metadata: authors: 0: author: Santamaria,Julieth
1: author: Hanmer,Lucia C.
2: author: Rubiano Matulevich,Eliana Carolina
count: Syrian Arab Republic,Jordan
docna: 0: docna: The Impact of Protracted Displacement on
            Syrian Refugees in Jordan : The Evolution of Household
            Composition and Poverty Rates
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the influence of
            gender inequality on poverty among Syrian refugees in Jordan
            between 2013 and 2018. Two waves of Home-Visit surveys,
            collected by the United Nations High Commissioner for
            Refugees, are analyzed to track the evolution of poverty
            among Syrian refugees in Jordan.  To compare changes in
            poverty between female- and male-headed households, the
            paper uses relative comparisons of deciles in the
            expenditure distribution and quantile regressions. The
            analysis adjusts the poverty measure for economies of scale
            as the cost per person of maintaining a given standard of
            living may fall as household size rises. The findings show
            that the spending distribution has shifted over time,
            negatively affecting female-headed households. In 2013,
            female-headed households below the median had lower
            expenditure than male-headed households. In 2018, this
            pattern occurs in all deciles. The findings also show small
            differences between poverty rates of female- and male-headed
            households whether the poverty measure is adjusted for
            economies of scale or not. Regardless of the poverty
            measure, the poverty gender gap has increased over time,
            with female-headed households experiencing poverty more
            intensely. Female single caregivers remain at the most risk
            of falling into poverty when compared with other types of
            households and over time. This approach can help policy
            makers design more effective programs of assistance that
            respond to gender-based differences in vulnerability to
            poverty and find durable solutions for displaced populations.
display_title: The Impact of Protracted
            Displacement on Syrian Refugees in Jordan : The Evolution of
            Household Composition and Poverty Rates
listing_relative_url: /research/2022/09/33907583/impact-protracted-displacement-syrian-refugees-jordan-evolution-household-composition-poverty-rates
topicv3: Poverty m1326687 695,Gender and Labor Market m1326905 923,Vulnerable Groups m1327186 756,Population Displacement m1327313 2489,Household Surveys m1504071 2761
publishtoextweb_dt: 2022-09-28T00:00:00Z
dois: 10.1596/1813-9450-10194
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 428158
2: author_key: 885272
model_extractions: Policy Research Working Paper 10194 The Impact of Protracted Displacement on Syrian Refugees in Jordan The Evolution of Household Composition and Poverty Rates _Julieth Santamaria Lucia Hanmer Eliana Rubiano_ Gender Global Theme September 2022
Policy Research Working Paper 10194

# **Abstract**

This paper examines the influence of gender inequality on poverty among Syrian refugees in Jordan between 2013 and 2018. Two waves of Home-Visit surveys, collected by the United Nations High Commissioner for Refugees, are analyzed to track the evolution of poverty among Syrian refugees in Jordan. To compare changes in poverty between female- and male-headed households, the paper uses relative comparisons of deciles in the expenditure distribution and quantile regressions. The analysis adjusts the poverty measure for economies of scale as the cost per person of maintaining a given standard of living may fall as household size rises. The findings show that the spending distribution has shifted over time, negatively affecting female-headed households. In 2013, female-headed households below the median had lower expenditure than male-headed households. In 2018, this pattern occurs in all deciles. The findings also show small differences between poverty rates of female- and male-headed households whether the poverty measure is adjusted for economies of scale or not. Regardless of the poverty measure, the poverty gender gap has increased over time, with female-headed households experiencing poverty more intensely. Female single caregivers remain at the most risk of falling into poverty when compared with other types of households and over time. This approach can help policy makers design more effective programs of assistance that respond to gender-based differences in vulnerability to poverty and find durable solutions for displaced populations.

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 and erubiano@worldbank.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
# **The Impact of Protracted Displacement on Syrian Refugees in Jordan: The Evolution of Household Composition and Poverty Rates** Julieth Santamaria<sup>a</sup> , Lucia Hanmer<sup>b</sup> Eliana Rubiano<sup>c</sup>

_<mark>The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is coled by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.</mark>_ _a Inter-American Development Bank. bc World Bank Group_

**Keywords:** Refugees; Gender; Poverty; Vulnerability; Syria **JEL Codes:** O53, D10, I32, J16

<mark>Acknowledgements: This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder 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.</mark>
# **1. Introduction**

Forced displacement causes abrupt changes in living conditions. Immediately after displacement, forcibly displaced people are often in a highly vulnerable situation due to the loss of assets, the disruption of family structures, the loss of support networks, and their limited access to labor markets and services. These changes occur within existing structures of gender inequality, and while men, women and children are all likely to be adversely affected, these changes may have different consequences for women and girls. However, we might also expect that the impacts of displacement may be mitigated over time as displaced people establish new homes and networks, find work, and integrate into their new communities. Empirical research on income poverty rates among displaced populations is growing, yet the impact of gender inequality on poverty among displaced populations is rarely discussed, nor is much known about the dynamics of poverty in situations of displacement, largely because of lack of data. This study seeks to fill this gap.

Understanding the impact of gender inequality on poverty rates among displaced populations is important as displacement changes social structures and access to opportunities in ways that differ for women and men. For example, during displacement, household composition changes due to separation, conflict-related mortality, or widowhood. This shock often results in the formation of new female-headed households. Evidence for Sub-Saharan Africa shows that female-headed households that have previously been highly dependent on male income – notably widow-headed households – are particularly prone to poverty, although other types of female-headed households may not be more likely to be poor than male-headed households (Brown & Van der Walle, 2020). For this reason, while it is important to analyze differences in poverty risk between households with female and male heads, it is also important to understand how different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels.

This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671,000 Syrians<sup>1</sup> who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR’s 2019 Vulnerability Assessment Framework (VAF)

> 1 UNHCR’s data portal records 671,551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https://data.unhcr.org/en/situations/syria/location/36.

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population study<sup>2</sup> (Brown et al, 2019) revealed that 78 percent of the population of registered refugees lived below the Jordanian poverty line, and between 2017 and 2019, there was only a 2- percentage point reduction of highly vulnerable cases.<sup>3</sup> UNHCR (2018a) also reports that close to 37% of the registered Syrian refugees in Jordan are separated from a family member. Household structures have often been fluid. Many Syrian families sent members ahead to Jordan to settle and sometimes, after the reunification of the rest of the family in Jordan, male family members traveled on to <mark>Türkiye</mark> or to Europe (UNHCR, 2018a). These separations likely affected families emotionally and economically. However, little is known about the changes in economic well-being over time or how gender inequality has shaped poverty and expenditure outcomes.

We use two waves of data from a unique household survey, the United Nations High Commissioner for Refugees (UNHCR) Home-Visits survey for 2013-14 and 2017-18. First, we create comparable measures of expenditure per capita over time. Then, we assess whether certain types of households are more likely to be below the median of the per capita expenditure distribution in each time period. The UNHCR registers households as having a male or female principal applicant (PA) and we use this classification as equivalent to male or female headship used in the poverty literature. We find that there is no significant difference in the share of female versus male- PA households below the median in either 2013-14 or 2017-18.

