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Households’ joint consumption spending and home production responses to retirement in the US

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Abstract

We analyze the effect of retirement on households’ joint consumption spending and home production decisions using a micro panel with detailed spending and time use categories of US households. For causal identification, we use panel data regressions. Our results suggest that the spending drop at retirement is partially compensated by increases in home production of the retiring household member. Home production can particularly make up for losses in spending categories that are well-substitutable by home production, although only about 12% of total pre-retirement consumption spending, but there is no evidence that households fully replace consumption spending at retirement. In a Life-Cycle Model, we find no empirical evidence for full consumption smoothing at retirement.

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Notes

  1. Aguila, Attanasio and Meghir (2011), Banks, Blundell and Tanner (1998), Battistin, Brugiavini, Rettore and Weber (2009), Bernheim, Skinner and Weinberg (2001), Haider & Stephens (2007), Hurd and Rohwedder (2003), Hurd & Rohwedder (2013), Hurst (2008), Lundberg, Startz and Stillman (2003), Mariger (1987), Miniaci, Monfardini and Weber (2010), Moreau & Stancanelli (2015), Robb & Burbidge (1989), Smith (2006).

  2. Our HRS/CAMS data combines detailed information on consumption spending with detailed information on time use of both spouses in a household over time. Most data sets with time use information do not have information on consumption spending (for example, American Time Use Survey (ATUS)) or less detailed information on time use categories (for example, German Socioeconomic Panel (GSOEP)), do not have information on both spouses within a household (for example, Panel Study on Income Dynamics (PSID)) or have a limited longitudinal dimension (for example, French, Italian and Spanish Time Use Survey). Compared to the Dutch Longitudinal Internet Studies for the Social Sciences (LISS), the HRS/CAMS has the advantage to span a wider time frame with information on consumption and time use.

  3. Schwerdt (2005) argues that increases in home production are bigger for households with lower income replacement rates which would suggest the replacement of consumption spending. However, he also finds increases in home production in households with replacement rates of 100+% who do not need to substitute consumption.

  4. In Table B1, we show that conclusions of the paper are robust to different consumption definitions that exclude work-related expenses (e.g., clothing, transport) from the total (about 12% of total consumption). Conclusions are also robust to definitions that exclude out of pocket health spending (about 8% of total spending). This implies that a substantial amount of the consumption drop at retirement is attributed to consumption spending other than work-related and health spending.

  5. Although we have only self-reported information to study the retirement behavior of US households, Behaghel & Blau (2012) verify that retirement trends observed in HRS are robust to trends observed in administrative data. Compared to administrative data, we have very detailed information on both spending and time use and the possible substitutability between these (see Appendix A).

  6. In Table B2 in the Appendix we show that our main estimations and conclusions do not change when we exclude the unemployed and disabled from the sample (top panel), which reduces the possibility of underestimation of the effect even further, or if we define retirement to be full only (bottom panel).

  7. Additional empirical checks show that the effect of retirement on spending and home production is not different for those whose spouse is already retired (not reported here).

  8. For the full tables with descriptive statistics we refer to Appendix A.

  9. For the full tables with descriptive statistics we refer to Appendix A.

  10. Part of this may be explained by a drop in work-related spending. However, our results that take into account such changes at retirement do not alter our main conclusions in the paper as can be seen in Table B1.

  11. This 12% is the share of average spending. The average share of spending is slightly smaller: 11%. These shares are for the total of the sample. For the non-retired households the shares are 11.6% and 10.4%, respectively.

  12. The actuarial reductions are steeper for spouses. Therefore, the change in the pension system may have a substantial effect on the retirement behavior of the spouse.

  13. Bloemen et al. (2010), Stancanelli and Van Soest (2012a), Stancanelli & Van Soest (2012b) use an RDD for causal estimation because of the cross-sectional nature of their data.

  14. Virtually all households have consumption expenditures (about 100%) and engage in some form of home production (about 99%) so issues regarding left-censoring of the dependent variables in the regression models are likely to be negligible.

  15. For empirical applications of this method to panel data, see for example Been & Knoef (2017), Christelis & Sanz-de Galdeano (2011), Haan & Wrohlich (2011), Michaud & Tatsiramos (2011).

  16. When the model is correctly specified, then full information estimation is more efficient than estimators that do not take into account the covariances between the equations. However, the FIML estimator is liable to the problem that misspecification of any one equation will affect the estimates in all the equations. This main disadvantage of the FIML implies that the estimators for a single equation are potentially less robust, since they will be inconsistent if the assumptions regarding the instrument fail in any equation, not just a particular one of interest. In Table B4 we show that our conclusions do not depend on the assumptions of FIML.

