Other Poverty Study
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Monitoring Social and Economic Impacts of COVID-19 on Refugees in Uganda: Results from the High-Frequency Phone - Third Round
The High-Frequency Phone Survey for refugees in Uganda (URHFPS) tracked the socioeconomic impacts of the COVID-19 (coronavirus) crisis on refugees throughout three rounds. The World Bank (WB) in collaboration with the Uganda Bureau of Statistics (UBOS) and the United Nations High Commissioner for Refugees (UNHCR) launched and conducted the URHFPS. The URHFPS tracked the impacts of the pandemic between October 2020 and March 2021. Data collection for the first round of the URHFPS took place between October 22 – November 25, 2020, the second round took place between December 5-24, 2020, and the final and third round was conducted between February 8-March 14, 2021. This brief discusses the results from the third round. Where possible and appropriate, the results are compared across the three rounds and also benchmarked against Ugandans by using the national High-Frequency Phone Survey on COVID-19 (UHFPS). Detailed results for the first round are available in Atamanov et al. (2021a) and for the second round in Atamanov et al. (2021b)
Monitoring COVID-19 Impacts on Firms in Ethiopia, Report No. 2: Results from a High-Frequency Phone Survey of Firms
The COVID-19 (coronavirus) pandemic and its negative economic effects create an urgent need for timely data and evidence to help monitor and mitigate the social and economic impacts of the crisis and protect the welfare of the least well-off in Ethiopia's society. To monitor the impacts of the COVID-19 (coronavirus) pandemic on Ethiopia's economy and people and inform interventions and policy responses, the World Bank Ethiopia team, in collaboration with the government, designed and implemented two high-frequency phone surveys, one with firms and one with households.
Does Fiscal Policy Benefit the Poor and Reduce Inequality in Namibia?
Reducing poverty and inequality continues to be an important national priority in Namibia. Vision 2030 – the country’s guiding development strategy – has a subordinate vision that points to several goals: “Poverty is reduced to the minimum, the existing pattern of income-distribution is equitable and disparity is at the minimum.” Vision 2030 is being implemented via a series of five-year National Development Plans, with the current National Development Plan IV (NDP4) covering 2012 through to 2017. NDP4 sets specific numerical targets. One is reducing the incidence of extreme poverty to less than 10 percent of individuals by the end of FY2016/17, measured at the national lower bound poverty line of N$277.54 in 2009/10. This report demonstrates that Namibia’s progressive income tax and generous social spending programs substantially reduce poverty and inequality, but the analysis also underscores the limits of what redistributive fiscal measures alone can accomplish. The economy must ultimately create more jobs for the poorest members of society to change the underlying distribution of what might be called “pre-fiscal” income; i.e., the income before households pay taxes and receive benefits from social programs. This will require structural transformation through greater investment in activities that create employment for unskilled workers and offer the potential for continuous productivity increases. This report aims to measure the effectiveness of these efforts and draws comparisons to the experiences of other countries. It estimates how major taxes and social spending programs affect individual incomes. It then assesses who benefits from or bears the burden of each instrument and by how much. This way, the analysis estimates the contribution of each instrument to reducing the poverty headcount and the Gini coefficient, a standard measure of inequality. The analysis provides evidence that can shape public debates over government spending and the design of social programs.
The Socio-Economic Impacts of Ebola in Liberia : Results from a High Frequency Cell Phone Survey
As of February 18, 2015 Liberia has reported over 9,000 cases of Ebola Virus Disease (EVD), and 3,900 deaths. Since the previous round of data collection, in December 2014, Liberia continues to show progress toward getting to zero cases, and in the 21 days leading up to January 28, there were only 20 confirmed cases nationally, down from 92 in the 21 days ending on December 31, 2014, and a high of 546 in late August and early September. It is believed that all those currently infected are seeking care in Ebola Treatment Units (ETUs), some of which have begun to close in recent weeks as a result of declining need. The World Bank Group, with the Liberia Institute of Statistics and Geo-Information Services and the Gallup Organization, has continued to monitor the socio-economic impacts of EVD on households through a series of mobile-phone surveys conducted in October, November, and December 2014, and January 2015. As the health crisis continues to abate in Liberia, it will be crucial to tackle the important medium- and long-term economic and welfare impacts of Ebola on households. As Liberia continues on the path to eradicating Ebola, it will be important to identify and support those who are most vulnerable to the sluggish economy and whose long-term welfare may be negatively impacted by coping decisions made in the name of immediate stability. As households send their children back to school in the next month, the World Bank Group and partners will continue to monitor the crisis and work to support the Liberian government and its people as they move into economic recovery.