However, there are important differences between male and female PA households that impacts poverty risk. As is evidenced in other countries (see for example Brown et al.’s study of 43 African countries (2020)), we find that Syrian refugee female PA households are smaller in size than malePA households. Once economies of scale in consumption are considered we find female-PA households are more likely to be poor than male PA households and that this gap has widened over time. Compared to other household heads, single caregivers (the vast majority of whom are women) exhibit some of the highest risks of falling in poverty and this risk has increased over time.

We also find that the distribution of expenditure changed over time and negatively affected femaleheaded households. The gap between male and female-headed households grew over time causing a

> 2 The Vulnerability Assessment Framework for 2019 is based on analysis of data collected from a random, representative sample of registered refugees in October and November 2018. The sample consisted of 2,248 Syrian refugee households, which comprised 3,712 cases and over 10,400 individuals and was fielded in October and November 2018.

> 3 A case is roughly equivalent to a household unit.

3
deeper gap to be experienced among the poorest households. The factors that affected this trend cannot be explained by the demographic variables of the household members or by the type of family arrangement but are possibly explained by factors exogenous to the household.

Our contributions to the existing literature are thus twofold: first, we present results on the evolution of poverty among refugees; and second, we show that identification of how poverty rates differ between households and their trends over time is highly sensitive to the choice of poverty measurement in the context of displacement. Policy design should consider these differences and consider the size and structure of the family, as well as its income and assets.

The paper proceeds as follows: in section 2, we describe the relevant context in Jordan and section 3 provides information about the data set. Section 4 presents our empirical strategy and section 5 documents changes in demographic characteristics, family composition, and their association with changes in poverty for Syrian refugees living in Jordan between 2013 and 2018. It explores the link between gender inequality and poverty through examining differences between male and female PA households and households with different demographic compositions, illustrating the sensitivity of the results to assumptions made about economies of scale. Finally, we consider the policy implications of our results.

# **2. Context**

The war in the Syrian Arab Republic has unleashed an unprecedented humanitarian crisis. Since the civil war started in 2011, 5.3 million Syrians out of a population of 21 million in have fled Syria (UNHCR, 2019a; World Bank, n.d). Most found asylum in neighboring countries. From the onset of the crisis, Jordan has opened its doors to refugees, accepting large numbers of people who fled across borders into the country. While some register with UNHCR, many others do not. As of June 2022, UNHCR reported there are 5.7 million registered Syrian refugees. About two-thirds of the refugees, 3.7 million, reside in <mark>Türkiye</mark> . Jordan hosts 675,000 registered Syrian refugees, about 12 percent of their total number (UNCHR, 2022c). Most Syrian refugees entered Jordan in 2013 when the total number of registered Syrian refugees increased from just over 120,000 in January 2013 to 576,000 and refugee numbers continued to increase as more people fled Syria up until 2016. Since then, the increases in the number of registered refugees in Jordan is predominantly due to births.

4
At the end of 2018 Jordan hosted an estimated 1.36 million Syrian refugees (with 665,884 registered), representing 15 percent of the total population. The majority of refugees live in host communities mainly in Amman and the northern governorates with around 10 percent living in camps (Government of the Hashemite Kingdom of Jordan, 2020). The Government of Jordan has implemented a series of policy reforms and initiatives that have aided the inclusion of the Syrian people in Jordan and is one of the first countries globally to pilot a development focused refugee response program (World Bank, 2021b).<sup>4</sup> However, little is known about the evolution of poverty among Syrian refugees over time.

As noted by Verme et al. (2016), Syrian refugees faced three main challenges on arrival: (i) a limited supply of basic services in the host economy due to institutional capacity constraints; (ii) limited access to the labor market and other economic opportunities due to skill mismatches between the Jordanian labor demand and Syrians’ qualifications; and (iii) risk of human capital depletion due to low access to education and lack of use of skills.

Poverty risk was found to be high and gendered among Syrian refugees in 2013. Hanmer et al. (2020) found that in 2013 half of the female principal applicants<sup>5</sup> for nonnuclear households – a household comprising a family group not composed solely of parents and their children - lived below the poverty line compared to only one-fifth of male principal applicants for nonnuclear households. Nonnuclear households represented about 92% of all female-headed households and 31% of all Syrian households. For widows, poverty risk was 10 percent higher than for couples with children. However, new households formed as a result of displacement, for example, households of siblings or unaccompanied children were by far the most likely to be poor than other types of households. For unaccompanied children, poverty risk was 20 percent higher than for couples with children after receiving cash assistance. At the same time, there were differences in poverty risk among female PA households –single women and single women caregivers were more likely to fall into poverty and more likely to depend on cash assistance than female-PA households who were married and/or had other male adults in the household.

> 4 Reforms and initiatives include granting work permits for Syrian refugees in sectors such as agriculture, construction, retail trade and manufacturing; providing free access to schools and waiving the documentation required for enrollment; and including refugees as part of national plans on COVID-19.

> 5 The principal applicant is the person who receives assistance from UNHCR for the family and is self‐ selected or selected by the family.

5
To extend education to Syrian children, the Government of Jordan quickly implemented a two-shift system, hired more teachers, and built schools. Although enrollment rates among Syrian refugees remain lower than those of Jordanians, their enrollment rates have improved over time. In 2014, fewer than 45 percent of refugee children were in school (ODI, 2018). As of 2018, nearly 70 percent of Syrian refugee children between the ages of 6 and 15 were enrolled, with rates slightly higher for boys than for girls. School enrollment rates are lower than before the war in Syria as while primary school enrollment was universal, secondary school enrollment rates were around 75 percent for both girls and boys. In contrast, 97 percent of Jordanian children are in school (UNICEF, 2020). HangerZanger et al. (2017) found distance, transportation costs, and harassment are among the reasons preventing access to schooling for refugee children of all ages. Safety concerns, particularly risks of gender-based violence, also limited access, especially for girls. For secondary school-age children, these factors combined with the need to generate income for their households – particularly for boys --exacerbated their likelihood of dropping out (UNHCR, 2018a).

Finding employment was particularly challenging, especially in the early years of displacement. Initially, on arrival, Syrians could apply for work permits in specific sectors subject to quotas. However, the majority of Syrians undertook informal sector jobs that often were subject to exploitation. They usually worked long hours, had very low wages, and were subject to arrest by the Ministry of Labour patrols (Bellamy et al. 2017). By 2015, one in two refugees wanted to leave Jordan because they saw no future (NRC, 2016). As of 2020, the unemployment rate for Syrian refugees stands at around 17 percent (ILO, 2020), compared to 13 percent in Jordan nationally, with women’s unemployment at 23 percent (World Bank, 2019). An assessment of individuals and enterprises that received support from the ILO and humanitarian organizations found that COVID-19 disproportionately affected Syrian refugee workers, with one-third losing their job following the 2020 pandemic outbreak compared to 17 percent of Jordanian workers (ILO & FAFO, 2020).

