Economics & Development

Women, Business and the Law 2026 offers the latest global assessment of how laws, policies, and practices shape women’s economic opportunities across 10 topics relating to their working lives: safety, mobility, work, pay, marriage, parenthood, childcare, entrepreneurship, assets, and pension. This 11th edition of Women, Business and the Law introduces methodological refinements that capture not only laws on the books but also the policies and institutions that support their implementation, as well as expert perceptions of whether those laws translate into real-world practices. Grounded in data current as of October 1, 2025, this edition covers 190 economies. The findings reaffirm the strong link between legal equality and women’s economic empowerment - but also show that laws alone are not enough to secure equal opportunities for women in practice.

The private sector is a key driver of economic growth and job creation, yet its performance depends on an enabling business environment that fosters entrepreneurship, productivity, and investment. While national-level data have long guided development policy, such aggregate measures often obscure the significant economic and regulatory disparities that exist across regions within countries. This report argues that subnational assessments of the business environment are essential for designing effective, context-specific reforms that promote inclusive and sustainable growth. Drawing on the World Bank Group’s Business Ready (B-READY) methodology, as well as the work of the Regulatory Efficiency Unit (DECRE) and the Urban, Subnational Finance, Tourism, and Disaster Risk Management Department, the report explores how regional-level data reveal bottlenecks in regulatory implementation, infrastructure, and public service delivery that constrain private sector activity. Through country examples from Zambia, Indonesia, Nigeria, Egypt, and others, the report demonstrates how subnational diagnostics have informed targeted reforms, improved service efficiency, and stimulated job creation in lagging regions. By bridging the gap between data and action, subnational assessments empower policymakers to tailor interventions that leverage regional strengths and reduce spatial inequalities. The report concludes by outlining a framework for integrating subnational diagnostics into the World Bank Group’s broader Jobs Agenda, positioning localized data as a cornerstone of evidence-based policymaking for inclusive economic transformation.

El desajuste entre la oferta y la demanda laboral constituye uno de los principales desafíos para el funcionamiento eficiente de los mercados de trabajo, al limitar la productividad y la competitividad económica y dificultar tanto el acceso de las empresas al talento requerido como la inserción laboral de los trabajadores. Para abordar este problema, el Banco Mundial desarrolló la metodología de Identificación de Ocupaciones Críticas (LOC), una herramienta que permite identificar ocupaciones con escasez de talento, analizar brechas de habilidades y orientar políticas públicas de formación, empleo y migración laboral. En Colombia, su aplicación resulta especialmente relevante en un contexto de desempleo persistente, en particular entre jóvenes y mujeres, y de débil articulación entre el sistema educativo y las demandas productivas. El análisis combina un enfoque cuantitativo top down, basado en datos administrativos y estadísticos y en la construcción y validación de más de cuarenta indicadores de escasez, con un enfoque cualitativo bottom up apoyado en talleres regionales con empleadores, gremios y actores institucionales. Como resultado se identificaron 37 ocupaciones críticas, concentradas principalmente en perfiles técnicos de nivel medio, oficios industriales y servicios. Los resultados evidencian marcadas diferencias sectoriales y territoriales, así como núcleos comunes de escasez en agricultura, servicios de cuidado y ocupaciones técnicas industriales. El estudio proporciona una base sólida para orientar políticas de empleo y formación, fortalecer la intermediación laboral y diseñar iniciativas de movilidad laboral internacional y Global Skills Partnerships.

EarthRanger is an integrated data visualization and analysis platform designed to enhance wildlife conservation, protected area management, and personnel safety across large and complex landscapes. It aggregates real-time and historical data from more than 100 types of hardware devices and software sources including wildlife tracking collars, satellite imagery, vehicles, radios, drones, and AI‑enabled cameras, and combines them with field reports to provide a unified, map‑based operational view. Through instant alerts delivered via SMS, WhatsApp, and email, EarthRanger enables rapid responses to critical events such as geofence crossings, immobility, abnormal movement patterns, and potential threats from poaching or human‑wildlife conflict. The platform’s mobile application further supports field operations by allowing users to share GPS locations, report observations, and collect data offline for later synchronization. By analyzing data through tools such as heatmaps and time‑series playback, EarthRanger supports both immediate operational decision‑making and long‑term conservation planning. Deployed at scale across hundreds of sites globally, the platform strengthens cross‑organizational collaboration and enables proactive, data‑driven approaches to protecting wildlife, ecosystems, and communities.

In many low-income countries, consumption surveys are too infrequent to track poverty during economic shocks. Survey-to-survey imputation can fill this gap, but its reliability depends on whether prediction models estimated in one period remain valid in another. This paper develops a formal identification framework for the transportability of survey-to-survey estimators and shows that failures during crises arise primarily from missing shock-responsive information rather than from insufficient model flexibility. When such information is omitted, both linear models and flexible learners yield biased poverty estimates. When fast-changing proxy variables that track unobserved welfare changes are included, out-of-sample validity can be restored under well-defined conditions. Poverty estimation, however, requires a stronger condition—stability of the full conditional distribution of welfare—beyond that needed for mean welfare estimation. Monte Carlo simulations and evidence from Afghanistan, Uganda, and Rwanda support these predictions. The findings imply that improving questionnaire content is more important than increasing model complexity for timely poverty measurement in the face of shocks.