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Original Article The association between commute time and both presenteeism and absenteeism: a cross-sectional analysis of the 6th Korean Working Conditions Survey
Tae Jin Ahnorcid, Young-Sun Minorcid, Soon-Chan Kwonorcid, Jisuk Yunorcid, Sang-Ho Ohorcid, Eun-Chul Jang,*orcid
Annals of Occupational and Environmental Medicine 2026;38:e31.
DOI: https://doi.org/10.35371/aoem.2026.38.e31
Published online: July 21, 2026

Department of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Korea

*Corresponding author Eun-Chul Jang Department of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea E-mail: oemdr10@gmail.com
• Received: April 17, 2026   • Revised: July 3, 2026   • Accepted: July 4, 2026

© 2026 Korean Society of Occupational & Environmental Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background
    This study examined whether longer commute time is associated with presenteeism and absenteeism among Korean wage workers, in the context of Korea’s comparatively long commutes and growing concerns about work-related productivity loss in an increasingly urbanized society.
  • Methods
    We analyzed 28,349 wage workers from the 6th Korean Working Conditions Survey, excluding individuals younger than 20 years, foreign nationals, telecommuters, self-employed workers, unpaid family workers, and those with missing or incomplete data. Commute time was categorized into ≤60, 61–120, and ≥121 minutes per day. Presenteeism was defined as having worked while sick in the past 12 months (or since starting the current job), and absenteeism as having at least one day of sickness-related absence during the same period. Sex-stratified, survey-weighted multiple logistic regression analyses were performed to estimate odds ratios and 95% confidence intervals while accounting for the complex survey design.
  • Results
    Overall, 82.1% of workers reported commuting ≤60 minutes, 11.2% reported presenteeism, and 3.9% reported absenteeism. Among men, commute times of 61–120 and ≥121 minutes were associated with higher odds of presenteeism, and ≥121 minutes with higher odds of absenteeism compared with ≤60 minutes. Among women, commute times of 61–120 and ≥121 minutes were associated with higher odds of presenteeism, and ≥121 minutes with higher odds of absenteeism, indicating a similar but slightly more pronounced pattern.
  • Conclusions
    Longer commute times were associated with increased odds of both presenteeism and absenteeism among Korean wage workers. In particular, women with commute times of ≥121 minutes showed higher odds of presenteeism than men. Commute time should be recognized as a modifiable factor relevant to occupational health in workplace and public policy, and further longitudinal research is needed to clarify causal pathways and to inform targeted interventions aimed at reducing commuting-related productivity loss.
  • Keywords: Absenteeism; Commute time; KWCS; Presenteeism
As urbanization and metropolitan concentration accelerate worldwide, commute distance and time have continued to increase.1 Korea is no exception. Due to overcrowding in the Seoul metropolitan area, Korea has one of the longest average commutes in the Organisation for Economic Co-operation and Development (OECD).2 According to Statistics Korea, the average daily commute time for Korean workers is 73.9 minutes, but for those in the Seoul metropolitan area, it jumps to 82.0 minutes.3 In this context, commute time is a key determinant of workers' quality of life.4 Critically, the growing burden of commute time extends beyond individual lifestyle choices and reflects a structural societal issue that demands policy-level attention.5,6
Long commutes deplete workers' time resources and reduce their opportunities to recover, thereby exacerbating occupational stress and contributing to productivity losses at the macro level.7,8 These stress-related burdens are closely tied to a wide range of adverse health outcomes. Empirical evidence consistently links long commute times to physical health consequences, including cardiovascular disease, musculoskeletal disorders, and sleep disturbances, as well as psychological outcomes, such as depressive symptoms, reduced job satisfaction, and diminished overall well-being.4,9-12
This study focuses on two key indicators of work-related productivity loss—presenteeism and absenteeism—and examines the consequences of commute time.13,14 Originally conceptualized as working while sick,15 presenteeism has since been broadened to encompass a range of health-related productivity impairments during working hours.16 Work environments increasingly recognize that sustaining health and concentration during work hours, rather than merely attending, is a critical determinant of work quality.16 Absenteeism, on the other hand, has been broadly defined as absence from scheduled work due to illness, injury, or other health-related reasons.15 It has long been regarded as a visible and immediate marker of workforce disruption. Recently, however, absenteeism has been understood to reflect not only objective health impairment, but also psychological factors, such as burnout and disengagement.17
Although several studies have examined the relationship between commute time and work-related productivity loss,13,18 studies that specifically address the situation in South Korea remain scarce. To our knowledge, no study has directly examined the association between commute time and both presenteeism and absenteeism among Korean wage workers using nationally representative data. Therefore, this study aimed to investigate the association between commute time and both presenteeism and absenteeism among Korean wage workers, using data from the 6th Korean Working Conditions Survey (KWCS).
Study participants
From 2020 to 2021, the Korea Institute of Occupational Safety and Health conducted the 6th KWCS. Since 2006, the KWCS has been a large-scale national survey investigating occupational and environmental factors to provide preliminary data for improving working conditions.19
A total of 50,538 people responded to the 6th KWCS. Of these, 153 individuals under the age of 20 and 288 foreign nationals were excluded. After accounting for commute time, an additional 2,788 telecommuters were excluded from the analysis. This study focused exclusively on wage workers, thereby excluding 15,134 self-employed individuals and unpaid family workers. Additionally, 3,826 participants with missing or incomplete data were excluded. Finally, the study included 28,349 wage workers (Fig. 1). After applying pre-designed weights to represent all workers at the national level, the total study population was 32,530 individuals.
Measurements
