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Kenya

Exploring youth’s role and engagement in African rural economies

Jerome Bossuet's picture
A boy at work in a maize field, Ethiopia.  Photo credit: C. Robinson/CIMMYT

How do young rural Africans engage in the rural economy? How important is farming relative to non-farm activities and the income of young rural Africans? What social, spatial and policy factors explain different patterns of engagement? These questions are at the heart of an interdisciplinary research project, funded by IFAD, that seeks to provide a stronger evidence base for policy and for the growing number of programs in Africa that want to “invest in youth.”

One component of the Challenges and Opportunities for Rural Youth Employment in Sub-Saharan Africa project, led by the Institute of Development Studies (IDS), exploits data from the Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) to develop a more detailed picture of young people’s economic activities. These household survey data cover eight countries in Sub-Saharan Africa, are taken at regular intervals, and in most cases follow the same households and individuals through time. While the LSMS-ISA are not specialized youth surveys and therefore may not cover all facets of youth livelihoods and wellbeing in detail, they provide valuable knowledge about the evolving patterns of social and economic characteristics of rural African youth and their households.

What’s an ambitious but realistic target for human capital progress?

Zelalem Yilma Debebe's picture

Globally, 56 percent of children live in countries with Human Capital Index (HCI) scores below 0.5. As these countries gear up to improve their human capital outcomes, it is vital to set a target that is ambitious enough to prompt action and realistic enough to be achieved. One way to get at this is to examine the historical rate of progress that countries demonstrated to be possible.

Using time-series data between 2000 and 2017, we estimated countries' progress in the health components of HCI (fraction of children not stunted, child survival and adult survival) using a non-linear regression model. [1] Our measure of progress is the fraction of gap to the frontier that is eliminated every year- the frontier being 100 percent child and adult survival, and no stunting.,[2]

We address the following two questions:

  1. What is the typical progress in the health components of HCI observed globally?

7 data innovation projects win funding to tackle local challenges

World Bank Data Team's picture

How can data be used to improve disease outbreak warning, urban planning, air quality, or agricultural production? Seven winning projects, which will receive support from the third round of funding for collaborative data innovation projects, do just that and more.

Following the success of the first round of funding in 2017 and the second round of funding in 2018 the World Bank’s Development Data Group and the Global Partnership for Sustainable Development Data launched the Collaborative Data Innovations for Sustainable Development Fund’s third round in June 2018.

This round called for ideas that had an established proof of concept that benefited local decision-making. We were looking for projects that fostered synergies, and collaborations that took advantage of the relative strengths and responsibilities of official and non-official actors in the data ecosystem.

Can modern technologies facilitate spatial and temporal price analysis?

Marko Rissanen's picture
Also available in: Français

The International Comparison Program (ICP) team in the World Bank Development Data Group commissioned a pilot data collection study utilizing modern information and communication technologies in 15 countries―Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam―from December 2015 to August 2016.

The main aim of the pilot was to study the feasibility of a crowdsourced price data collection approach for a variety of spatial and temporal price studies and other applications. The anticipated benefits of the approach were the openness, accessibility, level of granularity, and timeliness of the collected data and related metadata; traits rarely true for datasets typically available to policymakers and researchers.

The data was collected through a privately-operated network of paid on-the-ground contributors that had access to a smartphone and a data collection application designed for the pilot. Price collection tasks and related guidance were pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and related metadata using the application. The contributors were subsequently compensated based on the task location and degree of difficulty.

The collected price data covers 162 tightly specified items for a variety of household goods and services, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. The use of common item specifications aimed at ensuring the quality, as well as intra- and inter-country comparability, of the collected data.

In total, as many as 1,262,458 price observations―ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia―were collected during the pilot. The figure below shows the cumulative number of collected price observations and outlets covered per each pilot country and month (mouse over the dashboard for additional details).

Figure 1: Cumulative number of price observations collected during the pilot

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).

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