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Monitoring the Socio-Economic Impacts of COVID-19 on Djiboutian and Refugee Households in Djibouti : Results from the Third Wave of Survey (Inglês)

The third round of data collection on monitoring of socio-economic impacts of the COVID-19 (coronavirus) pandemic in Djibouti followed urban national households based on two previous waves of data collection as well as a replacement sub-sample. This round also includes a refugee sub-sample, covering urban refugees and those based in refugee villages. Economic recovery in Djibouti continues to follow a positive trend. Breadwinners from Djiboutian households continue to come back to work. Only 4 percent of those working before the pandemic were not working at the time of the survey. Even when counting those who were not working before the pandemic, 83 percent of all national households' breadwinners are now working – continuing strong trends from waves 1 and 2. Nationals with waged work grew from 22 to 76 percent in that time, and only 9 percent of those currently working report working less than usual. Djiboutian workers are also working more – but for less pay. Only one in five Djiboutian breadwinners are working less than they were before the pandemic or not at all. However, half of those who worked less than usual received no pay in wave 3 – 53 percent up from 35 percent in wave 2, and fewer received partial payment compared to the previous waves. Poor households were more likely to have received no pay for work performed. Refugees based in refugee villages face worse employment conditions than those living in urban areas or urban nationals. They were less likely to be employed prior to COVID-19, more likely to lose their job during pandemic, and do not exhibit similar signs of recovery. Around 68 percent of urban refugee breadwinners are currently working and 7 percent who worked before the pandemic are currently not working. In comparison, less than half (49 percent) of refugee breadwinners based in refugee villages are currently working, and 16 percent are no longer working relative to pre-COVID-19. A quarter of urban refugees and around 35 percent of refugees in refugee villages worked neither now nor before the pandemic, and nearly a third (29 percent) of the latter who are working report working less than usual. In addition, refugee breadwinners’ concentration in the informal sector (87 percent) highlights the precarity of their livelihood.

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    Malaeb,Bilal, Duplantier,Anne Marie, Gansey,Romeo Jacky, Konate,Sekou Tidani, Abdoulkader,Omar, Tanner,Jeffery, Mugera,Harriet Kasidi

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    Oriente Médio e Norte da África,

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    Monitoring the Socio-Economic Impacts of COVID-19 on Djiboutian and Refugee Households in Djibouti : Results from the Third Wave of Survey

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    refugee household; national household; female labor force participation; source income; source of income; family and friends; urban national; children from households; access to health-care; households with child; income generating activity; income-generating activity; distribution of household; access to goods; state owned enterprise; informal sector; place of residence; evolution over time; public sector job; signs of recovery; public health measures; increase in prices; accessing health services; reduction in consumption; types of expenses; basic good; wage work; urban village; partial payment; food insecurity; household head; response rate; small sample; work status; food stamp; food assistance; income source; daily rate; wheat flour; Cash Transfer; social affairs; refugee population; female national; sole proprietorship; urban one; asylum seeker; household use; coping mechanism; Coping Mechanisms; labor income; urban household; sampling strategy; employment rate; urban counterpart; first wave; poor household; employment condition; survey design; low share; frequency indicator; refugee child; Labor Market; household income; refugee family; in family; food voucher; refugee settlement; poverty status; refugee work; host population; refugee status; female counterparts; dietary diversity; registry data; module design; forced displacement; economic recovery; high probability; phone number; national population; urban dweller; urban population; education health; sampling frame; Child care; Informal Work; wealth quintile; chronic disease; emergency service; cooking oil; national service; restrictive measures; negative effect; price spike; healthcare services; dominant strategy; population census; sampling design; health expenditure; Real estate



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