associated with the greatest poverty risk reduction.

Our paper contributes to the growing literature that suggests that many forcibly displaced people face high and persistent barriers to achieving adequate living standards for themselves and their families and that these barriers are higher than those faced by other poor communities. It extends the literature by analyzing the association of gender-based disadvantage with poverty for displaced and non-displaced populations, respectively.

Forced displacement can impair people’s abilities to escape poverty in a way that sets them apart from non-displaced populations (Christensen & Harild, 2009). Loss of assets and trauma resulting from displacement lead to higher poverty rates among IDPs than non-displaced populations. For example, poverty rates among internally displaced persons in Azerbaijan are 25 percent, compared with 20 percent among the non-displaced (Bussolo & Lopez-Calva, 2014). Over 2
the medium term, IDPs may not be able to replace their assets. For example, in Afghanistan, 60 percent of IDPs in urban areas live in a tent or temporary shelter, and even after five years in displacement, 61 percent of them remained in temporary housing (World Bank & UNHCR, 2011). Asset losses may also impact people’s standing in the community; for example, the loss of cattle meant a change in social status among forcibly displaced pastoralists in the Sahel (World Bank, 2017).

The rest of this paper is structured as follows: Section 2 provides a brief overview of the literature related to poverty and gender. Section 3 describes the Somali context and provides some background to the drivers of forced displacement and poverty. Section 4 describes the data. Section 5 presents the empirical approach, before describing the results in Section 6. Section 7 concludes and discusses the policy implications of our findings.

# **2. Poverty and Gender**

Research examining why women as individuals or households dependent on women’s income may be particularly vulnerable to poverty often draws attention to how underlying norms about gender affect paid and unpaid work (Braunstein et al., 2011; Folbre, 2006; Nelson, 1995). Household allocation of time to paid work or employment or otherwise remunerated employment such as entrepreneurship is partly the outcome of an intra-household bargaining process. Power inequality between men and women combined with social norms leads to a gendered division of labor whereby women undertake the majority of unpaid work. Hence, the demand for labor for unpaid household-services restricts women’s ability to participate in labor markets (Floro, 1995; Folbre, 2006; Folbre et al., 2005) and restricts girls’ ability to attend school and acquire labor market skills (Arora, 2015; Bardasi & Wodon, 2010; DeGraff et al., 2017; Gammage, 2010). Compared to men, women have less access and control over financial, digital, land and property assets putting them at a disadvantage as entrepreneurs and self-employed workers (Deere & Doss, 2006; Deere et al, 2013; Morton et al. 2014; Kantor, 2002; Horrell & Krishnan, 2007; Klugman & Tyson, 2016).

3
A body of evidence shows that across the world, compared to men, women are less likely to participate in the labor market (Klasen, 2019). Legal and social norms shaping attitudes about women’s role in society underlie this outcome. These norms are often reflected in discriminatory laws, employment practices, gender wage penalties and social preferences that reinforce the gender division of labor (Connelly & Kongar, 2017; Rubiano-Matulevich & Viollaz, 2019). In developing countries, when women work, they are more likely to be informal workers, earn less, and are less likely to receive pensions and other work-related benefits (Bosch & Maloney, 2010; Ñopo et al., 2011; World Bank, 2011).

Empirical evidence of women’s time constraints, gender norms, and barriers to women’s access to economic opportunities have led researchers to examine whether households headed by a woman or their income are more vulnerable to poverty than male-headed households. Many studies have found that poverty rates among female-headed households are higher than those with a male head, but this finding is far from universal (Buvinić & Gupta, 1997; Chant, 2003; Lampietti and Stalker 2000; Quisumbing et al. 2001).

Analyses based on headship have been criticized because the concept hides substantial differences between the types of households in which individuals live, such as income transfers from male migrant workers, social norms surrounding widows, divorcees and lone parents and distribution of care for elders and children (Chant, 1997, 2003, 2004, 2008; Lampietti & Stalker, 2000; Quisumbing et al., 1995; Rosenhouse, 1989).

_De jure_ versus _de facto_ distinctions are often used to capture differences between female headship. _De jure_ female household heads are those who are divorced, separated, or widows. _De facto_ household heads are married or in a civil union often with their husband or partner living away from the family home. This distinction carries important, often culturally specific implications for poverty risk (for a recent review see Hanmer et al. 2020). Further, depending on their education, class, race, ethnicity, and other markers of social identity, including possibly displacement status, female-headed households have different degrees of access to income4
generating opportunities, asset ownership and use, and entitlements - for example, support from other family members, receipt of charitable giving from religious institutions or government transfers targeted to vulnerable groups like single parents or widows. In turn, these opportunities determine people’s vulnerability to poverty and their ability to cope with adverse events.

Results of research which focuses on subgroups of female-headed households show that distinguishing between different types of female-headed households is critical to understand the links between gender inequality and poverty (Appleton, 1996; Horrell & Krishnan, 2007; Klasen et al., 2015; van de Walle, 2013). Often _de facto_ female heads have access to earnings from migrant male workers and are not particularly disadvantaged compared with male-headed households (Appleton, 1996; Horrell and Krishnan, 2007). _De jure_ female-headed households, on the other hand, are more likely to be among the poorest of the poor. For example, in Mali, households with a widow head have significantly lower living standards than male or other female-headed households and the adverse effects of widowhood persist even after widows are absorbed into male-headed households (van de Walle, 2013).

