Since its adoption in December 2015 by the 21st Conference of Parties (COP21), within the United Nations Framework Convention on Climate Change (UNFCCC), 175 countries to date have ratified the Paris Agreement.
... See More + These countries have made commitments Nationally Determined Contributions (NDCs), in some cases contingent on financing by developed countries, to limit or reduce their Greenhouse gas (GHG) emissions through a variety of measures including more significant deployment of renewable power, energy efficiency, land-use controls such as conservation of forests and grasslands, carbon pricing, and other measures compatible with each country’s national circumstances and capabilities. Even with full ratification of the Agreement by all 197 signatories, the aggregate effect is projected only to slow the rate of GHG emissions growth from the 24 percent increase, between 1990 and 2010, to an anticipated increase between 2010 and 2030 of between 11 and 23 percent. To foster higher ambition and sustainable development, and also encourage large-scale financing towards the most effective mitigation measures, Article 6 of the Agreement recognizes that countries may engage in cooperative approaches, including the use of internationally transferred mitigation outcomes (ITMOs) towards their individual NDC. In this new, complex and diverse environment, this paper aims to examine emerging digital technologies and architectures that could be used to enhance and connect the heterogeneous climate actions across countries, thereby supporting post-2020 climate markets that facilitate the most cost-effective achievement of the highest possible ambition. Given the speed with which information technology, system architectures, domestic policy, and other relevant elements are developing, the roadmap laid out in this paper will likely continue to evolve significantly over the next few years.
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This note shows how big data can help combine experimental with non-experimental approaches in impact evaluations when take-up is low. In this study, author have access to a large administrative data set (of 660 MB), which follows the monthly financial indicators of each client for up to 18 months prior to the intervention and 6 months after it.
... See More + Moreover, from the experimental approach their also had a large pool of clients randomly assigned to the control group. This data enables us to obtain credible estimates by combining the experiment with two non-experimental approaches. Their first use propensity score matching to find, among the clients in the control group, a subset of clients that best mimics the pre-intervention financial trajectories of clients in the treatment group that received treatment. The effects of the workshops on the treated clients are summarized. Under our preferred specification, the author finds that participating in the workshop increases by 11 percentage points the likelihood of paying more than the minimum payment, and reduces by 3.4 percentage points the likelihood of delaying payment. Monthly credit card spending increases by 63.7 percent, and the likelihood of owning a deposit account with our partner bank also increases by 2.7 percentage points. The two financial education interventions help clients reach the minimum payment and pay their bills on time more often, without reducing their credit card spending. Both interventions increase the likelihood that clients are profitable for the bank.
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With unprecedented speed and scale, digital transformation is affecting multiple industries, including energy. A combination of technologies, and a more complex world demanding greater agility and new competences impact all aspects of the energy sector and manifest themselves in changing patterns of consumption, new ways of asset optimization, and cross-industry partnerships.
... See More + 'Smart solutions' are a product of this transformation and energy data are its source. By nature, the energy sector generates vast amounts of big data through meters, sensor networks, customer payments, credit history, satellite imagery, etc. It is not surprising that private and public energy companies are turning to the idea of leveraging big data analytics for performance optimization and improved service delivery. The transition to a digitized energy sector will not happen on its own, and a number of enablers are required to facilitate this change. Beyond improved digital infrastructure, digital skills and analytics capabilities will need to be strengthened. This new solutions brief aims to encourage the use of big data analytics in the energy sector by outlining opportunities and identify cases for where the use of big data analytics could help better address challenges faced by the energy sector today.
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The Subcommittee of the Committee on Development Effectiveness met to consider the IEG report entitled Data for Development – An Evaluation of World Bank Support for Data and Statistical Capacity.
... See More + The Subcommittee welcomed the report and was pleased that Management broadly concurred with IEG’s findings and recommendations and that the WBG Development Data Council had endorsed the report. They highlighted the importance of data in meeting the SDGs and the need for a vision that sets out how to get there by 2030. Members noted the importance of supporting client countries to develop capacity to generate, use and share data and asked how this could be implemented most effectively. Some members stressed the importance of the CPF process as a policy dialogue that could promote the allocation of domestic resources to statistical capacity building, of focusing on both national and subnational statistics offices, of knowledge transfer and technology and the need to use IDA resources in low-income and fragile countries.
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This toolkit attempts to address some of the challenges associated with difficulties inaccessing comparables data. While 'perfect' or ideal comparables may only rarely be available,commonly the data that is available will still allow a reasonably reliable analysis to be performedand a satisfactory approximation of an arm's length outcome to be determined.