In this study, the dependent variables were presenteeism and absenteeism, and the main independent variable was commute time, which was used to examine their associations. Covariates were constructed based on factors related to these variables. The definitions of the variables are as follows.
Dependent variables: presenteeism and absenteeism
Presenteeism was evaluated using the question "Over the past 12 months (or since you started your job), did you work when you were sick?" Responses to this question were categorized as "yes," "no," or "I wasn't sick." A "yes" response indicated presenteeism, while the other responses did not.
Absenteeism was evaluated using the question "Over the past 12 months (or since you started your job), how many days in total were you absent from work due to sick leave or health-related leave?" Responses to this question were coded as "yes" if the respondent was absent for at least one day, and as "no" if they were not.
Independent variable: commute time
Commute time was evaluated using the question: "In total, how many minutes per day do you usually spend traveling from home to work and back?" Responses were categorized as follows: ≤60 minutes, 61–120 minutes, and ≥121 minutes.20
Covariates
To account for potential confounding factors, this study incorporated a range of general and occupational characteristics.21-25 General characteristics are as follows. The age variable was divided into five groups (20–29, 30–39, 40–49, 50–59, and ≥60 years). Self-rated health status was assessed using the following question: "How is your health in general? Would you say it is…" There were five response options: "very good," "good," "fair," "bad," and "very bad." Responses were dichotomized into "good" (responses of "very good" or "good") and "bad" (responses of "fair," "bad," or "very bad"). The education variable was categorized into three levels: middle school or lower, high school, and college graduate or higher. Monthly income was divided into four groups, ranging from less than 2 million Korean won (KRW) to 4 million KRW or more. Regarding occupational factors, occupational roles were organized into three clusters: white-collar (managerial, professional, and office roles), pink-collar (service and sales), and blue-collar (agriculture, manual labor, and technical operations). Company size was assessed by the number of employees (<50, 50–299, or ≥300), and shift work was classified as either yes or no. Finally, weekly working hours were classified into three groups: <40, 40–52, and >52 hours.
Statistical methods
In this study, the χ2 test was used to determine the levels of presenteeism and absenteeism by general and occupational characteristics of the participants. To examine the association between commute time and both presenteeism and absenteeism, we performed sex-stratified survey-weighted multiple logistic regression analyses. Odds ratios (ORs) were reported across three models: a crude model, model 1 (adjusted for age, self-rated health status, education, and income), and model 2 (adjusted for model 1 + occupational type, company size, weekly working hours, and shift work). In addition to the sex-stratified analyses, an interaction term (commute time × sex) was incorporated into each model (crude, model 1, and model 2) applied to the total study population to formally evaluate whether sex modified the association between commute time and the outcomes. The statistical significance of the interaction was assessed using the Wald test. Finally, to further enhance the interpretability of the findings, adjusted predicted probabilities of both presenteeism and absenteeism were estimated across all three models using marginal standardization, and the absolute probability differences between the baseline commute group (≤60 minutes) and the long commute group (≥121 minutes) were calculated and stratified by sex. As a sensitivity analysis, we additionally modeled commute time as a continuous variable (minutes) and examined its association with presenteeism and absenteeism.
All analyses incorporated sampling weights and stratification to account for the complex survey design.
The number of participants, proportion (%), and all analyses were performed using weighted data. All statistical analyses were performed using R software version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria). A p-value < 0.05 was considered statistically significant.
Ethics statement
The present study protocol was reviewed and approved by the Institutional Review Board of Soonchunhyang University Hospital in Cheonan (IRB No. SCHCA 2023-08-037). The requirement for informed consent was waived due to the retrospective nature of the study.
The general and occupational characteristics of the study group are presented in Table 1. Tables 2 and 3 present general and occupational characteristics of participants according to presenteeism and absenteeism, by sex.
Table 1 shows the unweighted sample sizes and the weighted estimates. According to the weighted estimates in Table 1, the majority of participants (82.1%) reported a commute time of 60 minutes or less, while 15.1% reported a commute time of 61–120 minutes, and 2.8% reported a commute time of 121 minutes or more. The prevalence of presenteeism was 11.2%, and 3.9% of participants reported absenteeism.
Regarding presenteeism (Table 2), male workers showed statistically significant associations with age, self-rated health status, occupational type, shift work, working hours, and commute time. Among female workers, self-rated health status, income, occupational type, company size, shift work, working hours, and commute time were significantly associated with presenteeism. Regarding absenteeism (Table 3), self-rated health status, working hours, and commute time were significantly associated with absenteeism among male workers. Among female workers, self-rated health status, education, income, occupational type, company size, working hours, and commute time were statistically significantly associated with absenteeism.
Fig. 2 shows the ORs for the association between commute time and presenteeism, and Fig. 3 shows those for the association between commute time and absenteeism, both stratified by sex. As shown in Fig. 2, long commute times were significantly associated with presenteeism among male workers. The ORs were higher than those of the ≤60 minutes group in the 61–120 minutes group (OR: 1.35; 95% confidence interval [CI]: 1.07–1.70) and ≥121 minutes group (OR: 1.49; 95% CI: 1.00–2.23). Among female workers, significant associations with presenteeism were also observed in both the 61–120 minutes group (OR: 1.24; 95% CI: 1.01–1.52) and the ≥121 minutes group (OR: 2.59; 95% CI: 1.52–4.41). Notably, the OR for women in the ≥121 minutes group (2.59) was higher than that for men (1.49).