Another strand of the literature has focused on subgroups of female-headed households using demographic classifications. Milazzo and van de Walle (2017) examine how female-headed households have fared in the context of Africa’s remarkable progress in reducing poverty since the mid-1990s. Their classifications include households with and without a male adult and with and without a married head, as well as widow-headed households. They find that households with a married woman head and a male adult performed the best, whereas households with an unmarried female head and no male adult presence experience the least poverty reduction. Further, a number of studies show that allowing for economies of scale in consumption reverses conclusions about the incidence of poverty based on the headship comparison (Drèze & Srinivasan, 1997; Lanjouw & Ravallion, 1995; van de Walle, 2013). This is partly because female-headed households are, on average, smaller. Hence, the traditional per capita measures tend to underestimate their poverty and overestimate that of larger male-headed households (Brown & van de Walle, 2020).

5
Other research has classified households by the number of earners, their sex and the share of their contribution to the household income (Fuwa, 2000; ECLAC, 2004; Grown & Valodia, 2010; Rogan, 2013). For example, ECLAC (2004) classifies households based on the presence and number of people who contribute to the household income and finds when both spouses contribute to household income, households are less likely to be poor, compared to households with a single earner of either sex.

Our study builds on the foregoing literature. We use household headship, demographic characteristics, and gender and number of people contributing to household income to explore links between poverty and gender.

# **3. Somali Context**

In the aftermath of the war with Ethiopia and the ousting of General Siad Barre, rebellions over most of the territory led to the collapse of the Somali State in 1991 and to the separation of the former British colony of Somaliland.<sup>3</sup> The civil war is complex and has multiple causes, including competition for natural resources and power, large numbers of unemployed youth, and a politicized clan identity (Elmi & Barise, 2006). Thousands of Somali civilians have been killed, died of starvation or been forcibly displaced. To date, Somalia remains marked by high levels of insecurity exacerbated by the Al-Shabaab terrorist group and the weak capacity of state institutions (World Bank, 2018a).

Somalia is one of the poorest countries in the world. Gross domestic product (GDP) per capita is US$500, and more than 70 percent of its population is poor, living on less than US$1.90 PPP (2011) per day. With the absence of an effective government structure in most of the country since 1991, clan affiliation became a critical source of social, financial, and human protection.

> 3 Currently, Somaliland is an autonomous region in northern Somalia that has held independent elections since 2003; it considers itself an independent country, which seeks international recognition of its sovereignty (Council on Foreign Relations, 2018). The adoption of a Provisional Constitution and establishment of the Federal Government in 2012, as well as the subsequent formation of four new Federal Member States, provides space for peace and stability.

6
Half of Somalia’s estimated 12 million people live in rural areas, and many are pastoralists, highly vulnerable to climate shocks. The agriculture sector remains the backbone of the economy and accounts for about 75 percent of GDP, among the highest in the world (World Bank, 2019). The 2011 East Africa drought resulted in the deaths of a quarter million people, half of them children under the age of five years, as well as the displacement of nearly 1 million people. In 2016/17, the country experienced another large-scale drought, leaving an estimated 6.7 million people in urgent need of humanitarian assistance.

As of 2020 there were over 2,000 recorded IDP settlement sites in Somali ~~a~~<sup><u>[7]</u></sup> but information on the exact location of sites, living conditions, access to services and exact numbers of IDPs in each location is sparse (UN Office for the Coordination of Humanitarian Affairs [OCHA] & REACH 2019). UNHCR (2021) estimates that there were 3 million people (about 17 percent of the population) internally displaced at the end of 2020. Many IDPs have been displaced during different phases and types of armed conflict, others by clan conflict, drought or famine or by other disasters triggered by natural hazards, such as recurrent floods. Still others had to move due to forced evictions (Government of Somalia, 2017). It is estimated that the 2017 drought, followed by extreme floods in 2018, displaced more than 926,000 people (World Bank, 2019).

The majority of IDPs settle in informal and unplanned settlements characterized by overcrowding, poor shelter, unsanitary conditions, and limited access to basic services (UNDP, 2018). Access to humanitarian assistance and services provided by private contractors are often disrupted. One barrier to services delivery is that most settlements are controlled and dominated by gatekeepers who are often community leaders, members of militias, or landowners (Drumtra, 2014; Government of Somalia, 2017; Yarnell, 2019). IDPs outside settlements who have been disconnected from their networks and IDPs from minority groups tend to be excluded from access to jobs and opportunities and may have been denied permission to live in clan-controlled settlements (Drumtra, 2014). Also, high insecurity can restrict the delivery of services and monitor programs (Refugees International, 2019). In response to these challenges, the federal government 7
created a Durable Solutions Secretariat (DSS) and adopted a national IDP policy alongside National Evictions Guidelines. This response aims to protect the rights of IDPs, ensure that any evictions are carried out in legal way, and provide alternative housing options (Refugees International, 2019).

Prolonged conflict has had far reaching effects, including the emergence of a large number female-headed households, with reports of nearly half of all households being female headed going back for nearly a decade (Food Security and Nutrition Analysis Unit [FSNAU], 2012; United Nations Development Program [UNDP], 2012). This is often attributed to the conflict related death of male family members or the inability of men to flee to certain areas (World Bank, 2014). As has been observed in other conflict settings, family splitting is also a strategy to cope with lack of economic opportunities (Brück & Schindler, 2009; Ibáñez, 2008). In Somalia sometimes men opt to return to their areas of origin and women and children are left behind with the intention of returning if and when the situation improves (World Bank, 2014).

Somalia ranks among the bottom dozen countries on the Women, Peace and Security Index, which measures women’s inclusion, justice, and security.<sup>4</sup> It has one of the lowest female labor market participation rates in Sub-Saharan Africa with only 43 percent of women in the labor force compared to 67 percent of men. However, women have always contributed to work on family farms. Gender roles have changed; increased numbers of female-headed households have led to changes in herd management practices and women’s greater involvement in livestock trade (ElBushra & Gardner, 2016). Currently, women are engaged in the informal sector and microenterprises and work in agricultural production and livestock activities. Women are heavily involved in crop production, provide more than 60 percent of labor in subsistence farming, and are mainly responsible for marketing fruits and vegetables for domestic consumption and account for 60-80 percent of domestic meat and milk trade, hides and skins, as well as petty trade (UNDP,

> 4 See Georgetown Institute for Women, Peace and Security/Peace Research Institute of Oslo “Women Peace and Security Index”. https://giwps.georgetown.edu/wp-content/uploads/2019/12/WPS-Index-2019-20-Report.pdf

8
2018, World Bank, 2017). In urban areas, women rely on low skilled, low paying service jobs, including cleaning, washing, and childcare (Federal Government of Somalia, 2018).

