Issue 34 | Can Sleeping More Boost What You Earn?

Can Sleeping More Boost What You Earn?

by Thomas Wang

Sleep occupies a vast expanse over our lives. An average person spends 26 years of their life sleeping, a figure that increases to nearly 33 years when including the time it takes to fall asleep (1). Research supports the important role that sleep has in determining productivity and performance: sleep-deprived students do worse in class (2), less sleep causes athletes to perform worse in sports (3), and a lack of sleep contributes to a shorter life span (4). While the link between sleep and productivity is well-established, the economic implications are often overlooked. How does sleep impact your personal earning potential, and are there broader economic consequences of a sleep-deprived workforce?

Intuitively, sleep boosts earnings because it directly enhances cognitive function, productivity and decision-making ability (5). Adequate sleep also improves memory retention (6), problem-solving skills (7), likely also allowing individuals to exhibit better emotional regulation (8) and interpersonal skills (9). In a professional setting, these qualities can compound over time, resulting in significant income gaps between those who prioritise sleep and those who do not.

Sleep and Wages

A 2024 US study explores this link between sleep and earnings. Researchers found that increasing weekly sleep by just one hour boosted short-term earnings by 1.1%, with a more substantial 5% increase observed over the long term (10). The study used the varying sunset times across the US, because later sunsets misalign with fixed social and work schedules, delaying bedtimes without significantly altering wake times. This creates an overall reduction in sleep duration, which the study attributes to sleep deprivation directly diminishing workers' ability to focus, manage stress, and maintain psychological well-being. This ultimately reduces their efficiency and economic output. This approach minimised the typical confounding factors that complicate causation studies; later sunsets essentially act as an independent variable affecting sleep, separate from influences like demographics or socioeconomic status, allowing researchers to pinpoint the impact of sleep on earnings.

A similar study, this time using German data, also supports this causation, and claims that a one-hour increase in weekly sleep is associated with a 1.6% increase in employment and a 3.4% increase in weekly earnings (11). They further isolated sleep and wages through even stricter controls for factors like housing, weather, and job conditions.

We can use a graphical display to better demonstrate this trend (see Figure 1). US data from the American Time Use Survey (8) supports the broader consistency of the theory. Since 2004, sleep time has increased by 6%, which is correlated with a real median wage rise of almost the same percentage. Over the same period, average working hours have slightly decreased, from 3.67 hours per day to 3.58 (averaged over all 7 days).

The graph reflects the sleep pattern across a population over time, and the 2024 US study and German research provides the direct causal evidence. These results together demonstrate the connection between sleep and economic outcomes, as both societal trends and controlled studies demonstrate.

Figure 1

Indexed sleep and real median hourly wage is calculated by: each real value for every year of sleep time and median hourly wage divided by the ratio between the first (2004) values and first (2004) value of average daily working hours. Since percentage change is relative to the baseline of 3.67 hours, the constant scaling does not alter the relative percentage fluctuations between years.

Research using data from The Canadian Time Use Survey (12) gives an interesting addition to the findings of the German and American studies. Instead of sleep time causing an increase in income, this study claims that a 10% increase in wage rate actually causes a 11-12 minute decrease in weekly sleep.

The relationship between income and sleep can be understood through the income effect and the substitution effect. The income effect suggests that when wages increase, individuals have more income without needing to work additional hours, potentially allowing for more leisure or sleep. Conversely, the substitution effect implies that as wages rise, the opportunity cost of leisure increases, leading people to work more and sleep less. Evidence suggests that in Canada, the substitution effect is stronger. Reconciling these findings requires consideration of the time frame: while the Canadian Time Use Survey spans only five years (2005–2010), studies linking increased sleep to higher income often highlight long-term benefits. For instance, data from Figure 1 shows that as sleep time increases, hourly wages actually initially decrease. However, only in 2014 – a decade later – do hourly wages begin trending upward in correlation with increased sleep. Both the U.S. and German studies have compiled data for over more than ten years; this may suggest that while substitution and income effects can dominate in the short term, productivity gains from sleep could manifest more clearly in the long term. Over time, insufficient sleep could contribute to lower wages and reduced employment (13).

The Global Toll of Sleep Deprivation

So, how does the data vary across countries? A study from RAND (14) estimates that annual GDP losses across countries due to sleep deprivation is as follows:

Figure 2

As sleep times increase, GDP losses due to sleep deprivation appear to decrease. Logically, this leads to the hypothesis that if insufficient sleep leads to economic losses, we can expect to see a measurable correlation between sleep duration and GDP across more countries.

When plotting sleep time against GDP per capita (Figure 3), we can see that as average sleep time increases, GDP per capita also tends to rise. This positive correlation, reflected in an r-value of 0.458, indicates that wealthier countries generally report longer average sleep durations.

