|Sofala & Zambezia-rural|3.30|14.35|0.29|3.12|14.52|0.27|
|---|---|---|---|---|---|---|
|Sofala & Zambezia-urban|5.39|19.07|0.42|4.15|17.85|0.31|
|Manica & Tete-rural|4.21|19.39|0.29|4.34|19.54|0.28|
|Manica & Tete-urban|5.93|21.47|0.40|4.97|20.57|0.32|
|Gaza & Inhambane-rural|5.31|18.37|0.43|5.07|18.17|0.39|
|Gaza & Inhambane-urban|6.32|20.31|0.47|5.78|19.88|0.41|
|Maputo Province-rural|7.45|24.84|0.43|6.62|24.52|0.39|
|Maputo Province-urban|10.25|30.86|0.50|6.25|26.95|0.31|
|Maputo City|12.34|33.14|0.59|7.05|27.95|0.35|

**Official:** 2008/09 PLs and shares (MPD-DNEAP, 2010) **; Set A:** Consumption poor/ Wealth index non-poor households.

Columns 5, 6 and 7 of Table 2 report the results obtained by eliminating the underestimated observations identified above (Set A) from the calculation of the non-food PL. After eliminating observations that were singled out as suspicious we ran the estimation procedure, and once the final results were obtained we compared these with the baseline scenario, including the results obtained using the full sample.<sup>29</sup> As expected, both the revised non-food shares and non-food PLs were much lower than the baseline in Southern Mozambique and urban areas, which suggests that the construction of the non-food PLs was seriously affected by underestimated observations.

The direction of the bias is quite straightforward. The elimination of observations characterized by excessively high non-food shares produces lower non-food PLs and, consequently, total PLs tend to be lower. This effect becomes even clearer when comparing columns 2 and 3 of Table 2 with columns 5 and 6. Due to our simple modification of the non-food component, the total PLs decrease 5 percent and 12 percent, respectively, in the greater Maputo urban area and in Maputo City.<sup>30</sup> We then turn to the question of whether our alternative PLs minimize this effect. Our simulations indicate that non-food PLs calculated at the national or nationwide urban/rural levels are relatively insensitive to underestimated observations. The number of observations used to calculate the poverty line is crucial to its robustness: the more specific the PL, the higher the risk of magnifying measurement errors.

In Table 3 we extend the sensitivity test to the full PL methodology and compare the results with a national-basket alternative. Table 3 describes the revised PLs and their variation from the official figures. As noted above, whereas for the non-food PLs the impact of underestimation is easily detectable, for the food PLs the impact can be ambiguous: the bias can run in either or both directions depending on what type of information is missing.

The findings in Table 3 support our hypothesis. Location-specific PLs tend to be more strongly affected by the presence of underestimated observations in the data. The elimination of these observations

> 29 At this stage of the analysis we limit our simulation to a rather simple and mechanical procedure. We reiterate that a generalization of the results is beyond the scope of this analysis.

> 30 Similar results are reported in a recent paper on Mali (Delarue _et al._ , 2010). The use of provincial level PLs, also calculated based on a limited number of observations, leads to very high PLs in areas of the country traditionally regarded as relatively better-off, such as the Sikasso Province.  It follows that official poverty rates tend to be higher in Sikasso than in less developed areas of the country.

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