Women’s voice in society and their ability to exercise leadership are limited. They remained virtually excluded from the political and judicial structures that emerged in different parts of the country after the collapse of the state in 1991 (Gardener, 2007; Mahmood, 2018; Parke et al., 2017). Recent reforms have addressed women’s representation in a number of ways including through the introduction of quotas in national parliamentary elections (United Nations Development Program (UNDP), 2019). Nevertheless, the politicization of clan identity and stigma attached to women entering government and politics remain barriers to women’s political participation and adoption of leadership roles (Parke et al., 2017; UN Women, 2018).

Permeating women’s lives are high levels of gender-based violence (GBV). Conflictrelated violence against women and girls has been extensively documented (Perrin et al., 2019; UN Women, 2018; United Nations, 2020). GBV is particularly prevalent in IDP settlements, among minority clans, and in conflict zones where all parties have reportedly perpetrated sexual abuse and violence (Perrin et al., 2019; United Kingdom: Home Office, 2017). Increasing rates of violence in the home have been linked to changing gender roles as men experience changing dynamics within the household and more women move into breadwinner positions (OCHA, 2019). For women, heightened risk of sexual and gender-based violence is exacerbated by overcrowded makeshift facilities without adequate safety and security measures – from lighting to safe water, and sanitation and hygiene facilities with privacy. Child marriage, another form of GBV is common throughout the country, with 35 percent of women aged 20 to 24 married before the age of 18.<sup>5</sup> An estimated 98 percent of Somali women have undergone female genital mutilation (United Nations General Assembly, 2018).

> 5 World Bank’s Gender Data Portal based on Somalia Demographic and Health Survey (2020).

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# **4. Data and Descriptive Statistics**

The first wave of the Somali High Frequency Survey (HFS)<sup>6</sup> was conducted in 2016. The second wave conducted in 2017 extended coverage to include nomads and households in insecure areas. The sample of IDPs and non-displaced Somalians is representative of the Somali population living in secure areas.<sup>7</sup> We use the second wave of the HFS which samples 4,780 households (26,317 individuals). IDPs account for 33 percent of the survey population, most of whom reside in IDP settlements in the South West and in urban Jubbaland.

**Table 1. Household and household head characteristics, by displacement status and gender**

||Settl|ement I|DP|Non-se|ttlemen|t IDP||Non-IDP|||Total||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Female|Male|All|Female|Male|All|Female|Male|All|Female|Male|All|
|_Female-headed (%)_|||55.4|||41.7|||51.0|||50.5|
|De jure|31.0||31.0|25.6||25.6|26.7||26.7|||27.0|
|De facto|68.9||68.9|74.4||74.4|73.2||73.2|||72.9|
|_Household characteristics_|||||||||||||
|Urban (%)||||38|32|34|79|69|74|74|63|68|
|Rural (%)||||62|68|66|21|31|26|26|37|32|
|Household size|6|5|5|6|6|6|5|5|5|5|5|5|
|Number of children|3|3|3|3|4|3|3|3|3|3|3|3|
|Dependency ratio|2|1|1|1|2|1|1|1|1|1|1|1|
|Able bodied adults (%)|46|48|47|54|43|47|52|49|50|51|48|49|
|_Household head characteris_|_tics_||||||||||||
|Age|36|38|37|41|43|42|37|40|38|37|40|38|
|Education (%)|||||||||||||
|No education|88|64|76|90|69|77|51|38|44|68|50|58|
|Primary or below|9|24|16|8|21|16|24|26|25|17|25|21|
|Secondary and above|3|12|7|1|10|7|25|36|31|15|26|21|
|Number of households|226|184|410|186|263|449|1,998|1,923|3,921|2,410|2,370|4,780|
|Observations (number<br>of individuals)|1,254|970|2,224|1,009|1,426|<br>2,435|10,969|10,689|21,658|13,232|13,085|<br>26,317|

_Source:_ Estimated by authors using Somali HFS 2017-18. _Note:_ IDP stands for internally displaced persons. Notes HH = household head 6 Surveys were conducted by the World Bank in collaboration with the Somali statistical authorities. They can be accessed in the World Banks micro data library see https://microdata.worldbank.org/index.php/catalog/2738 and <u>https://microdata.worldbank.org/index.php/catalog/3181</u> 7 Rates of poverty and other markers of well-being may thus be different from those in different regions inaccessible to the HFS.

10
Poverty incidence was calculated using a consumption aggregate from the survey’s consumption module and the inflation-adjusted international poverty line of US$1.90 per person per day (Pape et al., 2019, Vol C).

Household heads were identified by the survey respondent who is asked who s/he considers to be the household head. About half of all households are headed by women in Somalia, although for IDP households living outside settlements, female headship is less common (36 percent). All IDP settlements are located in urban areas and about three-quarters of non-IDP households live in urban areas; however, a large share of IDPs living outside settlements (66 percent) are located in rural areas. The average household size is 5 people, three children and two adults. There is no difference between settlement IDPs and non-IDPs household size. Non-settlement IDP households are slightly larger than average with 6 people and settlement IDP households that are femaleheaded are also larger - 6 people on average (Table 1).