As shown in Fig. 3, a significant increase in the OR (1.77; 95% CI: 1.06–2.94) was observed only in the ≥121 minutes group among men. Among women, the OR was 2.99 (95% CI: 1.20–7.46) in the ≥121 minutes group, which was higher than that observed in men. The specific values from the overall results of the logistic regression analysis are presented in Supplementary Table 1.
According to Table 4, the predicted probability of presenteeism among men increased by 3.67 percentage points in the fully adjusted model (model 2) from 9.17% in the ≤60 minutes group to 12.84% in the ≥121 minutes group. Meanwhile, the predicted probability of absenteeism increased by 2.40 percentage points, from 3.44% to 5.84%. Among women, the predicted probability of presenteeism increased by 13.10 percentage points, from 12.56% to 25.66%, while the predicted probability of absenteeism increased by 6.46 percentage points, from 3.84% to 10.30%.
Supplementary Table 1 shows the ORs for the association between commute time and both presenteeism and absenteeism, stratified by sex, for the crude model, model 1, and model 2. Despite the stepwise addition of the adjustment variables, the direction and magnitude of the increased odds in the ≥121 minutes group remained consistent for both sexes, although the OR was higher for women. Furthermore, trend tests revealed statistically significant dose-response relationships for both presenteeism and absenteeism in both sexes. The trend OR for presenteeism was 1.28 (95% CI: 1.09–1.50) for men and 1.43 (95% CI: 1.18–1.73) for women, and the trend OR for absenteeism was 1.30 (95% CI: 1.03–1.66) for men and 1.51 (95% CI: 1.04–2.19) for women. The unweighted sample sizes for each commute time group, stratified by sex, are presented in the footnote of Supplementary Table 1.
According to Supplementary Table 2, the interaction term between commute time (≥121 minutes) and sex was statistically significant for presenteeism in the crude model and model 1, but not in model 2. For absenteeism, neither model showed a statistically significant interaction between commute time and sex.
In a sensitivity analysis treating commute time as a continuous variable (minutes), longer commute times were significantly associated with higher odds of both outcomes for both sexes (Supplementary Table 3). In this analysis, ORs represent the change in odds associated with a one-minute increase in commute time.
Due to concerns that self-rated health status might act as a mediator, we conducted a sensitivity analysis in which this variable was excluded from the models (Supplementary Table 4). There was no significant change to the estimated associations between commute time and both presenteeism and absenteeism. This suggests that the results of this study are robust with regard to this variable.
This study examined how commute time relates to two work-related health outcomes, presenteeism and absenteeism, among Korean workers using a sex-stratified analytical approach. A dose-response pattern was observed, in which the OR increased as commute time increased in both presenteeism and absenteeism. While a statistically significant association with presenteeism was found for moderate commute times (61–120 minutes) in both men and women, no significant association with absenteeism emerged at this level. However, significant associations were identified for both presenteeism and absenteeism when commute times exceeded 120 minutes. Notably, women had higher odds than men for both presenteeism and absenteeism in the ≥121 minutes category. In model 2, however, the interaction between commute time and sex was not statistically significant for either presenteeism or absenteeism. Therefore, these sex-specific differences should be interpreted as descriptive patterns rather than statistically confirmed effect modification.
Notably, women had a higher baseline level of presenteeism than men. In this study, the predicted probability of presenteeism was higher for women (12.56%) than for men (9.17%) in the ≤60 minutes group. This suggests that women were at a disadvantage even before considering the additional burden of commuting. Furthermore, women experienced a greater percentage point increase in both presenteeism and absenteeism. This suggests that the observed sex differences in the ORs are also reflected in substantial differences in the predicted probabilities.
In today’s workplace, there is growing recognition that presenteeism leads to greater and more hidden productivity losses than absenteeism, because reduced work performance during working hours may be difficult to detect within an organization.16,26 On the other hand, absenteeism is not merely about missing work due to illness; it is a phenomenon where burnout, apathy toward work, and chronic workplace stress accumulate over time, leaving individuals feeling exhausted.17 Taken together, the two capture distinct but overlapping facets of how health problems erode productivity, with real consequences not just for individual organizations but for society as a whole.27
Previous studies have consistently found a positive association between commute time and both presenteeism and absenteeism. In Germany, a study using longitudinal panel data found that long commute times were associated with increased absenteeism.28 A study using data from Chinese workplaces found a significant association between commute time and absenteeism, suggesting that long commute time may be associated with higher odds of absenteeism.29 A quasi-longitudinal study of Australians examined the direct association between commute time and presenteeism.14 Similarly, a study based on the British Household Panel Survey suggested that commute time has a negative impact on self-rated health and productivity.30 Furthermore, by empirically suggesting that the stress of the morning commute reduces work flow and engagement, the study explained the psychological mechanisms underlying presenteeism.31
Several mechanisms help explain why long commutes tend to increase both presenteeism and absenteeism. Being stuck in traffic or spending extended periods on crowded public transport is physically and mentally exhausting. This leaves individuals feeling stressed, fatigued, and sleep-deprived by the time they arrive at work. One effective way to frame this is through the Effort-Recovery Model: when commuting effectively adds unpaid hours to the workday, workers arrive at their next shift without having properly recovered, which negatively impacts their performance.32 Wang et al.29 reported that depressive symptoms and sleep loss are key factors in the commute-productivity pathway. This pattern also applies to the Korean context. Longer commutes have been linked to higher rates of depression and anxiety among Korean workers,33 both of which are well-known factors that contribute to presenteeism or absenteeism.34 Furthermore, work-family conflict has been linked to both presenteeism and absenteeism.35 Among Korean wage workers, in particular, longer commute times were associated with higher levels of work-family conflict.36 These findings suggest that the chronic time constraints resulting from long commutes can hinder recovery and increase chronic stress, thereby contributing to both presenteeism and absenteeism.
