Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Examining inequality in the time cost of waiting

Nature Human Behaviour volume 7, pages 545–555 (2023)Cite this article

Subjects

Abstract

Time spent waiting for services represents unproductive time lost while fulfilling needs. We use time diary data from the nationally representative American Time Use Survey to estimate the difference between high- and low-income people in time spent waiting for basic services. Relative to high-income people, low-income people are one percentage point more likely to wait on an average day, are three percentage points more likely to wait when using services, spend an additional minute waiting for services on a typical day and spend 12 more minutes waiting when waiting occurs. The unconditional gap in waiting time suggests low-income people spend at least six more hours per year waiting for services than high-income people. The income gap in waiting time cannot be explained by differences in family obligations, demographics, education, work time or travel time. Further, high-income Black people experience the same higher average wait times as low-income people regardless of race.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Marginal effect of income on time spent waiting for medical services for self or HH child (in minutes; T ∣ T > 0; weighted).
Fig. 2: Marginal effect of income on time spent waiting while shopping (in minutes; T ∣ T > 0; weighted).
Fig. 3: Marginal effect of income on probability of any waiting for services by race and ethnicity, with controls (weighted).
Fig. 4: Marginal effect of income on time spent waiting for services by race and ethnicity, with controls (in minutes; T ∣ T > 0; weighted).
Fig. 5: Marginal effect of income on time spent waiting for services by time of day, weekdays (in minutes; T ∣ T > 0; weighted).

Data availability

The ATUS data used in our analysis is publicly available and the multi-year microdata files used in our analysis can be found at https://www.bls.gov/tus/datafiles-0319.htm. The poverty thresholds used in the imputation-based robustness check in Supplemental Information can be found at https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html. The Annual Social and Economic Supplement to the Current Population Survey (also used in the imputation-based robustness check in Supplemental Information can be found at IPUMS (https://cps.ipums.org/cps/).

Code availability

Data files and the Stata17.do file for replicating the analysis is available on GitHub at https://github.com/stevebholt/waiting-time.

References

  1. Cohen, E. F. The Political Value of Time: Citizenship, Duration, and Democratic Justice (Cambridge Univ. Press, 2018).

  2. Gee, G. C., Hing, A., Mohammed, S., Tabor, D. C. & Williams, D. R. Racism and the life course: Taking time seriously. Am. J. Public Health 109, S43–S47 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bittman, M. Social participation and family welfare: the money and time costs of leisure in australia. Soc. Policy Adm. 36, 408–425 (2002).

    Article  Google Scholar 

  4. Kalenkoski, C. M., Hamrick, K. S. & Andrews, M. Time poverty thresholds and rates for the US population. Soc. Indic. Res. 104, 129–155 (2011).

    Article  Google Scholar 

  5. Vickery, C. The time-poor: a new look at poverty. J. Hum. Resour. 12, 27–48 (1977).

    Article  Google Scholar 

  6. Williams, J. R., Masuda, Y. J. & Tallis, H. A measure whose time has come: formalizing time poverty. Soc. Indic. Res. 128, 265–283 (2016).

    Article  Google Scholar 

  7. Zilanawala, A. Women’s time poverty and family structure: differences by parenthood and employment. J. Fam. Issues 37, 369–392 (2016).

    Article  Google Scholar 

  8. Strazdins, L. et al. Time scarcity: another health inequality? Environ. Plan. A 43, 545–559 (2011).

    Article  Google Scholar 

  9. Jabs, J. & Devine, C. M. Time scarcity and food choices: an overview. Appetite 47, 196–204 (2006).

    Article  PubMed  Google Scholar 

  10. Venn, D. & Strazdins, L. Your money or your time? How both types of scarcity matter to physical activity and healthy eating. Soc. Sci. Med. 172, 98–106 (2017).

    Article  PubMed  Google Scholar 

  11. Kossek, E. E. & Lautsch, B. A. Work–life flexibility for whom? Occupational status and work–life inequality in upper, middle, and lower level jobs. Acad. Manag. Ann. 12, 5–36 (2018).

    Article  Google Scholar 

  12. Loprest, P., Acs, G., Ratcliffe, C. & Vinopal, K. Who are low-wage workers? U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation https://aspe.hhs.gov/basic-report/who-are-low-wage-workers (2009).