Figure 1 shows that most female heads of both IDP and non-displaced households are married, hence, they are _de facto_ heads. About two-thirds of female household heads are _de facto_ in non-IDP communities compared to just over half of female-headed IDP households living outside settlements. There is a slightly higher proportion of _de jure_ female heads in the nonsettlement IDP sample (43 percent) than in the settlement and non-displaced samples (34 and 30 percent, respectively).

Figure 2 shows that education levels are strikingly low, particularly among IDPs, and there are large gender gaps. On average 93 percent of all IDP heads have primary or less education compared to 69 percent of non-IDP household heads. For both IDPs and non-IDPs the gender gap is large. Female heads in settlements are significantly less educated. Only about 1 in 10 female heads in IDP settlements has some secondary education compared to nearly 1 in 4 male heads in settlements. The gender gap is similar for non-settlement IDPs. In contrast, for non-IDPs, the gender gap emerges at the tertiary level and secondary schooling rates are similar for male and female heads at around 25 percent.

11
<!-- Start of picture text --> Settlement IDP<br>Married Divorced or separated Widow/er Never-married<br>Female HH | ead WM i23 4]<br>Non-settlement IDP<br>Married Divorced or separated Widow/er Never-married<br>Female HH Miss 0.1<br>Non-IDP<br>Married Divorced or separated Widow/er Never-married<br>FemaleHH EE 4s Bis 3.4<br><!-- End of picture text -->

BB No education [Primary or below [fj Secondary and above <!-- Start of picture text --> Settlement IDP<br>Female [Reksite 8.7<br>Male 63.6 24.1 12.3<br>Non-settlement IDP<br>Female Eom 8.4<br>Male 69.2 20.6 10.1<br>Non-IDP<br>Female [iyi 23.7 25<br>Male 38.3 26 35.8<br><!-- End of picture text -->
<!-- Start of picture text --> Hi Female [ij Male<br>0 20 AO 60 80<br>Settlement IDP<br>Non-settlement IDP<br>Non-IDP<br><!-- End of picture text -->
<!-- Start of picture text --> sane<br>aU eo<br>90%<br>80%<br>snaWem<br>én?TE<br>30%<br>Arye,<br>wee<br>30%<br>7yryD,ao<br>4 rye,<br>aero<br>rye,<br>eo<br>Female Male Female Néle Female Néle<br>Settlement IDP Non-Settlement IDP Non-IDP<br>@ Salaried or in-kind labor m Remittances Assets, property income<br>Agriculture Small family business Aid or zakat<br><!-- End of picture text -->
**Table 2. Household demographic and income profile categories**

|**Demographic Composition**|**Income profile**|
|---|---|
|**_Male single caregiver_**|_No earners, no remittances_|
|**_Female single caregiver_**|_Remittance recipients only_|
|**_Couple with children_**|_Female single earner_|
|**_Multigeneration with children_**|_Male single earner_|
|**_Family without children_**|_Equal number of male & female earners_|
||_Majority female earners_|
||_Majority male earners_|

To examine the role of gender-based factors in poverty rates among IDPs and non-IDPs, we estimate Linear Probability Models (LPMs) with robust standard errors for both groups and a pooled regression model, controlling for the effect of different household characteristics.<sup>8</sup> Our model is: Where i represents a household; =1 if household i is below the poverty line and =0 if the household is on or above the poverty line; ii represents a vector of household characteristics, ii PP PP including its size, dependency ratio, whether it has access to land and whether it has access to ii HH identity documents; is a vector of characteristics of the household head including gender, age, economically active, whether a widow or not and education; ii F is a vector of household types – II gender of the household head, the demographic categories and the employment categories ii described above; and is a normally distributed error term. Thus, is the vector of coefficients of interest since it captures the relative importance of the family types on the risk of falling into ii εε ββ poverty.

We draw on the economic literature in this field (Appleton, 1996; Buvinic & Gupta, 1997; Chant, 1997; Hanmer et al 2020; Milazzo & van de Walle 2020) to select other correlates that are

> 8 A Kolmogorov-Smirnov test rejects the hypothesis that the total per capita consumption for IDP and non-IDP households is drawn from the same distribution; hence, we run separate regressions to allow for heterogeneous effects of the independent variables on the incidence of household poverty for both groups.

15
expected to be important.<sup>9</sup> Our choices are supported by results of a Lasso algorithm run with a wide selection of household and household head characteristics. Some of the strongest predictors of poverty are large household size, having no male earners, lack of education, and being an IDP household living outside settlements (these results are available upon request). We run separate regressions for IDPs and non-IDPs to allow for heterogeneous effects of the independent variables on the incidence of household poverty for IDP and non-IDP households.<sup>10</sup> Finally, we assess whether the household classifications add value to an analysis based on headship alone. We use regression models to estimate the correlation between the different family types to assess whether specific family categories are complementary or substitutes of the others.

# **6. Results**

Poverty rates are high in Somalia, and highest among IDPs. Seven out of 10 households live below the poverty line. IDP households living in and outside settlements are 10 percentage points more likely to be poor than non-IDPs (77 compared to 67 percent) (Table 3). Poverty in Somalia is higher in rural than in urban areas (76 versus 64 percent). Almost all IDPs living in rural areas are poor (83 percent). Poverty rates of non-settlement IDPs living in urban areas are about the same as those for the rest of the urban population.

> 9 Lasso is a simple machine learning algorithm that selects the most relevant variables by minimizing the prediction error of the model, while penalizing the coefficients of the regression variables shrinking the coefficients of the variables that are least predictive of poverty to zero. (Zou & Hastie, 2005).

> 10 Pooled results with interactions are available on request from the authors. The definition of the intensity of poverty is called the normalized poverty gap in Foster, J. et al. (2013).