In Korean society, this phenomenon may be further exacerbated by a workplace culture characterized by low sick leave rates and an unspoken expectation that employees should come to work even when they are sick.24 Korea has one of the lowest rates of sick leave usage in the OECD,37 with a disproportionately high rate of presenteeism relative to absenteeism,38 suggesting that Korean workers feel compelled to attend work even when sick. When organizational culture frames working while sick as a sign of dedication, presenteeism becomes not just a health issue but also a matter of adapting to the group.24
Meanwhile, compared to men, women with commute times of ≥121 minutes had notably higher odds of both presenteeism and absenteeism. In light of the situation faced by women, who bear the double burden of the pressures of paid employment and disproportionate responsibilities for household chores and childcare,39,40 the finding that longer commutes are associated with both presenteeism and absenteeism can be understood. Previous studies suggest that women exhibit higher rates of both than men, particularly in poor psychosocial work environments.41,42 Roberts et al.39 found that commuting significantly impacted women's psychological health negatively but not men's, attributing this to women's greater domestic responsibilities. A Korean study also found that commute time was associated with increased depression among women across all long-commute categories, while no significant association was observed in men.33 These findings are consistent with previous research and suggest that women’s heightened sensitivity to the burden of commuting may partly explain the observed patterns. These sex differences may stem from structural factors in the Korean labor market, where women are more likely to occupy positions with low occupational autonomy and unstable employment.43 Consequently, the burden of going to work intensifies, adding to the strain already caused by household responsibilities.24,39 Although these structural and role-related factors are important, biological and psychosocial factors that were not assessed in this study may also influence sex differences in stress sensitivity and the recovery gap.44,45 This suggests that the observed gap is influenced by the combined effects of biological and social factors. However, this study did not measure these biological and social factors. As a result, this remains a mere hypothesis requiring further investigation.
The findings of this study have significant implications for occupational health policy. Presenteeism is associated with long-term productivity loss and deteriorating health. Therefore, the consistent relationship between commuting and presenteeism requires policy attention.5,6,16 Flexible work arrangements, such as remote work options and flexible start times, can help alleviate the health burden associated with commuting.46 Employers and policymakers should recognize commute time as a modifiable factor relevant to occupational health, particularly in densely urbanized areas like Korea, where long commutes are prevalent.2,6,47 However, it should be noted that alternatives such as remote work do not necessarily yield uniformly positive effects, and their implementation should be approached with careful consideration of contextual factors.48
This study has several limitations. First, it relies on secondary data from the 6th KWCS, a nationally representative cross-sectional survey conducted with standardized questionnaires. Due to the survey's large scale, respondents may not have fully grasped the nuances of certain constructs. Additionally, the KWCS lacks validated scales to measure absenteeism and presenteeism as defined in the occupational health literature.49,50 As in previous studies using large-scale survey data on commuting and work outcomes, unmeasured confounders such as commuting mode, caregiving responsibilities, job insecurity, and characteristics of the psychosocial work environment may have influenced the observed associations.20 Therefore, residual confounders cannot be ruled out, and the estimated associations should be interpreted with caution. Second, since most variables were self-reported, they may be subject to recall bias. Third, the cross-sectional design precludes definitive causal inference and prevents us from determining the temporal order between commute time and presenteeism or absenteeism. Longitudinal studies focusing on Korean populations are necessary to determine the temporal sequence of these associations.
Despite these limitations, this study has the following strengths. To the best of our knowledge, it is the first study to examine the association between commute time and work-related productivity losses using nationally representative data from the Korean workforce. Based on the 6th KWCS, which reflects the demographic and occupational characteristics of the Korean workforce, this study provides population-level evidence that goes beyond findings from single-firm or regional studies. Second, we assessed work-related productivity losses comprehensively by analyzing both presenteeism and absenteeism simultaneously. These are key indicators of work-related productivity loss, a primary concern in today’s labor market. Third, we quantitatively evaluated sex differences in the Korean context using sex-stratified analyses and tests of the interaction between commute time and sex. These findings highlight the potential significance of long commutes as a focus of occupational health policies and workplace interventions in Korea. Further investigation through longitudinal studies is warranted.
This study showed that among Korean wage workers, longer commute times are associated with higher odds of presenteeism and absenteeism. Notably, the association with presenteeism was more consistent across commute time categories than the association with absenteeism. These findings support the view that commute time should be recognized as a modifiable factor relevant to occupational health that warrants attention in workplace and public policy development. Further longitudinal studies focused on the Korean population are necessary to clarify causal relationships and to create more targeted interventions.

