This paper attempts to analyze these three potential sources of bias in Mozambique’s national poverty figures and to isolate their effects on the final estimates. Revised estimates are also proposed; although tentative, the revised estimates are more consistent with a range of poverty correlates and with the historic geographical welfare distribution of the country. The paper is organized as follows: Section 2 presents an overview of potential measurement errors in the 2002/03 and 2008/09 surveys, it also describes our strategy for identifying observations affected by measurement errors and re-estimating them; Section 3 deepens the analysis of the relationship between consumption underreporting and the PLs and proposes an alternative methodology for calculating the PLs; Section 4 presents the results of using new PLs based on revised estimates of observations likely affected by underreporting and discusses the consequent differences between the official and revised poverty levels and trends over time; Section 5 concludes the analysis.

# **2.  Measurement Errors in Consumption Estimates**

## **2.1 Underreported consumption**

This section reports the most relevant general findings regarding the apparent underestimation of consumption and describes the background and rationale for the present work. This analysis has two objectives: first, it seeks to identify a subset of potentially downward-biased observations in foodconsumption reporting in the IOF 2008/09 data and, second, it attempts to examine and evaluate the possible sources of this bias. We explore the consumption data from three perspectives: overall calorie intake, dietary diversity patterns, and food-share patterns.

About 30 percent of households reported consuming an average of less than 1,000 k-calories per capita per day, which is considered to be below the minimum standard for survival by the World Health Organization (WHO 1985). Moreover, in some Southern regions of Mozambique median calorie intake is below even this basic threshold. Following the strategy described above, we run an OLS regression model in which per-capita consumption expenditure is regressed against poverty correlates. A clear pattern of error terms (not shown) becomes apparent: observations with a calorie intake of below 1000 tend to show high a concentration of negative residuals. This suggests that for households reporting average calorie intakes below 1000 per capita per day, the model predicts higher fitted expenditures, based on characteristics highly correlated to consumption, than do the original expenditure figures. In other words, the official food-consumption statistics for certain populations are conspicuously low and imply that poverty among these groups is both more prevalent and more severe than other relevant indicators would suggest.

To further assess these apparent incongruities in the data, dietary diversity is analyzed. Unexpectedly, the share of “cereals”, a food category that is typically a mainstay in the diets of the poor, in fact increases with expenditures, whereas consumption of nutritionally superior (and usually much more expensive) items such as meat, fish, and dairy products does not increase with expenditures.

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