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**Table 3. Poverty rates by geographic area, headship, and displacement**

|||Poverty rate<br>Non-||||Observati<br>Non-|ons||
|---|---|---|---|---|---|---|---|---|
||Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|
|All|77|77.3|66.5|70|2,220|2,427|21,610|26,257|
|_Geographic_<br>_area_|||||||||
|Urban||62.7|64.1|64||1,562|17,469|19,031|
|Rural||83.5|72.8|76.3||865|4,141|5,006|
|_Headship_|||||||||
|Female|70.8|70|64.8|66.7|1,254|1,009|10,969|13,232|
|_de jure_|68.8|64.4|67.7|67.5|383|292|3,124|3,799|
|_de facto_|70.1|72.2|63.2|65.7|856|690|7,627|9,173|
|Male|84.7|81.1|68|72.9|966|1,418|10,641|13,025|

_Source:_ Own elaboration based on Somali HFS 2017-18 _. Note_ : IDP stands for internally displaced persons. Observations correspond to the number of individuals in the dataset.

# **_6.1. Are female-headed IDP households poorer than male-headed IDP households and maleand female-headed non-IDP households?_**

The incidence of poverty is higher for male-headed households than female-headed ones (73 versus 67 percent) (Table 3). This result is driven by the large gender gap in poverty (against maleheaded households) among IDPs (11-14 percentage points), as well as the smaller but significant difference in poverty between male- and female-headed non-IDP households (3 percentage points). Even when distinguishing between _de jure_ and _de facto_ female-headed IDP households poverty remains lower than male-headed households. For non-IDPs there is no significant 17
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Female-headed poor IDP households located outside settlements experience poverty more intensely than any other group: 64 percent of these households spend less than 50 percent of the amount an average household on the poverty line spends per capita. Poverty intensity experienced by non-IDPs is similar for male- and female-headed households although there are more female than male-headed households with expenditures close to the poverty line (12 versus 9 percent). For settlement IDPs poverty intensity is higher for male-headed household. 61 percent spend less than 50 percent of the average expenditure per capita of households at the poverty line and only 7 percent spend an amount within 10 percent of the poverty line compared to 17 percent of femaleheaded households.

# **_6.2. Beyond the gender of the household head, are specific types of IDP and non-IDP households more likely to be poor than others?_**

We examine the correlation between poverty rates and the demographic and income categories of households controlling for household characteristics, characteristics of the household head and displacement status (Table 4). Model 1 presents the results for IDPs, controlling for whether they live outside or inside settlements. Model 2 presents results for non-IDPs and model 3 presents results for all households controlling for displacement status. As a robustness check we pool all the data and interact the explanatory variables with IDP status.

**Table 4. Poverty Correlates: IDP and Non-IDP Households**

||(1)|(2)|(3)|
|---|---|---|---|
|Variables|IDPs|Non-IDPs|All|
|**Household characteristics**||||
|Size (number of members)|0.084***|0.075***|0.080***|
||(0.007)|(0.006)|(0.005)|
|Dependency ratio|-0.026*|0.056***|0.024**|
||(0.015)|(0.016)|(0.011)|
|Separated members (1=yes)|-0.052|-0.129**|-0.120***|

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||(0.050)|(0.051)|(0.040)|
|---|---|---|---|
|Access to land (1=yes)|-0.008|-0.070|-0.067**|
||(0.029)|(0.048)|(0.033)|
|Access to mechanisms to obtain/replace lost docs (1=yes)|-0.070***|-0.076***|-0.090***|
||(0.026)|(0.027)|(0.022)|
|**Characteristics of the household head**||||
|Male|0.119***|-0.001|0.060***|
||(0.024)|(0.028)|(0.020)|
|Age|-0.005|0.003|-0.003|
||(0.005)|(0.006)|(0.005)|
|Age squared|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|
|Active in the last 12 months|0.037|-0.021|0.013|
||(0.027)|(0.034)|(0.025)|
|Widow/er|0.178|-0.397***|-0.431***|
||(0.154)|(0.097)|(0.097)|
|Female*Widow|-0.082|0.422***|0.484***|
||(0.162)|(0.113)|(0.106)|
|_Education (base: No education)_||||
|Primary or below|-0.025|-0.000|-0.009|
||(0.026)|(0.034)|(0.028)|
|Secondary and above|-0.130**|-0.011|-0.030|
||(0.054)|(0.029)|(0.026)|
|**Household type**||||
|_Demographic classification (base: Families without children)_||||
|Male Single Caregivers|0.055|0.148|0.135|
||(0.108)|(0.122)|(0.094)|
|Female Single Caregivers|0.203**|0.094|0.139**|
||(0.084)|(0.080)|(0.063)|
|Couples with children|0.183***|0.060|0.093**|
||(0.050)|(0.050)|(0.040)|
|Multi-generation with children|0.170***|0.119**|0.121***|
||(0.049)|(0.050)|(0.040)|

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|_Income classification (base: No earners)_||||
|---|---|---|---|
|Remittance recipients only|0.058|-0.202***|-0.161***|
||(0.069)|(0.065)|(0.052)|
|Female single earner|-0.095*|-0.176***|-0.201***|
||(0.055)|(0.060)|(0.049)|
|Male single earner|-0.212***|-0.230***|-0.273***|
||(0.040)|(0.049)|(0.043)|
|Majority female earners|-0.403***|-0.237***|-0.328***|
||(0.054)|(0.065)|(0.055)|
|Equal contribution|-0.201***|-0.246***|-0.268***|
||(0.043)|(0.055)|(0.046)|
|Majority male earners|-0.360***|-0.308***|-0.400***|
||(0.065)|(0.065)|(0.053)|
|**Displacement status (base: settlement IDP)**||||
|Non-settlement IDP|-0.048*||-0.076***|
||(0.028)||(0.028)|
|Non-IDP|||-0.100***|
||||(0.023)|
|Constant|-0.256**|0.114|0.314|
||(0.116)|(0.205)|(0.196)|
|Observations|1,902|6,341|8,243|
|R-squared|0.583|0.299|0.368|

_Source:_ Estimated by authors based on Somali HFS 2017-18 _._ Note: Robust standard errors in parentheses. Estimations include regional dummies. *** p<0.01, ** p<0.05, * p<0.1. _Note:_ Following the Durable Solutions Analysis Guide (Joint IDP Profiling Service [JIPS], 2018), the variable mechanisms to obtain or replace lost documents refers to access to support systems that grant or restore personal or other documentation such as IDs, land and housing titles.

