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#3 from 2016: Delhi’s odd-even plan as a public policy experiment

Suvojit Chattopadhyay's picture
Our Top Ten blog posts by readership in 2016. This post was originally published on February 2, 2016.  

Late last year, Delhi’s Chief Minister, Arvind Kejriwal, announced a measure to tackle the severe air pollution crisis in the city. The proposal was to implement an odd-even plan for private cars on Delhi roads: cars with odd numbered registration plates would be allowed to ply on odd dates and those with even numbered registration plates allowed on the other days. There was an exemption list that included single women (or with children), public vehicles, medical emergencies, etc. This was to be piloted for a period of fifteen days, starting on 1st January 2016.

For a detailed account of how the city dealt with this rule, see here.  An excerpt:
During the odd-even period, the use of cars fells by 30 per cent while those car-pooling went up by a whopping 387.7 per cent, indicating the success of the government’s push towards that option. Delhiites using private auto-rickshaws went up by 156.3 per cent compared to the period before odd-even, while Metro use went up by 58.4 per cent.

On average, the respondents’ took 12 minutes less to commute from home to work during the odd-even period. Car and bus users reached their workplaces 13 and 14 minutes faster during the 15-day period
 

Latest from the LSMS: DNA fingerprinting, population mapping, energy access, and surveying forests and livestock

Raka Banerjee's picture


The LSMS team continues to support the World Bank's pledge to collaborate with the 78 poorest countries to collect high-quality national household survey data every three years, to better inform investments and policies to eradicate extreme poverty and boost shared prosperity. A big part of this effort involves improving data collection methods in key areas. Toward that end, under the aegis of the World Bank’s Household Survey Working Group, we have developed a methodological research plan that focuses on welfare, gender, agriculture, and data processing/dissemination. Work is underway, and LSMS is collaborating with UNESCO, ILO, FAO, and other international organizations to establish standards and validate methods for data collection. As part of this effort, at a recent expert consultation at our Center for Development Data in Rome (hosted with FAO), representatives from development agencies and national statistical offices agreed on draft guidelines for collecting data on food consumption. Currently, there are no internationally agreed-upon standards for household consumption and expenditure surveys, so bringing this agenda forward can greatly improve the quality and comparability of global poverty, food security, and nutrition data.

New Data from Niger and Uganda!

Niger: The data from wave 2 of the Niger Enquête Nationale sur les Conditions de Vie des Ménages et l'Agriculture (ECVMA 2014) are now available. This panel dataset follows from the 2011 survey; 3,614 of the original 3,859 households were re-interviewed. The ECVMA is implemented in collaboration with the Niger Institut National de la Statistique (INS).

Uganda: The Uganda National Panel Survey (UNPS) 2013/14 data are also available.  This round follows from the 2005/06, 2009/10, 2010/11, and 2011/12 rounds and includes 3,119 households. The UNPS is implemented in collaboration with the Uganda Bureau of Statistics.
 

DNA Fingerprinting, Drones and Remote Sensing in Ethiopia

CGIAR-Standing Panel on Impact Assessment (SPIA) implemented two data experiments in collaboration with LSMS, the World Bank, and the Ethiopian Central Statistical Agency. One experiment examined data accuracy on measuring improved sweet potato varietal adoption. It compared three household-based methods against DNA fingerprinting benchmark. These included: (i) farmer elicitation, (ii) farmer elicitation using visual-aid, and (iii) enumerator elicitation using visual-aid. Visual-aid protocols were better than farmer elicitation, but still far below the benchmark estimates. Another experiment focused on crop residue coverage measurement. It compared four survey-based (interviewee and enumerator estimations as well as use of visual-aid protocol) and two aerial (drones' images and remote sensing) methods against a line-transect benchmark. The results ranked measurement options for survey practitioners and researchers in conservation agriculture.

Data responsibility: a new social good for the information age

Stefaan Verhulst's picture

As climate change intensifies, catastrophic, record-setting natural disasters look increasingly like the “new normal” – from Hurricane Matthew killing at least 1,300 people in September to Typhoon Lionrock, the previous month, causing flooding that left 138 dead and more than 100,000 homeless in North Korea.

What steps can we take to limit the destruction caused by natural disasters? One possible answer is using data to improve relief operations.

