In a recent visit to El Salvador, the smallest, yet beautiful most densely populated country in Central America, I attended an international event organized by the Secretariat of the Framework Convention on Tobacco Control (FCTC) for the FCTC 2030 project. During this event, I had the opportunity to learn from government officials and the Solidarity Fund for Health (FOSALUD) team about the significant tobacco control steps taken by the country.
According to data presented at the event, 1 in 10 adults in El Salvador smoke; the prevalence of current cigarette consumption is 17 percent among men, 2 percent among women, and 10 percent among young people. Data from the IHME Global Burden of Disease study indicate that, in 2016, of the more than 1,600 tobacco-attributable deaths in El Salvador, almost half of them were premature deaths (before the age of 70 years). This contributed to an estimated 34,000 years of life lost due to tobacco-related premature mortality and disability. Besides these impacts, an assessment done by FOSALUD with the support of the FCTC Secretariat, UNDP and PAHO/WHO, estimates that tobacco use causes significant economic losses, including both health care costs (US$115.6 million) and loss of productivity (US$148 million), amounting to US$264 million or 1 percent of El Salvador’s GDP.
A few weeks ago, we had the opportunity to visit the "Federico Boquín" water treatment plant and dam in Tegucigalpa, one of the main sources of water supply for the Honduran capital. As we walked beside the local Mayor, "Tito" Asfura, who accompanied us during the visit, we discussed the relevance of this resource.
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
- 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
Activities of the Temporary Income Support Program, or PATI / World Bank
With collaboration of Emma Monsalve.
The 2008-09 financial crisis significantly affected El Salvador. The economy, as measured by gross domestic product, contracted 3.1 percent in 2009. The crisis seriously affected employment: between 2008 and 2009, more than 100,000 Salvadorans, or 3 percent of the labor force, became unemployed or under-employed.
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.
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.