Food-Away-from-Home Expenditure in Mexico during the COVID-19 Pandemic: A Micro-Econometric Analysis

The propagation of the coronavirus disease 2019 (COVID-19) reshaped the relationship between income and food-away-from-home (FAFH) expenditure in Mexico during 2020. Although the number of households participating in this market fell across income deciles and regions due to the pandemic, the impact on their budget shares is not uniform. Using data from the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) 2020, we estimated an Engel curve of the Working-Lesser functional form for FAFH. Among the independent variables are the number of family members 65 years of age and older, and dummies to indicate whether the household experienced food insecurity or received remittances. The estimation was carried out following the Heckman two-step method, suitable for censored-response data. The results suggest that the budget share for FAFH drops as income increases. The number of older adults and food insecurity discourage the decision to participate in FAFH expenditure and increase its budget share, whereas remittances encourage participation and reduce its budget share. The corrected conditional elasticity is 0.4609; the sign and the magnitude indicate that FAFH is a necessity good.

Strong convergence of the functional nonparametric relative error regression estimator under right censoring

In this paper, we investigate the asymptotic properties of a nonparametric estimator of the relative error regression given a functional explanatory variable, in the case of a scalar censored response, we use the mean squared relative error as a loss function to construct a nonparametric estimator of the regression operator of these functional censored data. We establish the strong almost complete convergence rate and asymptotic normality of these estimators. A simulation study is performed to illustrate and compare the higher predictive performances of our proposed method to those obtained with standard estimators.