We find some association between gender of the household head and poverty emerges for IDPs, controlling for other individual and household characteristics. Male headship remains associated with higher poverty for IDPs but not for non-IDPs. Among non-IDPs widow-headed households emerge as a group that with high risk of poverty but this result does not hold for IDPs.

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The demographic and income categories are significant across all three models, which shows there is an association between poverty risk and specific household types after controlling for the gender of household head and other individual and household characteristics. However, the strength of the association varies significantly between IDPs and non-IDPs, especially for demographic household types.

Results shown in Table 4 support our assumption that caregiving responsibilities impact poverty risk for IDP households. Compared to families without children, IDP households consisting of female single caregivers, couples with children, and multiple generations with children are between 17-20 percentage points more likely to be poor. In contrast, among non-IDPs, only households consisting of multiple generations with children are more likely to be poor than families without children.

Household income profiles affect poverty risk for both IDPs and non-IDPs. Multiple earners, regardless of their gender are associated with lower poverty risk. For example, compared with households of no earners, IDP households with multiple female or male earners are between 36-40 percentage points less likely to be poor. However, households with a male single earner or a female single earner are only 21 and 9 percentage points less likely to be poor, compared to no earners.

Household type aside, other household characteristics are associated with poverty risk. Some of them are worth highlighting:

- Having an additional household member in the household increases the odds of falling into

- poverty by about 8 percentage points. This risk factor is slightly higher for IDP households.

- For IDPs, higher dependency ratios reduce the likelihood of being poor, possibly as a high dependency ratio is a criterion for humanitarian assistance (UNOCHA & Reach Initiative, 2019). In contrast, higher dependency ratios are linked to higher poverty risk for non-IDPs.

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- Having one or more members separated from the household reduces the probability of experiencing poverty for non-IDPs, but not for IDPs. Remittances for absent household members could explain this result.

- Having an ID or a means of legally identity is strongly associated with reduced poverty risk for both IDPs and non-IDPs, possibly as an ID enables access to public services and more formal labor markets.

- Compared to having no education or only primary education, having secondary education or above reduces poverty risk by 13 percentage points for IDPs but not for non-IDPs.  As unemployment is high and there are few economic opportunities in Somalia, the lack of association is not surprising, however the association between education and poverty reduction among IDPs deserves further exploration.

In sum, IDP and non-IDP households in Somalia face large and specific risks of poverty. Many of these differences are associated with differences in the demographic characteristics and income profiles of their households rather than gender of the household head, although widows emerge as a vulnerable group of non-IDPs.

The demographic features of households have strong associations with poverty risk for IDPs but not for non-IDPs: specifically, among IDPs female single caregivers have the highest poverty risk and followed other types of families with children (couples and multi-generational families). The income profile of the households is associated with poverty risk for both IDPs and non-IDPs, having more earners is associated with a reduced risk of poverty for both IDPs and nonIDPs and for IDP living in a majority female earner household is associated with more poverty risk reduction than living in a majority male earner household.

# **_6.3 Do household classifications add value to the analysis of poverty?_**

Our results show that different household types are associated with different degrees of poverty risk. But do these categories add value to the traditional comparison between male and female 23
household heads? We estimate the correlation coefficients between all categories used in this analysis to investigate this question. If two of the household categories are highly correlated (correlations close to 1 or -1), the two variables are likely collinear and hence including both variables in a regression will not improve the precision of the estimates because both variables are proxies of the same dimension of gender-based disadvantage.

We use linear regressions to estimate the correlations as we have categorical variables formed by multiple mutually exclusive categories. High joint significance tests between gender of household heads and demographic or income categories would suggest that these classifications do not add value to the gender analysis by household headship.

The correlation coefficients estimates are reported in Table 5. The results show that the income classification is weakly linked with the gender of the household head; correlations are low (below 40 percent) indicating that these different household types are only slightly related to the gender of the household head. In contrast, the household types in the demographic composition are highly correlated with the headship classification, suggesting that both definitions capture similar dimensions of vulnerability for households in Somalia. Hence, while the income classification provides information not captured by headship definition, there is less value added by using the demographic categories and the differentiation between female and male heads together. The larger correlation between demographic categories and headship may be partially explained because demographic household types are not associated with poverty risk for non-IDPs. Columns (2)-(7) present the results of the same exercise restricting the sample to only femaleheaded households to examine the extent to which alternative household classifications capture the heterogeneity of this group. Overall, the coefficients confirm that the income and demographic classifications provide complementary information in analyzing the vulnerability of femaleheaded households.

Distinguishing between _de jure_ and _de facto_ female heads adds value relative to the employment classification but appears to somewhat overlap with some of the demographic 24
features. For instance, _de facto_ female-headed households and couples with children are highly correlated as well as _de jure_ female-headed households and multigeneration families with children.

