A comprehensive theoretical discussion of this methodology is beyond the scope of the present paper. In principle, the entropy correction methodology seems to provide a valid tool to produce utilityconsistent bundles and might thus enable the establishment of consistent location-specific PLs. Our purpose, however, is different. We question the validity of using location-specific poverty lines—with or without entropy correction—in the presence of substantial measurement errors that conform to a clear geographical pattern.<sup>16</sup> Any location-specific PL, being constructed based on a smaller sample than a national poverty line, has the potential to be more strongly influenced by measurement errors and to produce inconsistent, invalid, or counterintuitive results. There is a clear trade-off between the advantages of creating more locally-representative PLs and their relative robustness and consistency. There are also specific concerns regarding the entropy correction methodology, since to our knowledge the procedure doesn’t include any correction mechanism for the measurement errors described above; if the information used to construct the location-specific bundles is itself flawed, the entropy-adjusted bundles are bound to be affected by the same inaccuracy. As discussed in detail in Section 3.3, constructing location-specific PLs without entropy correction produce results that are very similar to the official PLs, which include entropy correction.<sup>17</sup> It follows that the impact of entropy correction is minimal, but that the problems in the raw data used to create these PLs can significantly compromise the resulting poverty figures.

# **3.1 Why focus on poverty lines?**

Some measurement error is always present in household survey data, and the IAF and IOF are no exception. Recalling in the analysis presented in Section 2, we identify 27 percent (2002/03) and 21 percent (2008/09) of observations (Sets A and B) as substantially inconsistent with other data and therefore likely to be inaccurate. This was a very conservative conclusion: early attempts to circumscribe the problem produced even broader sets of questionable observations. However, in all these cases the pure re-estimation of these observations didn’t produce the expected results, in particular regarding the regional distribution of poverty. After our re-estimation the Southern provinces still appeared poorer than the Central and Northern provinces, though all other indicators would suggest the opposite. This counterintuitive spatial distribution of poverty figures prompted us to examine the PLs themselves and their high degree of spatial variation.

> 16 Here we implicitly assume that measurement error in each location occurs in only one direction, either negative or positive. Section 2 confirms that this is the case for Mozambique. In the South and in urban areas we observe considerable underestimation of food consumption, while in some areas of the rural North we observe a less severe, but still significant, incidence of consumption overestimation.

> 17 The re-construction of official PLs with entropy correction is a sophisticated iterative procedure that we were not able to fully reproduce. However, when constructing the 13 provincial-level bundles, we followed the same procedure used in the poverty assessment, with the same calorie intake value and procedure to identify most-consumed food items, as well as the same outliers’ treatment for prices and quantities consumed.  As the results show, the variation from the official figures is minimal, and the geographical pattern is fully reproduced.

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