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Heterogeneity in food expenditure among US families: evidence from longitudinal quantile regression

Author

Listed:
  • Arjun Gupta

    (Indian Institute of Technology, Kanpur)

  • Soudeh Mirghasemi

    (Hofstra University)

  • Mohammad Arshad Rahman

    (Indian Institute of Technology, Kanpur)

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 are taken from the Panel Study of Income Dynamics (PSID) and comprise of 2174 families in the United States (US) observed between 2001 and 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.

Suggested Citation

  • Arjun Gupta & Soudeh Mirghasemi & Mohammad Arshad Rahman, 2021. "Heterogeneity in food expenditure among US families: evidence from longitudinal quantile regression," Indian Economic Review, Springer, vol. 56(1), pages 25-48, June.
    • Handle: RePEc:spr:inecre:v:56:y:2021:i:1:d:10.1007_s41775-020-00101-6
    DOI: 10.1007/s41775-020-00101-6
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    More about this item

    Keywords

    Bayesian quantile regression; Great Recession; Heterogeneity bias; Longitudinal data; Mixed-effects; Mortgage;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D10 - Microeconomics - - Household Behavior - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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