CI

confidence interval

KWCS

Korean Working Conditions Survey

OECD

Organisation for Economic Co-operation and Development

OR

odds ratio

Funding

This work was supported by the Soonchunhyang University Research Fund.

Competing interests

Young-Sun Min, a contributing editor of the Annals of Occupational and Environmental Medicine, was not involved in the ed­itorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Author contributions

Conceptualization: Ahn TJ, Jang EC. Data curation: Ahn TJ, Jang EC, Kwon SC, Min YS, Yun J, Oh SH. Formal analysis: Ahn TJ, Jang EC, Kwon SC, Min YS, Yun J. Investigation: Ahn TJ, Jang EC, Yun J. Writing - original draft: Ahn TJ, Yun J. Writing - review & editing: Jang EC.

Acknowledgments

I would like to speak to my colleagues who have had a hard time. It's not your fault.

Supplementary Table 1.
ORs of presenteeism and absenteeism according to commute time.
aoem-2026-38-e31_Supplementary-Table-1.pdf
Supplementary Table 2.
Statistical significance of interaction term between sex and commute time.
aoem-2026-38-e31_Supplementary-Table-2.pdf
Supplementary Table 3.
ORs of presenteeism and absenteeism according to continuous commute time.
aoem-2026-38-e31_Supplementary-Table-3.pdf
Supplementary Table 4.
ORs of presenteeism and absenteeism according to commute time, excluding self-rated health status.
aoem-2026-38-e31_Supplementary-Table-4.pdf
Fig. 1.
Flow chart of the selection of study subjects. KWCS: Korean Working Conditions Survey.
aoem-2026-38-e31f1.jpg
Fig. 2.
ORs of presenteeism according to commute time. Model 1: adjusted for age, self-rated health status, education, and income (10,000 KRW/month). Model 2: adjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week). OR: odds ratio; CI: confidence interval.
aoem-2026-38-e31f2.jpg
Fig. 3.
ORs of absenteeism according to commute time. Model 1: adjusted for age, self-rated health status, education, and income (10,000 KRW/month). Model 2: adjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week). OR: odds ratio; CI: confidence interval.
aoem-2026-38-e31f3.jpg
Table 1.
General and occupational characteristics of the study population
Characteristic No. (%) (n = 28,349) No. (%) (n = 32,530)a
Sex
 Male 13,311 (47.0) 18,499 (56.9)
 Female 15,038 (53.0) 14,032 (43.1)
Age (years)
 20–29 3,706 (13.1) 5,409 (16.6)
 30–39 6,176 (21.8) 7,379 (22.7)
 40–49 6,894 (24.3) 8,045 (24.7)
 50–59 6,551 (23.1) 7,134 (21.9)
 ≥60 5,022 (17.7) 4,563 (14.0)
Self-rated health status
 Good 20,037 (70.7) 23,554 (72.4)
 Bad 8,312 (29.3) 8,977 (27.6)
Education
 Middle school or below 3,138 (11.1) 2,720 (8.4)
 High school 9,684 (34.2) 10,439 (32.1)
 College or above 15,527 (54.8) 19,371 (59.5)
Income (10,000 KRW/month)
 <200 8,919 (31.5) 8,938 (27.5)
 200–299 9,675 (34.1) 10,556 (32.5)
 300–399 5,750 (20.3) 7,170 (22.0)
 ≥400 4,005 (14.1) 5,866 (18.0)
Occupational type
 White collar 12,613 (44.5) 15,552 (50.8)
 Pink collar 6,590 (23.2) 5,575 (16.6)
 Blue collar 9,146 (32.3) 11,404 (32.6)
Working hours (hours/week)
 <40 6,021 (21.2) 6,144 (18.9)
 40–52 20,388 (71.9) 24,287 (74.7)
 >52 1,940 (6.8) 2,099 (6.5)
Company size
 <50 19,271 (68.0) 20,592 (63.3)
 50–299 4,789 (16.9) 6,186 (19.0)
 ≥300 4,289 (15.1) 5,753 (17.7)
Shift work
 No 25,612 (90.3) 29,227 (89.8)
 Yes 2,737 (9.7) 3,304 (10.2)
Commute time (minutes/day)
 ≤60 24,253 (85.6) 26,733 (82.1)
 61–120 3,523 (12.4) 4,898 (15.1)
 ≥121 573 (2.0) 900 (2.8)
Presenteeism
 No 25,170 (88.8) 28,873 (88.8)
 Yes 3,179 (11.2) 3,657 (11.2)
Absenteeism
 No 27,262 (96.2) 31,272 (96.1)
 Yes 1,087 (3.8) 1,258 (3.9)