The Government of Jordan was acutely aware of the problems facing Syria refugees and has taken important steps to respond to the impact of the Syrian crisis. Several Response Plans sought to integrate policies on livelihoods and education and move from a short-term humanitarian approach to a development perspective (Ministry of Planning and International Cooperation 2017). In 2014 the Jordan Response Platform for the Syria Crisis (JRPSC) was established to coordinate and monitor the preparation and implementation of Response Plans in various matters including social protection, food security and livelihoods, water and sanitation, health, and education, among others 6
(Government of Jordan, 2015). Humanitarian assistance supported by the United Nations, including UNHCR and the World Food Programme and international donors such as USAID, the International Rescue Committee, and CARE, is also a crucial element of support to refugees in Jordan. A significant component of this assistance is cash-based programming. By 2018, Jordan was UNHCR’s second largest cash operation worldwide (after Lebanon) (UNHCR, 2019b), providing US$98 million in assistance to over 435,000 refugees (Brown et al, 2019).

In 2016, Jordan became the first country in the Arab region to facilitate Syrian refugees’ access to the labor market (ILO, 2019). The Jordan Compact sought to advance long-term solutions through promoting access to education and to employment opportunities for Syrian refugees – international donors pledged $700 million in the form of grants, $1.9 million in concessional loans from the Multilateral Development Banks at concessionary rates, trade benefits with the European Union, and investments in the Jordanian economy (OECD, 2016). The payment of grants and loans was tied to specific targets for refugees that included the provision of an additional 50,000 school places in 2017, the issue of 200,000 work permits by 2020 (United Nations, 2016), and employment quotas in particular sectors (Kelberer, 2017; Barbelet et al., 2018). In 2021, the Jordanian government issued a record 62,000 work permits to Syrians, increasing opportunities for employment (UNCHR, 2022a). Work permits have increased wages and employment stability for Syrian refugees. In 2020, 25 percent of Syrian refugees with a work permit had a written contract with their employers and were covered by social security, compared to 9 percent of those without a permit (Stave, Kebede, & Kattaa, 2021).

Although the number of Syrians with work permits has increased, they still mainly find jobs in the informal sector since there is reportedly a mismatch between their skills profiles and the sectors in which they are allowed to work (Barbaret, Hagen-Zanker, & Mansour-Ille, 2018). Interview and survey data of Syrian refugees further revealed that a majority of workers, including those with higher skill levels, felt unsafe in their jobs due to frequent harassment, low bargaining power, harsh labor conditions, and little agency to change their situation due to fear of the authorities (IRC & Airbel Center). In this context, women are still disadvantaged. Given the direct and indirect costs of acquiring work permits (such as the direct cost of the transaction, the costs of providing all required documents at different institutions, transportation costs to complete those transactions, time and resources forgone that would have been used to work or care for children), it is often only male family members that are in a position to apply for work permits. Refugee women’s options in the labor 7
market are limited; for the most part they are limited to the informal economy, mainly carrying out activities such as tailoring, cleaning houses, and cooking for neighbors and family (UN Women, 2018).

The limited availability of employment opportunities for women is strongly associated with social norms that bind women to specific roles in society (Kelberer, 2017; Felicio et al., 2018). Data from the World Development Indicators (WDI) show that in 2019 only about 13 percent of women over 15 participate in the labor market. Changing gender roles in the labor market can cause tensions and have been linked to increased rates of domestic violence in refugee households in Jordan (HagenZanker et al. 2017; Culcasi 2019).

The most common reason for low levels of female labor force participation is family responsibilities such as childcare and housework, reported by 44 percent of Syrian refugee women and Jordanian women of working age (Stave, Kebede, & Kattaa, 2021). This is tied to deeply engrained norms surrounding gender roles. A recent analysis of gender norms in Jordan found that women and girls hold more equitable gender role attitudes than their male counterparts and that there are no significant differences between Syrians and Jordanians (Krafft, Assaad, & Pastoor, 2021). Restrictive norms perpetuated by men continue to severely restrict women’s access to the formal labor market. Fortunately, the Jordanian government has worked in recent years to address this problem. For example, in the amendment made to the Jordanian labor law in 2019, the provision of childcare services was made mandatory in every business that has 15 or more employees, male or female, with children under 5 (World Bank, 2019). However, implementation is not consistent and low-income households report less access to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan’s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important for female entrepreneurs in both host and refugee communities (Slimane et al, 2020; Turner, 2019).

# **3. Data and descriptive statistics**

We use two rounds of household-level data collected by UNHCR in 2013-14 and 2017-18.<sup>6</sup> These data come from two sources, namely, the Profile Global Registration System (ProGres) and Jordan Home

> 6 For simplicity, from here on we refer to the first wave as the 2013 wave, and to the second wave as the 2018 wave.

8
Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)).

Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014).

At registration, each household is assigned a unique registration number that serves to crossreference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. In both waves, the selected households to be interviewed are those UNHCR considers to be most vulnerable in terms of education, specific needs and the use of livelihood coping strategies,<sup>7</sup> thus, they are a nonrandom sample of all registered Syrian households in Jordan. During Home Visits, data on household expenditures, aid, income, schooling, and other demographics are collected. Each wave of data is a snapshot of a different nonrandom sample of registered refugees because the pool of registered households changed over time. However, because of data confidentiality, we could not link records across years. For this reason, we treat each wave as an independent sample of cross-sectional data.

We compare 204,941 individuals in 54,900 cases (households) in the 2013 wave, with 195,930 individuals in 43,292 cases in 2018. We focus on comparisons between different types of households; however, we use individual characteristics to determine the household categories. A case is formed when a Principal Applicant (PA) registers at a UNHCR office seeking asylum. Initially the selfidentified PA reports all members of the household and their demographic characteristics, which are recorded in ProGres. Subsequently UNHCR carries out individual interviews with each family

> 7 See UNHCR (2018b) available at https://data2.unhcr.org/en/documents/download/65143.

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member and records their data in ProGres. The PA is also the person who receives assistance from UNHCR although he/she may not be the only recipient of humanitarian assistance.<sup>8</sup> Although income data are available, we use expenditure data from JD-HV. The existing literature leans toward computing poverty estimates based on expenditure rather than income because (i) income does not capture the possibility that there are people who can live off their savings or have assets to meet their immediate needs; (ii) consumption is smoother and less variable than income, especially in agricultural economies; and (iii) income reporting appears to be more misleading, especially for those with more resources who tend to underreport income more (Carletto et al, 2021; Cutler & Katz, 1991; Deaton & Zaidi, 2002; Meyer and Sullivan, 2003, 2011, 2012).

Aggregations of expenditure categories were used to compute total expenditure; however, the main challenge was constructing comparable poverty estimates across waves given a change in the number of spending categories included in the questionnaire. In the 2013 wave, there were six expenditure categories, namely, rent, utilities, food, health treatment, education, and others. In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits.

To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation. These five categories average 97.5 percent and 77 percent of household expenditure in the first and the second waves, respectively. While spending on healthcare, education, and household items were also recorded in both waves, the amounts were not large and were excluded because health care and education expenditures may reflect a vulnerability (for example, caused by illness or disability in the case of health care), or a luxury for those who can access the service, and

> 8 The monthly per capita cash assistance Syrian refugees receive is roughly 60%-65% of the Survival Minimum Expenditure Basket (SMEB) and is intended to cover rent, water and sanitation costs. Assistance levels are mainly determined by family size. Since 2014, PAs often make the withdraws or their allocated cash assistance using iris scan enabled ATM machines (UNHCR, 2018b).