**Table 5.  Regression results for correlations between headship and demographic composition and income composition of households.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Variables|FHH|_De facto_<br>FHH|_De jure_<br>FHH|Single<br>Caregiver<br>s|Couples<br>with<br>children|Multigeneration<br>with children|Families<br>without<br>children|
|**Income composition**||||||||
|Remittance recipients|-0.079**|-0.161***|0.161***|0.253***|0.071*|-0.178***|-0.146***|
||(0.040)|(0.035)|(0.035)|(0.033)|(0.041)|(0.043)|(0.035)|
|Female single earner|0.122***|-0.206***|0.206***|0.387***|0.058|-0.219***|-0.225***|
||(0.036)|(0.032)|(0.032)|(0.029)|(0.036)|(0.038)|(0.031)|
|Male single earner|-0.229***|0.033|-0.033|0.054**|0.245***|-0.097***|-0.203***|
||(0.031)|(0.029)|(0.029)|(0.027)|(0.033)|(0.035)|(0.028)|
|Equal contribution|-0.120***|0.016|-0.016|0.043|0.245***|-0.076**|-0.212***|
||(0.032)|(0.029)|(0.029)|(0.026)|(0.033)|(0.035)|(0.028)|
|Majority female earners|0.024|-0.167***|0.167***|-0.005|0.012|0.060|-0.067*|
||(0.041)|(0.035)|(0.035)|(0.033)|(0.041)|(0.043)|(0.035)|
|Majority male earners|-0.196***|||||||
||(0.039)|||||||
|No earners||-0.143***|0.143***|0.243***|0.097**|-0.213***|-0.127***|
|||(0.035)|(0.035)|(0.032)|(0.040)|(0.042)|(0.034)|
|**Demographic composition**||||||||
|Single Caregivers|0.834***|0.271***|0.729***|||||
||(0.035)|(0.033)|(0.033)|||||
|Couples with children|0.583***|0.991***|0.009|||||
||(0.030)|(0.027)|(0.027)|||||
|Multigeneration with<br>children|0.686***|0.474***|0.526***|||||

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||(0.032)|(0.029)|(0.029)|||||
|---|---|---|---|---|---|---|---|
|Families without children|0.633***|0.648***|0.352***|||||
||(0.034)|(0.030)|(0.030)|||||
|**Female headship**||||||||
|_De facto_FHH||||-0.061**|0.591***|0.203***|0.267***|
|||||(0.024)|(0.031)|(0.032)|(0.026)|
|_De jure_FHH||||0.228***|-0.054|0.517***|0.309***|
|||||(0.027)|(0.033)|(0.035)|(0.028)|
|Observations|4,749|2,359|2,359|2,359|2,359|2,359|2,359|
|R-squared|0.556|0.872|0.714|0.504|0.782|0.297|0.145|

_Source:_ Own elaboration based on Somali HFS 2017-18. _Note:_ ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. Standard errors in parentheses.

# **7. Conclusions**

Our findings show that even when poverty rates are very high, there are important differences between poverty risk according to gender and displacement status. Distinguishing between different types of households beyond the gender of the household head improves our understanding of poverty in situations of forced displacement. The association between a household’s demographic composition, its income profile and poverty risk shows that women’s lack of economic empowerment and their caring responsibilities elevate poverty risk. If we had simply stopped at female headship, we may have concluded that – aside from widows - women are better off in Somalia.

Our analysis extends the gender and poverty literature by establishing a link between the demographic and income profiles of households and the risk of experiencing poverty in situations of displacement. Using data that is typically available in household surveys, we showed how this can be done. Our starting point is that women as individuals or households dependent on women’s income may be at greater risk of poverty due to two interrelated gender norms. These two norms are first, the norms assigning women a disproportionate responsibility for care work and the provision of household services, and second the norms that restrict women’s ability to access paid 26
economic opportunities. We show that, for both IDP and non-displaced households, the risk of experiencing poverty decreases with more earners, both male and female.

Interestingly, IDP households with majority female earners have a lower likelihood of poverty than other IDP households, but this result does not hold for non-displaced households. This may be because some of the normative constraints to women’s employment in waged or paid in kind work outside the household are lifted during displacement. We also show that there are some marked differences between IDPs and non-IDPs and the link between a household’s likelihood of being poor and its demographic composition. For IDPs their household’s demographic composition is linked to poverty, but this does not hold for non-IDPs. Specifically, compared to IDP families without children, IDP households consisting of female single caregivers and couples with children are between 17-20 percentage points more likely to be poor. These results are consistent with the view that disruptions to family structure during displacement increase IDPs risk of poverty.

Our analysis highlights the importance of poverty reduction policies and programs for all households, especially those with children. The Government of Somalia’s new policy framework on social protection – which post-dates the data analyzed here – appears to respond to this need. It aims to create a formal safety net delivery system that provides predictable, reliable, and scalable assistance to households facing chronic food insecurity, complementing systems of humanitarian assistance that provide emergency relief to prevent famine and save lives (World Bank, 2019). The _Baxnaano_ program is the first government cash transfer program in Somalia developed to provide a shock responsive and scalable social safety net system. It provides a benefit set worth USD20 per household per month to poor and vulnerable households that suffer chronic food insecurity. Its focus is on rural areas that are not covered by humanitarian assistance programs, which mainly target urban areas and IDP settlements where populations suffer emergency and crisis levels of food insecurity. Within each state, targeting is conducted in three stages, namely, selection of districts, selection of communities within districts, and cash-based targeting of households using 27
specific eligibility criteria. Key features of the targeting include using a ‘distress index’ consisting of nutrition, trends of shocks, etc.; criteria for geographic targeting favoring rural districts; and criteria specific per household composition, with a strong focus on women with children under five. Recognizing the gender gap in access to income and livelihood opportunities, women are the main recipients of the transfer. In addition, the program provides emergency cash assistance to households affected by the locust crisis to protect their food security and productive assets (Gentilini et al., 2020; World Bank, 2019).

