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World Bank
Country: United States
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20 Projects, page 1 of 4
  • Funder: UKRI Project Code: ES/T013567/1
    Funder Contribution: 473,031 GBP
    Partners: University of Surrey, WB

    International trade is of vital importance for modern economies, and governments around the world try to shape their countries' exports and imports through numerous interventions. Given the problems facing trade negotiations through the World Trade Organization (WTO), countries have increasingly turned to preferential trade agreements (PTAs) involving only one or a small number of partners. At the same time, attention has shifted from reductions of import tariffs to the role of non-tariff barriers such as differences in regulations and technical standards. Accordingly, modern PTAs contain a host of provisions besides tariff reductions, in areas as diverse as services trade, competition policy or public procurement. A key question in international trade research is how to estimate the effects of PTAs and their individual provisions on trade flows. We argue that methods from the machine learning literature can help address this challenge, and that such methods are often superior to existing approaches. We use the term 'machine learning' to refer to algorithms used for statistical prediction that are trained on subsets of the available data to make forecasts of quantifiable outcomes (here: trade flows). While such algorithms have started to be applied in economic research, they have not been used for the analysis of PTAs nor in international economics more generally. First, machine learning can help evaluate the suitability of existing methods for estimating PTA effects. Such methods evaluate PTAs by comparing the trade flows observed after the implementation of an agreement to a so-called counterfactual outcome that shows what would have happened to trade flows in the absence of a PTA. This counterfactual is invariably based on a specific statistical model. Currently, by far the most common model is the so-called gravity equation. The estimated effect does of course depend on how well the gravity equation predicts counterfactual trade flows. We will use machine learning to develop a more flexible forecast to which we can compare the gravity equation's predictive power. Machine learning can also help improve existing methods for PTA evaluation. Implicitly, approaches based on the gravity equation construct a counterfactual by using an average of the changes in trade flows between countries not involved in a PTA. Similar approaches have been applied in a range of contexts besides international trade. Recent methodological advances have shown how these approaches can be improved by applying machine learning to select more complex combinations of control units (here: countries not participating in a PTA) than simple averages. Despite their potential, these techniques have not been applied in international trade research, and we propose to adapt them to this context. Finally, machine learning can be used to determine the relative importance of individual PTA provisions. The key challenge existing research has faced is that many PTAs contain similar provisions, making it difficult to estimate their effect on trade flows separately. Thus, researchers usually aggregate provisions in some way, for example by combining them into broad groups. This limits the relevance to policymakers who need to know if they should include a given individual provision in a PTA. This problem is reminiscent of the issue of 'feature selection' in machine learning where algorithms must decide which of many potentially relevant variables to include for forecasting purposes. We plan to use a subgroup of these methods that allow to identify the subset of variables (here: provisions) with the largest effect and to accurately estimate their impact. Overall, the proposed research will deepen our understanding of how PTAs impact trade flows. This, and the empirical techniques we plan to develop, will help researchers and policymakers involved in the design and evaluation of PTAs and ultimately contribute to a better, more evidence-based trade policy.

  • Funder: UKRI Project Code: ES/R004293/1
    Funder Contribution: 243,548 GBP
    Partners: Global Alliance for Banking on Values, University of Exeter, WB

