# **1. Introduction**

Livestock ownership comprises a large part of rural wealth and well-being in the developing world, serving diverse functions from food source to savings and investment vehicle. The sector, however, has recently come under increasing pressure from a number of sources, including increased demand for meat and dairy products from the expanding middle class, climate change, and loss of traditional pasture land to development. Efforts to understand these evolving dynamics, and their impact on the welfare of livestock-owning households, are hampered by a lack of high quality data on which to base analyses. Beyond the general data collection issues of definition and quantification, livestock statistics present particular challenges due to the nomadic and semi-nomadic nature of many pastoralists.

The most common sample selection methodology for household surveys in the developing world is a multi-stage stratified sample (Grosh and Munoz, 1996). In the first stage, primary sampling units are selected from census enumeration areas. In the second stage, dwellings are selected from a housing unit frame, usually compiled through costly in-field listing. However, semi-nomadic households who are temporarily absent, as well as fully nomadic households without fixed dwellings, are undercovered by this approach. In areas where a large portion of the poor and vulnerable population engages in pastoralist activities, this undercoverage could lead to substantial bias in livestock and welfare estimates.

This paper considers the use of an alternative approach to collecting data from livestock-owning households, Random Geographic Cluster Sampling (RGCS). Similar methods are commonly used by developed world agricultural statistics agencies, such as the United States Department of Agriculture, to measure agricultural production and livestock (USDA, 2010), and have also been used by researchers to study farms in Scotland and livestock in Somalia, South Africa, Thailand, and Laos (Emerson et. al., 1995; Cameron, 1997; Soumare et al 2007; von Hagen, 2002). They are also common in forestry surveys (Husch 1982; Roesch et al 1993). This paper describes a pilot project to test the RGCS methodology in the Afar region of Ethiopia, carried out in collaboration by the World Bank Development Economics Research Group and the Ethiopian Central Statistical Agency (CSA).

In an RGCS design, the study area is stratified using data from Geographic Information Systems (GIS) sources. Within each stratum, points (latitude and longitude) are randomly selected, and then a circular cluster of a given radius is created around the point. All eligible respondents found within this cluster are selected for the survey. The main advantage of this design is that it captures everyone who resides in the selected circles at the time of the interview, including those who do not have a permanent dwelling or who are temporarily away from their dwelling. Properly implemented, this design eliminates undercoverage resulting from mobile populations.

There are other alternative methodologies for measuring livestock ownership which we do not use directly in this study. The CSA used a flyover survey in 2004 to estimate the total number of livestock for areas in the Ethiopian Somali region not covered in the agricultural census due to security concerns. In addition to high costs and difficulties in implementation, flyover surveys do not allow researchers to link livestock to households, which severely constrains the use of the data for socioeconomic analysis. Water point surveys are also common, but are biased as they exclude all livestock not found at a known watering point. Adaptive sampling is an approach often used for wildlife studies that are rare and unevenly distributed. However, such a design requires ongoing and close supervision by a sampling statistician, and often multiple trips to the same area, neither of which was possible in this project (Thompson 1990; Thompson 1991; Thompson and Seber 1996). Other geographic sampling methods use 2