# **3.2 Sensitivity of PLs to measurement error**

As noted in previous sections, measurement error is highly concentrated in certain parts of the country. In this section we use the 2008/09 data<sup>24</sup> to show how the construction of location-specific PLs is likely to be affected by measurement errors and how this might produce, in the areas where measurement error is more concentrated, PLs that do not reflect the baskets consumed by the poor (see also Maia and van den Berg, 2010). Southern Mozambique and urban areas nationwide appear to be particularly affected by observations in which the food consumption component is excessively low (due to the systematic underestimation of food consumption in urban areas described above) and whose non-food consumption share is consequently overestimated.<sup>25</sup> These observations, we posit, have a significant impact on both the food and non-food components of the location-specific PLs.

The large share of non-food consumption in these observations makes the non-food component of the PLs inordinately influential (see Appendix). In the Maputo area, for instance, the non-food component accounts for about 1/3 of the total PL, causing the Maputo poverty line to be roughly twice as high as all other PLs in the country. This differential is not justified by corresponding differences in any spatial price index (see Appendix).

Regarding the food component, the direct impact of underestimated consumption (beyond the consequent overestimation of non-food shares) is less clear. Broadly speaking, relying on these observations to construct ‘local baskets’ may produce counterintuitive results. Two types of measurement errors typically occur.

The first type of error results from the fact that many food transactions are not reported in the survey; this causes the aggregate level of food expenditures to be lower than expected. The consequences of this error are clear in terms of food/non-food shares but less so in terms of their impact of the food component of the food poverty line. This is because calculating the food PL relies on the pattern of consumption, not the level.<sup>26</sup> Baskets relying on underreported consumption may imply a very different dietary pattern than would more precise consumption data. It is uncertain, a priori, whether the resulting food PLs will be biased upward or downward: this depends on which types of items are underreported. If relatively inexpensive items are underreported, the resulting basket will be biased upward (as will the food PL); if more expensive foods are underreported, the opposite will be true.

> 24 As an additional check, we reproduced the test and procedures for 2002/2003, obtaining similar results. These findings are available upon request.

> 25 Food consumption is calculated by aggregating food expenditures. Non-food consumption expenditures, instead, are constructed by using information on education, health and some utilities fees/expenditures by calculating an implicit value, such as for housing or the use value of durable goods (see Deaton and Zaidi, 2002). In the specific situation discussed, we argue, the level of non-food consumption of urban households was correctly estimated. The problem arises when we merge food with non-food consumption and we look at their shares. Being food consumption dramatically under-estimated, non-food consumption share is relatively overestimated; hence it looks like these households spend a disproportionate amount in nonfood items -a typical consumption behavior of better-off households- while at the same time they cannot meet a minimal dietary requirement. Indeed, a clear contradiction.

> 26 This is because PLs are rescaled to meet the minimum caloric requirement (2150 calories per capita per day).

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