Syndicate content

Household Surveys

A massive new dataset to help promote health equity and financial protection in health

Adam Wagstaff's picture

Today we’re (re)launching HEFPI—aka the Health Equity and Financial Protection Indicators database. HEFPI sheds light on two major concerns in global health: a concern that the poor do not get left behind in the rush to achieve global health goals; and a concern that health services should be affordable. Neither concern featured in the MDGs; both feature prominently in the SDGs.

The HEFPI database draws on data from over 1,600 household surveys, including the Demographic and Health Survey and the Multiple Indicator Cluster Survey. Most of the 1,600 surveys have been re-analyzed in-house to ensure comparability across surveys and years, since published indicators from different surveys often use different definitions. We have settled on a definition based on recommendations in the relevant literature, and have used that across all surveys and time periods. As a result, the numbers in HEFPI are often different from (and more comparable than) numbers published elsewhere.

The database is, in effect, the fourth in a series. The first was in 2000. That database focused entirely on MDG-era health service and health outcome data—so no financial protection data. It covered just 42 countries, each with one year’s worth of data. The second (in 2007) and third (in 2012) gradually expanded the scope, with the 2012 dataset covering both financial protection and health equity, and getting up to 109 countries, including some high-income countries.

Behind Closed Doors: how traditional measures of poverty mask inequality inside the household and a new look at possible solutions

Caren Grown's picture

During the days coming up to, and after October 17, when many stories, numbers, and calls for action will mark the International Day for the Eradication of Poverty, we want to invite you to think for a second on what you imagine a poor household to be like. Is this a husband, wife, and children, or maybe an elderly couple? Are the children girls or boys? And more importantly, do all experience the same deprivations and challenges from the situation they live in?  In a recent blog post and paper, we showed that looking at who lives in poor homes—from gender differences to household composition more broadly—matters  to better understand and tackle poverty.

Globally, female and male poverty rates—defined as the share of women and men who live in poor households—are very similar (12.8 and 12.3 percent, respectively, based on 2013 data). Even in the two regions with the largest number of poor people (and highest poverty rates)—South Asia and Sub-Saharan Africa—gender differences in poverty rates are quite small. This is true for the regions, but also for individual countries, irrespective of their share of poor people. Why is that the case? As Chapter 5 of the 2018 Poverty and Shared Prosperity Report explains, our standard monetary poverty indicator is measured by household, not by individual. So, a person is classified as either poor or nonpoor according to the poverty status of the household in which she or he lives. This approach critically assumes everyone in the household shares equally in household consumption—be they a father, a young child, or a daughter-in-law.  By design, it thus masks differences in individual poverty within a household.

Notwithstanding this shortcoming, when we look a bit deeper the information we have today still shows visible gender differences in poverty rates. Take age, for example. We know that there are more poor children than poor adults, and while we do not find that poverty rates differ much between girls and boys at the early stages of life, stark differences appear between men and women during the peak productive and reproductive years.

C4D2-Training: Working with regional statistics training centers to improve household surveys in Africa

Shelton Kanyanda's picture

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.

Participants listening to a presentation by the Bank of Italy on Measuring Wealth

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.

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.

What do household surveys and project monitoring have in common?

Michael Wild's picture

For verification purposes Survey Solution's picture question allows to document the installation and their new owner.
The implementation and the monitoring of large infrastructure projects is always a challenge. This challenge is even more pronounced, when the beneficiaries are located at the grassroots level. In the case of the Myanmar national electrification project (NEP), the challenge was the implementation and monitoring of around 145,000 households, community centers and schools, which did not have proper access to electricity and are being newly equipped with solar panels under the first contract. The basic information to be collected and monitored include who receives which type of solar PV systems, when, and by which supplier, and whether the users have been satisfactory with the quality of the equipment and installation, etc. The project is expected to eventually benefit 1.2 million households and more than 10,000 villages over 6 years with new electricity services.

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.

New Partnership for Capacity Development in Household Surveys for Welfare Analysis

Vini Vaid's picture

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].

Tanzania Conference on LSMS Data

Gwendolyn Stansbury's picture

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).