  17. A heterogeneity analysis shows that cross-effects exist if the household expects a substantial income drop at retirement (Table D1) or if the partner retires with a bad health (Table D2).

  18. Not conditioning on unobserved heterogeneity does not change the correlations between the error terms of consumption and home production, except that the correlation between consumption spending and female home production is more significant.

  19. Behaghel & Blau 2012, Blau & Goodstein 2010, Mastrobuoni 2009, Song & Manchester 2007.

  20. We do not focus on the opportunity cost approach as this method assumed an hour of home production to be valued at the wage which, in our case, is difficult for retirees.

  21. Total consumption spending prior to male retirement is, on average $53,794.62. The yearly consumption drop at retirement is 16%. The weekly increase in home production is 3.7. In order to calculate yearly home production we multiply by 52: (0.16*53, 794.62)/(3.7*52).

  22. Total consumption spending prior to female retirement is, on average $51,122.41. The yearly consumption drop at retirement is 5%. The weekly increase in home production is 3.2. In order to calculate yearly home production we multiply by 52. (0.11*51, 122.41)/(6.2*52).

  23. The same conclusion holds for the results based on the fuzzy RDD: (0.18*53, 794.62)/(4.8*52) = 39 for men and (0.12*51, 122.41)/(6.7*52) = 18 for women.

  24. (0.42*6429.997)/(3.7*52) = 14 and (0.13*5683.881)/(3.2*52) = 4.5.

  25. (0.36*2452.26)/(1*52) = 16

  26. 14.5 from Table 3 and 3.7 from Table 7 give a percentage increase of 26 after retirement compared to pre-retirement.

  27. 24.1 from Table 3 and 3.2 from Table 7 give a percentage increase of 13 after retirement compared to pre-retirement.

  28. Aguila et al. (2011), Banks et al. (1998), Battistin et al. (2009), Bernheim et al. (2001), Haider & Stephens (2007), Hurd and Rohwedder (2003, 0), Hurd & Rohwedder (2013), Hurst (2008), Lundberg et al. (2003), Mariger (1987), Miniaci et al. (2010), Moreau & Stancanelli (2015), Robb & Burbidge (1989), Smith (2006).

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Acknowledgements

The work was supported by grants from the Social Security Administration through the Michigan Retirement Research Center (Grant #RRC08098401 − 06), the National Institute on Aging (R01AG035010, PI: Rohwedder), and the Leiden University Fund/van Walsem (Grant #4414/3 − 9 − 13/V, vW). We thank participants at the Department of Economics Research Seminar Series, 3 October 2015, Leiden, ILS Seminar Series, 29 May 2017, Leiden, Society of Economics of the Household, 25–26 June 2017, San Diego CA, the CeRP Workshop “Household Finance and Retirement Savings”, 19–20 October 2017, Turin, and the Netspar International Pension Workshop, 17-19 January 2018, Leiden. More particularly, we have benefited from discussions with Marie Briere, Koen Caminada, Flavia Coda Moscarola, Enrica Croda, Andrew Davis, Thomas DeLeire, Sean Fahle, Elsa Fornero, Charles Horioka, Egbert Jongen, Alain Jousten, Daniel Kemptner, Marike Knoef, Francesco Lippi, Henriette Prast, Robert Sauer, Sarah Grace See, Pascal St-Amour, Frank Stafford, Elena Stancanelli, Federica Teppa, Salvador Valdes, Hendrik Vrijburg, Jessie Wang, and Matthias Wibral. Finally, we thank the editor José Ignacio Giménez-Nadal and three anonymous referees. The findings and conclusions expressed are solely those of the authors and do not represent the opinions or policy of the Social Security Administration, any agency of the Federal government, or the Michigan Retirement Research Center.

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  1. Department of Economics, Leiden University and NetsparLeiden, Leiden, ZH, Netherlands

    Jim Been

  2. RAND Corp., MEA and Netspar, Santa Monica, CA, USA

    Susann Rohwedder

  3. RAND Corp., NBER, MEA and Netspar, Santa Monica, CA, USA

    Michael Hurd

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  1. Jim Been
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Correspondence to Jim Been, Susann Rohwedder or Michael Hurd.

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Been, J., Rohwedder, S. & Hurd, M. Households’ joint consumption spending and home production responses to retirement in the US. Rev Econ Household 19, 959–985 (2021). https://doi.org/10.1007/s11150-020-09528-5

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