The World Bank Group (WBG) is a family of five international organizations that make leveraged loans to developing countries. It is the largest and best-known development bank in the world and an observer at the United Nations Development Group. The bank is headquartered in Washington, D.C. in the United States. It provided around $98.83 billion in loans and assistance to "developing" and transition countries in the 2021 fiscal year. The bank's stated mission is to achieve the twin goals of ending extreme poverty and building shared prosperity. Total lending as of 2015 for the last 10 years through Development Policy Financing was approximately $117 billion. Its five organizations are the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), the Multilateral Investment Guarantee Agency (MIGA) and the International Centre for Settlement of Investment Disputes (ICSID). The first two are sometimes collectively referred to as the World Bank.The World Bank's (the IBRD's and IDA's) activities focus on developing countries, in fields such as human development (e.g. education, health), agriculture and rural development (e.g. irrigation and rural services), environmental protection (e.g. pollution reduction, establishing and enforcing regulations), infrastructure (e.g. roads, urban regeneration, and electricity), large industrial construction projects, and governance (e.g. anti-corruption, legal institutions development). The IBRD and IDA provide loans at preferential rates to member countries, as well as grants to the poorest countries. Loans or grants for specific projects are often linked to wider policy changes in the sector or the country's economy as a whole. For example, a loan to improve coastal environmental management may be linked to the development of new environmental institutions at national and local levels and the implementation of new regulations to limit pollution.C.C.
Although international trade has become increasingly seamless, developing countries continue to face significant delays in export shipment processing due to inadequate inland transport infrastructure and unreliable logistics systems. Border delays and uncertainty in travel time to ports severely constrain freight mobility and export performance. Despite recent infrastructure investments and institutional reforms, exporters in Southern Africa still encounter frequent road disruptions, long waits at border crossings, and persistent inefficiencies in logistics operations. Using micro-level data on export shipments from ten Southern African countries to the United States, we analyze how inland border delays and uncertainty are associated with routing patterns to ports. A mixed-logit discrete choice model applied to disaggregated shipment data shows that the likelihood of choosing a port-corridor decreases with the increase in travel-time variability and border delays, indicating restricted exporters' access to global markets. Ports connected by corridors with high travel-time uncertainty are systematically less likely to be selected, even when they take longer time from the shipment origin to the port. The results underscore the importance of reducing inland transport delays and uncertainty to enhance freight reliability, strengthen trade competitiveness, expand port hinterlands, and support export diversification in the region.
Extreme heat is a growing threat to both individual livelihoods and broader economies, killing a growing number of people each year as temperatures rise in many parts of the world and limiting productivity. Many studies document the link between heat waves and mortality or morbidity, and others explore the economic consequences of them, but few are able to determine how populations respond to the shock of extreme heat in day-to-day activity. Toward this end, we investigate how ambient heat affects urban activity, measured as the number of trips ending in a geohash cell per day and by time of day. In India and Mexico, extreme heat suppresses activity by up to 10
Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone. Satellite imagery offers global coverage and the possibility of predicting economic livelihoods at scale, yet existing approaches to predicting livelihoods with imagery or other non-traditional data often fail to reliably identify local-level variation and, as we show, degrade under temporal shift. Here we introduce Tempov, a satellite foundation model pretrained by self-supervision on three million bi-temporal Landsat pairs and adapted with parameter-efficient fine-tuning to sparse survey labels. The model enables large-scale, high-resolution wealth mapping and dynamic measurement, including zero-shot nowcasting up to a decade after observed labels, retrospective hindcasting, and decadal change tracking, while outperforming existing neural network and geospatial foundation-model baselines. In low-label regimes, Tempov achieves competitive accuracy with only 10
General-purpose LLMs pose misinformation risks for development and policy experts, lacking epistemic humility for verifiable outputs. We present AVA (AI + Verified Analysis), a GenAI platform built on a curated library of over 4,000 World Bank Reports with multilingual capabilities. AVA’s multi-agent pipeline enables users to query and receive evidence-based syntheses. It operationalizes epistemic humility through two mechanisms: citation verifiability (tracing claims to sources) and reasoned abstention (declining unsupported queries with justification and redirection). We conducted an in-the-wild evaluation with over 2,200 individuals from heterogeneous organisations and roles in 116 countries, via log analysis, surveys, and 20 interviews. Difference-in-Differences estimates associate sustained engagement with 2.4–3.9 hours saved weekly. Qualitatively, participants used AVA as a specialized “evidence engine”; reasoned abstention clarified scope boundaries, and trust was calibrated through institutional provenance and page-anchored citations. We contribute design guidelines for specialized AI and articulate a vision for ‘ecosystem-aware’ Humble AI.
We develop and calibrate a quantitative spatial equilibrium model of Indonesia to analyze the potential impact of the Trans-Sumatra Toll Road project on the country's economy. The estimated aggregate welfare gains vary from 0.25% of the country's output if there is only trade in final goods to 1.37% if sectoral linkages and trade in intermediates are allowed, and they further increase to 3.54% if labor mobility is considered. About 39% of gains result from improved product market integration and 61% from easier worker migration. Finally, the welfare effect concentrates on Sumatra, whereas the overall national inequality declines.