In Somalia mobile money has potential to provide the platform needed to scale up social protection and humanitarian assistance (Daniels & Anderson, 2018). In other FCV settings, cash transfers delivered through electronic payments to address mobility and security constraints have had promising results. For example, in Niger, Aker et al. (2016) find that recipients of electronic transfers (m–transfer) were able to save up to 20 hours in terms of travel time and wait–time and had more flexibility on which day to travel to get their transfers relative to the women who received cash transfers. This “extra” time translated into a 7-13 percentage point higher probability of growing crops. In Somalia almost three-quarters of the population ages 16 and older use mobile money. Penetration rates are highest in urban areas (83 percent) and camps for internally displaced people (72 percent), and 55 percent of the population uses mobile money in rural areas. Mobile money is now the main transaction instrument used by both individuals and businesses in Somalia (World Bank, 2018b). However, government regulation is needed to address concerns and uncertainties about risk and vulnerabilities of the system and promote greater competition and innovation (World Bank, 2018b), and lack of IDs and limited rural coverage are challenges that need to be addressed (Daniels & Anderson, 2018).

Moving beyond cash transfers to other social protection policies, our findings suggest that the design of work programs accounts for the occupations and sectors where forcibly displaced women and men work, and whether specific activities are needed to increase women’s earnings and productivity. For example, public works programs with a heavy manual labor component often 28
put women at a disadvantage (Palmer-Jones & Jackson, 1997). Such constraints can be alleviated by including specific activities that are suitable for both men and women, e.g., as material preparation and planting trees. Equally important is to ensure that livelihood programs are based on market analysis and participatory approaches that proactively include women. In some cases, as is the case of Liberia, forcibly displaced women have crossed over into non-traditional activities such as reforestation and construction projects (Fuest, 2008).

Livelihood programs that aim to increase women’s economic empowerment should incorporate complementary components to address barriers preventing women from benefitting from these interventions. For instance, embedding support services such as community-childcare facilities, flexible working hours, and facilitating transport arrangements or assigning women to work close to their residences in large-scale programs can increase women's participation in income-generating activities (de Silva et al., 2020). Equally important is to ensure equal wages between women and men in public works programs.

Many humanitarian and social protection programs in Somalia use community-based targeting, often with women as the main beneficiaries. Extending this type of programming by targeting men and community leaders with communication campaigns and engaging them in all stages of the program can contribute to women’s empowerment. For example, in Burundi, a microfinance program consisting of village savings and loan associations combined with discussion groups on the role of women in household decision-making improved financial autonomy for women, reduced exposure to and acceptance of violence among beneficiaries, and increased the consumption of household goods relative to luxury goods such as alcohol (Iyengar & Ferrari, 2016).

Ultra-poor graduation programs that combine multiple interventions at once also hold promise to lift several constraints affecting IDP and non-IDP women at once. These programs combine safety nets, life skills training, saving strategies, as well as support for income generation activities such as seed capital, and business mentoring. These programs have the potential to 29
increase self-employment opportunities, improve women’s access to income, help them better manage their money through financial education and access to credit. For example, BRAC’s ultrapoor graduation program in Bangladesh combines large-scale business asset transfers and skills training to target the most disadvantaged women in selected communities that were not recipients of anti-poverty government transfers or microfinance lending. An impact evaluation revealed that as a result of the program, targeted women shifted their working hours from casual wage labor towards livestock rearing, increasing both total hours worked and earnings. After four years, beneficiary women worked 22 percent more hours and 25 percent more days, while their earnings had increased by 37 percent (Balboni et al., 2015).

Finally, IPV is a significant challenge in FCV contexts, and particularly in Somalia. Displacement linked to conflict and disaster can worsen the risk of violence. Programs that address norms and beliefs that underlie gender inequality, while providing women with economic opportunities and enhancing life skills, have proven successful in reducing intimate partner violence in many low income and conflict settings (Arango et al, 2014). At the same time, it is critical to ensure that poverty reduction interventions do not exacerbate the situation and are developed with good understanding of available GBV response services and community advocates and partners.

30
# Annex 1: Definitions of household categories

The demographic profile divides households into five mutually exclusive categories:

- _Male single caregiver_ : One male adult living with children of his/her own below the age of 18; and/or children of others (classified by UNHCR as separated children) and/or one or more elderly and/or disabled persons.

- _Female single caregiver_ : One female adult living with children of his/her own below the age of 18; and/or separated children; and/or one or more elderly and/or disabled persons.

- _Couple with children_ : Married or cohabiting couple (at least one of whom is over the age of 18, not disabled and/or elderly) with children (below the age of 18) of their own and/or children of others.

- _Multigeneration with children_ : Includes extended family households, households of siblings, and polygamous households.

- _Family without children_ : Includes married or cohabiting couples (at least one of whom is over the age of 18, disabled and/or elderly) without children and single person households reporting no other dependents or cohabitees.

The income profile divides households into seven mutually exclusive categories:

- _No earners:_ Household where none of its members are engaged in economic activity and do not receive remittances.

- _Remittance recipients only:_ Household whose only source of income is remittances. No household member is economically active.

- _Female single earner:_ Household composed of two or more people, one of whom is a woman engaged in at least one economic activity. This woman is 15-64 years old and either employed, a farm worker or self-employed. Her contribution to the household income can also come from remittances she receives.

- _Male single earner:_ Household composed of two or more people, one of whom is a man engaged in at least one economic activity. This man is 15-64 years old and either employed, a farm worker or self-employed. His contribution to the household income can also come from remittances he receives.

31
- _Equal contribution earners_ : Household with more than one member who receives remittances or engages in economic activity through paid employment, farm employment or self-employment. Exactly 50 percent of those who contribute to the household income are women.

- _Majority female earners_ : Household with more than one member who receives remittances or is engaged in economic activity through paid employment, farm employment or self-employment. More than 50 percent of those who contribute to the household’s income are women.

- _Majority male earners:_ Household with more than one member who receives remittances or is engaged in economic activity through paid employment, farm employment or selfemployment. More than 50 percent of those who contribute to the household’s income are men.

32