... See More + Part II of thistoolkit thus focuses on making the best use of available data. It discusses the sources of data and how the use of available data may be optimized through widening the criteria for data-selectionand the use of comparability adjustments. It also stresses that the selection of the mostappropriate transfer pricing method, on the basis of a detailed factual analysis, is central to theapplication of the arm’s length principle, and in many cases, is likely to have a greater impact onthe outcome than the accuracy of the data used in the method’s application. Part III focuses on issues that arise, and solutions that may be available, where adequate data on transactions between independent parties are not available, including the potential for developing safe harbours or prescriptive approaches. It also explores how data in the possession of tax administrations, typically derived from tax returns, may be used to identify arm’s length results in a way that preserves confidentiality. Such data may have a part to play in setting safe harbour margins. Part III also includes a discussion of some of the policy considerations with regard to such approaches, as well as outlining a number of country practices, and providing practical tools in the form of sample legislation or regulations which could be used to implement such approaches. The final part, Part IV, sets out areas where further work is planned, and summarizes a number of conclusions.
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This toolkit attempts to address some of the challenges associated with difficulties inaccessing comparables data. While 'perfect' or ideal comparables may only rarely be available,commonly the data that is available will still allow a reasonably reliable analysis to be performedand a satisfactory approximation of an arm's length outcome to be determined.
... See More + Part II of thistoolkit thus focuses on making the best use of available data. It discusses the sources of data and how the use of available data may be optimized through widening the criteria for data-selectionand the use of comparability adjustments. It also stresses that the selection of the mostappropriate transfer pricing method, on the basis of a detailed factual analysis, is central to theapplication of the arm’s length principle, and in many cases, is likely to have a greater impact onthe outcome than the accuracy of the data used in the method’s application. Part III focuses on issues that arise, and solutions that may be available, where adequate data on transactions between independent parties are not available, including the potential for developing safe harbours or prescriptive approaches. It also explores how data in the possession of tax administrations, typically derived from tax returns, may be used to identify arm’s length results in a way that preserves confidentiality. Such data may have a part to play in setting safe harbour margins. Part III also includes a discussion of some of the policy considerations with regard to such approaches, as well as outlining a number of country practices, and providing practical tools in the form of sample legislation or regulations which could be used to implement such approaches. The final part, Part IV, sets out areas where further work is planned, and summarizes a number of conclusions.
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Harnessing the power of big data is the next frontier for governments. Big data analytics uses data science and machine learning for better understanding, tracking and prediction of trends to improve government delivery of existing public services in education, procurement, public safety, transportation, taxation and civic engagement, without compromising citizen rights and privacy.
... See More + In this technical analytics will inform and transform government decision-making and help solve complex problems that were once impossible. Governments around the world are beginning to utilize big data to solve a range of pressing policy problems. The Australian Ministry of Finance, for instance, has developed a big data strategy, which provides a whole-of-government approach to use big data in order to enhance existing services, deliver new services and provide better policy advice, while incorporating best practice privacy protections and leveraging existing ICT investments.
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Governments have an opportunity to harness big data solutions to improve productivity, performance and innovation in service delivery and policymaking processes.
... See More + In developing countries, governments have an opportunity to adopt big data solutions and leapfrog traditional administrative approaches
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: For the 2017 World Government Summit, ¬the World Bank’s Governance Practice partnered with the Big Data Program in Global Operations Knowledge Management (GOKMU) and SecondMuse to deliver a “Big Data in Action” workshop and high-level panel.
... See More + Both activities explored the opportunity to use big data applications in government service delivery, policy-making, and citizen engagement. ¬e workshop gathered 25 big data experts in public and private sector to advance thinking on these areas. ¬e workshop included: • Seven lighting talks featuring case stories of big data in government • Exercise to identify high priority areas for applications of big data within service delivery, policy-making, and citizen engagement • Team based exercises to design practical big data prototypes in areas like pu Big data is not just about the size or quantity of the data now available. It is also about how governments use data driven capabilities to gather citizen feedback on services and put it into action toward more responsive and proactive service delivery, enabling governments to continuously improve public service delivery, and build trust. As governments look into adopting the use of big data, we need clear regulations and guidelines developed for data use, data ownership, privacy, civil liberties and equality, and more public-private data partnerships to make private sector data accessible for public and social good. ¬
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This evaluation’s objective was to assess how effectively the World Bank has supported development data production, sharing, and use, and to suggest ways to improve its approach.
