Poverty reduction consistently ranks among the most prioritized tasks of developing countries as well as the international community. Indeed, the Sustainable Development Goals (SDGs) recently adopted by the United Nations General Assembly call for eliminating poverty by 2030 in its very first goal. A good understanding about poverty trends and dynamics could result in more efficient policies and better use of resources. For example, social protection programs may be most suitable to prevent vulnerable households from falling into poverty, but are not the best options to fight a situation of entrenched chronic poverty.
Several questions typically come up in the context of poverty measurement. One set of questions concerns, unsurprisingly, how best to track the trends of poverty over time? Put differently, how do we know which trajectory country A’s poverty is on: is it upward, downward, or does it remain flat over time? The other set of questions are related to the composition of poverty transitions over time. In particular, what is the proportion of the poor in one period that remain poor (i.e., chronic poverty) or escape poverty (i.e., upward mobility) in the next period? Or what is the proportion of the non-poor that fall into poverty (i.e., downward mobility) in the next period?
Yet, finding the answers to these questions are challenging tasks, simply because comparable household consumption data for a specific country from multiple time periods are often unavailable, particularly for low-income countries. As an example, using the World Bank’s database, we plot in Figure 1 the number of data points of poverty estimates for a country against its consumption level. For better presentation, we also graph the fitted line for the regression of the former outcome on the latter outcome.
The estimated slope of this regression line is positive and strongly statistically significant, suggesting that a 10 percent increase in a country’s household consumption is associated with almost one-third (i.e., 0.3) more surveys. Figure 1 thus helps highlight the—perhaps paradoxical—fact that poorer countries with a stronger need for poverty reduction also face a more demanding challenge of poverty measurement given their smaller numbers of surveys. This is unsurprisingly consistent with a prevailing among some development practitioners that collecting survey data may not be the top priority for many developing countries.
Figure 1: Number of Household Surveys vs. Countries’ Income Level, 1981- 2014
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 (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.
A new guidebook published by the World Bank and the UNESCO Institute for Statistics () casts light on how to measure the heavy burden of education spending that falls on the world’s families. will help countries report on indicator 4.5.4: education expenditure per student by level of education and source of funding. The guidebook also aims to ensure proper representation of education expenditures in consumption-based poverty and inequality measures, and enable more micro-econometric research on resource allocation in households.
The burden of education spending by families
We already know that the burden on families can be heavy. found that families in low-income countries pay more for their children’s education: households in many developing countries spend a far greater share of average GDP per capita on education than those in developed countries. Household spending on secondary education amounts to 20-25% of average GDP per person in Benin, Chad, Côte d’Ivoire, Guinea, and Niger, and more than 30% in Togo. In stark contrast, the share does not exceed 5% in almost all high-income countries.
The data also reveal that families—including the poorest—are providing much of the world’s education spending. For example, households provide about one-quarter of education expenditure in Viet Nam, one-third in Côte d’Ivoire, half in Nepal, and more than half in Uganda.
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 . 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 ) and third (in ) 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.
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 , 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 of the 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. 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.
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 —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 (NSO), in collaboration with the World Bank’s (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 , 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.
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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.
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.