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because the data was often missing. We do not include humanitarian cash assistance in the computation of total expenditure because survey respondents would not count cash assistance as part of their spending but rather as part of their income.

**Table 1.** Characteristics of male and female Principal Applicant (PA) Households

||**20**<br>|**13-14 Wa**<br>|**ve**<br>|**20**<br>|**17-18 Wa**<br>|**ve**<br>|
|---|---|---|---|---|---|---|
|Variable|Male<br>PA|Female<br>PA|All<br>PAs|Male<br>PA|Female<br>PA|All<br>PAs|
|All PAs, %|72.6|27.4||62.0|38.0||
|Age of PA|38.0|40.5|38.7|41.9|44.1|42.7|
|Elderly PA (%)|3.9|6.5|4.6|6.5|10.2|7.9|
|Married PAs below 18 (#)|10.0|11.0|22.0|0.0|2.0|2.0|
|Marital Status of PA|||||||
|Married with spouse in the hh|75.3|9.9|57.4|88.0|18.4|61.6|
|Married without spouse in the|||||||
|hh|8.8|54.3|21.2|6.0|40.3|19.0|
|Single or engaged|14.6|7.7|12.7|4.9|7.9|6.0|
|Divorced or separated|0.6|5.4|1.9|0.7|8.6|3.7|
|Widowed|0.8|22.7|6.8|0.5|24.8|9.7|
|Education of PA|||||||
|Less than 6 years|16.6|28.5|19.8|18.8|33.1|24.2|
|6-11 years|61.8|54.2|59.7|61.8|52.2|58.1|
|More than 12 years|18.5|14.9|17.5|19.5|14.7|17.6|
|Family Type|||||||
|Couple with children|65.7|8.8|50.1|73.5|13.9|50.9|
|Couple without children|6.0|0.8|4.6|6.3|1.7|4.6|
|Single caregivers|4.6|47.8|16.5|5.0|36.5|16.9|
|Single -person|16.6|20.3|17.6|7.3|21.3|12.6|
|Unaccompanied children|0.7|1.1|0.8|0.1|0.2|0.1|
|Non-nuclear and other hhs|6.5|21.2|10.5|7.7|26.5|14.9|

_Source_ : Own calculations based on ProGres and JD-HV database. _Note_ : PA = principal applicant. PA is determined at the time of registration. This table contains data on 42,505 observations in 2013 and 40,483 observations in 2018. To make both waves comparable, we excluded observations in wave one that did not receive the home visit questionnaire or that reported having no expenditure. For this reason, this table contains figures that slightly differ from Table 1 in Hanmer et al. (2019), which had 54,408 observations.

Our data enable insights into the change in refugee households’ composition five years after their original displacement. The share of PAs (that we classify as household heads) that are women has risen from 27 to 38 percent between 2013 and 2018. Interestingly however, the characteristics of Syrian households observed in the data seem to be similar in both waves. For example, PAs in 201718 are about four years older on average compared to PAs in 2013-14. This is partly because most 11
Syrian refugees arrived and were registered by 2013. By December 2017, registrations increased by only 14 percent relative to December 2013, which suggests that we are observing a similar pool of individuals in both waves; according to UNHCR most of the population increase in 2017-18 is accounted by babies born to Syrian refugee women living in Jordan.<sup>9</sup> Hanmer et al. (2019) reported striking differences between the characteristics of male and female PA households in terms of education, marital status, and family types. Table 1 confirms the persistence of these differences in the 2018 wave. In both waves, about 80 percent of PAs are married, however most male PAs are accompanied by their spouse (75 percent in 2013 and 88 percent in 2018). In contrast only 10 percent female PAs were married and accompanied by their spouse in 2013 but by 2018 this share had increased to 18 percent. Part of the explanation for this shift may be associated with the spouses being able to reunite years after the crisis. Among female PAs the share of divorced or separated women increased by about 4 percentage points and widows by about 2 percentage points in 2018 compared to 2013.

Most principal applicants have completed primary education – only 20 percent in 2013 and 24 percent in 2018 have less than 6 years of education. Taking a gender lens to human capital achievements, the share of female PAs with more than 12 years of education is about the same in both waves (15 percent), while the share of female PAs who had not completed primary school rose by about 4 percentage points. In contrast the percentage of male PAs with more than 12 years education increased slightly by about 1 percent and the percentage of male PAs who had not completed primary school rose by about 2 percentage points. So, on average male PAs have more years of schooling than female PAs and this gap widened between 2013 and 2108.

Household composition of refugee households can change over time as family splitting is a common response to conflict ( Brück & Schindler, 2009; Ibáñez, 2008). However, between 2013 and 2018 on average the composition of Syrian refugee households has not changed. In 2013 and 2018 one in two households are couples with children and one in six households has a single caregiver, the vast majority of whom are women. However, there are differences in distribution of different types of female households in 2018 compared with 2013. There are fewer female single caregivers (49 percent in 2013 compared to 37 percent in 2018) and more female PA couples with children and female PA non-nuclear and other household types. Thus, some households that had female single

> 9 We do not have the information to be able to merge the data sets based on PA case numbers.

12
care givers in 2013 may have been joined by their husbands and others absorbed into the households of relatives by 2018. The other notable change over time is in the distribution of male PA households across family types. Most male PA households are in couples with children (66 and 72 percent in 2013 and 2018 respectively). However, the share of single male PAs falls from one in six male PA households in 2013 to one in 14 in 2018 (17 to 7 percent). In contrast among female principal applicants single women account for one in five principal applicants in both 2013 and 2018.

**Table 2.** Household composition by head

||**20**<br>|**13-14 Wav**<br>|**e**<br>|**20**<br>|**17-18 Wa**<br>|**ve**<br>|
|---|---|---|---|---|---|---|
|Variable|Male<br>PA|Female<br>PA|All<br>PAs|Male<br>PA|Female<br>PA|All<br>PAs|
|Household size|4.2|3.6|4.0|4.7|3.5|4.2|
|Children (%)|52.6|59.2|54.2|52.7|53.1|52.9|
|Boys (%)|27.3|30.5|28.1|26.9|27.1|27.0|
|Girls (%)|25.3|28.8|26.1|25.8|26.0|25.9|
|Adults (%)|47.4|40.8|45.8|47.3|46.9|47.1|
|Men (%)|26.6|7.8|22.0|25.7|12.8|21.6|
|Women (%)|20.8|33.0|23.8|21.6|34.0|25.6|
|Dependency ratios|||||||
|Children|1.09|1.76|1.28|1.19|1.43|128.|
|Children and elderly|1.12|1.82|1.31|1.23|1.53|1.34|
|Children, elderly &|||||||
|disabled|1.14|1.84|1.34|1.27|1.58|1.38|

_Source_ : Own calculations based on ProGress and JD-HV database. _Note_ : PA = principal applicant. PA is determined at the time of registration. Dependency ratios are defined as the proportion of the groups in the rows to adults.