... See More + This evaluation defines development data as data produced by country systems, the World Bank, or third parties on countries’ social, economic, and environmental issues. At the global level, the World Bank has a strong reputation in development data and has been highly effective in data production. It produces influential, widely used data and cross-country indicators that fill important niches, benchmark countries, and stimulate research and policy action. The World Bank has also taken a prominent leadership role in global data partnerships so far. However, the World Bank needs to determine its future role carefully because the global partnership landscape is becoming more uncertain—as old partnerships phase out, the complementarity of new partnerships is unclear. This makes the World Bank’s future role especially pivotal because the sustainability of funding from global data partnerships at both the national level and for some global data efforts is at risk. Without sustained funding, past progress will be in jeopardy, as observed in some countries where data quality worsened when trust fund support ended. At the national level, the World Bank has been mostly effective at fostering its client countries’ data production through its own financing and through financing from small trust fund grants. It has been less effective in promoting data sharing; while the World Bank has used its leverage in some of its client countries, it needs to do a better job at encouraging other countries to share data. The World Bank has been even less effective in promoting data use by governments and citizens. The World Bank’s systemwide approach to building the capacity of national statistical organizations yielded significant successes in countries where it was deployed, and it should now add a focus on building subnational capacity and strengthening client countries’ administrative data systems. The World Bank needs to make sure it clearly understands when and how big data can complement traditional data in answering key development questions related to its mission, and use big data analytics appropriately to underpin its own decisions and to ensure that it supports its country clients effectively in big data use. The World Bank still needs to address the implications for organizing big data work internally, entering into corporate agreements with private providers (typically the producers of big data), and seriously considering and addressing privacy and ethical concerns related to big data use.
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Big data solutions have the potential to accelerate the work of our teams by deriving timely, accurate, and actionable insights from alternative data sources in order to close data gaps and inform policymaking.
... See More + For example, pilot projects underway in the Trade and Competitiveness practice are exploring the use of data science techniques to harness publicly available government and commercial data to aid competition authorities in detecting cartels and other anti-competitive practices. We are also mining Internet data to measure innovative economic activity in cities, so agencies can make better informed policy decisions and, we are collecting regulatory data to classify and assess the impacts of non-tariff measures on economies and their competitiveness. This paper, prepared in collaboration with Deloitte and with other global practices within the Bank Group, highlights data-driven pilot projects underway in the Trade & Competitiveness Global Practice and shares compelling cases of how big data is changing the way we look at the challenges countries are facing and how we can best support them.
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Ninety percent of all world data was only produced in the last two years. In 2020, forty-four times more data will be produced than in 2009. The term big data refers to the vast amount of information now being generated and captured in a variety of formats and from a number of different sources, and which require innovative forms of processing for better analysis and decision making.
... See More + Big data analytics uses data science and machine learning for better understanding, tracking, and prediction of trends to improve government delivery of public services in areas like education, procurement, public safety, transportation, taxation, and civic engagement, without compromising citizen rights and privacy. The objective of this high-level panel will be to discuss the state of big data in government and the opportunities to utilize it to improve the delivery and design of public services.
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Economists and other social scientists are increasingly using big data analytics to address longstanding economic questions and complement existing information sources.
... See More + Big data produced by online platforms can yield a wealth of diverse, highly granular, multidimensional information with a variety of potential applications. This paper examines how online job-portal data can be used as a basis for policy-relevant research in the fields of labor economics and workforce skills development, through an empirical analysis of information generated by Babajob, an online Indian job portal. The analysis highlights five key areas where online job-portal data can contribute to the development of labor market policies and analytical knowledge: (i) labor market monitoring and analysis; (ii) assessing demand for workforce skills; (iii) observing job-search behavior and improving skills matching; (iv) predictive analysis of skills demand; and (v) experimental studies. The unique nature of the data produced by online job-search portals allows for the application of diverse analytical methodologies, including descriptive data analysis, time-series analysis, text analysis, predictive analysis, and transactional data analysis. This paper is intended to contribute to the academic literature and the development of public policies. It contributes to the literature on labor economics through application of big data analytics to real-world data. The analysis also provides a unique case study on labor market data analytics in a developing-country context in South Asia. Finally, the report examines the potential for using big data to improve the design and implementation of labor market policies and promote demand-driven skills development.
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Policy Research Working Paper WPS7966 FEB 09, 2017
