How does poverty differ among refugees? Taking a gender lens to the data on Syrian refugees in Jordan

ABSTRACT

Many reports document the hardships experienced by refugees, highlighting that women and children are a highly vulnerable group. However, empirical analysis of how gender inequality impacts poverty among refugees is limited. We combine registration data for Syrian refugees in Jordan collected by the United Nations High Commissioner for Refugees with data from its Home Visit surveys to analyze income poverty rates among refugee households. We use an approach that captures the disruption to household structures that results from displacement to evaluate the poverty impacts, comparing refugee households with male and female principal applicants (PAs). We find that distinguishing between different types of principal applicant households is important. Half of the female PAs for nonnuclear households live below the poverty line compared to only one-fifth of male PAs for nonnuclear household. PAs who are widows and widowers also face high poverty risks. Households that have formed because of the unpredictable dynamics of forced displacement, such as unaccompanied children and single caregivers, emerge as extremely vulnerable groups. We show that differences in household composition and individual attributes of male and female PAs are not the only factors driving increased poverty risk. Gender-specific barriers which prevent women accessing labor markets are also a factor. Our findings show that gender inequality amplifies the poverty experienced by a significant number of refugees. Our approach can be used to help policy-makers design more effective programs of assistance and find durable solutions for displaced populations.

KEYWORDS:

• Refugees
• gender
• poverty
• vulnerability
• Syria

JEL CLASSIFICATION:

• O53
• D10
• I32
• J16

Acknowledgements

This paper is a collaboration between the World Bank and the United Nations High Commissioner for Refugees. We would like to thank our UNHCR partners: Shelley Gornall; Joanina Karugaba; Tanya Axisa; Petra Nahmias, Kimberly Roberson, Kirstin Lange; Nur Amalina Abdul Majit; Hussein Watfa; Theresa Beltramo and Stephane Savarimuthu for their contributions to this work and acknowledge with thanks the support of Louise Aubin Deputy Director Division of International Protection. We would also like to thank Elizabeth Barnhart and Harry Brown (UNHCR Jordan) and Sima Kanaan (UNHCR MENA regional Office) for their comments and feedback. Mariana Viollaz provided excellent research assistance at earlier stages of the project. The research was supported by the Multi Donor Trust Fund for Forced Displacement a World Bank Group-managed trust fund supported by Denmark, Germany, Norway, and Switzerland.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 http://www.unhcr.org/globaltrends2018/, accessed 27 January 2020.

2 http://www.unhcr.org/globaltrends2018/, accessed 27 January 2020.

3 https://data2.unhcr.org/en/documents/download/73473, accessed 27 January 2020.

4 https://reliefweb.int/sites/reliefweb.int/files/resources/64114.pdf, accessed 2 August 2018.

5 Identification of the head of the case (as household groupings are referred to in the UNHCR ProGres database) is determined by who best represents the household for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures.

6 Even when conventional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self-identified `most knowledgeable' household member, which overwhelmingly corresponds to the ‘head’ of the household. In the case of a household survey that solicits information on ‘headship’, this information is gathered often through the question: ‘Who is the head of this household?’'

7 Calculated as the share of household members that are below or above working age (15–64).

8 Calculated as the share of household members who are above or below working age (18–60). The ratio is disability adjusted, i.e. if a household member of age 18–60 is chronically ill or is disabled, the person has a condition which affects their ability to be economically active or manage daily activities (UNHCR, VAF Baseline Study Report, 2015). The data are gathered from refugees through periodic home visits and refugees requesting UNHCR multi-purpose cash assistance.

9 Unemployment rate for 2015. https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.ZS?locations=JO

10 In Lebanon, Jordan, and Turkey.

11 It is worth noting that these documented consequences of child marriage are drawn from populations that are not forcibly displaced. The impact of displacement is likely to exacerbate some of the profound consequences of early marriage.

12 While there is no specific guidance on who should be designated as the principal applicant, UNHCR benefits and food assistance are allocated to this person. It is therefore reasonable to consider the principal applicant as the household head.

13 The UNHCR defines a case as: ‘A processing unit similar to a family headed by a Principal Applicant. It comprises (biological and non-biological) sons and daughters up to the age 18 (or 21) years, but also includes first degree family members emotionally and/or economically dependent and for whom a living on their own and whose ability to function independently in society/in the community and/or to pursue an occupation is not granted, and/or who require assistance from a caregiver’ (Verme et al., 2016).

14 Verme et al. (2016) note that the non-random nature of the JD-HV sample means that it is not necessarily representative of the population (ProGres). However, they also note that it is not possible to draw a sample of refugees which is perfectly representative of the refugee population as the exact refugee population is not known and one does not have a master sample for sampling purposes.

15 For example, UNHCR specifically identifies single woman at risk, single older person, and single parent or caregiver.

16 LPM models produce approximately the same estimates as marginal effects calculated using probit and logit models. However, there are three main differences between the LPM and logit and probit models. First, the error term of the model may have a Bernoulli structure. However, LPM models that correct for heteroskedasticity can address this issue. Second, predicted values of LPM models can lead to predictions below 0 or above 1, which is problematic in forecasting models. However, we do not intend to forecast poverty likelihoods. Third, LPM models impose a linear relationship between dependent and independent variables while logit/probit models impose a nonlinear relationship. However, as there is no way to know whether a nonlinear structure or a linear structure is the most appropriate structure to impose on the model, OLS estimation is as valid a choice as a logit/probit. Because of its simplicity, its computational efficiency, and the straightforward interpretation of the coefficients, many authors use LPM models.

17 The results of the model selection exercise for the other poverty models are available on request.

18 Estimates produced by the logit models confirmed that the marginal effects were nearly identical to the estimates of the LPM models. As only few estimates lie outside the unit interval, an LPM is expected to produce largely unbiased and consistent estimates (see Appendix 1).

19 PSM is increasingly used to preprocess data before applying parametric techniques. Evidence suggests that this approach makes parametric models produce more accurate and inferences less dependent on the model.

20 The density function of the matched male principal applicant households resembles the density function of female principal applicant households, as confirmed by the balancing test, which shows a reduction of 100% in the differences between the matched households across all covariates (see Appendix 3).

21 See, for example, UNHCR’s Emergency Handbook (2015). https://reliefweb.int/report/world/unhcr-emergency-handbook.

22 Verme et al. (2016)) calculated these indexes using reported expenditure during the home visits.

23 Gender poverty gaps against female PAs exist for all nonnuclear households apart from nonnuclear households without children where a gender poverty gap against males exists (5% of households) and couples without children where these is no gender poverty gap (50% of households).

24 Definitions of the variables included in the main models as well as in the robustness checks are available in Appendix 4.

25 We estimate whether differences between the coefficients obtained for female and male principal applicant models are statistically significant from 0. Results are included as part of the text and the exact calculations are available upon request.

26 See Appendix 2 for details.

27 UNHCR policies guiding targeting of assistance to Syrian refugees have changed since 2013/14, and so policy recommendations for Syrian refugees in Jordan cannot be based on the findings presented here.

28 UNOCHA Humanitarian Development Nexus accessed 19 August 2019 https://www.unocha.org/es/themes/humanitarian-development-nexus.

Additional information

Funding

The research was supported by the Multi Donor Trust Fund for Forced Displacement Trust Fund a World Bank Group-managed trust fund supported by Denmark, Germany, Norway, and Switzerland.