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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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    1. Hilary W. Hoynes & Diane Whitmore Schanzenbach, 2009. "Consumption Responses to In-Kind Transfers: Evidence from the Introduction of the Food Stamp Program," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 109-139, October.
    2. Zheng, Yuqing & Dong, Diansheng & Burney, Shaheer & Kaiser, Harry M., 2019. "Eat at Home or Away from Home? The Role of Grocery and Restaurant Food Sales Taxes," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 44(1), January.
    3. Antelo, Manel & Magdalena, Pilar & Reboredo, Juan C., 2017. "Economic crisis and the unemployment effect on household food expenditure: The case of Spain," Food Policy, Elsevier, vol. 69(C), pages 11-24.
    4. George Davis, 2014. "Food at home production and consumption: implications for nutrition quality and policy," Review of Economics of the Household, Springer, vol. 12(3), pages 565-588, September.
    5. Andreas Drichoutis & Rodolfo Nayga & Panagiotis Lazaridis, 2012. "Food away from home expenditures and obesity among older Europeans: are there gender differences?," Empirical Economics, Springer, vol. 42(3), pages 1051-1078, June.
    6. James K. Binkley, 2008. "Calorie and Gram Differences between Meals at Fast Food and Table Service Restaurants," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 30(4), pages 750-763.
    7. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    8. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    9. Miaoru Liu & Panagiotis Kasteridis & Steven T. Yen, 2013. "Who are consuming food away from home and where? Results from the Consumer Expenditure Surveys," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(1), pages 191-213, February.
    10. Atif Mian & Kamalesh Rao & Amir Sufi, 2013. "Household Balance Sheets, Consumption, and the Economic Slump," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(4), pages 1687-1726.
    11. Cai, Yongxia & Alviola, Pedro A. & Nayga, Rodolfo M. & Wu, Ximing, 2008. "The Effect of Food-Away-from-Home and Food-at-Home Expenditures on Obesity Rates: A State-Level Analysis," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(2), pages 507-521, August.
    12. Miki Kohara & Yusuke Kamiya, 2016. "Maternal employment and food produced at home: evidence from Japanese data," Review of Economics of the Household, Springer, vol. 14(2), pages 417-442, June.
    13. Mancino, Lisa & Todd, Jessica & Lin, Biing-Hwan, 2009. "Separating what we eat from where: Measuring the effect of food away from home on diet quality," Food Policy, Elsevier, vol. 34(6), pages 557-562, December.
    14. Steven T. Yen, 1993. "Working Wives and Food away from Home: The Box-Cox Double Hurdle Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(4), pages 884-895.
    15. Patrick J. Byrne & Oral Capps & Atanu Saha, 1996. "Analysis of Food-Away-from-Home Expenditure Patterns for U.S. Households, 1982–89," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 614-627.
    16. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    17. Barbara J. Redman, 1980. "The Impact of Women's Time Allocation on Expenditure for Meals Away from Home and Prepared Foods," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 62(2), pages 234-237.
    18. Stewart, Hayden & Yen, Steven T., 2004. "Changing household characteristics and the away-from-home food market: a censored equation system approach," Food Policy, Elsevier, vol. 29(6), pages 643-658, December.
    19. Lee, Jonq-Ying & Brown, Mark G, 1986. "Food Expenditures at Home and Away from Home in the United States-A Switching Regression Analysis," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 142-147, February.
    20. Handa, Sudhanshu, 1996. "Expenditure behavior and children's welfare: An analysis of female headed households in Jamaica," Journal of Development Economics, Elsevier, vol. 50(1), pages 165-187, June.
    21. Jean Kinsey, 1983. "Working Wives and the Marginal Propensity to Consume Food Away from Home," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(1), pages 10-19.
    22. Stewart, Hayden & Blisard, Noel & Jolliffe, Dean & Bhuyan, Sanjib, 2005. "The Demand for Food Away from Home: Do Other Preferences Compete with Our Desire to Eat Healthfully?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 30(3), pages 1-17, December.
    23. Liu, Haiyan & Wahl, Thomas I. & Seale, James L. & Bai, Junfei, 2015. "Household composition, income, and food-away-from-home expenditure in urban China," Food Policy, Elsevier, vol. 51(C), pages 97-103.
    24. James K. Binkley, 2008. "Calorie and Gram Differences between Meals at Fast Food and Table Service Restaurants," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 30(4), pages 750-763.
    25. Nayga, Rodolfo M., Jr., 1996. "Wife'S Labor Force Participation And Family Expenditures For Prepared Food, Food Prepared At Home, And Food Away From Home," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 25(2), pages 1-8, October.
    26. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    27. Mark Aguiar & Erik Hurst, 2005. "Consumption versus Expenditure," Journal of Political Economy, University of Chicago Press, vol. 113(5), pages 919-948, October.
    28. Sonja Greven & Thomas Kneib, 2010. "On the behaviour of marginal and conditional AIC in linear mixed models," Biometrika, Biometrika Trust, vol. 97(4), pages 773-789.
    29. Manini Ojha & Mohammad Arshad Rahman, 2020. "Do Online Courses Provide an Equal Educational Value Compared to In-Person Classroom Teaching? Evidence from US Survey Data using Quantile Regression," Papers 2007.06994, arXiv.org.
    30. Mohammad Arshad Rahman & Shubham Karnawat, 2019. "Flexible Bayesian Quantile Regression in Ordinal Models," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 211-251, Emerald Group Publishing Limited.
    31. Bruno A. Lanfranco & Glenn C.W. Ames & Chung L. Huang, 2002. "Food expenditure patterns of the Hispanic population in the United States," Agribusiness, John Wiley & Sons, Ltd., vol. 18(2), pages 197-211.
    32. Burney, Shaheer, 2018. "In-kind benefits and household behavior: The impact of SNAP on food-away-from-home consumption," Food Policy, Elsevier, vol. 75(C), pages 134-146.
    33. Alexander M. Gelber & Joshua W. Mitchell, 2012. "Taxes and Time Allocation: Evidence from Single Women and Men," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 863-897.
    34. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
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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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