  13. Cohen, P. N. Replacing housework in the service economy: gender, class, and race-ethnicity in service spending. Gend. Soc. 12, 219–231 (1998).

    Article  Google Scholar 

  14. Herd, P. & Moynihan, D. Administrative burdens in health policy. J. Health Hum. Serv. Adm. 43, 3–16 (2020).

    Google Scholar 

  15. Herd, P. & Moynihan, D. P. Administrative Burden: Policymaking by Other Means (Russell Sage Foundation, 2019).

  16. Ray, V., Herd, P. & Moynihan, D. Racialized burdens: applying racialized organization theory to the administrative state. J. Public Adm. Res. Theory 33, 139–152 (2020).

    Article  Google Scholar 

  17. Chen, M. K., Haggag, K., Pope, D. G. & Rohla, R. Racial disparities in voting wait times: evidence from smartphone data. Rev. Econ. Stat. 104, 1341–1350 (2019).

    Article  Google Scholar 

  18. Pettigrew, S. The racial gap in wait times: why minority precincts are underserved by local election officials. Political Sci. Q. 132, 527–547 (2017).

    Article  Google Scholar 

  19. Kugelmass, H. ‘Sorry, I’m not accepting new patients’: an audit study of access to mental health care. J. Health Soc. Behav. 57, 168–183 (2016).

    Article  PubMed  Google Scholar 

  20. Massey, D. S. & Lundy, G. Use of black English and racial discrimination in urban housing markets: new methods and findings. Urban Aff. Rev. 36, 452–469 (2001).

    Article  Google Scholar 

  21. Liederbach, E. et al. Wait times for breast surgical operations, 2003–2011: a report from the national cancer data base. Ann. Surgical Oncol. 22, 899–907 (2015).

    Article  Google Scholar 

  22. Hsiang, W. R. et al. Medicaid patients have greater difficulty scheduling health care appointments compared with private insurance patients: a meta-analysis. INQUIRY: J. Health Care Organ., Provis., Financing 56, 0046958019838118 (2019).

    Google Scholar 

  23. Oostrom, T., Einav, L. & Finkelstein, A. Outpatient office wait times and quality of care for Medicaid patients. Health Aff. 36, 826–832 (2017).

    Article  Google Scholar 

  24. Chetty, R., Hendren, N. & Katz, L. F. The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment. Am. Economic Rev. 106, 855–902 (2016).

    Article  Google Scholar 

  25. Chetty, R. & Hendren, N. The impacts of neighborhoods on intergenerational mobility I: childhood exposure effects. Q. J. Econ. 133, 1107–1162 (2018).

    Article  Google Scholar 

  26. Dutko, P., Ver Ploeg, M. & Farrigan, T. Characteristics and influential factors of food deserts (United States Department of Agriculture, 2012).

  27. Cooksey-Stowers, K., Schwartz, M. B. & Brownell, K. D. Food swamps predict obesity rates better than food deserts in the United States. Int. J. Environ. Res. Public Health 14, 1366 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schuetz, J., Kolko, J. & Meltzer, R. Are poor neighborhoods ‘retail deserts’? Regional Sci. Urban Econ. 42, 269–285 (2012).

    Article  Google Scholar 

  29. Dunham, I. M. Forgotten landscapes of financial exclusion: a geographic analysis of banking deserts and the two-tiered financial service system. In Proc. of the International Association for Business and Society Vol. 29 (ed Maas, K.) 96–109 (IABS, 2018).

  30. Hegerty, S. W. Commercial bank locations and ‘banking deserts’: a statistical analysis of Milwaukee and Buffalo. Ann. Regional Sci. 56, 253–271 (2016).

    Article  Google Scholar 

  31. Pendall, R. et al. Driving to opportunity: understanding the links among transportation access, residential outcomes, and economic opportunity for housing voucher recipients (Urban Institute, 2014).

  32. Baker, C., Floros, T. & Kristensen, K. Vehicle financing inequality: what can be done? Federal Reserve Bank of St. Louis https://www.stlouisfed.org/publications/bridges/volume-1-2020/vehicle-financing-inequality (2020).

  33. Quinton, S. How car ownership helps the working poor get ahead. The Atlantic https://www.theatlantic.com/politics/archive/2014/07/how-car-ownership-helps-the-working-poor-get-ahead/431160/ (2014).

