Syndicate content

surveys

SDGs 1 & 2: Meeting the demand for more and better household survey data

Alberto Zezza's picture

Household survey data constitute a cornerstone of the statistical toolkit for addressing the data needs for the monitoring of the Sustainable Development Goals (SDGs) on poverty and hunger. A seminar convened today by the FAO and the World Bank, under the aegis of the Inter-Agency and Expert Group on Food Security, Agricultural and Rural Statistics during the 48th Session of the UN Statistical Commission will provide chief statisticians of several low- and middle-income countries an opportunity to discuss a common agenda for fostering the adoption and implementation of a new set of guidelines for the measurement of food consumption data in household surveys.
 
Food constitutes a key component of a number of fundamental dimensions of well-being: food security, nutrition, health, and poverty. It makes up the largest share of total household expenditure in low-income countries, accounting on average for about 50 percent of the household budget. Low levels of food access contributed to an estimated 800 million individuals who were chronically undernourished in 2014-16.
 
Proper measurement of food consumption is therefore central to the assessment and monitoring of the well-being of any population, and to several development domains: social, economic, and human. Food consumption data are needed to monitor global and national goals including the SDGs. But the measurement of food consumption data is also crucial for assessing and guiding FAO’s mandate to eradicate hunger, food insecurity, and malnutrition, as well as the World Bank’s twin goals of eliminating extreme poverty and boosting shared prosperity.

Seeing the forests and the trees

Gwendolyn Stansbury's picture

Forests and trees are sources of energy, food, shelter, and medicine—and, as such, contribute in multiple ways to reducing food insecurity, supporting sustainable livelihoods, and alleviating poverty.

But measuring forests’ socioeconomic benefits has been difficult due to methodological limitations and the lack of reliable data. As a consequence, the contribution of forests to sustainable development is not only underestimated, but is in some cases invisible, preventing policy makers from considering forest production and consumption benefits when developing social-welfare policies.

A new multi-partner publication provides a landmark contribution to data collection on the socioeconomic benefits of forests. Countries can use the modules and guidance in the book to help close the information gap on the multiple relationships between household welfare and forests. This, in turn, will help capture the true value of forests and other environmental products in gross domestic product measurements and increase understanding of their roles in livelihoods, ultimately leading to evidence-based policy decisions that ensure appropriate recognition of the socioeconomic benefits of forests in post-2015 development programs.

The publication is the result of collaboration between the Food and Agriculture Organization of the United Nations (FAO), Center for International Forestry Research (CIFOR), International Forestry Resources and Institutions (IFRI) Network, and the World Bank's Living Standards Measurement Study (LSMS) team and Program on Forests (PROFOR).

Link to the webcast publication launch: http://www.fao.org/webcast/home/en/item/4227/icode/

For practical guidance on household survey design, visit the LSMS Guidebooks page: http://go.worldbank.org/0ZOAP159L0 
 

New paper: "Milking the Data"

Tariq Khokhar's picture
Quick: how much milk did you drink last year?
 
If you can answer that accurately, you’re either taking the “quantified self” thing a bit far, or you may have been reading some of our research.
 
A new paper co-authored by our colleges on the Living Standards Measurement Study (LSMS) team compares different methods for estimating how much milk is being taken from livestock for human consumption.
 
Alberto wrote about this research last year and the work has been published in Food Policy under an open access license. I think the findings are super-interesting - the authors are trying to understand how to accurately find out from individuals “how much milk did you collect from your animals this year?”
 
Simply asking that question isn’t likely to get you an accurate answer, but if you had to rely on questions in a survey, which questions would you pick? The study compares the answers provided by different survey “recall methods” in Niger against benchmark data gathered by actually measuring the volume of milk taken (weighing it in a jug... ) one day every 2-weeks over the course of a year.