On average, family size has remained the same over time for female PAs but has increased slightly – by about 0.6 to 4.7 – in male PA households. In other words, about half of male-PA households have an additional family member in 2017-18. Households with a female PA have fewer male adults than households with male PAs in both waves. In the average household of four people, in male PA households’ adult males account for one of the household members, in contrast most female PA households have no male adults. In 2013 only 8 percent of female PA households had a male adult member, increasing to 13 percent in 2018. This reflects that the path to female headship is often separation or widowhood, which are common after displacement.

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Dependency ratios – the proportion of children and people not of working age or unable to work to working age adults – showed mixed trends over time by household type, rising among male PA, and falling for female PA households, possibly in part because their children reached working age. However, the dependency ratios were larger for female PA households throughout. These trends suggest that compared to male PA households, female PA households are under greater pressure to provide for their families, although the pressure for male PA households has increased over time.

# **4. Empirical strategy**

The objectives of this paper are (i) to understand whether, in the context of displacement, the type of expenditure measure leads to different predictions about which types of households are vulnerable, and (ii) to assess the role that household and PA characteristics play in the level and distribution of expenditure over time.

To address the _first objective_ , we compare per capita expenditure with measures that consider an adjustment for economies of scale. Per capita expenditure measures assume that there are no economies of scale in household consumption, that is, the per capita cost of reaching a specific measure of welfare, does not fall as household size increases (Lanjouw et al., 2004). Research in various settings has shown that poverty measurement is sensitive to this assumption. For example, Brown and Van de Walle (2020) indicate that in Sub-Saharan Africa, a small adjustment for economies of scale can reverse conclusions about which households fall below the poverty line. Judged by traditional poverty measures, female-headed households have on average lower poverty rates than male-headed households. However, once consumption is adjusted for economies of scale, female-headed households tend to fare significantly worse in most of Sub-Saharan Africa.

Building on Brown and Van de Walle (2020), we adjust for economies of scale by rescaling expenditure in the following manner: where yi is total expenditure in household i, ni is the number of family members, and θ represents economies of scale in consumption. When θ equals 1, yie(θ) is expenditure per capita.

After computing per capita and economies-of-scale-adjusted expenditure, we compare spending levels across different groups and over time to identify whether these measures lead to different 14
conclusions. In this exercise, we compare conditional and unconditional distribution plots and linear regressions that use each expenditure measure as dependent variable. In particular, we estimate the following linear model: Where yie(θ) is expenditure adjusted for economies of scale (or per capita when θ= 1), Wt takes the value of 1 for households observed in the second wave (2017-18) and 0 in households observed in the first wave (2013-14); Hi is set of demographic characteristics of the household, including a set of family categories that disentangle the effect by household headship; and Ii is a set of characteristics of the PA.

In addition to using expenditure as the dependent variable, we also use models in which we evaluate how the probability of being below the 40th percentile of the expenditure distribution changes according to the variables in model 1. We chose to use this relative poverty cutoff because we compute expenditure based on a subset of expenditure categories, thus, using the national poverty line would overestimate headcounts. Nevertheless, this approach allows us to determine whether, for example, female-PA households are more likely to be below the bottom tail of the expenditure distribution in each wave and whether this likelihood has changed over time.

We use quantile regressions to evaluate how the expenditure distribution has changed over time and what role demographic variables play in the location of a household in that distribution ( _second objective)_ . As is well known, the linear regression approach estimates the effects at the mean. However, in this case it is also important to know the distribution of those effects. For example, if we found that expenditure gender gaps have widened over time, one also would like to understand whether that effect is more prominent among households at the 10th percentile of the expenditure distribution or among those at the 90th percentile. For that reason, we estimate conditional quantile regressions as follows: Where Q<sup>τ</sup> (yie(θ)|Hi, Ii) denotes the value at the τth percentile of yie(θ), conditional on household (Hi) and PA characteristics (Ii). Notice that from this regression method we obtain different μ<sup>τ</sup> and

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γ<sup>τ</sup> for each value of τ. Thus, the effect that demographic characteristics have on expenditure may vary over the distribution of expenditure.

# **5. Results**

# **5.1. Does the type of expenditure measure matter?**

Figure 1 shows different expenditure distributions, which vary somewhat depending on the wave of observation and assumptions about economies of scale (θ). Panels A and B show the distribution of expenditure per capita with no economies of scale for the first and the second waves of data, respectively. The per capita expenditure of the household at the median in 2013-14 is JOD$44.2— marked by the grey vertical line, while that of 2017-18 is JOD$44.2. In panels C and D economies of scale are assumed (using θ= 0.5). Expenditure levels in those cases are JOD$96.6 and JOD$89.3 for the first and second waves, respectively. If we assume no economies of scale, there are only minor differences between the expenditure distributions of female and male-PA households, with the former’s expenditure cumulating slightly faster in the 2017-18 wave. For example, in Panels A and B, the light blue cumulative distribution curve (female PA households) is just above the curve for malePA households below the 50<sup>th</sup> percentile. Above the 50<sup>th</sup> percentile in 2013-14 the cumulative distribution curve for female PAs lies below that of male PAs, indicating female PAs are spending slightly more. This gender expenditure gap, although statistically significant (see Table A1), is quite small. By 2017-18 the difference between curves increases, especially below the 50<sup>th</sup> percentile. This significant difference suggests that the poverty gap between female and male-headed households slightly widened over time, negatively affecting female-headed households.

Differences between female and male-PA households and trends over time emerge when adjusting for economies of scale (panels C and D). Both graphs show that female-PA households are slightly worse off, below the 50<sup>th</sup> percentile and that by 2018, female PA households have lower per capita expenditures across the entire expenditure distribution. The largest differences between these two distributions in 2017-18 is 0.156, which is significant according to a Kolmogorov-Smirnov test (see table A1).

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<!-- Start of picture text --> 1) —— Male HHH<br>———— Female HHH<br>8<br>=<br>3 6<br>3sa4@<br>5<br>oO<br>2<br>0<br>0 2 .<br>In (y; (8=1)) 4 6<br><!-- End of picture text --> <!-- Start of picture text -->
|) —— Male HHH<br>——— Female HHH<br>8<br>2<br>a<br>86<br>a<br>2<br>B4<br>oO|<br>0<br>0 2 In{(y; (8=0.5)) 4 6<br><!-- End of picture text -->

<!-- Start of picture text --> ‘) —— Male HHH<br>———— Female HHH<br>8<br>3<br>8 6<br>3g<br>3s 4<br>Oo5<br>2<br>0<br>0 2 In (y" (8=1)) 4 6<br><!-- End of picture text --> <!-- Start of picture text --> ') —— Male HHH<br>——— Female HHH<br>8<br>2<br>a<br>8.6<br>a<br>&<br>S4<br>oO2<br>0<br>0 2 In (y, . (8=0.5)) 4 6<br><!-- End of picture text -->
**Figure 2.** Percentage of households at the bottom 40 of the expenditure distribution, by gender of the household head.

_Note_ : Own calculations based on ProGres and JD-HV database. The graph shows the percentage of households below the percentile 40<sup>th</sup> by gender of the PA.

