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LSMS-ISA

Alternative methods to produce poverty estimates when household consumption data are not available (Part I)

Hai-Anh H. Dang's picture

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

Source: Dang, Jolliffe, and Carletto (in press).

Exploring youth’s role and engagement in African rural economies

Jerome Bossuet's picture
  Photo credit: C. Robinson/CIMMYT

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.

Can satellites deliver accurate measures of crop yields in smallholder farming systems?

Talip Kilic's picture

How much food is produced on a plot of land? The answer is central to several pressing questions in agricultural and development economics: How efficiently do smallholders use their labor and land? What interventions are most effective at lifting smallholders out of poverty? Are smallholders better off investing more time and resources on the farm, or intensifying their reliance on off-farm employment? The answers in part depend on the ability to accurately measure crop production. This is why household and farm surveys across the developing world, such as those supported by the , attempt to obtain precise, within-farm measures of crop production and productivity.

Latest from the LSMS: New data from Malawi, measuring soil health & food consumption and expenditure in household surveys

Vini Vaid's picture

 

Message from Gero Carletto (Manager, LSMS)

A few weeks ago, I attended a meeting of the in Muscat, Oman, where I joined a panel discussion on how global survey initiatives like the or can help us measure and monitor many of the SDG indicators. We also discussed how global initiatives like the UN Statistical Commission’s can help coordinate these efforts and position the household survey agenda within the global data landscape. Everyone seems to agree that monitoring more than 70 SDG indicators will require high-quality, more frequent, and internationally comparable household surveys. Yet, the narrative on household surveys continues to be lopsided. In my view, this is partly because strengthening traditional data sources like surveys and censuses is seen as outmoded and ineffective when compared with the more glittering promises offered by alternative data sources like Big Data.

At the risk of sounding like a luddite, I believe that it’s important for countries and donors alike to continue investing in household surveys to both validate and add value to new types of data. In many of the countries we work in, leapfrogging to the digital revolution without having gone through an analog evolution may be an ephemeral proposition. This in no way means that we should continue doing things the same way: during the past decade, household surveys have evolved dramatically, increasingly relying on technological innovation and new methods to make survey data cheaper, more accurate, and more policy relevant. Methodological and technological innovation remains at the core of the LSMS’s raison d’être and, together with our partners, we will continue pushing the frontier. Until more robust and fully validated alternatives materialize, household survey critics may want to recall the old saying, “Can’t live with ‘em, can’t live without ‘em!”

Latest from the LSMS: New data from Tanzania and Nigeria, dynamics of wellbeing in Ethiopia & using non-standard units in data collection

Vini Vaid's picture

Message from Gero Carletto (Manager, LSMS)

It has been a busy few months for the LSMS team! Together with several Italian and African institutions, we recently launched the Partnership for Capacity Development in Household Surveys for Welfare Analysis. The initiative cements a long-term collaboration to train trainers from regional training institutions in Sub-Saharan Africa to harmonize survey data and promote the adoption of best practices in household surveys across the region (see below for more details). In addition, we have contributed to several international conferences and meetings, such as the Annual Bank Conference on Africa (featured below), where we witnessed the creative use of the data we helped collect and disseminate. Finally, LSMS was part of a documentary on the Public Broadcasting Service (PBS) called The Crowd & The Cloud. The fourth episode featured our very own Talip Kilic and the Uganda Bureau of Statistics, working hand in hand to produce household and farm-level panel data, which have been game changers in informing government policymaking and investment decisions, as well as in advancing the methodological frontier. We look forward to many more exciting quarters as we continue to work with our partners to improve the household survey landscape!

Why are women farmers in Sub-Saharan Africa less productive?

Kevin McGee's picture
Researchers have documented a wide array of gender disparities in sub-Saharan Africa that have important implications for individual and household well-being. Perhaps one of the most significant disparities is in agricultural production, the primary economic activity for the majority of the population in sub-Saharan Africa. Closing this gender gap in agricultural productivity would not only improve the welfare of female farmers but could also have larger benefits for other members of the household, especially children.


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