Rethinking Poverty Metrics: How Outlier Management Affects Poverty Estimates in Mexico

The Food Energy Intake (FEI) method is among the most widely used approaches for establishing income poverty lines, particularly in low- and middle-income countries where it is often used as a baseline to define official poverty lines. This method links poverty measurement to the consumption of calories and other essential nutrients, identifying households that lack the resources necessary to meet minimum nutritional requirements. However, due to data limitations, the construction of poverty lines based on the FEI method relies on a series of assumptions that are often left to the discretion of specialists, without adequate discussion regarding their appropriateness or potential implications. These assumptions can significantly influence the resulting poverty lines and, consequently, the poverty estimates derived from them. In this study, we focus on the Mexican official method of poverty measurement to explore an often overlooked step in poverty measurement: the treatment of extreme values in nutrient availability estimates constructed using household survey data. While this may initially appear to be a minor technical consideration, we demonstrate that the criteria used to identify and potentially exclude outlying observations exert a substantial influence on poverty metrics, which reveals weak fulfillment of the FEI assumptions, as well as important limitations in the expenditure data. This underscores the critical importance of adopting transparent, rigorous, and consistent methodologies for managing outliers, alongside a careful evaluation of other assumptions that are commonly overlooked during the poverty line construction process. We assess the sensitivity of poverty estimates in Mexico employing twelve different outlier-handling methodologies. For each method, we compute the corresponding poverty lines and poverty rates. Our findings reveal substantial variation in extreme poverty rates—ranging from 10.7% to 26.7%—solely attributable to the criteria adopted for the treatment of extreme values. Our analysis suggests that such sensitivity may originate in the decision to use income, rather than expenditure, as the underlying welfare measure in the implementation of the FEI method. We conclude that addressing the treatment of outliers is essential for ensuring consistent and reliable poverty measures, but a thorough revision of the method adopted by the Mexican government to measure poverty is essential.