This past spring, Honduras took an important step in improving transparency and accountability with respect to Public-Private Partnerships (PPP) by launching an online platform that allows public access to detailed information about these activities.
The portal, created with the support of the World Bank and in coordination with the Construction Sector Transparency Initiative (CoST), allows access to information related to PPP projects through their entire project cycle. This is a significant achievement that promotes transparency in PPP planning, procurement, implementation and monitoring in Honduras, by making information easily accessible to citizens.
It is well established in the economic literature that it’s the rich who benefit from the lion’s share of energy subsidies. Yet, it is often the poor and vulnerable who protest loudly against these reforms. Why does this happen? What are we missing?
- Latin America & Caribbean
- Venezuela, Republica Bolivariana de
- Trinidad and Tobago
- St. Vincent and the Grenadines
- St. Pierre & Miquelon
- St. Lucia
- St. Kitts and Nevis
- Puerto Rico
- French Guiana
- El Salvador
- Dominican Republic
- Costa Rica
- Bahamas, The
- Antigua and Barbuda
Trading across borders in Central America has been a severe problem for many years. In 2017, cargo trucks used to spend 10 hours to travel less than one kilometer across the borders between Guatemala and Honduras. Such delays at border crossings made trade throughout the region slow and expensive.
Four years ago, Juan Angel Sandoval, a resident of Barrio Buenos Aires in the Honduran municipality of Siguatepeque, received water at home only three times a week. His was not an isolated reality. Most of his neighbors, were in the same situation. "It was annoying because the water was not enough," says Juan Angel.
By Liliana D. Sousa
It might be surprising, but the majority of Central American households receive electricity subsidies, benefiting up to 8 out of 10 households in some cases. Without a doubt, this provides many poor and low-income families with access to affordable electricity.
Some months ago, during a visit to one of the Central American countries, while we were on a call with the head of the electricity dispatch center, we noticed by the tone of his voice, that he was becoming nervous. Shortly after, background voices could be heard on the line. They were experiencing a crisis and he quickly asked to continue our conversation at another time.
The first time a World Bank education team tried classroom observations in Brazil, it nearly provoked a state-wide teachers’ strike. It was October 2009 in the northeast state of Pernambuco and two members of the team, Barbara Bruns and Madalena Dos Santos, had handed out stopwatches to school supervisors newly trained in using the Stallings “classroom snapshot” method to measure teacher activities.
Two days later, the stopwatches were on the front page of Pernambuco’s leading newspaper: the teachers’ union called for a state-wide strike to protest an evaluation tool they dubbed the “Stalin method.”
“I thought the grant money we had used to train observers was down the drain,” recalled Bruns, a World Bank retiree now a visiting Fellow at the Center for Global Development. “But the governor, Eduardo Campos, was unfazed. He publicly declared: ‘No one is going to stop me and my secretariat from going into public schools to figure out how to make them better.’ The union backed down and the fieldwork went ahead.”
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.