Household surveys are an important source of development data, but in low- and middle-income countries the capacity to conduct and analyze them varies widely. To help address this issue, the World Bank’s Rome-based hub for innovation in household surveys and agricultural statistics—the Center for Development Data (C4D2)—and several Italian partners launched the C4D2 Training Program to increase the capacity of lecturers from statistical training centers in Africa to design and implement sound and modern household surveys.
The Program’s first initiative, a week-long training course on “Designing Household Surveys to Measure Poverty” was held from November 27 to December 1 in Perugia, Italy, at facilities provided by the Bank of Italy. Participants included lecturers from the Eastern African Statistics Training Center, the Ecole Nationale Supérieure de Statistique et d'Economie Appliquée, and experts from the African Center for Statistics of the United Nations Economic Commission for Africa. Instructors included staff from the World Bank, the Bank of Italy, the Italian National Institute of Statistics, and the Italian Institute of Health. The Italian Agency for Cooperation and Development is providing funding for this initiative.
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.
Survey Solutions is already well known for its capacity to deal with large scale household surveys with highly complex questionnaires. One of the main strengths of Survey Solutions is its flexibility in designing a questionnaire. Users can easily create complex survey questionnaires through the browser based interface without the use of any complex syntax. For most of the standard survey questionnaires, the provided basic functions are sufficient.
However, it also offers the possibility to modify the questionnaire beyond the basic capabilities, by using the C# programming language. This allows the users to create questionnaires for very specific, non-standard tasks.
In low- and middle-income countries, household surveys are often the primary source of socio-economic data used by decision makers to make informed decisions and monitor national development plans and the SDGs. However, household surveys continue to suffer from low quality and limited cross-country comparability, and many countries lack the necessary resources and know-how to develop and maintain sustainable household survey systems.
The World Bank’s Center for Development Data (C4D2) in Rome and the Bank of Italy— with financial support by the Italian Agency for Development Cooperation and commitments from other Italian and African institutions—have launched a new initiative to address these issues.
The Partnership for Capacity Development in Household Surveys for Welfare Analysis aims to improve the quality and sustainability of national surveys by strengthening capacity in regional training centers in the collection, analysis, and use of household surveys and other microdata, as well as in the integration of household surveys with other data sources.
On Monday, nine partners signed an MoU describing the intent of the Partnership, at the Bank of Italy in Rome. The signatories included Haishan Fu (Director, Development Data Group, World Bank), Valeria Sannucci (Deputy Governor, Bank of Italy), Pietro Sebastiani (Director General for Cooperation and Development, Ministry of Foreign Affairs and International Cooperation of the Italian Republic), Laura Frigenti (Director, Italian Agency for Development Cooperation), Giorgio Alleva (President, Italian National Institute of Statistics), Stefano Vella (Research Manager, Italian National Institute of Health), Oliver Chinganya (Director, African Centre for Statistics of the UN Economic Commission for Africa), Frank Mkumbo (Rector, Eastern Africa Statistical Training Center), and Hugues Kouadio (Director, École Nationale Supérieure de Statistique et d’Économie Appliquée).
The Partnership will offer a biannual Training Week on household surveys and thematic workshops on specialized topics to be held in Italy in training facilities made available by the Bank of Italy, as well as regular short courses and seminars held at regional statistical training facilities to maximize outreach and impact. The first of a series of Training-of-Trainers (ToT) courses will be held in Fall 2017.
For more information, please contact: [email protected].
Data producers and users from Sub-Saharan Africa meet at the First International Conference on the Use of Tanzania National Panel Survey and LSMS Data for Research, Policy, and Development
Earlier this month, researchers, policymakers, and development practitioners gathered in Dar es Salaam to attend the first of a series of conferences to discuss the use of household panel data produced with support from the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) program.
The event—co-sponsored by the Tanzania National Bureau of Statistics (NBS) and LSMS of the World Bank’s Development Data Group—brought together more than 100 people, with a large representation of researchers from Sub-Saharan Africa.
The opening session featured the Hon. Dr. Philip Mpango (Minister for Finance and Planning, United Republic of Tanzania), Dr. Albina Chuwa (Director General, Tanzania National Bureau of Statistics), Mr. Roeland Van De Geer (European Union Ambassador to the United Republic of Tanzania and the East African Community), Ms. Bella Bird (Country Director Tanzania, World Bank), Ms. Mayasa Mwinyi (Government Statistician, Office of the Chief Government Statistician–Zanzibar), and Dr. Gero Carletto (Manager, LSMS program, World Bank)—as well as a keynote speech by Dr. Blandina Kilama (Senior Researcher, Policy Research for Development–REPOA).
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.
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This is the first of three blog posts on recent trends in national inequality.
Inequality has featured prominently in the public debate in recent times. Media outlets highlight the apparent surge in the incomes of the richest, many books have been written on this issue, and numerous academic studies have attempted to assess the nature and magnitude of inequality over time. Most studies of inequality focus on the extent of inequality within a country; this makes sense since most policies operate at this level, too. Despite the attention this issue has received, it has been constrained by the quality of data on inequality. Household surveys collected by national authorities around the world are the most readily available source of data on inequality. However, compiling and harmonizing household surveys from different countries is extremely difficult as they are not always collected consistently or frequently enough. It is also well-known that household surveys often fail to capture the top tail of the distribution, as we will discuss in more detail in a future blog.
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John Grunsfeld, former NASA Chief Scientist and veteran of five Space Shuttle flights, had several chances to look down at Earth, and noticed how poverty can be recognized from far away. Unlike richer countries, typically lined in green, poorer countries with less access to water are a shocking brown color. During the night, wealthier countries light up the sky whereas nations with less widespread electricity look dim.
Dr. Grunsfeld’s observation might have important implications. Pictures from satellites could become a tool to help identifying where poverty is, by zooming in to the tiniest villages and allowing a constant monitoring that cannot be achieved with traditional surveys.