Miscellaneous Knowledge Notes

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  • Publication
    COVID-19 in LAC: High Frequency Phone Surveys - Technical Note
    (World Bank, Washington, DC, 2021-04) Mejía-Mantilla, Carolina; Olivieri, Sergio; Rivadeneira, Ana; Lara Ibarra, Gabriel; Romero, Javier
    Latin American and the Caribbean is one of the regions in the world most affected by the COVID-19 pandemic, and the welfare impacts for households have been severe. At the macroeconomic level, the World Bank estimates a contraction of 6.9 percent of the region’s GDP in 2020, due to pandemic-control measures and the deceleration of the global economy (World Bank, 2021). Regional export prices significantly dropped in the first semester of 2020 (5.2 percent) (Inter-American Development Bank, 2020), and although they began to recover in the second half of the year, the volume of goods-exports dropped by 8 points by the third quarter of 2020 (World Bank, 2021).
  • Publication
    Improving the Productivity of National Offices for Statistics
    (World Bank, Washington, DC, 2020-06) Medina Giopp, Alejandro; Montes, Jose; Martinez, Jorge
    For decades, policy experts’ and practitioner consensus has been growing about the crucial role quality data plays in informing policy making. This has led to investment and projects to increase quality data availability. But progress has been slow, as reflected by slow improvement in country statistical capacity, many countries remain data deprived. The IPNOS toolkit is based on the notion that producing more and better statistics, while requiring adequate resources, should also be efficient. Various initiatives assess national statistical systems’ (NSS) and data production. The World Bank’s IPNOS initiative complements existing kits, providing in-depth analytical tools to evaluate the efficiency of national statistical offices (NSOs), including costs, data quality, and NSO management. IPNOS application in 3 countries has identified bottlenecks and areas for improvement to inform data policies.
  • Publication
    Data for Policy Initiative
    (World Bank, Washington, DC, 2020-06) Himelein, Kristen; Dabalen, Andrew; Rodriguez Castelan, Carlos
    The Data for Policy (D4P) initiative (D4P) is a new World Bank engagement to improve National Statistical Systems (NSS) by enhancing the availability, timeliness, quality, and relevance of key data for evidence-based decision making. Working at national and regional levels, the D4P ‘package’ includes production of a core set of economic, social, and sustainability statistics essential for monitoring and evaluating public policies and programs. Good quality, timely, and relevant statistics are crucial to monitor social and human development outcomes. They can also help identify what policies work, and which do not, in promoting inclusive growth and eradicating poverty. Having reliable, timely data is particularly important for poor countries to allow them to allocate limited resources most efficiently. At the same time, the World Bank’s support for countries’ statistical capacity has become even more critical as the world strives to achieve the Sustainable Development Goals (SDGs).
  • Publication
    Collecting Robust Real-Time High Frequency Price Data in Fragile Settings
    (World Bank, Washington, DC, 2019-03) Pape, Utz J.; Nunez Chaim, Gonzalo I.
    To embark on a sustainable pathway toward development, effective policy responses must be implemented quickly and based on evidence. This requires reliable, timely data, which is often unavailable especially in fragile settings. An innovative High Frequency Survey (HFS) infrastructure offers a modern data collection system to fill critical data gaps. It can provide quantitative data to inform programs and policies, often linked to resilience in fragile settings. Using the cases of Somalia and South Sudan, this note describes the design and setup of such a HFS infrastructure and illustrates how high frequency price data can effectively support decision-making even in the event of an economic or humanitarian crisis.
  • Publication
    We Feel Fine: Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion
    (World Bank, Washington, DC, 2015-06) Lemieux, Victoria
    Motivated by the significant decline in citizen’s trust in governments over the past decades, this paper explores how policy decision makers and researchers can use social media analytics to investigate trust, specifically the relationship among trust in government, trust in state institutions, and citizens’ collective behavior. Analysis of these complex socio-political issues using online social data requires a human in the inference loop while also benefiting from computational methods to handle large amounts of unstructured data and the inference of relevant data features. To highlight the power of a mixed-initiative visual analytics-data science approach, this technical note describes the exploratory analysis work undertaken for analysis of collections of Tweets from Brazil, and describes further work that conceives data science methods to assist the analysis process by supporting definition of constructs of concepts of interest using social media data, and assisting the evaluation of evidence for hypotheses evaluation in an interactive-machine learning fashion. The outcomes of this project aim to support social sciences inquiry using observational social media data and World Bank operations.
  • Publication
    Case Study in Outcomes Evaluation : Mongolia
    (Washington, DC, 2014-05) World Bank
    From 2010 to 2013, the World Bank governance partnership facility (GPF) and the Swiss Agency for Development and Cooperation (SDC) helped build the capacity of Mongolian civil society organizations (CSOs) to promote good governance and an effective civil society engagement in procurement and service delivery monitoring. An assessment of results from the interventions was needed to satisfy accountability and learning needs and to inform decisions on future programs and funding. However, the short-term, complex nature of the interventions, numerous CSOs involved, and scarce documentation meant that knowledge of results was largely limited to activities and impact will be difficult to measure. In fall 2013, the World Bank and Mongolia office of SDC decided to use an outcome mapping approach to evaluate the effectiveness, sustainability, and relevance of these interventions. Outcome mapping is a participatory methodology useful for evaluating complex programs that involve capacity and coalition building, multiple actors, and tacit knowledge. It looks beyond outputs and delivery efficiency to institutional behavioral changes that occur in and among social actors influenced by interventions. The evaluation provided benefits to the stakeholders in several ways: results were packaged into an accessible, narrative format for various communication purposes; lessons were identified on what worked and did not work to inform the design of future CSO support, particularly concerning social actors and their roles, innovative solutions, and how to adapt or scale up a program; and the participatory process promoted stakeholder learning and ownership of results achieved to date. Thus, the evaluation generated robust, locally validated data that demonstrated the value of the interventions to stakeholders and donors and revealed ways to improve implementation and management for future efforts.
  • Publication
    Workforce Development: Matching Education Systems to Workforce Needs
    (World Bank, Washington, DC, 2014-01) World Bank
    Equipping national workforces with job-relevant skills is a continuing challenge, and mismatches are a present concern. Many school graduates cannot find jobs commensurate with their education and training. Employers complain of difficulty in filling vacancies and bemoan the scarcity of soft skills for boosting productivity. More broadly, skills constraints make it difficult for companies to innovate and invest in more lucrative economic areas. A goal of SABER-Workforce Development is to help countries improve their workforce development framework.