Let’s look at the aftermath of the April 2015 Gorkha earthquake, the worst to hit Nepal in over 80 years. Nearly 9,000 people were killed, some 22,000 injured, hundreds of thousands were rendered homeless and entire villages were flattened.

Yet for all the destruction, the toll could have been far worse.

Without in any way minimising the horrible disaster that hit Nepal that day, I want to make the case that data — and, in particular, a new type of social responsibility — helped Nepal avoid a worse calamity. It may offer lessons for other disasters around the world.

In the wake of the Nepal disaster, a wide variety of actors – from government, civil society and the private sector alike – rushed in to address the humanitarian crisis. One notable player was Ncell, Nepal’s largest mobile network operator. Shortly after the earthquake, Ncell decided to share its mobile data (in an aggregated, de-identified way) with the the non-profit Swedish organisation, Flowminder.
 

A first look at Facebook’s high-resolution population maps

Talip Kilic's picture

Facebook recently announced the public release of unprecedentedly high-resolution population maps for Ghana, Haiti, Malawi, South Africa, and Sri Lanka. These maps have been produced jointly by the Facebook Connectivity Lab and the Center for International Earth Science Information Network (CIESIN), and provide data on the distribution of human populations at 30-meter spatial resolution. Facebook conducted this research to inform the development of wireless communication technologies and platforms to bring Internet to the globally unconnected as part of the internet.org initiative.

Figure 1 conveys the spatial resolution of the Facebook dataset, unmatched in its ability to identify settlements. We are looking at approximately a 1 km2 area covering a rural village in Malawi. Previous efforts to map population would have represented this area with only a single grid cell (LandScan), or 100 cells (WorldPop), but Facebook has achieved the highest level of spatial refinement yet, with 900 cells. The blue areas identify the populated pixels in Facebook’s impressive map of the Warm Heart of Africa.
 

Figure 1: Digital Globe Imagery from Rural Malawi Overlaid with Facebook Populated Cells

Facebook’s computer vision approach is a very fast method to produce spatially-explicit country-wide population estimates. Using their method, Facebook successfully generated at-scale, high-resolution insights on the distribution of buildings, unmatched by any other remote sensing effort to date.  These maps demonstrate the value of artificial intelligence for filling data gaps and creating new datasets, and they could provide a promising complement to household surveys and censuses. 

Beginning in March 2016, we started collaborating with Facebook to assess the precision of the maps and explore their potential uses in development efforts. Here, we describe the analyses undertaken to date by the Living Standards Measurement Study (LSMS) team at the World Bank to compare the high-resolution population projections against the ground truth data. Among the countries that were part of the initial release, Malawi was of particular interest for the validation exercise given the range of data at our disposal.

Is predicted data a viable alternative to real data?

Roy Van der Weide's picture

The primary motivation for predicting data in economics, health sciences, and other disciplines has been to deal with various forms of missing data problems. However, one could also make a case for adopting prediction methods to obtain more cost-efficient estimates of welfare indicators when it is expensive to observe the outcome of interest (in comparison with its predictors). For example, consider the estimation of poverty and malnutrition rates. The conventional estimators in this case require household- and individual-level data on expenditures and health outcomes. Collecting this data is generally costly. It is not uncommon that in developing countries, where poverty and poor health outcomes are most pressing, statistical agencies do not have the budget that is needed to collect these data frequently. As a result, official estimates of poverty and malnutrition are often outdated: For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of stunting for children under five are on average reported only once every five years and once every ten years in the World Development Indicators.

Think you know who the manager's favorite is? You may be right: Technology Aided Gut Checks

Tanya Gupta's picture

Welcome to the sixth blog of the technology aided gut (TAG) checks series. So far in this series, we have focused on the tools and techniques of a just-in-time learning strategy. We will now switch gears and show how, with very little effort, we can use TAG checks to make simple yet (occasionally) profound conclusions about data - big and small.

As we delve into the details of TAG checks in the next several blogs, we will be using web programming tools and techniques to gather, process and analyze data. While we will try to be as comprehensive as possible in our explanations, it may not be always as detailed as we would like it to be. This forum, after all, is a blog and not a training tutorial. We hope by applying the just-in-time learning strategy that we have discussed so far in the series, you will be able to supplement what we miss in our explanations. Our goal for the overall series has been to empower you. We hope the first part of the series has made you an empowered self-learner.

