and the ERSS. These numbers are also in the range of secondary source estimates of herd size and composition, though the available estimates are dated (see Sabates-Wheeler et al. 2013, Getachew 2004, and Said 1994 for further discussion). There are, however, large differences with regard to the totals. While the ERSS and RGCS estimates are not statistically different for camels due to the wide confidence intervals on the estimates, the ERSS estimate is more than 70 percent higher than even the higher of the two RGCS estimates. The gap is even wider for goats, where the ERSS estimate is more than 1.5 times higher than the adjusted RGCS, and for cattle, where the ERSS estimate is nearly 5 times higher (Table 3).

We hypothesize two issues that could have led to these discrepancies. The first possible explanation is the _interviewer effort hypothesis_ : RGCS interviewers did not make efforts to reach all portions of the circles that they could have and/or did not interview all households in the circles and all holders at those households, and thus systematically excluded many livestock from the survey. The lower levels of effort could be attributed to the weather, which was extremely hot during this period, flooding, which would have made access more difficult by requiring interviewers to take long detours on foot or forge swollen rivers, and also the Ramadan period, which would have limited access to local guides to assist the teams. Low effort by the interviewers could lead to undercoverage of livestock and thus to underestimates of totals in Table 3. The second possible explanation is the _ERSS over-estimation hypothesis_ : implementation issues with the ERSS upwardly biased the livestock totals. The following two sections explore these two hypotheses in more detail.

# **4.3 Test of Interviewer Effort Hypothesis**

To further explore the interviewer effort hypothesis, we estimate three regression models in which measures of effort are the dependent variables. The covariates in each of the models are similar and are of two types: measures about the area and the land, and measures about the workload and the interviewers. The models are all run at the level of the circle, rather than on the household or holder level.

The first two models use a logistic regression in which the dependent variable is whether a selected circle was visited (1) or not (0) by a field team, regardless of whether any livestock households were found. Recall that there were 23 circles that were selected but never visited, and this failure to complete assigned work load is one measure of interviewer effort. Since the unit of observation is the circle neither weighting nor stratification are necessary to include. In the model, α is a constant term, X is a vector of relevant household and team characteristics, and ε is the error term. In the first model, the variables included in vector X are the distance of the center point of the circle to the nearest paved road, the distance from the center to the nearest locality, the distance from the center to the nearest large body of water, the relief roughness of the terrain (the maximum elevation minus the minimum elevation divided by site radius, Meybeck et al, 2001), the radius of the circle, a historical mean vegetation index (NDVI, a measure of ‘greenness’) value, and supervisor (or team) level fixed effects. The distance measures are included to capture how difficult it was for interviewers to access the selected circle; interviewers may have been less likely to visit circles which were further from the road or from a town. Similarly if the circle was situated in rough terrain, it may 11