Besides being more prone to poverty, households with female Pas are more likely than male Pas to be in lower deciles of the expenditure distribution. In Figure 3, Panels A and B show the percentage of households below the 40<sup>th</sup> percentile (deciles 1-4). Female-PA households in both waves are slightly over-represented in the bottom decile of the expenditure distribution: 1 in 4 poor female-PA households are in the first decile, the equivalent figure is 1 in 5 for their male counterparts. Panel B of figure 3 shows that when adjustment is made for economies of scale, the gap of how intensely female-PA households experience poverty is about the same.

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**Figure 3.** Intensity of poverty, by wave and gender of the household head

Panel A. Distribution of poor households using the unadjusted expenditure (θ= 1) <!-- Start of picture text --> Female PA<br>Male PA<br>Decile 1<br>Decile 2<br>Female PA<br>Decile 3<br>Decile 4<br>Male PA<br>0% 20% 40% 60% 80% 100%<br>% of households, per decile<br>2013-14<br>2017-18<br><!-- End of picture text --> Panel B. Distribution of poor households using poverty adjusted by economies of scale (θ= 0.5) <!-- Start of picture text --> Female PA<br>Male PA<br>Decile 1<br>Decile 2<br>Female PA<br>Decile 3<br>Decile 4<br>Male PA<br>0% 20% 40% 60% 80% 100%<br>% of households, per decile<br>2013-14<br>2017-18<br><!-- End of picture text --> _Note_ : Own calculations based on ProGres and JD-HV database. Panel A shows the intensity of poverty using the headcount measure, while Panel B displays the intensity of poverty using a measure that accounts for economies of scale. The horizontal axis indicates the percentage of households in each decile of the bottom 40, for each wave.

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Table 3 shows that using the unadjusted poverty line, couples with children and non-nuclear and other households with children are more likely to be in the bottom 40 percent of the expenditure distribution than other household types. However, this likelihood fell over time, especially for couples with children – in 2013-14, 54 percent of couples with children were in the bottom 40 percent, compared to 48 percent in 2018. On the other hand, unaccompanied children (although numbering very few – only 0.14 percent of all registered households by 2018) stand out as an increasingly vulnerable group, with the share of households below the 40<sup>th</sup> percentile going from 34 to 46 percent over the two waves.

Adjusting for economies of scale, single caregivers and single person household are most likely to be in the bottom 40 percent. The share of female single care givers in the bottom 40 percent rose from 38 to 48 percent between the two time periods. This is in contrast to couples with children where the share falls by around 5 percentage points. The share of non- nuclear households with children at the bottom of the distribution also decreased.

**Table 3.** Share of households below 40<sup>th</sup> percentile, by demographic characteristic

||Unad<br>expen<br>θ|justed<br>diture<br>= 1|Expen<br>adjus<br>economi<br>θ=|diture<br>ted for<br>es of scale<br> 0.5|
|---|---|---|---|---|
||2013-14|2017-18|2013-14|2017-18|
|Male HHH|41.0%|39.8%|35.7%|32.6%|
|Female HHH|37.8%|40.3%|38.0%|49.0%|
|Couples with children|54.1%|47.9%|39.9%|34.8%|
|Couples without children|14.7%|15.9%|25.6%|31.8%|
|Single caregivers|40.0%|42.3%|37.9%|47.9%|
|Single person households|10.3%|19.9%|29.1%|51.2%|
|Unaccompanied children|34.5%|46.4%|57.5%|73.2%|
|Non-nuclear and other households with children|46.7%|45.9%|36.3%|35.3%|
|Non-nuclear and other households without children|21.0%|20.3%|26.4%|31.6%|
|No children in the hh|13.4%|19.4%|28.1%|42.1%|
|Children in the hh|50.6%|47.2%|39.5%|37.7%|
|Dependency ratio <1|18.1%|21.7%|27.0%|32.3%|
|Dependencyratio >=1|54.3%|50.9%|42.0%|42.7%|

_Note_ : Own calculations based on ProGress and JD-HV database. This table shows the percentage of households below the 40<sup>th</sup> percentile of the expenditure distribution, for each wave and each demographic group.

Table 3 also compares households with and without children over time. Using the unadjusted expenditure measure, households with children in both waves are significantly more prone to be at 20
the bottom of the distribution (50.6% and 47.2% vs 13.4% and 19.4% in 2013-14 and 2017-18, respectively).

However, this picture is highly sensitive to assumptions about economies of scale. In 2013-14 households with children are more likely to be below the poverty line than households without children (40 vs 28%). In contrast by 2017-18 the share of households without children below the poverty line is 42 percent compared with 38 percent of households with children.

Turning to differences between households with higher and lower dependency ratios, households with higher dependency ratios (>1) are less likely to be in the bottom 40 percent of the distribution by 2017-18 compared with 2013-14 using the unadjusted poverty line (54 percent compared to 51 percent) and using a line adjusted for economies of scale there is no significant difference between the two waves.

Up to this point, we have analyzed whether there are differences in the distribution of unconditional spending using an unadjusted and an adjusted-for-economies-of-scale expenditure measure. We found that the level of spending is indeed affected by economies of scale adjustment, making female PA households more likely than male PA households be more vulnerable to poverty when correcting for economies of scale. We will now assess whether spending levels, conditional on demographic characteristics, are also sensitive to economies of scale adjustment. With this in mind, we first run a linear regression using the expenditure measured that adjusts for economies of scale (assuming θ= 0.5). Then, we implement a sensitivity analysis of the size of the coefficient associated to the gender of the household PA using different θs to understand the extent to which the conditional estimators change under these adjustments.

Table 4 shows correlates of log expenditure with individual characteristics of the PA, children, the elderly, family type, marital status, and household composition. We discuss results of our preferred model, model 4. Model 4 shows that expenditure of female Pas in 2017-18 is 21.3 percent lower than expenditure of male PA households in 2013-14, suggesting that gender-related differences in poverty are relatively large. With respect to the characteristics of the Pas, we found significant effects for age, marital status, and education for all models. Models 4 shows being older and more educated is associated with increased spending for both female-PA and male-PA households in both time periods. Female headed households are particularly advantaged when the PA is more educated, as their level 21
of spending is significantly higher. Being single or widow/er reduces expenditure by 13.0 and 7.6 percent, respectively, compared to married PA households. This result was as expected since these types of households tend to have smaller numbers of able-bodied male adults who are most likely to be able to get work.