Values are presented as numbers (%).

aNumbers reflect weighted frequencies rounded to the nearest whole number.

Table 2.
General and occupational characteristics of the participants according to presenteeism
Variable Male Female
Total No presenteeism Presenteeism p-valuea Total No presenteeism Presenteeism p-valuea
Age (years) <0.001 0.358
 20–29 2,563 2,423 (94.5) 140 (5.5) 2,846 2,512 (88.3) 333 (11.7)
 30–39 4,565 4,116 (90.2) 449 (9.8) 2,814 2,427 (86.2) 387 (13.8)
 40–49 4,839 4,269 (88.2) 571 (11.8) 3,206 2,767 (86.3) 438 (13.7)
 50–59 4,024 3,590 (89.2) 434 (10.8) 3,110 2,676 (86.1) 433 (13.9)
 ≥60 2,507 2,289 (91.3) 218 (8.7) 2,056 1,803 (87.7) 253 (12.3)
Self-rated health status <0.001 <0.001
 Good 13,594 12,764 (93.9) 830 (6.1) 9,960 9,111 (91.5) 849 (8.5)
 Bad 4,905 3,923 (80.0) 981 (20.0) 4,072 3,075 (75.5) 997 (24.5)
Education 0.227 0.123
 Middle school or below 1,162 1,038 (89.4) 123 (10.6) 1,559 1,371 (88.0) 188 (12.0)
 High school 5,973 5,347 (89.5) 626 (10.5) 4,466 3,916 (87.7) 550 (12.3)
 College or above 11,364 10,302 (90.7) 1,062 (9.3) 8,007 6,899 (86.2) 1,108 (13.8)
Income (10,000 KRW/month) 0.078 <0.001
 <200 2,824 2,607 (92.3) 217 (7.7) 6,114 5,443 (89.0) 671 (11.0)
 200–299 5,114 4,614 (90.2) 501 (9.8) 5,442 4,640 (85.3) 802 (14.7)
 300–399 5,471 4,916 (89.8) 555 (10.2) 1,698 1,455 (85.7) 243 (14.3)
 ≥400 5,089 4,551 (89.4) 538 (10.6) 777 648 (83.4) 129 (16.6)
Occupational type <0.01 0.018
 White collar 8,346 7,583 (90.9) 763 (9.1) 7,206 6,177 (85.7) 1,028 (14.3)
 Pink collar 1,945 1,816 (93.4) 129 (6.6) 3,630 3,201 (88.2) 429 (11.8)
 Blue collar 8,207 7,288 (88.8) 919 (11.2) 3,196 2,807 (87.8) 389 (12.2)
Company size 0.154 <0.001
 <50 10,508 9,540 (90.8) 967 (9.2) 10,084 8,865 (87.9) 1,219 (12.1)
 50–299 3,822 3,404 (89.1) 417 (10.9) 2,364 1,993 (84.3) 371 (15.7)
 ≥300 4,169 3,743 (89.8) 426 (10.2) 1,584 1,328 (83.8) 256 (16.2)
Shift work <0.01 <0.001
 No 16,329 14,795 (90.6) 1,533 (9.4) 12,898 11,288 (87.5) 1,610 (12.5)
 Yes 2,170 1,892 (87.2) 278 (12.8) 1,133 897 (79.2) 236 (20.8)
Working hours (hours/week) <0.001 <0.001
 <40 2,121 1,975 (93.1) 146 (6.9) 4,023 3,637 (90.4) 386 (9.6)
 40–52 14,866 13,442 (90.4) 1,424 (9.6) 9,421 8,068 (85.6) 1,354 (14.4)
 >52 1,512 1,271 (84.1) 241 (15.9) 587 481 (81.9) 106 (18.1)
Commute time (minutes/day) <0.001 <0.001
 ≤60 14,557 13,235 (90.9) 1,322 (9.1) 12,176 10,678 (87.7) 1,499 (12.3)
 61–120 3,318 2,919 (88.0) 400 (12.0) 1,580 1,326 (83.9) 254 (16.1)
 ≥121 624 535 (85.7) 89 (14.3) 276 183 (66.2) 93 (33.8)

Values are presented as numbers (%).

All numbers reflect weighted frequencies rounded to the nearest whole number.

aχ2 test for categorical variables.