  34. Aguir, M. & Hurst, E. The Increase in Leisure Inequality, 1965–2005. National Research Initiative (American Enterprise Institute for Public Policy Research, 2009).

  35. Sevilla, A., Gimenez-Nadal, J. I. & Gershuny, J. Leisure inequality in the United States: 1965–2003. Demography 49, 939–964 (2012).

    Article  PubMed  Google Scholar 

  36. Schwartz, B. Waiting, exchange, and power: the distribution of time in social systems. Am. J. Sociol. 79, 841–870 (1974).

    Article  Google Scholar 

  37. Roy, D. F. Work satisfaction and social reward in quota achievement: an analysis of piecework incentive. Am. Sociol. Rev. 18, 507–514 (1953).

    Article  Google Scholar 

  38. Pfeffer, J. Managing with Power: Politics and Influence in Organizations (Harvard Business Press, 1992).

  39. Scott, J. C. Domination and the Arts of Resistance (Yale Univ. Press, 2008).

  40. Auyero, J. Patients of the state: an ethnographic account of poor people’s waiting.Lat. Am. Res. Rev. 46, 5–29 (2011).

    Article  PubMed  Google Scholar 

  41. Roy, K. M., Tubbs, C. Y. & Burton, L. M. Don’t have no time: daily rhythms and the organization of time for low-income families. Fam. Relat. 53, 168–178 (2004).

    Article  Google Scholar 

  42. Bonilla-Silva, E. & Baiocchi, G. Anything but racism: how sociologists limit the significance of racism. Race Soc. 4, 117–131 (2001).

    Article  Google Scholar 

  43. Kaufman, J. S. & Cooper, R. S. Commentary: considerations for use of racial/ethnic classification in etiologic research. Am. J. Epidemiol. 154, 291–298 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Nuru-Jeter, A. M. et al. Relative roles of race versus socioeconomic position in studies of health inequalities: a matter of interpretation. Annu. Rev. Public Health 39, 169–188 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Singh, M. & Venkataramani, A. Capacity strain and racial disparities in hospital mortality. Preprint at https://www.nber.org/papers/w30380 (NBER, 2022).

  46. Wolfe, J., Jones, J. & Cooper, D. ‘Fair workweek’ laws help more than 1.8 million workers. Economic Policy Institute https://www.epi.org/publication/fair-workweek-laws-help-more-than-1-8-million-workers/ (2018).

  47. Kesavan, S., Lambert, S., Williams, J. & Pendem, P. Doing well by doing good: improving store performance with employee-friendly scheduling practices at the Gap, Inc. Manage. Sci. 68, 7818–7836 (2022).

    Article  Google Scholar 

  48. Juster, F. T. & Stafford, F. P. The allocation of time: empirical findings, behavioral models, and problems of measurement. J. Econ. Lit. 29, 471–522 (1991).

    Google Scholar 

  49. Kimbrough, G. Measuring commuting in the American Time Use Survey. J. Econ. Soc. Meas. 44, 1–17 (2019).

    Article  Google Scholar 

  50. Allard, M. D. What is the impact of recoding travel activities in the American Time Use Survey? Mon. Labor Rev. 141, 1–13 (2018).

    Google Scholar 

  51. Sturman, M. C. Multiple approaches to analyzing count data in studies of individual differences: the propensity for type I errors, illustrated with the case of absenteeism prediction. Educ. Psychol. Meas. 59, 414–430 (1999).

    Article  Google Scholar 

  52. Stewart, J. Tobit or not Tobit? J. Econ. Soc. Meas. 38, 263–290 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. University at Albany, SUNY, Rockefeller College of Public Affairs and Policy, Albany, NY, USA

    Stephen B. Holt

  2. The Ohio State University, John Glenn College of Public Affairs, Columbus, OH, USA

    Katie Vinopal

Authors
  1. Stephen B. Holt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katie Vinopal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.B.H. contributed to the conceptualization, data analysis, writing and methods of the work. K.V. contributed to the conceptualization, writing, background research, and editing and review of the work.

Corresponding author

Correspondence to Stephen B. Holt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Chiara Monfardini, Lyndall Strazdins and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12, Discussion and Tables 1–15.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Holt, S.B., Vinopal, K. Examining inequality in the time cost of waiting. Nat Hum Behav 7, 545–555 (2023). https://doi.org/10.1038/s41562-023-01524-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-023-01524-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing