Heterogeneity in Food Expenditure amongst US families: Evidence from Longitudinal Quantile Regression

TL;DR

This study uses longitudinal quantile regression to analyze US family food expenditure data, revealing heterogeneity in effects across different expenditure levels and emphasizing the importance of modeling dependencies over time.

Contribution

It introduces a longitudinal quantile regression approach to examine heterogeneity in food expenditure determinants, addressing limitations of previous mean-focused models.

Findings

Age, education, income, family structure, and recession significantly influence food expenditure.

Quantile analysis uncovers heterogeneity in covariate effects across expenditure levels.

Modeling temporal dependence improves fit and reduces heterogeneity bias.

Abstract

Empirical studies on food expenditure are largely based on cross-section data and for a few studies based on longitudinal (or panel) data the focus has been on the conditional mean. While the former, by construction, cannot model the dependencies between observations across time, the latter cannot look at the relationship between food expenditure and covariates (such as income, education, etc.) at lower (or upper) quantiles, which are of interest to policymakers. This paper analyzes expenditures on total food (TF), food at home (FAH), and food away from home (FAFH) using mean regression and quantile regression models for longitudinal data to examine the impact of economic recession and various demographic, socioeconomic, and geographic factors. The data is taken from the Panel Study of Income Dynamics (PSID) and comprises of 2174 families in the United States (US) observed between 2001-2015. Results indicate that age and education of the head, family income, female headed family, marital status, and economic recession are important determinants for all three types of food expenditure. Spouse education, family size, and some regional indicators are important for expenditures on TF and FAH, but not for FAFH. Quantile analysis reveals considerable heterogeneity in the covariate effects for all types of food expenditure, which cannot be captured by models focused on conditional mean. The study ends by showing that modeling conditional dependence between observations across time for the same family unit is crucial to reducing/avoiding heterogeneity bias and better model fitting.