World Bank Customized Data Download
Justina Pniewski
The Private Participation in Infrastructure (PPI) Project Database has data on over 6,400 infrastructure projects in 137 low- and middle-income countries. The database is the leading source of PPI trends in the developing world, covering projects in the energy, transport, water and sewerage, ICT backbone, and Municipal Solid Waste (MSW) sectors (MSW data includes projects since 2008) Projects include management or lease contracts, concessions, greenfield projects, and divestitures.
The Trust Fund and Partner Relations unit of the World Bank is at the forefront of the bank's engagement in global funds and innovative financing initiatives. The Bank is currently Trustee for 26 Financial Intermediary Funds (FIFs).
FIFs are financial arrangements that typically leverage a variety of public and private resources in support of international initiatives, enabling the international community to provide a direct and coordinated response to global priorities. FIFs often involve innovative financing and governance arrangements as well as flexible designs which enable funds to be raised from multiple sources, both sovereign and private. Funds can be channeled in a coordinated manner to a range of recipients in the public and private sectors through a variety of arrangements. FIF structures are customized, depending on the needs of the partnership and agreements with the Bank.
Most FIFs support global programs often focused on the provision of global public goods; in particular, communicable diseases and responses to climate change. Recently established FIFs have aimed to address food security challenges and natural disasters.
2023 marks the midpoint for achieving these goals. The World Bank's Atlas of Sustainable Development Goals presents interactive storytelling and data visualizations, to describe progress towards each of the 17 SDGs. The first SDG calls for an end to extreme poverty in all its forms everywhere. That is also one of the World Bank's goals. The good news is since 1990, the world has made extraordinary progress in reducing extreme poverty.
But again, our report tries to offer data too in a way that really can point, especially in this landscape of data scarcity, we have to make the best use of the data that we do have and really use the data in the most impactful way to raise awareness on key policy shortcomings, and also areas where the most urgent actions are needed. Also, it's important to provide a good understanding to the public of the fact that data is central to the implementation of the SDGs, and the lack of data is a cause of concern, also raising awareness on the importance of improving our data statistical systems around the world. But I don't want to be all negative. Probably the data statistical systems we have today are not where we would want them to be in all countries, but we've made some good progress. If you look back at when we proposed the SDG indicator framework in 2016, we only had data for 115 indicators of the whole framework. Now, we have data for 225, almost doubling the coverage for the overall SDG framework, which is composed of 231 unique indicators.
One of the biggest things that we are struggling with at Gapminder at this point, I would say, is that even though the data exists, and it is findable, we still see that people seem to have a worldview that is not totally relying on the data that is actually available for them to upgrade the worldview.
Data helps to measure progress, identify gaps, and inform decision-making. Without accurate data, it is impossible to track progress and ensure that resources are being allocated effectively. The World Bank is not only a development and knowledge bank, our goal is to also become a data bank. This involves not only generating more data, but also maximizing the value of existing data and reaching a wider audience.
Yes, the list is compiled on an ongoing basis but may not be comprehensive. If you have research that is not in the list, please send an email to enterpri...@worldbank.org and it will be added to the list.
Unfortunately, the Enterprise Analysis team can not provide access to, or support for, many of the surveys conducted prior to 2005. We are unable to support many of the older datasets in their raw form. There is however a standardized dataset spanning 2002-2005 on the Data Portal that contains a core set of matched variables. Please inquire at enterpri...@worldbank.org to see if a specific survey is available.