**Table 4.** Correlates of log expenditure per capita adjusted by economies of scale using OLS, by wave and gender of the household head

|VARIABLES|(1)<br>Female|(2)<br>Male|(3)<br>Pooled|(4)<br>Pooled|
|---|---|---|---|---|
|Female PA|||0.010<br>|-0.011<br>|
||||(0.008)|(0.023)|
|Wave 2017-18 (base: 2013-14)|-0.212***<br>|0.002<br>|-0.003<br>|0.001<br>|
||(0.008)|(0.004)|(0.004)|(0.004)|
|Female x Wave 2017-18 (base: 2013-14)|||-0.203***<br>(0.009)|-0.213***<br>(0.009)|
|_Characteristics of the PA_|||||
|Age|0.004***|0.002***|0.002***|0.003***|
||(0.001)|(0.000)|(0.000)|(0.000)|
|Marital Status (base: married)|||||
|Single|-0.148***|-0.121***|-0.130***|-0.130***|
||(0.020)|(0.014)|(0.012)|(0.012)|
|Divorced|-0.017|-0.051|-0.027*|-0.021|
||(0.016)|(0.033)|(0.014)|(0.014)|
|Widowed/Widower|-0.082***|-0.120***|-0.082***|-0.076***|
||(0.010)|(0.034)|(0.009)|(0.010)|
|Education (base: less than 6 years)|||||
|6-11 years|0.136***|0.134***|0.131***|0.136***|
||(0.010)|(0.006)|(0.005)|(0.006)|
|More than 12 years|0.213***|0.234***|0.224***|0.235***|
||(0.013)|(0.007)|(0.006)|(0.007)|
|Female PA|||||
|x 6-11 years||||0.125***<br>(0.009)|
|x More than 12 years||||0.201***<br>(0.012)|
|_Characteristics of the household_|||||
|Family size|-0.015***|-0.043***|-0.035***|-0.044***|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Able bodied male adults, %|0.152***|0.164***|0.236***|0.172***|
||(0.027)|(0.016)|(0.011)|(0.016)|
|Number of children below 5|-0.076***|-0.035***|-0.044***|-0.031***|
||(0.005)|(0.003)|(0.002)|(0.003)|
|Number of elderly in the hh|<br>-0.181***|<br>-0.085***|<br>-0.106***|<br>-0.094***|
||(0.017)|(0.011)|(0.009)|(0.011)|
|Family type (base: couples with children)|||||

22
|Couples without children|-0.173***|0.000|-0.011|-0.000|
|---|---|---|---|---|
||(0.049)|(0.011)|(0.011)|(0.011)|
|Single caregivers|-0.058***|0.054***|0.007|0.050***|
||(0.012)|(0.012)|(0.007)|(0.012)|
|Single person households|-0.130***|-0.011|-0.063***|-0.008|
||(0.020)|(0.015)|(0.011)|(0.014)|
|Unaccompanied children|-0.252***|-0.235***|-0.241***|-0.216***|
||(0.078)|(0.048)|(0.042)|(0.047)|
|Non-nuclear and other households with children|-0.004|-0.012|0.059***|-0.011|
||(0.012)|(0.013)|(0.008)|(0.012)|
|Non-nuclear and other households without children|0.060***|0.013|0.049***|0.008|
||(0.018)|(0.012)|(0.010)|(0.012)|
|Female PA|||||
|x family size||||0.030***<br>|
|||||(0.003)|
|x able bodied male adults, %||||-0.010<br>(0.031)|
|x number of children below 5||||-0.050***<br>(0.005)|
|x number of elderly in the hh||||-0.065***<br>(0.018)|
|x couples without children||||-0.172***<br>(0.050)|
|x single caregivers||||-0.108***|
|||||(0.017)|
|x single person households||||-0.114***|
|||||(0.023)|
|x unaccompanied children||||-0.072|
|||||(0.089)|
|x non-nuclear and other households with children||||0.010<br>(0.017)|
|x non-nuclear and other households without children||||0.059***<br>(0.021)|
|Constant|4.420***|4.504***|4.460***|4.481***|
||(0.029)|(0.015)|(0.013)|(0.014)|
|Observations|26,857|56,745|83,602|83,602|
|R-squared|0.075|0.095|0.094|0.100|

_Note_ : Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The dependent variable is the log of household expenditure per cápita.

With respect to household characteristics, model 4 shows that each additional household member is associated with a decrease in per capita spending of 4.4 percent. Interacting family size with female headship shows the decrease in expenditure associated with larger families is highest for male PA households.

The presence of able-bodied males increases spending and thus, reduces the risk of poverty. In contrast the presence of children and elderly in the household increase poverty risk, especially for 23
female-PA households. Table A2 also shows that without adjusting for economies of scale, these patterns remain, but the effects are slightly smaller.

Analyzing how family configurations affect spending, we find that unaccompanied children are the most vulnerable: They spend 21.6 percent less than the average household formed by a couple with children. The table also shows that female-headed households with a family arrangement other than a couple with children have sizeable and significantly lower levels of spending, compared to their male-headed counterparts. Table 4 also shows that a greater number of dependents, whether children under 5 or elderly, can have more adverse effects on female-headed households, decreasing their expenditure by 5-6.5 percent, compared with their male counterparts. In summary, these results show that female-headed households tend to be more vulnerable to poverty than those headed by men.

To analyze the sensitivity of these results to different economies of scale adjustments, linear regressions are run following Equation (1) using different measures of expenditure as the dependent variable. Each measure varies according to the θs shown in the horizontal axis of Figure 4. The figure shows on the vertical axis the size of the coefficient estimate associated with the interaction between the gender of the PA and the data wave. The vertical dotted gray line shows where the coefficient from the regression shown in Table 4 is located.

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**Figure 4.** Sensitivity of the OLS regression models using different expenditure measures.

_Note_ : Own calculatios based on ProGress and JD-HV database. This table shows, in the vertical axis, the estimated coefficients and confidence intervals associated to the interaction Female× Wave 2017 −18, following the model presented in Table 4 and Equation 1. The x axis shows the values of θ used to compute the dependent variable in each case. The vertical gray line points at the estimate shown in Table 4, where θ= 0.5 Contrary to the findings of the unconditional distributions, we find that the size of the estimates does not vary in the presence of different economies of scale adjustments. If anything, a higher θ (i.e., a measure of spending closer to unadjusted per capita spending), makes the differences between men and women over time appear smaller. However, it is worth noting that the differences are so subtle that they are non-significant across models. This suggests that once the demographic characteristics of the household are controlled for, the choice of expenditure measure used is of little relevance for statistical inference on the average household. This result is consistent with the fact that displacement situations affect all types of households, many of which are in new family arrangements. Once we control for the different type of family arrangements, economies of scale do not capture differences in expenditure better than the headcount measure.

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# **5.2. How does the gender gap in spending evolve over time and across the expenditure distribution?**

To answer this question, we run quantile-conditional regressions following the model described by equation 2. We run two models per wave independently shown in Tables A.3. and A.4. These regressions allow us to identify how gender effects vary at different points in the income distribution. With this in mind, Figure 5 shows how the coefficient associated with the principal applicant's gender changes for each expenditure level. Recall that in the last section we had found that, on average, female-headed households had lower levels of spending compared to those headed by men, especially in the 2017-18 wave.

**Figure 5.** Gender gap between PAs (Female – Male). Estimated coefficients from log expenditure quantile regressions

Note: This figure shows estimated coefficients from a quantile regression model. The y axis, thus, measure log changes in expenditure or percent changes (i.e., -0.1 is a 10 percent reduction).