    Global banking regulation is on the verge of a ground-breaking transformation from the mainstream dominant economic model to a people-oriented sustainable approach. This innovative global research, informed by transitional and regulatory theories, aims at maximising the opportunities to inform and underpin such a paradigm shift in the banking sector. It will do so by exploring the role and potential of 'values-based banks' - that is small and niche banks which adhere to Principles of Sustainable Banking (2011) and are values-driven rather than exclusively profit-driven - to inform the re-shaping of global banking regulation. In order to realise this ambition, it is proposed to co-create and conduct this timely and topical research over three years in partnership with both the Global Alliance for Banking on Values (GABV) which currently represents 40 small commercial banks, credit unions, microfinance banks globally (including the UK), and with the World Bank (WB), which made a formal offer to provide the necessary leverage for this research. The research is set within the ongoing debate over the role of the financial and banking sector in achieving sustainable development (SD). The concept of sustainable banking is complex as it relates to the goals of balancing the social, environmental and financial aspects of banking business and using banking services to achieve social and environmental long-term impacts. The current focus of the global banking regulation is on the largest and frequently described as the 'too big to fail' banks identified by the Financial Stability Board (FSB). Small banks are well positioned to have SD as the core of their practices but their potential to inform global banking regulation is unexplored. Following innovative sampling informed by transitional and regulatory theories, the research will draw from the experience of GABV niche banks. The study will be conducted over two Phases by taking a mixed methods approach: a desk based review and a triangulation of quantitative and qualitative data. Two placements will facilitate access and the adequate collection of data: a 6-month placement at GABV and a 3-month placement at the WB. The research will actively involve NGOs, which follow the current debates on SD banking regulation (eg. Friends of the Earth, Finance Watch). Phase 1 will identify values-based banks' innovative and niche sustainable development practices and assess how far these practices can and should inform global banking regulation via surveys and interviews. For example values-based banks' social engagement practices may constitute best SD practice but may be of little use for global banking regulation. Alternatively, these banks may have particularly strong environmental disclosure practices which could be included in global regulation. The research will identify the preferred regulatory strategies for embedding the innovative and niche SD strategies into global regulation via a focus group, which will meet several times. Phase 2 will assess and test Phase 1 proposals with the global banking regulators (such as FSB) and the largest global banks (such as Barclays Bank) via surveys, interviews and focus groups. Ultimately, it will identify those values-based banks' practices that can be taken up realistically by the global regulators. Phase 2 will also lead to establishing a Stakeholder Forum, whose mandate will be to promote Phase 2 findings and ensure they are on the global regulators' agenda. The research will lead to producing several high-quality outputs, including a planned monograph, four journal articles, two impact reports, and developing interdisciplinary links. It will benefit several stakeholders such as GABV, the World Bank, global regulators, NGOs, and large banks.

  • Funder: UKRI Project Code: ES/M004740/1
    Funder Contribution: 151,215 GBP
    Partners: State University of New York at Potsdam, THE REPUBLIC OF UGANDA, WB

    Worldwide, there has been growing interest in understanding the nature of quality education. A major key to this quest lies in what goes on inside classrooms, where children derive the bulk of their daily experiences in academic and social learning. While factors like the physical condition of the school building, textbooks, and teacher degrees play a role in children's learning and life outcomes, they are small and indirect. Teacher instructional practices and classroom processes, in terms of supportiveness and organization, play considerable roles in children's learning and well being outcomes. Yet, the focus of many attempts to improve (and evaluate) educational programs has been based on classic, though simple, input-output model. In other words, an intervention takes place, and then the change in child academic or social outcomes are measured. Studies of this type can be viewed as "black box" studies; they tell us little more than whether the program worked or not. They fail to provide us with insights on how to more effectively facilitate deeper learning. To do this, we first need to be able to effectively measure instructional practices and classroom processes. The most accurate way of measuring instructional practices and classroom processes is with the use of observational methods. To date, available methods have been too labor-intensive and costly for large-scale evaluation studies or for use in daily practice. Reliable, valid, cost-effective, and practically useful tools are needed. Nowhere is this truer than in low-income and fragile states. This is the goal of the proposed investigation. To achieve these ends, we capitalize on a large-scale experimental school and classroom-based intervention program undertaken in Ugandan public secondary schools by the World Bank (WB), in partnership with the Ministry of Education and Sports (MoES). In a second phase of this project, the WB enlisted New York University (NYU) to supplement the impact evaluation by examining the instructional practices and classroom processes with live observations using an innovative tool, known as TIPPS, before, in the middle, and at the end of the intervention year. Samples of these classrooms are also videotaped for more intensive analysis. This data provides a unique opportunity to further develop and validate an innovative, affordable, scalable, and practically useful tool for assessing teacher practices and classroom processes. It also has the potential to provide feedback to teachers, especially when used in tandem with mentoring and reflected practice for improved teacher performance. We conduct a series of scientific studies to assure the viability, validity, and utility of this instrument. In addition to the development and validation of an effective classroom observational instrument, we want to assure its use in policy and practice in Uganda and eventually in other low-income and fragile states. Thus, we begin the project year by working closely with the various stakeholder groups - ministry, union officials, school administrators, teachers, and World Bank Africa Region staff - to facilitate buy-in and ownership. We will engage them in interviews and focus groups to both inform them about the instrument and to gain their assistance in structuring the end of the year workshops for maximum effectiveness. The goals of these workshops are to explain our findings with regard to the intervention and the tool, and more importantly, so that the tool can be implemented at policy levels by the ministry, with the aide of the unions. In this manner, this tool could then be put into practical use in secondary schools around the country, and eventually primary schools as well.

