Innovations in youth employment programs are critical to addressing this enormous development challenge effectively. Rapid progress in digital technology, behavioral economics, evaluation methods, and the connectivity of youth in the developing world generates a stream of real-time insights and opportunities in project design and implementation. Part of the challenge is the sheer number of projects (just in Egypt, there are over 180 youth employment programs). And even without being aware, projects often innovate out of necessity in response to situations they face on the ground. But innovations need to be tested in different country contexts to be able to make an impact at scale.
Through the new Solutions for Youth Employment (S4YE) report, our team ventured to curate a few such ongoing innovations as they were being implemented through S4YE’s Impact Portfolio — a group of 19 youth employment projects from different regions being implemented by different partners across the globe. This network of youth employment practitioners serves as a dynamic learning community and laboratory for improving the jobs outcomes of youth globally.
Can we rely only on satellite? How accurate are these results?
It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.
So, it’s important to learn how well the resulting maps reflect the reality on the ground.
Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.
Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.
|Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).|
The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.
But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.
The question remains:
In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.
A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.
Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.
What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.
Want to explore our maps? Download our data here. Click here for an interactive land cover map of La Ceiba.
Between 2005 and 2014, due to natural disasters, the region had a nominal cumulative loss of around US$5.8 billion, and witnessed more than 3,410 deaths and hundreds of thousands of displaced people. More recently, in October 2011, Tropical Depression 12-E hit the coasts of El Salvador and Guatemala with damages amounting to nearly US$1 billion.
In two recent studies, we evaluated the causal impacts of hurricane windstorms on poverty and income as well as economic activity measured using night lights at the regional and country level. In both cases, we applied a fully probabilistic windstorm model developed in-house, and calibrated and adjusted it for Central America. The first study (on poverty) used yearly information at the household level (for income and poverty measures) as well as the national level (GDP per capita). Due to the limited comparable household data between the countries, we decided to follow up with the second study (on economic activity) using granular data at the highest spatial resolution available (i.e., 1 km2) to understand more deeply the (monthly) impact over time.
Our results are striking:
In the fiscal transparency arena, people often hear two conflicting claims. First, governments complain that few people take advantage of fiscal information that they make publicly available. Many countries - including fragile and low-income countries such as Togo and Haiti – have been opening up their budgets to public scrutiny by making fiscal data available, often through web portals.
Increasing the supply of fiscal information, however, often does not translate to the adequate demand and usage required to bring some of the intended benefits of transparency such as increased citizen engagement, and accountability. Providing a comprehensive budget dataset to the public does not guarantee that citizens, Civil Society Organizations (CSOs) and the media will start digging through the numbers.
In an effort to address this issue, the World Bank Group and the United Nations embarked on a three-year partnership that led to the publication of a new report titled Securing Development: Public Finance and the Security Sector. It is a sourcebook providing guidance to governments and development practitioners on how to use a tool called “Public Expenditure Review (PER)” adapted to examine the financing of security and criminal justice institutions.
Social safety nets – predictable cash grants to poor households often in exchange for children going to school or going for regular health check-ups – have become one of the most effective poverty reduction strategies, helping the poor and vulnerable cope with crises and shocks. Each year, safety net programs in developing countries lift an estimated 69 million people living in absolute poverty and uplifting some 97 million people from the bottom 20 percent – a substantial contribution in the global fight against poverty.
On her daily walk down the muddy road that connects her home with school, Beatriz would sing a cumbia and dream of becoming a professional dancer. However, she would soon find out that her aspirations were short lived. At the age of 14, Beatriz got pregnant and never went back to school. In the six years following her pregnancy, she struggled with an unstable and low-paid job, cleaning rich houses in Guatemala City. By the age of 20, without minimum skills and a secure job, Beatriz had little control over her life and a murky picture of her future loomed.
- crime and violence
- Urban Development
- Latin America & Caribbean
- Venezuela, Republica Bolivariana de
- Trinidad and Tobago
- St. Vincent and the Grenadines
- St. Lucia
- St. Kitts and Nevis
- El Salvador
- Dominican Republic
- Costa Rica
- Bahamas, The
- Sustainable Communities
Translations available in Chinese and Spanish.
Many of you are already familiar with the PPP (Public-Private Partnerships) Group’s Private Participation in Infrastructure (PPI) Database. As a reminder for those who aren’t, the PPI Database is a comprehensive resource of over 8,000 projects with private participation across 139 low- and middle-income economies from the period of 1990-2015, in the water, energy, transport and telecoms sectors.
We recently released the 2015 full year data showing that global private infrastructure investment remains steady when compared to the previous year (US$111.6 billion compared with US$111.7 the previous year), largely due to a couple of mega-deals in Turkey (including Istanbul’s $35.6 billion IGA Airport (which includes a $29.1 billion concession fee to the government). When compared to the previous five-year average, however, global private infrastructure investment in 2015 was 10 percent lower, mainly due to dwindling commitments in China, Brazil, and India. Brazil in particular saw only $4.5 billion in investments, sharply declining from $47.2 billion in 2014 and reversing a trend of growing investments over the last five years.
- private sector
- Private Sector Development
- Global Economy
- Financial Sector
- The World Region
- South Asia
- Middle East and North Africa
- Latin America & Caribbean
- Europe and Central Asia
- East Asia and Pacific
- El Salvador