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The 2030 Agenda for Sustainable Development has an unprecedented ambition, but also confronts countries with an enormous challenge given the complex and integrated nature of the Agenda with its 17 Goals, underpinned by 169 Targets. To assist national governments with their implementation, the OECD has developed a unique methodology allowing comparison of progress across SDG goals and targets, and also over time.
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The adoption of crowdsourced geographic data, or volunteered geographic information (VGI), as a valuable source of spatial data is growing at all levels of government. VGI is crowdsourced geographic information provided by a wide range of participants with varying levels of education, knowledge and skills. Despite some initial concerns about data quality during early development of VGI approaches, extensive research now demonstrates that the reliability and accuracy of VGI is suitable for official or government use. Such concerns should no longer be a reason for the lack of government adoption of VGI. Nonetheless, significant challenges remain for governments seeking to take full advantage of the benefits that crowdsourcing offer. This research used a case study approach to understand factors that have contributed to the success of government VGI efforts, some of which include supportive organizational or legal contexts, the presence of local champions, and project design elements. This policy brief summarizes the findings of the research report identifying success factors in crowdsourced geographic information use in government produced by the World Bank global facility for disaster reduction and recovery (GFDRR) in partnership with scholars from University College London (UCL). This brief explains the report's context, methodology, main findings and recommendations.
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This paper describes a framework of supply and demand factors that could affect birth registration coverage rates, particularly in the context of social transfers. Within this framework, a review of the empirical literature (academic and grey) was conducted on incentives that have been demonstrated to increase birth registration coverage. More than two hundred articles were reviewed, and forty-two (twenty-three academic and nineteen grey) were selected for this study based on relevance. The literature encompassed evidence from Asia, Africa, and Latin America on linking birth registration with social transfer programs, such as cash transfers, which have resulted in increased birth registration rates. The methods in the literature on incentives for countries to increase birth registration coverage vary. There is a lack of scholarly research on incentives to address both supply and demand barriers for birth registration and a need for more robust literature on the topic.
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A fast-paced and practical guide to demystifying big data and transforming it into operational intelligence About This Book Want to get started with Splunk to analyze and visualize machine data? Open this book and step into the world of Splunk. Leverage the exceptional analysis and visualization capabilities to make informed decisions for your business This easy-to-follow, practical book can be used by anyone, even if you have never managed any data before Who This Book Is For This book will be perfect for you if you are a Software engineer or developer or System administrators or Business analyst who seek to correlate machine data with business metrics and provide intuitive real-time and statistical visualizations. Some knowledge or experience of previous versions of Splunk will be helpful but not essential. What You Will Learn Install and configure Splunk Gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields Be comfortable with the Search Processing Language and get to know the best practices in writing search queries Create stunning and powerful dashboards Be proactive by implementing alerts and scheduled reports Use the Splunk SDK and integrate Splunk data into other applications Implement the best practices in using Splunk. In Detail Splunk is a search, analysis, and reporting platform for machine data, which has a high adoption on the market. More and more organizations want to adopt Splunk to use their data to make informed decisions. This book is for anyone who wants to manage data with Splunk. You’ll start with very basics of Splunk— installing Splunk—and then move on to searching machine data with Splunk. You will gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields. After this, you will learn to create various reports, XML forms, and alerts. You will then continue using the Pivot Model to transform the data models into visualization. You will also explore visualization with D3 in Splunk. Finally you’ll be provided with some real-world best practices in using Splunk. Style and approach This fast-paced, example-rich guide will help you analyze and visualize machine data with Splunk through simple, practical instructions. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.Packt...
Big data. --- Data mining. --- Automatic data collection systems.
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Established in 2016, the World Bank living standards measurement study - plus (LSMS+) program works to enhance the availability and quality of intra-household, self-reported, individual-disaggregated survey data collected in low- and middle-income countries on key dimensions of men's and women's economic opportunities and welfare. This report presents an overview of the LSMS+ program and provides operational guidance regarding individual-disaggregated data collection in large-scale household surveys, based on the experience with and analysis of the national surveys that have been implemented by the respective national statistical offices (NSOs) in Cambodia, Ethiopia, Malawi, Tanzania over the period 2016-2020, with support from the LSMS+ program.
Data Collection --- Employment --- Labor Market --- Living Standards --- Poverty Monitoring and Analysis --- Poverty Reduction
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As countries around the world battle the Coronavirus (COVID-19) pandemic, the importance of sharing and using data effectively has never been more apparent. Data collection and analysis tools for diagnostics, detection, and prediction are of critical importance to respond intelligently to this crisis and prevent more lives from being lost. An effective response requires data to be shared between institutions, across sectors, and beyond national borders. Because data is critical to understanding, anticipating, and responding to the crisis, new approaches to share data are being tried, some which may have concerning consequences for individual data protection. It is an extraordinary moment where the use of personal data for helping society may potentially come into conflict with data protection norms. The aim of this report is to highlight emerging practices and interesting features of countries' current approaches to establishing these safeguards and enablers of data sharing.
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Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face trade-offs in survey design that may reduce measurement error or increase coverage. This paper first reviews the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, it provides examples of how agricultural data structure affects testable empirical models. Finally, it reviews the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and research.
Agricultural Knowledge and Information Systems --- Agricultural Research --- Agricultural Sector Economics --- Agriculture --- Data Collection --- Survey Design
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Telephone surveys enable us to collect data in a cost-effective and timely manner, but may not be conducive for collecting detailed consumption or income data for measuring poverty due to the required length of the interview and complexity of the questions. Combining telephone surveys with a survey-to-survey imputation technique may be a solution, as this technique can produce reliable poverty estimates from only 10 to 20 simple questions. However, this approach may lead to biased results if the interview mode, that is, face-to-face versus telephone interviews, affects how households respond to questions. By conducting the first survey experiment to examine potential differences in poverty estimates between interview modes, this study finds that the reporting patterns changed very little between the two interview modes, and the bias in poverty estimates due to interview mode is statistically insignificant. These findings suggest that poverty monitoring via telephone surveys is promising, but additional experiments in other country contexts are encouraged.
Data --- Data Collection --- Imputation --- Poverty --- Randomized Experiment --- Real-Time Poverty --- Telephone Interview
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The July 2021 release of learning poverty estimates involves several changes to the data underlying the country-level learning poverty figures. This document provides details of the key changes made. Some country-level estimates have changed or become available for the first time due to new data from recent assessments: TIMSS 2019, PASEC 2019, and SEA-PLM 2019. In cases where new assessment data call for a change to the learning poverty estimates, the corresponding enrollment data used for learning poverty calculations have also been updated so that the enrollment year is as close as possible to the assessment year, depending on data availability. In the latest release, country-level estimates of learning poverty are available for 120 countries.
Data Collection --- Education --- Education For All --- Inequality --- Poverty Monitoring and Analysis --- Poverty Reduction
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