Kenya ranks among the top countries with road traffic deaths per capita in the world. Working in Nairobi, DIME undertook a massive effort to collate multiple different data sources to tackle this challenge. The researchers obtained administrative data through a data sharing agreement with the National Police Service (NPS). The NPS provided access to paper records stored in police stations across the city. A total of 12,546 crash records were manually digitized for a nine-year period across the city of Nairobi. The reports include crash details, location, and severity; these were then mapped to show crash hot spots. A preliminary finding showed that 98 percent of these reports included injuries or deaths, indicating that crashes without injuries or deaths had not been reported or stored. To supplement these records, the team then used crowdsourced crash data processed through machine learning algorithms to identify and geolocate crashes reported by an established platform on Twitter feeds (Milusheva et al. 2020). To ground truth the crashes reported on Twitter, an app-based delivery firm was contracted to dispatch motorcycle drivers to the crash location within minutes after the crash was reported. The researchers also integrated private sector data on speed, road events, weather conditions, and land use from AccuWeather, Google Maps, Uber, and Waze. Data from Uber and Waze were accessed through a combination of publicly available data and specific partnerships leveraging the World Bank Development Data Partnership (DDP) initiative. The administrative data was combined with primary survey data collected at 200 hot spots to ascertain infrastructure conditions and video analytics were used to ascertain road user behavior, which generated in excess of 100 new variables on high-risk locations.
DIME Analytics ensures the credibility of DIME research by developing best practices, providing implementation tools and workflows, and monitoring compliance across all research teams. Research assistants (RAs) follow a formal annual training program designed to teach recent graduates the skills they need for a future in research. The program includes ten full-day courses, twelve seminar-style continuing education courses, and customized academic development through office hours. The training program covers standard data structures, safe and reproducible data practices, data collection and cleaning workflows, and the tools created by DIME Analytics to implement these standardized practices. Institutionalizing RA recruitment and training means that researchers spend less time onboarding staff and that technical skills are standardized across the portfolio, so research assistants can more easily be assigned to different tasks across an impact evaluation portfolio without significant retraining. The approach facilitates portfolio-level improvements to reproducible research. For example, when RAs follow consistent coding conventions, reviewing code is more efficient. The training program provides a mechanism to address problems and inefficiencies identified by DIME Analytics and to disseminate newly developed tools and workflows. In 2019 alone, DIME Analytics offered 28 reproducible research trainings.
Research assistants and field coordinators are trained in DIME Data Security Standards, and they in turn work closely with government counterparts and implementing partners to make sure that the standards are applied. Field coordinators play a particularly important role in identifying data security challenges and helping to implement secure protocols, as the field coordinators are physically present with partners. DIME Analytics offers customized support to DIME project teams for setting up secure data infrastructure and provides advising on safe handling, transfer, and storage of confidential data.
Arianna Legovini heads the Development Impact Evaluation (DIME) Department of the World Bank. She established this group in 2009 based on a model of collaboration between research and operations to optimize project design, secure higher returns to development investments, and empower governments to generate contextually relevant evidence to guide their policymaking process. She successfully aligned US$200 million in donor and government client financing to support a systematic approach to data analytics and causal evidence across a large program of World Bank and other development banks operations. She now presides over a team of 186 people conducting research in sixty countries across all sectors, working with 200 agencies, and shaping US$20 billion in development finance. She started similar groups with the Inter-American Development Bank and the Africa region of the World Bank. She provides advisory services to the thirty largest multilateral and bilateral development agencies in the world.
However, this is a minor quibble with an otherwise very promisingapproach to data. The World Bank has clearly thought about how to meetthe needs of different user groups and the results show it. Inlaunching this site, the World Bank has also made a strong statement insupport of open data - even to the extent of eliminating the revenuestream from WDI Online subscriptions. This is a rare move in theinformation world and worthy of praise.
The UNESCO Institute for Statistics (UIS) is the official and trusted source of internationally-comparable data on education, science, culture and communication. As the official statistical agency of UNESCO, the UIS produces a wide range of state-of-the-art databases to fuel the policies and investments needed to transform lives and propel the world towards its development goals. The UIS provides free access to data for all UNESCO countries and regional groupings from 1970 to the most recent year available. The UIS encourages developers and researchers to build websites and applications that make rich use of UIS dissemination data. In addition to a powerful standards-based API, the UIS supports a data browser and a bulk data download service (BDDS).
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