Table 3.
General and occupational characteristics of the participants according to absenteeism
Variable Male Female
Total No absenteeism Absenteeism p-valuea Total No absenteeism Absenteeism p-valuea
Age (years) 0.109 0.246
 20–29 2,563 2,470 (96.3) 94 (3.7) 2,846 2,735 (96.1) 111 (3.9)
 30–39 4,565 4,384 (96.0) 181 (4.0) 2,814 2,676 (95.1) 138 (4.9)
 40–49 4,839 4,621 (95.5) 218 (4.5) 3,206 3,070 (95.8) 136 (4.2)
 50–59 4,024 3,894 (96.8) 129 (3.2) 3,110 2,978 (95.7) 132 (4.3)
 ≥60 2,507 2,447 (97.6) 60 (2.4) 2,056 1,997 (97.1) 59 (2.9)
Self-rated health status <0.001 <0.001
 Good 13,594 13,283 (97.7) 311 (2.3) 9,960 9,692 (97.3) 268 (2.7)
 Bad 4,905 4,533 (92.4) 372 (7.6) 4,072 3,764 (92.4) 308 (7.6)
Education 0.976 <0.01
 Middle school or below 1,162 1,120 (96.4) 42 (3.6) 1,559 1,528 (98.0) 31 (2.0)
 High school 5,973 5,755 (96.4) 218 (3.6) 4,466 4,285 (96.0) 181 (4.0)
 College or above 11,364 10,941 (96.3) 423 (3.7) 8,007 7,643 (95.5) 364 (4.5)
Income (10,000 KRW/month) 0.051 <0.001
 <200 2,824 2,743 (97.1) 82 (3.6) 6,114 5,941 (97.2) 173 (2.8)
 200–299 5,114 4,876 (95.3) 238 (4.7) 5,442 5,182 (95.2) 260 (4.8)
 300–399 5,471 5,280 (96.5) 191 (3.5) 1,698 1,612 (94.9) 86 (5.1)
 ≥400 5,089 4,917 (96.6) 171 (3.4) 777 720 (92.7) 57 (7.3)
Occupational type 0.080 0.012
 White collar 8,346 8,053 (96.5) 293 (3.5) 7,206 6,862 (95.2) 344 (4.8)
 Pink collar 1,945 1,895 (97.4) 50 (2.6) 3,630 3,507 (96.6) 123 (3.4)
 Blue collar 8,207 7,869 (95.9) 339 (4.1) 3,196 3,087 (96.6) 109 (3.4)
Company size 0.595 <0.001
 <50 10,508 10,098 (96.1) 410 (3.9) 10,084 9,749 (96.7) 334 (3.3)
 50–299 3,822 3,690 (96.6) 131 (3.4) 2,364 2,225 (94.1) 139 (5.9)
 ≥300 4,169 4,027 (96.6) 141 (3.4) 1,584 1,481 (93.5) 102 (6.5)
Shift work 0.944 0.437
 No 16,329 15,725 (96.3) 604 (3.7) 12,898 12,375 (95.9) 523 (4.1)
 Yes 2,170 2,092 (96.4) 79 (3.6) 1,133 1,081 (95.4) 53 (4.6)
Working hours (hours/week) <0.01 <0.001
 <40 2,121 2,055 (96.9) 66 (3.1) 4,023 3,931 (97.7) 92 (2.3)
 40–52 14,866 14,336 (96.4) 530 (3.6) 9,421 8,964 (95.1) 456 (4.9)
 >52 1,512 1,426 (94.3) 87 (5.7) 587 560 (95.4) 27 (4.6)
Commute time (minutes/day) 0.036 <0.001
 ≤60 14,557 14,060 (96.6) 496 (3.4) 12,176 11,726 (96.3) 451 (3.7)
 61–120 3,318 3,172 (95.6) 146 (4.4) 1,580 1,498 (94.8) 82 (5.2)
 ≥121 624 583 (93.5) 40 (6.5) 276 233 (84.3) 43 (15.7)

Values are presented as numbers (%).

All numbers reflect weighted frequencies rounded to the nearest whole number.

aχ2 test for categorical variables.

Table 4.
Adjusted predicted probability of presenteeism and absenteeism
Baseline probability (%) Long commute probability (%) Absolute difference (%p)
Male
 Presenteeism
  Crude 9.08 14.31 5.23
   Model 1a 9.21 12.33 3.12
   Model 2b 9.17 12.84 3.67
 Absenteeism
  Crude 3.41 6.46 3.05
   Model 1a 3.46 5.62 2.16
   Model 2b 3.44 5.84 2.40
Female
 Presenteeism
  Crude 12.31 33.85 21.54
   Model 1a 12.51 26.22 13.71
   Model 2b 12.56 25.66 13.10
 Absenteeism
  Crude 3.70 15.67 11.97
   Model 1a 3.80 11.04 7.24
   Model 2b 3.84 10.30 6.46

Baseline probability: predicted probability in the ≤60 minutes commute group; Long commute probability: predicted probability in the ≥121 minutes commute group; Absolute difference: absolute difference between these two probabilities (percentage points).

aAdjusted for age, self-rated health status, education, and income (10,000 KRW/month);

bAdjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week).

