With the right kind of reforms, public employment services can do a better job of matching job seekers from poor households. In low and middle-income countries, individuals from poor households find jobs through informal contacts; for example asking friends and family and other members of their limited network. But this type of informal job search tends to channel high concentrations of the poor individuals into informal, low-paid work.
Job seekers especially from poor households need bigger, more formal networks to go beyond the limited opportunities offered by the informal sector in their local communities. This is where public employment services can help, but in developing countries many of these services just simply do not work well: they suffer from limited financing and poor connections to employers, and governments are looking for ways to reform and modernize them to today’s job challenges.
There are lots of cases where developing countries have improved their public employment services and these can serve as models. The lessons from these successful reforms can be distilled and replicated. Based on our recent publication, here are three case-tested strategies that improved the performance, relevance and image of public employment services.
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:
Globally 2.9 million people died from household air pollution in 2015, caused by cooking over foul, smoky fires from solid fuels such as wood, charcoal, coal, animal dung, and agricultural crop residues. Well over 99% of these deaths were in developing countries, making household air pollution one of their leading health risk factors.
Many women across the world spend their days and evenings cooking with these fuels. They know the fumes are sickening, which is why some cook in a separate outhouse or send the children to play while they cook. Sadly, these small actions cannot fully protect the young. As for the women themselves, they suffer incredible morbidity and mortality from household air pollution.
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.
- Sustainable Communities
- crime and violence
- Urban Development
- Latin America & Caribbean
- Trinidad and Tobago
- St. Vincent and the Grenadines
- St. Lucia
- St. Kitts and Nevis
- El Salvador
- Dominican Republic
- Costa Rica
- Bahamas, The
When seeking to engage private partners, one thinks of large, high-cost national infrastructure projects. But subnational governments are also effectively partnering with the private sector by leveraging assets, rethinking “infrastructure,” and establishing mechanisms to give long-term security.
Some Latin American governments are capitalizing on legislative frameworks for Public-Private Partnerships (PPPs)—in some cases tailoring laws for subnational use, and using experience gained from large-scale national projects.
While not always technically PPPs, this private sector capacity can be harnessed to deliver innovative smaller projects, from using drones to deliver medicines to health centers in rural communities in the Dominican Republic to building market stalls in a new Honduran bus terminal to spur the development of small businesses.
Here are three ways cities and municipalities can mobilize capital and innovation in infrastructure.
Although it may take the form of domestic violence, Associated with certain societies' social norms and many other risk factors, such violence leads to severe social and economic consequences that can contribute to ongoing poverty in developing and developed countries alike.
Because violence affects everyone, it takes us all—from individuals to communities, and from cities to countries—to tackle the pandemic of violence against our women and girls.
On Day 15 of the global #16Days campaign, let’s take a look at a few examples of how community groups, civil society organizations, and national governments around the world are making informed efforts to prevent and respond to various forms of gender-based violence.
- women business and the law
- #16Days of Activism Against Gender-Based Violence
- Sustainable Communities
- Urban Development
- Social Development
- Middle East and North Africa
- Latin America & Caribbean
- Sri Lanka
- Egypt, Arab Republic of