Finally, the FCS formula is not found to be very effective in identifying households that are food insecure, at least based on measures during the harvest season. This may be explained by different reasons. The FCS formula was developed to measure food insecurity during the lean season, while the current indicator was measured during the harvest season (because of the project timing, data could not be collected during the lean season). The correlation of the food consumption score between the lean and the harvest seasons is limited, and during the harvest season the food insecurity prevalence is usually much lower (Schnitzer 2016).

# 4.3 Implementation issues in formula‐based methods

Finally, we turn to potential implementation issues with formula‐based methods. This includes (i) measurement errors in the registry survey, (ii) external validity of the formulas, and (iii) the use of exclusion filters. Overall, results show that these implementation aspects have little effects on targeting method performance.

Measurement errors may affect the performance of formula‐based targeting methods. Errors are expected to happen. Registry surveys are usually collected at a very large scale, often under tight time pressures and with less quality control than for more advanced surveys. A particular source of concern is that respondents may manipulate their answers to increase their chances of being selected, creating a strategic response bias. This risk may be higher when the selection formula is widely understood, and has been used for some time (Camacho and Conover, 2011). To assess the extent of measurement error, we use the fact that the baseline survey included similar variables as those in the registry survey used to apply the PMT and FCS formulas. We can thus compute a PMT and FCS score both from the registry survey and baseline survey. We can then compare the two PMT and the two FCS scores to (i) assess the size of the measurement error; (ii) assess how these errors affect targeting performance; and (iii) check if these errors are correlated with poverty. In interpreting these differences, we assume that baseline survey responses are those reflecting the real status of the household, and any deviations with the registry survey are errors. Of course, this assumption does not necessarily hold, and in practice there can be various reasons for observed differences, including real changes in household characteristics, or errors in the baseline survey itself. Still, the comparisons provide a conservative, upper bound estimate of the effect of measurement error on targeting efficiency. In reality, the administrative survey errors are likely to be lower.

We find that measurement errors in the registry survey affect targeting performance, but that the effects are small. Figure 6 shows that there is substantial variation between the scores estimated from the registry survey and the baseline survey, and this is especially the case for the FCS score. The fact that the FCS score fluctuates more may be due to different reasons. First, variables contributing to the food consumption score may be more prone to measurement errors. Alternatively, as demonstrated in Schnitzer (2016), unlike consumption, food security can fluctuate more between seasons. Part of the registry data was collected during the lean season, while the baseline was mostly collected during the harvest season. As such, differences may be due to seasonal changes as opposed to measurement errors. The errors also seem randomly distributed around zero along the consumption and food consumption score distribution (Figure 7). This suggests that, even if there are strategic responses, these are not correlated with the welfare measures used. Table 11 and Table 12 illustrate how measurement errors 20