The second part of the series will make you an empowered and savvy data consumer, a development professional who can confidently rely on the story the data tells to accomplish her tasks.

For the readers who are just joining in, we suggest that you become somewhat familiar with the just-in-time learning strategy by skimming the series so far.

Seeing the forests and the trees

Gwendolyn Stansbury's picture

Forests and trees are sources of energy, food, shelter, and medicine—and, as such, contribute in multiple ways to reducing food insecurity, supporting sustainable livelihoods, and alleviating poverty.

But measuring forests’ socioeconomic benefits has been difficult due to methodological limitations and the lack of reliable data. As a consequence, the contribution of forests to sustainable development is not only underestimated, but is in some cases invisible, preventing policy makers from considering forest production and consumption benefits when developing social-welfare policies.

A new multi-partner publication provides a landmark contribution to data collection on the socioeconomic benefits of forests. Countries can use the modules and guidance in the book to help close the information gap on the multiple relationships between household welfare and forests. This, in turn, will help capture the true value of forests and other environmental products in gross domestic product measurements and increase understanding of their roles in livelihoods, ultimately leading to evidence-based policy decisions that ensure appropriate recognition of the socioeconomic benefits of forests in post-2015 development programs.

The publication is the result of collaboration between the Food and Agriculture Organization of the United Nations (FAO), Center for International Forestry Research (CIFOR), International Forestry Resources and Institutions (IFRI) Network, and the World Bank's Living Standards Measurement Study (LSMS) team and Program on Forests (PROFOR).

Link to the webcast publication launch: http://www.fao.org/webcast/home/en/item/4227/icode/

For practical guidance on household survey design, visit the LSMS Guidebooks page: http://go.worldbank.org/0ZOAP159L0 
 

How to use evidence to improve student learning

David Evans's picture
Access to education is improving but so must the quality of learning. (Arne Hoel/World Bank)

See if you can spot the pattern:
  • “Although the quantity of schooling has expanded rapidly, quality is often abysmal.” (Kremer et al.)
  • “Between 1999 and 2009, an extra 52 million children enrolled in primary school…Yet the quality of education in many schools is unacceptably poor.” (Krishnaratne et al.)
  • “Progress over the last decade in regards to school access and enrollment has been promising.” But “current learning levels for primary as well as secondary school students are extremely low in much of Sub-Saharan Africa” (Conn)
  •  “The most consistent focus of investment has been on increasing primary and secondary school enrollment rates… More recently, however, attention has begun to swing toward the quality of schools and the achievement of students—and here the evidence on outcomes is decidedly more mixed.” (Glewwe et al.)
  • “Over the past decade, low- and middle-income countries have made considerable progress in increasing the number of children and youth who enroll in school and stay long enough to learn basic skills… Learning in many low- and middle-income countries remains appallingly low.” (Murnane & Ganimian)

Again and again, we hear the refrain: access is improving, but learning lags. Thankfully, an increasing number of studies reveal interventions that work – and those that don’t – to improve learning around the world.

Mapping the World Bank’s support for education

Luis Benveniste's picture
Also available in: Español  |  Francais  |  العربية


Every year, the World Bank generates a wealth of useful information about education systems across the globe, from project-driven appraisal documents and results stories to country-specific data and news to impact evaluations and everything in between. Through the Smarter Education Systems tool, this information, which can often be overwhelming to navigate and curate, is becoming more easily accessible, digestible, and searchable. The Smarter Education Systems tool demonstrates how the World Bank helps countries ensure "Learning for All" through support to countries on both the financing (loans, grants, and more) and knowledge (research, publications, and more) fronts.

The data revolution continues with the latest World Bank Innovation challenge

Marianne Fay's picture

On September 22, 2016, we launched the World Bank Big Data Innovation Challenge – a global call for big data solutions for climate resilience and sustainable development.

As the world grows more connected--through mobile phones, social media, internet, satellites, ground sensors and machines—governments and economies need better ways to harness these data flows for insights toward targeted policies and actions that boost climate resilience, especially amongst the most vulnerable. To make this data more useful for development, we need more data innovations and innovative public-private arrangements for data collaboration.

The World Bank Big Data Innovation Challenge invites innovators across the world to reimagine climate resilience through big data solutions that address the nexus areas of food security and nutrition, and forests and watersheds – high priority areas of the World Bank’s Climate and Forest Action Plans and the UN Sustainable Development Goals.


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