COVID-19 Impact Monitoring: Uganda, Round 4-5
In June 2020, the Uganda Bureau of Statistics, with the support from the World Bank, has launched the High-Frequency Phone Survey on COVID-19 (coronavirus) to track the impacts of the pandemic on a monthly basis for a period of 12 months. The survey aimed to recontact the entire sample of households that had been interviewed during the Uganda National Panel Survey 2019/20 round and that had phone numbers for at least one household member or a reference individual. This report presents the findings from the fourth and fifth rounds of the survey that were conducted respectively between October 27th and November 17th, 2020 and February 2nd and February 21st, 2021.
How Much Will Poverty Rise in Sub-Saharan Africa in 2020?
The ongoing coronavirus pandemic is expected to drastically slow 2020 GDP per capita growth in Sub-Saharan Africa (SSA) by about 5 percentage points compared to pre-pandemic forecasts. This note presents results from an analysis of a comprehensive database of surveys from 45 of 48 SSA countries to examine the effects of the project fall in growth on poverty in the region. An additional 26 million people in SSA, and as much as 58 million, may fall into extreme poverty defined by the international poverty line of 1.90 US Dollars per day in 2011 PPP. The poverty rate for SSA will likely increase more than two percentage points, setting back poverty reduction in the region by about 5 years.
Alternative Social Safety Nets in South Sudan: Costing and Impact on Welfare Indicators -- Poverty Note
The purpose of this note is to provide the monetary cost of various social safety net targeting schemes that can be deployed to reduce vulnerability and increase resilience. It is believed that gradually switching to the provision of social safety nets can reduce the chronic dependency on humanitarian (mainly food) aid. At the same time, it could help to alleviate reliance on patronage networks and switch a portion of the public spending from unproductive uses (e.g., military expenditure) toward strengthening the resilience and supporting the livelihoods of South Sudanese. In addition, a social safety net would address the fatigues of years of weak governance and ongoing efforts to find more direct, transparent ways to enable citizens to reap the benefits of independence and – once it materializes – peace.
COVID-19 Impact Monitoring: Nigeria, Round 4
The COVID-19 pandemic and its economic and social effects on households have created an urgent need for timely data to help monitor and mitigate the social and economic impacts of the crisis and protect the welfare of Nigerian society. To monitor how the COVID-19 pandemic is affecting the economy and people of Nigeria and to inform policy interventions and responses, the National Bureau of Statistics with technical support from the World Bank implemented the Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS). The fourth round of this survey was conducted between August 9 and 24, 2020.
Analysis of Spatial Patterns of Settlement, Internal Migration, and Welfare Inequality in Zimbabwe
This report aims to assess the spatial dimensions of settlement, internal migration, and welfare inequality in Zimbabwe, explore their relationship and implications, and identify policy options for addressing spatial disparities in social outcomes. It is exploratory in nature and identifies areas for further research to continue to unravel the drivers of the pattern that is observed. The study looks at where people are today (chapter 2), unpacks urbanization trends, and reviews population density and connectivity (chapter 3). Chapter 4 assesses the reasons behind the spatial settlement patterns and looks at Zimbabwe’s historical land allocation, land reform, and economic crisis in the 2000s. Chapter 5 discusses the consequences of this spatial distribution of the population in terms of poverty, nonfarm employment, and service delivery outcomes. Chapter 6 discusses policy implications.
Geography of Poverty in Mali
This study discusses the impact of economic geography and (low) population density on development outcomes in Mali and explores how policies to reduce poverty can be made more effective by taking these two factors into account. The crisis in north Mali which started in 2012 and continues to date has brought questions of economic geography to the center of attention. To help answer such questions, and to analyze how to reduce poverty in Mali as a whole, this study uses different sources of information to analyze the diversity of livelihood patterns, in access to services and in living standards. The study uses quantitative information from household surveys, population and firm censuses, administrative and geographic data, and qualitative information about livelihoods. This study argues that the authorities will need to employ all three policy instruments, while emphasizing that if the objective is poverty reduction, most attention should be focused on spatially blind approaches. The study is organized as follows: chapter one gives introduction. Chapter two emphasizes differences in population density which allows distinguishing between types of agglomeration from villages, to rural town, to large cities. Chapter three categorizes the country into various livelihood zones and considers how the agro-physical environment affects the way people live. In chapter four authors turn to household welfare. Chapter five considers access to services. Chapter six is forward looking.