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Malawi

Can modern technologies facilitate spatial and temporal price analysis?

Marko Rissanen's picture

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

Why technology will disrupt and transform Africa’s agriculture sector—in a good way

Simeon Ehui's picture
© Dasan Bobo/World Bank
© Dasan Bobo/World Bank


Agriculture is critical to some of Africa’s biggest development goals. The sector is an engine of job creation: Farming alone currently accounts for about 60 percent of total employment in sub-Saharan Africa, while the share of jobs across the food system is potentially much larger. In Ethiopia, Malawi, Mozambique, Tanzania, Uganda, and Zambia, the food system is projected to add more jobs than the rest of the economy between 2010 and 2025. Agriculture is also a driver of inclusive and sustainable growth, and the foundation of a food system that provides nutritious, safe, and affordable food. 

Announcing Funding for 12 Development Data Innovation Projects

World Bank Data Team's picture

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

A Smarter Way to Keep Teachers in Malawi’s Remote Schools

Salman Asim's picture
 
Alberto Gwande, the Headteacher at the Khuzi school near Nathenje, Lilongwe Rural East District, Malawi.
Photo: Ravinder Casley Gera


Alberto Gwande and his students at Khuzi school in Malawi need more teachers. The school is severely understaffed, with only six teachers for nearly 800 students. “I was supposed to receive new teachers last year, but they never came,” recalls Alberto, the headteacher.

Khuzi is 20 kilometres away from Nathenje, the nearest large village with a trading center, and its Pupil-Teacher Ratio (PTR) is 131 pupils per teacher. In contrast, Chibubu school, located four kilometers from Nathenje, has a PTR of 65, while Mwatibu school, located inside the village, has a PTR of just 49. And yet, despite the shortage at Khuzi, it was Chibubu which received four new teachers last year.

Good fences make good neighbors

Hasita Bhammar's picture
© Center for Conservation and Research, Sri Lanka
© Center for Conservation and Research, Sri Lanka

The members of the community in the Bulugolla village in Sri Lanka breathed a sigh of relief. It was the month of October and the rice harvest had gone well. The rains had been plentiful and their meddlesome neighbors (seen in picture above) were abiding by their boundaries. This has not always been the case.

As the head of the village explained, “We depend upon a rice harvest to earn our livelihood. While we culturally and traditionally have lived in harmony with elephants, we cannot survive without our paddy farms and so we have to keep the elephants out”.

Human wildlife conflict is currently one of the greatest conservation challenges. As human populations grow, wildlife habitat shrink and humans and wildlife come in contact with each other as they compete for resources. In addition, wildlife such as elephants cannot be limited to the boundaries of protected areas as many protected areas can only support a certain number of elephants. In Sri Lanka, most elephant live outside protected areas amidst paddy fields, community villages, highway railways and other development infrastructure that is intended to support the growing human population. Conflict is inevitable but failure to reduce it will result in extinction of wildlife species.   

Why gender parity is a low standard for success in education

Stephanie Psaki's picture
Gender parity in educational attainment may mask other important inequalities. (Photo: Vuong Hai Hoang / World Bank)


In many ways, girls’ education is a success story in global development. Relatively simple changes in national policies – like making primary schooling free and compulsory – have led to dramatic increases in school enrollment around the world. In Uganda, for example, enrollment increased by over 60 percent following the elimination of primary school fees.  

As more young people have enrolled in school, gaps in educational attainment between boys and girls have closed. According to UNESCO, by 2014, “gender parity (meaning an equal amount of men and women) was achieved globally, on average, in primary, lower secondary, and upper secondary education.”

Yet, more than 250 million children are not in school. Many more drop out before completing primary school. And many young people who attend school do not gain basic literacy skills. These challenges remain particularly acute for poor girls.

In a new paper, published in Population and Development Review, we explore recent progress in girls’ education in 43 low- and middle-income countries. To do so, we use Demographic and Health Survey data collected at two time points, the first between 1997 and 2007 (time 1), and the second between 2008 and 2016 (time 2).

Bouncing back: Resilience as a predictor of food insecurity

Erwin Knippenberg's picture

One in eight people worldwide still go to bed hungry every night, and the increased severity of natural disasters like droughts only exacerbates this situation. Humanitarian agencies and development practitioners are increasingly focused on helping the most vulnerable recover from the effect of these shocks by boosting their resilience. 

Malawi’s Fourth Integrated Household Survey 2016-2017 & Integrated Household Panel Survey 2016: Data and documentation now available

Heather Moylan's picture
Malawi IHS4 Enumerator administering household questionnaire
using World Bank Survey Solutions
Photo credit: Heather Moylan, World Bank

The Malawi National Statistical Office (NSO), in collaboration with the World Bank’s Living Standards Measurement Study (LSMS), disseminated the findings from the Fourth Integrated Household Survey 2016/17 (IHS4), and the Integrated Household Panel Survey 2016 (IHPS), on November 22, 2017 in Lilongwe, Malawi. Both surveys were implemented under the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) initiative, with funding from the United States Agency for International Development (USAID).

The IHS4 is the fourth cross-sectional survey in the IHS series, and was fielded from April 2016 to April 2017. The IHS4 2016/17 collected information from a sample of 12,447 households, representative at the national-, urban/rural-, regional- and district-levels.

In parallel, the third (2016) round of the Integrated Household Panel Survey (IHPS) ran concurrently with the IHS4 fieldwork. The IHPS 2016 targeted a national sample of 1,989 households that were interviewed as part of the IHPS 2013, and that could be traced back to half of the 204 panel enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11.

The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. The IHPS 2016 maintained a 4 percent household-level attrition rate (the same as 2013), while the sample expanded to 2,508 households. The low attrition rate was not a trivial accomplishment given only 54 percent of the IHPS 2016 households were within one kilometer of their 2010 location.

How can Malawi move from falling behind to catching up?

Richard Record's picture
A bypass under construction in Lilongwe. A sign that Malawi is inching its way forward. Photo: Govati Nyirenda/World Bank


A new Country Economic Memorandum gives us a chance to step back and look at the deep drivers of growth since Malawi’s independence in 1964. What stands out, though, is just how far Malawi has fallen behind its peers. It’s easy to look at the seemingly insurmountable challenges the country faces—from droughts and floods to the country’s landlocked status—yet other countries in the region have experienced just as many climate-related disasters, and overcome them better. And throughout the 50 plus years of its independence, Malawi has been fortunate to be at peace and mostly politically stable.

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

Vini Vaid's picture
Download PDF

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: http://go.worldbank.org/0ZOAP159L0


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