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Announcing Funding for 12 Development Data Innovation Projects

World Bank Data Team's picture
Also available in: Français | 中文

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: http://go.worldbank.org/0ZOAP159L0

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.

Boosting demand for open aid data: lessons from Kenya’s e-ProMIS

Daniel Nogueira-Budny's picture

One journalist used it as a data source for a story on solar energy in Makueni County. Another accessed the data for inclusion in a piece on sanitary napkin distribution in East Pokot. Development partners reported relying on the data to coordinate specific activities in the Central Highlands of Kenya. And this is to say nothing of the government users of the data managed by the Electronic Project Monitoring Information System for the Government of Kenya (e-ProMIS), Kenya’s automated information management system on development projects funded by both domestic and foreign resources.
 

 

Kenyan firms benefit from increased use of financial services and lower crime-related losses

Silvia Muzi's picture

The private sector continues to be a critical driver of job creation and economic growth. However, several factors can undermine the private sector and, if left unaddressed, may impede development.  Through rigorous face-to-face interviews with managers and owners of firms, the World Bank Group’s Enterprise Surveys benchmark the business environment based on actual experiences of firms.

This blog focuses on surveys conducted of 781 Kenyan firms across five regions (including Nairobi and Mombasa) and six business sectors—i) food, ii) textiles and garments, iii) chemicals, plastics and rubber, iv) other manufacturing, v) retail, and vi) other services.

Under Kenya’s new constitution, the country recently embarked on several major business reforms that promoted a more market-friendly environment. Some examples of positive benefits include boosts in public investment in infrastructure, increased interest from foreign investors, and lowered transaction costs from information technology improvements. The Kenya Enterprise Surveys sheds light on how the country’s private sector fared amidst these reforms.

More firms use financial services than before

According to the Kenya Enterprise Surveys (ES) data, the use of financial services has improved since 2007.  On average, 44% and 41% of Kenyan firms use banks to finance investment and working capital, respectively. The corresponding figures in 2007 were much lower at 23% and 26%. Moreover, the percentage of Kenyan firms with a bank loan is 36%, which is on par with the global average yet higher than the average of countries in the same income group (do note that when this survey was conducted, Kenya was classified as a low income country, having since graduated to a lower middle income country).

Kenya’s re-based national accounts: myths, facts, and the consequences

Johan Mistiaen's picture

A month ago, the Kenya National Bureau of Statistics (KNBS) Kenya released a set of re-based and revised National Accounts Statistics (NAS), the culmination of an exercise that started in 2010.  Press coverage, reactions from investors and the public have been generally favorable, but some confusion still looms regarding some of the facts and consequences.  We wrote this blog post to debunk some of the myths.

NAS, including Gross Domestic Product (GDP), are typically measured by reference to the economic structure in a “base” year.  Statisticians sample businesses in different industries to collect data that measures how fast they are growing.  The weight they give to each sector depends on its importance to the economy in the base year.  As time passes and the structure of the economy changes, these figures become less and less accurate.

Re-basing is a process of using more recently collected data to replace an old base year with a new one to reflect the structural changes in the economy.  Re-basing also provides an opportunity to add new or more comprehensive data, incorporate new or better statistical methods, and apply advancements in classification and compilation standards. The current gold standard is the 2008 System of National Accounts (SNA).

Open data on the ground: Kenya’s Data Science

Samuel Lee's picture
How are individuals and organizations taking advantage of the data that governments are publishing? This is part of a series looking at how data are being used for social good.  Last time we covered Nigeria’s Follow the Money Initiative, this time we’re heading to East Africa.

In Kenya, Data Science, LTD (www.datascience.co.ke) is a data analysis and research company providing services to government, local organizations, and businesses. The company seeks to promote greater understanding and use of available data to gain insights for better planning, resource allocation, and entrepreneurship.  This blog post is based on a recent Google Hangout discussion with Data Science, LTD founder Linet Kwamboka.

So what is it like being a data analysis company in Kenya, and what can others learn from Linet’s experience?

Open data roots 
Linet worked on the World Bank supported opendata.go.ke as a project manager in the lead up to the initiative's launch in 2011.  The company works with clients seeking to utilize data to make better decisions.  They include private companies involved in marketing, jobs, retail, and consumer products. With government and civil society clients, the focus is to improve decision-making that lead to better public services and advocacy efforts.

Overcoming gaps in data
Linet has learned that the tasks of sourcing, analyzing, and transforming data into more readily consumed and actionable forms can take a significant amount of effort and time.  In many situations, the data simply do not exist or are out of date.  
 

Between 1960 and 2012, the world average fertility rate halved to 2.5 births per woman

Emi Suzuki's picture
Also available in: العربية | Español

There were more than 7 billion people on earth in 2013. While this is the highest number ever, the population growth rate has been steadily declining, in part due to declining fertility rates.  Tomorrow, Friday, July 11, is World Population Day, and in this spirit, I'd like to talk about a key component of population growth: fertility rates.
 

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Can our parents collect reliable and timely price data?

Nada Hamadeh's picture
Also available in: 中文 | Español | Français | العربية

During the past few years, interest in high-frequency price data has grown steadily.  Recent major economic events - including the food crisis and the energy price surge – have increased the need for timely high-frequency data, openly available to all users.  Standard survey methods lag behind in meeting this demand, due to the high cost of collecting detailed sub-national data, the time delay usually associated with publishing the results, and the limitations to publishing detailed data. For example, although national consumer price indices (CPIs) are published on a monthly basis in most countries, national statistical offices do not release the underlying price data.

 
Crowd sourced price data

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