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COVID-19 vaccination rates in Sub-Saharan Africa lag behind other world regions, with just over 20 percent of the population in Sub-Saharan Africa fully vaccinated. To reach widespread coverage, high willingness to get vaccinated for COVID-19 among the population is considered an important prerequisite. Drawing on two years of panel survey data, this paper studies the dynamics of vaccine acceptance, its correlates, and reasons for hesitancy over time. The data come from multiple rounds of national High-Frequency Phone Surveys in five countries in East and West Africa (Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda), covering the period between 2020 and 2022. The surveys are cross-country comparable and draw their samples from nationally representative sampling frames. The paper finds that COVID-19 vaccine acceptance has been high throughout the study period (68 to 98 percent). However, acceptance levels were lower in 2022 than in 2020 in three countries (Burkina Faso, Malawi, and Nigeria) and higher in one country (Uganda). Moreover, individuals are observed to change their stated vaccine attitudes between survey rounds, to a limited extent in some countries (Ethiopia) and more frequently in others (Burkina Faso, Malawi, Nigeria, and Uganda). Vaccine hesitancy is higher in richer households and among those residing in urban areas, women, and the better educated. Conversely, hesitancy is lower in larger households and among heads of the household. The main reasons for hesitancy are concerns about side effects of the vaccine, its safety and efficacy, as well as assessments of COVID-19 risk, although these reasons fluctuate over time. The findings suggest that vaccine hesitancy is not the primary obstacle to reaching greater vaccine coverage in Sub-Saharan Africa. Instead, low coverage rates may be related to access and delivery barriers as well as supply shortages. Nevertheless, vaccine attitudes appear malleable so that continued efforts are needed to retain high levels of vaccine acceptance.

Vaccination.

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This paper assesses the relationship between the length of recall and nonrandom error in agricultural survey data. Using data from the World Bank's Living Standards Measurement Study-Integrated Surveys on Agriculture in Malawi and Tanzania, the paper shows that key input and output variables are systematically related to the length of the recall period, indicating the presence of nonrandom measurement error. With longer recall periods, farmers report greater quantities of harvest, labor, and fertilizer inputs. Farmers list fewer plots as the recall period increases. The paper argues that it is plausible that farmers overestimate plot-level outcomes, or they forget some of their more marginal plots due to longer recall periods. The analysis also finds evidence of measurement error related to the length of recall in common measures of agricultural productivity. The size of the recall effect typically varies between 2 and 5 percent per additional month of recall length, which is economically significant. With data reliability affecting policy effectiveness, improving agricultural survey data quality remains an important concern. Mainstreaming objective measures where possible and reducing the risk of recall error through shorter recall periods appear to be promising avenues to improve the quality of key variables in agricultural surveys.

Agricultural Sector Economics --- Agriculture --- Food Security --- Living Standards Measurement Study --- Measurement --- Poverty Reduction --- Recall --- Rural Development --- Survey Design --- Survey Methods

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Previous studies have explored potential conflicts between ending poverty and limiting global warming, by focusing on the carbon emissions of the world's poorest. This paper instead focuses on economic growth as the driver of poverty alleviation and estimates the emissions associated with the growth needed to eradicate poverty. With this framing, eradicating poverty requires not only increasing the consumption of poor people, but also the consumption of non-poor people in poor countries. Even in this more pessimistic framing, the global emissions increase associated with eradicating extreme poverty is small, at 2.37 gigatonnes of equivalent carbon dioxide in 2050, or 4.9 percent of 2019 global emissions. These additional emissions would not materially affect the global climate change challenge: global emissions would need to be reduced by 2.08 gigatonnes of equivalent carbon dioxide per year, instead of the 2.0 gigatonnes of equivalent carbon dioxide per year needed in the absence of any extreme poverty eradication. Lower inequality, higher energy efficiency, and decarbonization of energy can significantly ease this trade-off: assuming the best historical performance in all countries, the additional emissions for poverty eradication are reduced by 90 percent. Therefore, the need to eradicate extreme poverty cannot be used as a justification for reducing the world's climate ambitions. When trade-offs exist, the eradication of extreme poverty can be prioritized with negligible emissions implications. The estimated emissions of eradicating poverty are 15.3 percent of 2019 emissions with the lower-middle-income poverty line at $3.65 per day and or 45.7 percent of 2019 emissions with the $6.85 upper-middle-income poverty line. The challenge to align the world's development and climate objectives is not in reconciling extreme poverty alleviation with climate objectives but in providing middle-income standards of living in a sustainable manner.

Climatic changes.

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Somalia is highly data-deprived, leaving policy makers to operate in a statistical vacuum. To overcome this challenge, the World Bank implemented wave 2 of the Somali High Frequency Survey to better understand livelihoods and vulnerabilities and, especially, to estimate national poverty indicators. The specific context of insecurity and lack of statistical infrastructure in Somalia posed several challenges for implementing a household survey and measuring poverty. This paper outlines how these challenges were overcome in wave 2 of the Somali High Frequency Survey through methodological and technological adaptations in four areas. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability-based representative sample. Therefore, geospatial techniques and high-resolution imagery were used to model the spatial population distribution, build a probability-based population sampling frame, and generate enumeration areas to overcome the lack of a recent population census. Second, although some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. To address security concerns, the survey adapted logistical arrangements, sampling strategy using micro-listing, and questionnaire design to limit time on the ground based on the Rapid Consumption Methodology. Third, poverty in completely inaccessible areas had to be estimated by other means. Therefore, the Somali High Frequency Survey relies on correlates derived from satellite imagery and other geo-spatial data to estimate poverty in such areas. Finally, the nonstationary nature of the nomadic population required special sampling strategies.

Armed Conflict --- Conflict and Development --- Consumption Measurement --- De Facto Governments --- Democratic Government --- Governance --- Hydrology --- Inequality --- Poverty --- Poverty Assessment --- Poverty Diagnostics --- Poverty Impact Evaluation --- Poverty Lines --- Poverty Monitoring and Analysis --- Questionnaire Design --- Water Resources

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Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings in policy implementation, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing at a rapid pace, yet their transformational impacts have not been realized fully. Why has the full value of these data not been realized yet This paper outlines 12 conditions needed for the production and use of public sector data to generate value for development and presents case studies substantiating these conditions. The conditions are that data need to have adequate spatial and temporal coverage (are complete, frequent, and timely), are of high quality (are accurate, comparable, and granular), are easy to use (are accessible, understandable, and interoperable), and are safe to use (are impartial, confidential, and appropriate).

Development Data --- Economic Theory and Research --- Information and Communication Technologies --- Information Technology --- Macroeconomics and Economic Growth --- Public Sector Development --- Public Service Delivery --- Statistical and Mathematical Sciences --- Statistics