  • Funder: UKRI Project Code: EP/V028200/1
    Funder Contribution: 79,522 GBP
    Partners: Imperial College London, Ghana Education Service, Ministry of Education (Ghana), WB

    The COVID-19 pandemic and related social and economic crises are undermining children's education in low- and middle-income countries through school closures, unequal access to remote-learning activities, and increased household food insecurity and poverty. Groups at greater risk, including girls and children from the poorest families, are likely being disproportionately affected, amplifying existing inequalities in child education, health and broader development. We embed in an ongoing longitudinal project, Quality Preschool for Ghana, a study of the pandemic's repercussions on children's education and broader development for a representative sample of urban Ghanaian boys and girls aged 10-12 years (N=~2,000), their households, and teachers (N=~400). We have four main goals. First, we investigate household and child vulnerability and resilience to the crisis, with three phone surveys with parents and one phone survey with children starting in late summer, followed by already-funded child and parent direct assessments later in the 2020-2021 school-year. Second, with three additional phone surveys with teachers, we generate new data on how children, parents and teachers are faring with the remote-learning implemented during school closures and with re-entry into in-person schooling should that happen in the 2020-21 school year. Third, by piggy-backing on already-funded data collection activities planned for later in the Fall 2020 and Spring 2021, and combined with four prior rounds of data on these children starting in preschool, we examine inequalities in the effects of the crisis on learning and broader child development domains (health, psycho-social outcomes). Fourth, we monitor changes in poverty and food security and examine their associations with later-in-life children's educational outcomes. The proposed study provides the Ghanaian government with unique, real-time data to inform remote-learning, school-reentry, how children, families and teachers are coping with the crisis, and social-protection efforts. Results will provide timely and much-needed academic and policy insights for Ghana and broader global educational efforts to protect children from the long-term effects of the pandemic on their learning and development.

  • Funder: UKRI Project Code: ES/P006329/1
    Funder Contribution: 130,841 GBP
    Partners: University of Salford, Microsoft Research Lab India Private Ltd, Digital Divide Data, International Development Research Ctr, WB

    As digital technologies - the internet, web, mobile phones, social networks, 3D printers, etc - spread around the world, both work and business are changing via creation of digital economies. There has already been impact in developing countries: thousands of digital startups, millions working in the ICT sector, millions more undertaking online work for platforms like Upwork. And the potential is even greater: hundreds of millions could access online work platforms, digital businesses like Uber and Airbnb are spreading rapidly, demand for digital enterprises is high, 3D-printing could level the manufacturing playing field, etc. But problems are also arising: most digital startups and digital careers fail; most citizens are unable to participate in digital economies; the benefits of digital work and trade seem to flow more to big corporations in the global North than to workers, enterprises or governments in the global South. The speed of change means much of this is happening in a knowledge vacuum. Researchers are playing catch-up to try to understand these new trends in the global North, but very little research on digital economies looks at developing countries or is done by researchers in those countries. As a result, there are four knowledge gaps about digital economies in the global South. We don't know: - what's going on and where; - the development impact e.g. whether digital economies are increasing or reducing inequalities; - what governments, NGOs and businesses should be doing to create an effective "digital ecosystem" that works for the benefit of all. And as researchers we are not sure what concepts and methods to apply. The "Development Implications of Digital Economies" (DIODE) Strategic Network aims to help fill these knowledge gaps. It has three main objectives: - To assess the current state-of-play and identify a future research agenda around the four knowledge gaps above. - To create a research network with the capacities to implement this research agenda on digital economies and development. - To develop specific research proposals that address identified research priorities. The network consists of senior and junior researchers from the UK, developing countries and other locations around the world who - along with those working in digital economy policy and practice - will work together to fulfil these objectives. Following initial synthesis studies to understand the current state-of-play, we will meet in four workshops - two in developing countries, two in the UK - each of which will address particular knowledge gaps through presentations and working group sessions. Alongside the network itself, by the end we will have produced a final report that provides a future research agenda; a strategy brief to guide those involved in digital economy policy/practice; and a set of research proposals that can put the agenda into practice. Initially, the main beneficiaries will be network members: we will have a far better understanding of what to research next and how to research it, with stronger capacity to undertake this work, and dense contacts to ensure our research is relevant to policy and practice. We will have created research capacity within and between developing countries, so that work on this important and growing phenomenon can be driven from and undertaken in those countries. GCRF and other researchers - especially in developing countries - will benefit from having a clear sense of research priorities and tools, but also from understanding how important digital economies are becoming in the global South. Digital economy policy-makers, entrepreneurs, worker organisations and other practitioners will understand what good-practice actions to take. Through that policy/practice connection, and through the outputs from later implementing our research agenda, we will make a difference to development: helping ensure digital economies work to deliver development goals.

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