Figure 5 shows that the estimate of this coefficient does indeed change depending on the quantile used. In particular, we found that at the lowest expenditure quantile (the 10th), the expenditure gap between male- and female-headed households is the largest, disadvantaging households with female PAs. The result in this quantile holds regardless of the wave of data used. The figure also shows that 26
in 2013 the adjusted expenditure differences between female-and male headed households were less than 10 percent and, below the median, statistically non-significant. For those households located above the median, we found that in 2013 the gender expenditure gap disadvantaged male PA households. That is, male-PA households had higher risk of poverty compared to female PA households. In contrast, in 2018, regardless of location on the expenditure distribution, femaleheaded households are the most disadvantaged: they have between 10 and 25 percent less spending compared to their male counterparts. This result emphasizes that the risk of poverty has increased steadily for female PA households over time.

Next, we decompose the results understand what the components of the gap between female and male principal applicants. Figure 6 shows RIF decompositions for both waves by decile. In summary, we find similar results to those found in the quantile regression models. In 2013, female-headed households were the most disadvantaged, especially among the poorest. Demographic characteristics of these households seem to be explaining most of the gap (age, education, family size, etc.). Above the median, the most disadvantaged households are those headed by males. The scenario changed in 2018. In this wave, female-headed households are the most vulnerable across the spending distribution. The gap remains largely unexplained. In other words, we find a gap between households with a female PA and those with a male PA even when taking into account demographic factors. The unexplained part of the gap can be associated with factors leading female PAs to experiencing lower economic standing that are unobserved in the data we have, and which point to the need of more data to further explore this component.

27
**Figure 6.** RIF results: Differences in per capita expenditure between Female and Male PA households Wave 2013-2014 Wave 2017-2018

<!-- Start of picture text --> 17.1 p90 p90-22.0<br>Unexplained<br>8.6 p80 p80 -18.1<br>Explained:<br>4.0 p70 p70 -16.2<br>Demographic<br>characteristics<br>2.6 p60 p60 -15.0<br>Explained:<br>Family type<br>-0.4 p50 p50 -13.9<br>-2.2 p40 p40 -13.4<br>-5.4 p30 p30 -13.0<br>-9.1 p20 p20 -14.1<br>-11.2 p10 p10 -16.4<br>0.8 Mean Mean -16.2<br>-20.0 -10.0 0.0 10.0 20.0 -25 -5 15<br><!-- End of picture text --> Note: Both graphs show the results coming from RIF decompositions. These decompositions use two groups of variables: demographic characteristics and family types. Each bar is an estimated gap between female and male-headed households. A negative sign means that female-headed household spending is smaller than that of male-headed households. Complete tables can be found in Annex, Tables A5 and A6.

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# **6. Final remarks**

Our analysis shows how using a gender lens that differentiates between types of households and considers economies of scale can enrich the understanding of poverty in situations of forced displacement. In the case of Syrian refugees, specific household structures, particularly those that result from disruptions caused by displacement, are more prone to experiencing poverty.

However, while we have described how patterns of poverty have changed over time, lack of data on critical variables, most importantly the amount of social assistance received by households and labor force participation, employment and earnings data limits our analysis. While some of the changes in poverty over time experienced by male and female PA households are consistent with some of the changes in the circumstances of Syrian refugees, specifically their improved access to labor markets and better access to services, it is not possible to understand the extent to which these and potentially other factors have made a difference to their risk of poverty.

Overall, our results show that the share of female PA households has increased substantially from from 26 to 38 percent of all Syrian refugee households between 2013 and 2018. There are differences between the characteristics of male and female PA households in terms of education, marital status, and family types. While male PAs are likely to be married, the vast majority of female PAs are either single, divorced or widowed or married but separated from their spouse. However, by 2018 the share of married female PAs living with their spouse has increased from 10 percent in 2013 to 18 percent, suggesting some men who had stayed behind in Syria left to rejoin their families in Jordan in this time period.

We use a relative poverty line and examine changes in the composition of the types of households that lie in the bottom 40 percent of the distribution between 2013-14 and 2017-18. We show that assessment of the evolution of poverty over time is highly sensitive to assumptions made about economies of scale. Assuming a moderate amount of economies of scale, we find that female PA households have fared worse than male PA households between 2013-14 and 2017-18 and are disproportionately represented in the bottom 40 percent of the distribution. Furthermore, female PA households have lower per capita expenditures than male PA households across the whole consumption distribution. Female PA households have on average 22 percent lower consumption 29
per capita and are more likely to be in the bottom 40 percent of the distribution. By 2017-18 a 16percentage point gap has emerged between male and female PA households compared with a 2- percentage point gap in 2013-14.

Particular household types emerge as being especially vulnerable to increased poverty in 2017-18; single caregivers – mostly women – and single persons, both male and female face increasingly high poverty risks. Unaccompanied children remain at high poverty risk, but thankfully their numbers have decreased substantially between 2013-14 and 2017-18. Further analysis of risk factors is consistent with women’s care burden driving some of their increased risk of poverty over time. The number of children below age 5 and the number of elderly increased poverty risk more for female PAs than male PAs. In contrast, for male Pas, large household size is most associated with higher poverty risk.

Factors that reduce poverty risk include post primary education and having more adult male household members, the latter we take as a proxy for the potential to access labor market. While households with these characteristics are associated with lower poverty risk for both male and female PA households, the effect is largest for female PA households.

Displacement is long-term for many Syrian refugees. The challenges and opportunities faced by the displaced in protracted situations are different from those in the phase of emergency. In principal, results from our analysis can be used to inform the design of better targeted social assistance programs that recognize poverty risk of smaller households and aim to design special measures to support those who are caring for young children, the elderly or other dependents. Our results underline the importance of continued access to secondary education for both boys and girls. It is also important to build capacity for both refugee women and men to access economic opportunities, which can eventually replace social assistance. Although it is important to note that not everyone will be able to work. Social protections will continue to be needed by those who are elderly, people with disabilities that limit the work they can do, people in poor health and other vulnerable groups.

Program interventions should identify occupations and sectors where they could work given their skills and include components providing support services, particularly for women, such as flexible working arrangement to address specific constraints related to domestic responsibilities.

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The environment for job creation is particularly challenging in Jordan. Before the impact of the COVID-19 crisis, Jordan experienced only modest level and low productivity growth. With the impact of COVID-19, the unemployment rate increased to nearly 25% and women and youth, whose unemployment rates were already the highest, were hardest hit by the crisis. Creating meaningful economic opportunities for refugees and Jordanians alike will require tackling underlying economic constraints that lead to persistently high levels of unemployment and informality. According to the World Bank’s Job Diagnostic for Jordan (Winkler, Hernan, & Alvarez, 2019), the low level and quality of job creation in the private sector is mostly explained by firm dynamics: limited entry and growth of firms not driven by the most productive firms, while firms exiting are not always the least productive, all resulting in a productive structure dominated by small/micro, low-productivity firms employing half of private sector workers, including two-thirds of informal workers. The Government of Jordan launched a National Employment Plan in Response to the Crisis, but structural reforms are also needed to help the private sector create more jobs (World Bank, 2021a).

However, providing access to economic opportunities is not a guarantee that gender gaps in access to economic opportunities for either refugees or Jordanians will be reduced if men have full control of gains in access to economic opportunities, as determined by patriarchal norms. Economic empowerment programs for refugee women, in particular, should build in guidelines for their protection and should engage refugees in promoting more gender-equitable relationships (Heilman & Barker, 2018).

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