The world has entered a period of elevated volatility after the global financial crisis, underpinning the need for policy tools to stabilize economies in the short term. In that context, monetary policy has become an increasingly important tool. However, there is often skepticism about its efficacy in developing economies, despite the central role it plays in advanced economies. A natural question for those who study and monitor economies in East Asia and Pacific (EAP) is: how effective is monetary policy in the region? This blog, based on our latest economic report East Asia and Pacific Economic Update, October 2017, intends to shed some light on this.
The dramatic decrease in the cost of renewable energy technologies seen in recent years presents an unprecedented opportunity to improve our access to energy—and create employment in the process. This is especially true in Somaliland, where more than 80% of the local population of 3.5 million does not have access to modern electricity.
Somaliland’s small economy cannot afford large investments in the infrastructure needed for generating energy in the more traditional, 20th century sense. Running electricity lines over long distances to reach a geographically dispersed, off-grid population is simply uneconomical. Moreover, at US$0.85 per kilowatt, the cost of electricity in Somaliland is among the highest in the world.
- Data Colada on how to properly pre-register a study: “it may be helpful to imagine a skeptical reader of your paper. Let’s call him Leif. Imagine that Leif is worried that p-hacking might creep into the analyses of even the best-intentioned researchers. The job of your preregistration is to set Leif’s mind at ease. This means identifying all of the ways you could have p-hacked – choosing a different sample size, or a different exclusion rule, or a different dependent variable, or a different set of controls/covariates, or a different set of conditions to compare, or a different data transformation – and including all of the information that lets Leif know that these decisions were set in stone in advance”…but on the other hand “it should contain only the information that is essential for the task at hand… We have seen many preregistrations that are just too long… you don’t need to say in the preregistration everything that you will say in the paper. A hard-to-read preregistration makes preregistration less effective…” – comes with a nice example table of what bad specifications and good specifications look like.
- development impact links
Reform communications explains and promotes reforms to all concerned audiences, and ensures consistency, balance, and participation, all the way from a reform’s design to its implementation. It can also make sure that audiences understand the reform, contribute to stakeholder inclusion, and hold the owners of the reform accountable.
What does that mean in a country like Somalia? More importantly, what does that mean for a country like Somalia right now?
The growing availability of satellite imagery and analysis means that all kinds of things we used to think were hard to quantify, especially in conflict zones, can now be measured systematically.
For example, estimating ISIS oil production. Soon after it proclaimed itself the Islamic State in Iraq and the Levant (a.k.a. ISIL/ISIS, the Islamic State, or Daesh, its Arabic acronym), the group was quickly branded the richest terrorist organization in history and oil was believed to be its major revenue source. A typical headline in Foreign Policy proclaimed “The Islamic State is the Newest Petrostate.”
Yesterday I posted a round-up of the research presented at NEUDC, a major conference on development economics. Although most economic research aspires to uncover principles relevant across multiple contexts, empirical research happens at a place and time. I mapped out the distribution of research presented at NEUDC, fully recognizing that this makes no claim to be representative of the profession as a whole.
Below, I charted the number of studies per country (for all countries that had at least two studies). If a study used data from multiple countries (up to four), I counted each of them. If a study used a data set that spans 30 countries, I didn’t use it.
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.
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