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Labor supply and household meal production among working adults in the Health and Retirement Survey

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Abstract

In this paper, I consider how working adults near retirement age in the United States allocate time and monetary resources to meal production. Using the Consumption and Activities Mail Survey supplement to the Health and Retirement Survey, I use a fixed-effects tobit specification to estimate the effect of hours worked, labor income, non-labor income and assets on meal production decisions for respondents between 45 and 75 years of age who either live alone or with their spouse/partner. These relationships are estimated separately by gender and household structure (single-headed and dual-headed households). Among single males, increasing labor supply by 10 h per week was associated with 33.8 fewer minutes per week allocated to at-home meal preparation, 39.5 fewer minutes per month eating at restaurants, and $6.73 more per week spent on groceries. In contrast, the time and expenditure allocations of single females did not respond to changes in hours worked. Within dual-member households, increasing own-labor supply by 10 h per week was associated with a decrease in time allocated to preparing meals for both the male (30.4 min per week) and female member (30.5 min per week) with only weak evidence that the spouse/partner compensated by increasing their allocation of time.

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Notes

  1. George Davis (forthcoming) provides an excellent review of the literature on household food production.

  2. The relationship between maternal labor supply and household meal production is also of particular interest to health economists studying childhood obesity outcomes (Anderson, Butcher and Levine 2003; Ruhm 2004; Classen and Hokayem 2005; Fertig et al. 2009; Phipps et al. 2006; García et al. 2006; Cawley and Lui 2012).

  3. See Bernheim et al. (2001), Haider and Stephens (2007), Hurd and Rohwedder (2003, 2006, 2008), Hurst (2003), and Aguiar and Hurst (2005) for analysis of US data. Banks et al. (1998) and Miniaci et al. (2003) find similar patterns in UK and Italian data, respectively.

  4. See Becker (1965) for the classic presentation.

  5. Even for those already working, the effect of additional hours depends on how they are added: hours added to existing work-days may not increase commuting time or expense whereas working an additional day may do so.

  6. Fixed monetary and time costs to employment yield a non-convex choice set and a reservation number of hours worked in addition to a reservation wage.

  7. For a full description and analysis of the CAMS data, see Hurd and Rohwedder (2005).

  8. Although the 2011 CAMS data have been published, neither the 2012 HRS Core data nor the 2012 income and wealth imputations constructed by the RAND Corporation are yet available.

  9. Although individuals were also asked how much time they spent shopping, the CAMS does not ask specifically about shopping for groceries. Imputing the amount of time spent shopping for groceries using the 2006 ATUS to regress time spent food shopping on a cubic in total shopping time, a cubic in food preparation time, age and gender for individuals between 50 and 70 years of age did not affect coefficient estimates.

  10. Individuals were specifically asked for actual time use rather than typical time use with the following prompt: “We realize that last week might have been unusual, and that your answers may not reflect your typical activity patterns. It is important, however, to report the actual amount of time spent on each activity, rather than the usual amount.”

  11. For discussions of the reliability of consumer expenditure data, see Battistin (2004) and Ahmed et al. (2006).

  12. The sample size of working males in dual-headed households is smaller than the sample size of working females in dual-headed households because the age restriction only binds on the individual, not their partner. That is, a working females less than 75 years of age with a working spouse older than 75 would be eligible for inclusion, but the spouse would not.

  13. The only way to incorporate individual fixed-effects into a tobit analysis is to define a set of dummy variables for each respondent. Neyman and Scott (1948) demonstrate that estimation of these incidental parameters can cause inconsistent estimates of the parameters of interest (Lancaster 2000). But, the alternative of treating individual effects as random draws from distribution is not tenable, as time-invariant attributes that influence meal production decisions may also influence labor supply. Through a series of Monte Carlo exercises, Greene (2004) examines the small sample properties of the incidental parameters problem in maximum likelihood estimators of limited dependent variables models. For the tobit, he finds that as long T ≥ 3, the estimated slope parameters are not meaningfully affected by the estimation of N dummy variables. Therefore, in the subsequent analysis, only respondents who provided allocation information in at least three CAMS waves are included in the regression sample and a vector of respondent indicator variables is included in each regression.

  14. Grossbard-Shechtman (2003) also highlights the potential importance of local marriage markets to the allocation of production activities within the home. As the most disaggregated geographic identifier in the publically available HRS data is Census region, controls for local marriage market attributes are not readily available.

  15. Coefficient estimates are reported in Table 4 of a working paper version of this manuscript (Dunn 2013).

  16. Coefficient estimates are reported in Table 6 of a working paper version of this manuscript (Dunn 2013).

  17. The interested reader is directed to a working paper version (Dunn 2013) that fully describes these robustness tests and reports estimation results in Tables 8–10.

  18. Previous authors have used the structure of retirement benefits—early collection of Social Security at age 62, Medicare eligibility at age 65, etc.—to argue that age is a valid instrument for the retirement decision (Aguiar and Hurst 2005). However, Haider and Stephens (2007) provide evidence that age is not a valid instrument for retirement and instead suggest using expected retirement age. They find that replacing age with expected retirement age cuts the effect of retirement on food expenditure by roughly half. In the present context, using age as an instrument is similarly unattractive. As individuals get older, it may become more difficult to both work and prepare meals. Therefore, using age as an instrument for labor supply rather than an explanatory variable in meal production could bias coefficient estimates upwards.

  19. The interested reader is directed to a working paper version of this manuscript (Dunn 2013) that fully describes these robustness tests and reports estimation results in Table 11.

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Acknowledgments

The author wishes to thank Barbara Wolfe, John Mullahy, Karl Scholz, Daniel Hamermesh and Mark Aguiar for their valuable input along with seminar participants at Texas A&M University. This research was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award No. T32 MH18029-21 from the National Institute of Mental Health.

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    Richard A. Dunn

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Dunn, R.A. Labor supply and household meal production among working adults in the Health and Retirement Survey. Rev Econ Household 13, 437–457 (2015). https://doi.org/10.1007/s11150-013-9223-8

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