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Announcing Funding for 12 Development Data Innovation Projects

World Bank Data Team's picture
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We’re pleased to announce support for 12 projects which seek to improve the way development data are produced, managed, and used. They bring together diverse teams of collaborators from around the world, and are focused on solving challenges in low and lower middle-income countries in Sub-Saharan Africa, East Asia, Latin America, and South Asia.

Following the success of the first round of funding in 2016, in August 2017 we announced a $2.5M fund to support Collaborative Data Innovations for Sustainable Development. The World Bank’s Development Data group, together with the Global Partnership for Sustainable Development Data, called for ideas to improve the production, management, and use of data in the two thematic areas of “Leave No One Behind” and the environment. To ensure funding went to projects that solved real people’s problems, and built solutions that were context-specific and relevant to its audience, applicants were required to include the user, in most cases a government or public entity, in the project team. We were also looking for projects that have the potential to generate learning and knowledge that can be shared, adapted, and reused in other settings.

From predicting the movements of internally displaced populations in Somalia to speeding up post-disaster damage assessments in Nepal; and from detecting the armyworm invasive species in Malawi to supporting older people in Kenya and India to map and advocate for the better availability of public services; the 12 selected projects summarized below show how new partnerships, new methods, and new data sources can be integrated to really “put data to work” for development.

This initiative is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the United Kingdom’s Department for International Development (DFID), the Government of Korea and the Department of Foreign Affairs and Trade of Ireland.

2018 Innovation Fund Recipients

The Dirty Truth – Measuring Soil Health

Vini Vaid's picture
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The importance of soil health in agrarian societies is indisputable – soil health has a direct relationship with agricultural productivity and sustainability. Yet, its highly complex nature renders it much more challenging to measure than other agricultural inputs, such as fertilizers or pesticides. Household surveys, particularly those in low-income contexts where agriculture is the primary means of livelihood, have generally relied on subjective assessments of soil health – and for good reason. Subjective assessment is relatively inexpensive, and alternative methodological options have historically been prohibitively expensive. Recent advances in rapid low-cost technologies, namely spectral soil analysis, however, have increased the feasibility of integrating objective plot-level soil health measurement in household surveys.

This new Guidebook provides practical guidance for survey practitioners aiming to implement objective soil health measurement via spectral analysis in household and farm surveys, particularly in low-income smallholder farmer contexts. Two methodological experiments, in Ethiopia and Uganda, provide the foundation for this Guidebook. In each study, plot-level soil samples were collected following best-practice protocols and analyzed using wet chemistry and spectral analysis methods at ICRAF’s Soil-Plant Diagnostics Laboratory, in addition to a subjective module of soil health questions asked of the plot manager. The Guidebook offers (i) a comparison of subjective farmer assessments of soil health with laboratory testing, and (ii) step-by-step guidance on how to implement spectral soil analysis in a household- or farm-level survey, from questionnaire design to soil sample collection, labeling, and processing.

The Guidebook is the result of collaboration between the World Bank's Living Standards Measurement Study (LSMS) team, the World Agroforestry Centre, the Central Statistical Agency of Ethiopia, and the Uganda Bureau of Statistics.

For practical advice on household survey design, visit the LSMS Guidebooks page:

Using Non-Standard Units in Data Collection: The Latest in the LSMS Guidebook Series

Vini Vaid's picture
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Food consumption and agricultural production are two critical components for monitoring poverty and household well-being in low- and middle-income countries. Accurate measurement of both provides a better contextual understanding and contributes to more effective policy design.

At present, there is no standard methodology for collecting food quantities in national surveys. Often, respondents are required to estimate quantities in standard units (usually metric units), requiring respondents to convert into kilograms, for example, when many respondents are more comfortable reporting their food consumption and production using familiar “local” or “non-standard” units. But how many tomatoes are in one kilogram? How much does a local small tin or basket of maize flour weight? This conversion process is often an uncommon or abstract task for respondents and this added difficulty can introduce measurement error. Allowing respondents to report quantities directly in NSUs places less of a burden on respondents and may ultimately lead to better quality data by improving the accuracy of information provided.

This new Guidebook provides guidance for effectively including non-standard units (NSUs) into data-collection activities — from establishing the list of allowable NSUs to properly collecting conversion factors for the NSUs, with advice on how to incorporate all the components into data collection. An NSU-focused market survey is a critical part of preparing the conversion factors required for effectively using NSU data in analytical work. As such, the bulk of this Guidebook focuses on implementing the market survey and on calculating conversion factors to ensure the highest-quality data when using NSUs.

The Guidebook is the result of collaboration between the World Bank's Living Standards Measurement Study (LSMS) team, the Central Statistical Agency of Ethiopia, the National Bureau of Statistics in Nigeria, the National Statistics Office of Malawi, and the Uganda Bureau of Statistics.

For practical advice on household survey design, visit the LSMS Guidebooks page:

On the road to sustainable growth: measuring access for rural populations

Edie Purdie's picture
Also available in: العربية | 中文

This is part of a series of blogs focused on the Sustainable Development Goals and data from the 2016 Edition of World Development Indicators.  This blog draws on data from the World Bank’s Rural Access Index and on results presented in the report Measuring Rural Access: using new technologies

In Nepal, 54 percent of the rural population lives within 2 kilometers of an all season road.

Nepal, Rural Access Index: 2015

Just over half of the rural population in Nepal lives within 2 kilometers of a road in good or fair condition as measured by the Rural Access Index (RAI) in 2015, leaving around 10.3 million rural residents without easy access. The map shows how the RAI varies across the country: in the southern lowlands, where both road and population density are high, the RAI is around 80 percent in some districts. In the more rugged northern regions, lower road density and poor road quality leave many disconnected, resulting in a low RAI figure – in many places less than 20 percent.