COVID-19 has imposed unprecedented challenges to society. As the pandemic evolves, the social distancing measures that have been globally enforced, while essential, are having undesirable socioeconomic side effects particularly among vulnerable populations. In Mexico, families who depend upon informal employment face increased threats to their wellbeing, and households who in addition have young children may face long-term consequences. The Mexican government has not yet taken actions, but a coalition of non-governmental organizations is advocating in partnership with academic institutions for social protection actions such as a cash transfer and basic services subsidies for families with young children, subsisting from the informal sector economy. To facilitate governmental action, we estimated the costs for implementation of these recommendations. The methodology used could be replicated in other countries facing similar challenges.

In this study, we explore how to use household expenditures and income surveys (HEIS) to provide replicable and comparable measures of nutrients availability at the population level. Our method formalizes the common practice in the literature and consists of three steps: identification of relevant food categories, pairing of food contents food groups in HEIS data, and calculation of the typical amount of nutrients by food group. We illustrate the usage of the method with Mexican data and provide a publicly available data set to readily convert food purchases into six nutrients: calories, proteins, vitamins A and C, iron, and zinc. We perform a descriptive analysis of the evolution of nutrients intake among Mexican households between 2008 and 2020, considering differences by income level. Our results reflect the effect of the COVID-19 pandemic on nutrient availability in Mexican households, mainly driven by a substantial reduction in the expenditure in food consumed away from home, although for most nutrients the trend was stable over most of the period.

Background: Household food insecurity (HFI) increased in Latin America by 9% between 2019 and 2020. Scant evidence shows who was unable to recover from the COVID-19 pandemic. Our aim was to use a Machine Learning (ML) approach to identify consistent and influential predictors of persistent moderate or severe HFI over 2 years.Methods: We use a three-wave longitudinal telephone survey with a probabilistic sample representative of the Mexican population. With a response rate of 51.3 and 60.8% for the second and third waves, the final sample size consisted of 1,074 individuals. The primary outcome was persistent HFI, i.e., respondents who reported moderate or severe HFI in 2021 and 2022. Twelve income-related predictors were measured in 2020, including baseline HFI. We employed 6 supervised ML algorithms to cross-validate findings in models, examined its precision with 4 standard performance indicators to assess precision, and used SHAP values (Shapley Additive exPlanations) to identify influential predictors in each model.Results: Prevalence of persistent moderate/severe HFI in 2021 and 2022 was 8.8%. Models with only a HFI 2020 baseline measure were used as a reference for comparisons; they had an accuracy of 0.79, a Cohen’s Kappa of 0.57, a sensitivity of 0.68, and a specificity of 0.88. When HFI was substituted by the suite of socioeconomic indicators, accuracy ranged from 0.70 to 0.84, Cohen’s Kappa from 0.40 to 0.67, sensitivity from 0.86 to 0.90, and specificity from 0.75 to 0.82. The best performing models included baseline HFI and socioeconomic indicators; they had an accuracy between 0.81 and 0.92, a Cohen’s Kappa between 0.61 and 0.85, a sensitivity from 0.74 to 0.95, and a specificity from 0.85 to 0.92. Influential and consistent predictors across the algorithms were baseline HFI, socioeconomic status (SES), adoption of financial coping strategies, and receiving government support.Discussion: Persistent HFI can be a relevant indicator to identify households that are less responsive to food security policies. These households should be prioritized for innovative government support and monitored to assess changes. Forecasting systems of HFI can be improved with longitudinal designs including baseline measures of HFI and socioeconomic predictors.

El estudio examina cómo los efectos de los shocks económicos afectaron la inseguridad alimentaria (IA) entre 2020 y 2021, durante la pandemia por COVID-19. Utilizando la base de datos ENCOVID-19 Longitudinal se analizó cómo cambió la IA en hogares entre 2020 y 2021. Se examinaron los efectos de shocks económicos en la IA, evaluando pérdida de empleo, reducción de ingresos, gastos de salud catastróficos y endeudamiento. Los resultados muestran que la pérdida de empleo y la disminución de ingresos aumentaron la probabilidad de agravar la IA en un 10 % - 19 %. Los gastos catastróficos en salud tuvieron un efecto menor. El endeudamiento en 2021 incrementó las probabilidades de empeorar la IA en un 12 % - 23 %. Se confirmó que múltiples shocks económicos aumentan la probabilidad de empeorar la IA. Estos hallazgos resaltan la necesidad de abordar los impactos socioeconómicos para mitigar la inseguridad alimentaria durante la pandemia.

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.