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.
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The data used in publicly available from the Panel Study of Income Dynamics, University of Michigan, Ann Arbor.
The codes are available on request.
The American Recovery and Reinvestment Act (ARRA) of 2009 renamed the FSP to SNAP and increased benefits by an average of $80 per household. However, a common variable to capture SNAP participation pre- and post-ARRA is not available in PSID.
Within the SNAP literature, the central debate is whether households respond similarly to an increase in cash income and in-kind transfer (food coupons). While some researchers, such as Hoynes and Schanzenbach (2009), have found that the response is similar; others such as Beatty and Tuttle (2014) have found that households increase in food expenditure is more when given an in-kind transfer (food stamps) as compared to cash income.
The terms fixed-effects and random-effects have been used to mean different things in the literature and there is no agreed-upon definition. In this paper, fixed-effects refers to regression coefficients that do not differ across i (or individuals) and random-effects mean regression coefficients that differ across i (see Greenberg 2012, Ch. 10). Andrew Gelman lists five different definitions of fixed-effects and random-effects at https://statmodeling.stat.columbia.edu/2005/01/25/why_i_dont_use/. But again, there are other popular definitions such as in Classical econometrics where fixed-effects means that the unobserved individual-specific heterogeneity is correlated with the regressors, while random-effects imply zero correlation (or more strongly statistical independence) between individual-specific heterogeneity and the regressors (see Cameron and Trivedi 2005; Wooldridge 2010; Hsiao 2014; Greene 2017).
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Gupta, A., Mirghasemi, S. & Rahman, M.A. Heterogeneity in food expenditure among US families: evidence from longitudinal quantile regression. Ind. Econ. Rev. (2021). https://doi.org/10.1007/s41775-020-00101-6
- Bayesian quantile regression
- Great Recession
- Heterogeneity bias
- Longitudinal data