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 As a result, official estimates of poverty and malnutrition are often outdated: For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of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.