Figure 3

A multiple linear regression analysis of this data

GDP per capita= 𝛽0+ 𝛽1(Sleep Time) + 𝛽2(Mean Years of Schooling) + 𝛽3(Unemployment Rate) + 𝛽4(Urbanization Level) + ϵ

yields the following results:

Figure 4

This finding is supported by a t-value of 3.427 and a p-value of 0.001377, indicating strong statistical significance at the 0.01 level. Additionally, the low variance inflation factor (VIF) of 1.466 for sleep time suggests minimal multicollinearity, strengthening the reliability of this result.

This indicates that sleep time is a significant positive predictor of GDP per capita, suggesting a potential causal relationship. The model estimates that each additional minute of sleep corresponds to a $470 increase in GDP per capita, if all other variables are discounted.

Between countries, there is a clear positive correlation between increasing sleep time and increasing GDP per capita, which supports RAND’s findings on a broader scale. This trend suggests that wealthier countries typically have both more sleep and higher economic output. However, when isolating Asian countries, the positive correlation between sleep time and GDP per capita in non-Asian countries inverses (Figure 5). Why is this the case?

Figure 5

Here, the answer may not come from the productivity differences in sleep, but may be driven by regional differences in labour structure, cultural norms, and stages of economic development.

The most intuitive answer is that Asian countries may have a stronger substitution effect, and that higher GDP per capita causes people to work more instead of less. However, when we plot working hours against sleep time, instead of working less when sleep increases, we see that time worked increases alongside sleep (Figure 6).

Figure 6

This seems surprising, as the countries who are sleeping less and working less have a higher GDP per capita. These results can be explained in several ways. One possible explanation is the productivity effect. Sleep is known to enhance cognitive function, memory, and attention (15). Well-rested workers are more effective during the day, boosting their individual productivity. When a worker's productivity increases, their marginal product of labour increases as well. In turn, firms and workers may be incentivised to extend working hours. This is especially true in countries like China, where overtime culture and "996" work schedules (working from 9 a.m. to 9 p.m., 6 days a week) are the norm (16). With more sleep, workers can withstand these grueling schedules for longer periods.

However, this increased work effort does not necessarily translate to higher GDP per capita. High-GDP countries like Japan (17), South Korea (18), and Singapore (19) rely on capital-intensive sectors like technology, finance, and precision manufacturing. In these industries, the contribution of individual labour hours to output is relatively small because much of the work is done by automation and advanced machinery. Therefore, while individual productivity may decrease from less sleep working in such capital-intensive sectors, the productivity gains from working in more productive sectors outpaces any productivity lost from sleeping less.

Bottom Line

On an individual level, sleeping more can help to increase your income over the longer term – given that other factors remain unchanged. Research supports the fact that sleep enhances productivity, which leads to better performance and higher earnings. However, the impact between countries depends on the nature of work and unique characteristics. While western countries generally report longer sleep durations correlated strongly with higher GDP per capita, this trend can reverse in regions like Asia. Sleeping more may improve efficiency and well-being, but its benefits may be overshadowed in the difference between the level of industrialisation and technology between countries. For some, with cultures of overtime or a greater income effect, the opportunity cost of not working — whether to pursue overtime or meet financial obligations — makes sacrificing sleep a compelling choice.

Unfortunately, this creates a cycle where economic pressures incentivise individuals to sleep less, potentially undermining long-term health and career sustainability. Sleep is an economic resource, and achieving a good balance between rest and economic demands contributes to sustainable growth both on an individual and global scale.

