The second type of error involves discrepancies between recorded food expenditures and actual food quantities consumed. A household may correctly report its expenditures on an item, but may not be able to accurately report the specific quantity of that item, particularly given the indefinite measures (heaps, sacks, etc.) used by the survey. Such an error would clearly affect the unitary price of the item.

In order to compare the relative error sensitivity of PLs for urban and rural areas, we constructed a single national-basket PL, as well as national-level urban and rural PLs (not reported), the latter deflated with a location-based Paasche index, and then compared these with the location-specific PLs used to determine the official poverty statistics. It is important to recall that “location” in this context means each of the 13 regions listed in Table 1, a division of the country that groups homogenous urban and rural areas, sometimes from different provinces, into the same region. Our hypothesis is that locationbased PLs are more strongly affected by measurement errors in consumption data than are the overall national and national-level urban and rural PLs. Sensitivity tests on consumption data classified as unreliable (Set A and Set B observations, described above) have been constructed. Two tests are reported: (i) a test of the non-food component of the PLs; and (ii) a test of the final (food and non-food) PLs.

Table 2 illustrates our first sensitivity test of the non-food component of the PLs, constructed exactly like those in the three NPAs.<sup>27</sup> Column 4 of Table 2 shows, for each of the 13 regions, the non-food share of observations used to calculate the non-food PLs. The amount that these households spend on non-food items becomes the non-food poverty line; their share of non-food consumption is the value reported. As noted above, the non-food PLs rely heavily on observations in which the share of non-food consumption is very large due to the underestimation of food consumption.<sup>28</sup> To what extent these observations condition the construction of non-food PLs is shown below.

**Table 2: Sensitivity of the Official Non-Food Component of Poverty Lines to Set A Observations**

|13 regions|**Official**<br>**Non-**<br>**Food**<br>**Comp**<br>**of PL**|**Poverty**<br>**Line**|**Non-**<br>**food**<br>**share**|**Simulati**<br>**underes**<br>**observa**<br>**Non-**<br>**Food**<br>**Comp**<br>**of PL**|**on elimina**<br>**timated**<br>**tions(Set**<br>**Poverty**<br>**Line**|**ting**<br>**A)**<br>**Non-**<br>**food**<br>**share**|
|---|---|---|---|---|---|---|
|Niassa & Cabo Delgado-rural|3.43|15.95|0.30|3.75|16.25|0.30|
|Niassa & Cabo Delgado-urban|5.01|18.91|0.39|5.17|19.17|0.37|
|Nampula-rural|3.21|14.33|0.33|3.63|14.73|0.33|
|Nampula-urban|4.22|16.72|0.39|4.06|16.56|0.33|

> 27 We took a simple weighted average of non-food expenditures for households with expenditures between 80-120% of the food-poverty line. A triangular weighting scheme is then used: weights are higher the closer a household’s expenditure is to the poverty line.

> 28 As discussed in Section 2, the non-food share of these observations is significantly higher than that of all other observations.

16