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Bibliography

Curtis, Gemma. 2017. “Your Life in Numbers.” The Sleep Matters Club. September 28, 2017. https://www.dreams.co.uk/sleep-matters-club/your-life-in-numbers-infographic.
Taras, Howard, and William Potts-Datema. 2005. “Sleep and Student Performance at School.” Journal of School Health 75 (7): 248–54. https://doi.org/10.1111/j.1746-1561.2005.tb06685.x.
‌Fullagar, Hugh H K, Sabrina Skorski, Rob Duffield, Daniel Hammes, Aaron J Coutts, and Tim Meyer. 2015. “Sleep and Athletic Performance: The Effects of Sleep Loss on Exercise Performance, and Physiological and Cognitive Responses to Exercise.” Sports Medicine (Auckland, N.Z.) 45 (2): 161–86. https://doi.org/10.1007/s40279-014-0260-0.
‌Sambou, Muhammed Lamin, Xiaoyu Zhao, Tongtong Hong, Jingyi Fan, Til Bahadur Basnet, Meng Zhu, Cheng Wang, Dong Hang, Yue Jiang, and Juncheng Dai. 2021. “Associations between Sleep Quality and Health Span: A Prospective Cohort Study Based on 328,850 UK Biobank Participants.” Frontiers in Genetics 12 (June). https://doi.org/10.3389/fgene.2021.663449.
Harrison, Y., and J.A. Horne. 1999. “One Night of Sleep Loss Impairs Innovative Thinking and Flexible Decision Making.” Organizational Behavior and Human Decision Processes 78 (2): 128–45. https://doi.org/10.1006/obhd.1999.2827. (16.b) Killgore, William D.S. 2010. “Effects of Sleep Deprivation on Cognition.” Progress in Brain Research 185 (185): 105–29. https://doi.org/10.1016/b978-0-444-53702-7.00007-5.
Dahat, Purva, Stacy Toriola, Travis Satnarine, Zareen Zohara, Ademiniyi Adelekun, Kofi D Seffah, Lana Dardari, Korlos Salib, Maher Taha, and Safeera Khan. 2023. “Correlation of Various Sleep Patterns on Different Types of Memory Retention: A Systematic Review.” Cureus 15 (7). https://doi.org/10.7759/cureus.42294.
Berg, N H van den, A Pozzobon, Z Fang, J Al-Kuwatli, B Toor, L B Ray, and S M Fogel. 2022. “Sleep Enhances Consolidation of Memory Traces for Complex Problem-Solving Skills.” Cerebral Cortex 32 (4): 653–67. https://doi.org/10.1093/cercor/bhab216.
Vandekerckhove, Marie, and Yu-lin Wang. 2017. “Emotion, Emotion Regulation and Sleep: An Intimate Relationship.” AIMS Neuroscience 5 (1): 1–17. https://doi.org/10.3934/Neuroscience.2018.1.1.
Dwivedi, Neelima, Sakshi Patidar, and Krati Bhosle. 2024. “Impact of Sleep Quality on Interpersonal Communication in Early Adulthood.” Indian Journal of Health and Wellbeing 15 (1): 105–8. https://www.proquest.com/docview/3041529533?fromopenview=true&pq-origsite=gscholar&sourcetype=Scholarly%20Journals.
Gibson, Matthew, and Jeffrey Shrader. 2018. “Time Use and Labor Productivity: The Returns to Sleep.” The Review of Economics and Statistics 100 (5): 783–98. https://doi.org/10.1162/rest_a_00746.
‌Costa-Font, Joan, Sarah Fleche, and Ricardo Pagan. 2024. “The Labour Market Returns to Sleep.” Journal of Health Economics 93 (January): 102840. https://doi.org/10.1016/j.jhealeco.2023.102840.
Sedigh, Golnaz, Rose Anne Devlin, Gilles Grenier, and Catherine Deri Armstrong. 2017. “Revisiting the Relationship between Wages and Sleep Duration: The Role of Insomnia.” Economics & Human Biology 24 (February): 125–39. https://doi.org/10.1016/j.ehb.2016.11.010.
Huyett, Phillip, and Neil Bhattacharyya. 2022. “The Association between Sleep Disorders on Employment and Income among Adults in the United States.” Journal of Clinical Sleep Medicine, May. https://doi.org/10.5664/jcsm.10040.
‌Hafner, Marco, Martin Stepanek, Jirka Taylor, Wendy M Troxel, and Van Stolk. 2016. “Why Sleep Matters — the Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis.” Rand.org. RAND Corporation. 2016. https://www.rand.org/pubs/research_reports/RR1791.html.
Vallejo, Rubén González, and Mary Daohne P. Silvestre. "Effects of Sleep Deprivation on cognitive functions and academic achievement in students." Sage Science Review of Educational Technology 6, no. 1 (2023): 59-70.
yunyunyunaaa. 2023. “Working Overtime or Being Laid Off: The Pressure under Hopelessness among Workers in Chinese Internet Companies.” Developing Economics. July 10, 2023. https://developingeconomics.org/2023/07/10/working-overtime-or-being-laid-off-the-pressure-under-hopelessness-among-workers-in-chinese-internet-companies/.
Ito, Keiko, and Kyoji Fukao. 2005. “Title: Physical and Human Capital Deepening and New Trade Patterns in Japan.” NBER-East Asia Seminar on Economics 14. https://www.nber.org/system/files/chapters/c0189/c0189.pdf.
Lee, Chung Min. 2024. “The Future of K-Power: What South Korea Must Do after Peaking.” Carnegie Endowment for International Peace. 2024.
Cherif, Reda, Fuad Hasanov, and Min Zhu. 2016. Breaking the Oil Spell. INTERNATIONAL MONETARY FUND. https://doi.org/10.5089/9781513537863.071.