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        The association between commute time and both presenteeism and absenteeism: a cross-sectional analysis of the 6th Korean Working Conditions Survey
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      Related articles
      The association between commute time and both presenteeism and absenteeism: a cross-sectional analysis of the 6th Korean Working Conditions Survey
      Image Image Image
      Fig. 1. Flow chart of the selection of study subjects. KWCS: Korean Working Conditions Survey.
      Fig. 2. ORs of presenteeism according to commute time. Model 1: adjusted for age, self-rated health status, education, and income (10,000 KRW/month). Model 2: adjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week). OR: odds ratio; CI: confidence interval.
      Fig. 3. ORs of absenteeism according to commute time. Model 1: adjusted for age, self-rated health status, education, and income (10,000 KRW/month). Model 2: adjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week). OR: odds ratio; CI: confidence interval.

      Fig. 1.

      Fig. 2.

      Fig. 3.

      The association between commute time and both presenteeism and absenteeism: a cross-sectional analysis of the 6th Korean Working Conditions Survey
      Characteristic No. (%) (n = 28,349) No. (%) (n = 32,530)a
      Sex
       Male 13,311 (47.0) 18,499 (56.9)
       Female 15,038 (53.0) 14,032 (43.1)
      Age (years)
       20–29 3,706 (13.1) 5,409 (16.6)
       30–39 6,176 (21.8) 7,379 (22.7)
       40–49 6,894 (24.3) 8,045 (24.7)
       50–59 6,551 (23.1) 7,134 (21.9)
       ≥60 5,022 (17.7) 4,563 (14.0)
      Self-rated health status
       Good 20,037 (70.7) 23,554 (72.4)
       Bad 8,312 (29.3) 8,977 (27.6)
      Education
       Middle school or below 3,138 (11.1) 2,720 (8.4)
       High school 9,684 (34.2) 10,439 (32.1)
       College or above 15,527 (54.8) 19,371 (59.5)
      Income (10,000 KRW/month)
       <200 8,919 (31.5) 8,938 (27.5)
       200–299 9,675 (34.1) 10,556 (32.5)
       300–399 5,750 (20.3) 7,170 (22.0)
       ≥400 4,005 (14.1) 5,866 (18.0)
      Occupational type
       White collar 12,613 (44.5) 15,552 (50.8)
       Pink collar 6,590 (23.2) 5,575 (16.6)
       Blue collar 9,146 (32.3) 11,404 (32.6)
      Working hours (hours/week)
       <40 6,021 (21.2) 6,144 (18.9)
       40–52 20,388 (71.9) 24,287 (74.7)
       >52 1,940 (6.8) 2,099 (6.5)
      Company size
       <50 19,271 (68.0) 20,592 (63.3)
       50–299 4,789 (16.9) 6,186 (19.0)
       ≥300 4,289 (15.1) 5,753 (17.7)
      Shift work
       No 25,612 (90.3) 29,227 (89.8)
       Yes 2,737 (9.7) 3,304 (10.2)
      Commute time (minutes/day)
       ≤60 24,253 (85.6) 26,733 (82.1)
       61–120 3,523 (12.4) 4,898 (15.1)
       ≥121 573 (2.0) 900 (2.8)
      Presenteeism
       No 25,170 (88.8) 28,873 (88.8)
       Yes 3,179 (11.2) 3,657 (11.2)
      Absenteeism
       No 27,262 (96.2) 31,272 (96.1)
       Yes 1,087 (3.8) 1,258 (3.9)
      Variable Male Female
      Total No presenteeism Presenteeism p-valuea Total No presenteeism Presenteeism p-valuea
      Age (years) <0.001 0.358
       20–29 2,563 2,423 (94.5) 140 (5.5) 2,846 2,512 (88.3) 333 (11.7)
       30–39 4,565 4,116 (90.2) 449 (9.8) 2,814 2,427 (86.2) 387 (13.8)
       40–49 4,839 4,269 (88.2) 571 (11.8) 3,206 2,767 (86.3) 438 (13.7)
       50–59 4,024 3,590 (89.2) 434 (10.8) 3,110 2,676 (86.1) 433 (13.9)
       ≥60 2,507 2,289 (91.3) 218 (8.7) 2,056 1,803 (87.7) 253 (12.3)
      Self-rated health status <0.001 <0.001
       Good 13,594 12,764 (93.9) 830 (6.1) 9,960 9,111 (91.5) 849 (8.5)
       Bad 4,905 3,923 (80.0) 981 (20.0) 4,072 3,075 (75.5) 997 (24.5)
      Education 0.227 0.123
       Middle school or below 1,162 1,038 (89.4) 123 (10.6) 1,559 1,371 (88.0) 188 (12.0)
       High school 5,973 5,347 (89.5) 626 (10.5) 4,466 3,916 (87.7) 550 (12.3)
       College or above 11,364 10,302 (90.7) 1,062 (9.3) 8,007 6,899 (86.2) 1,108 (13.8)
      Income (10,000 KRW/month) 0.078 <0.001
       <200 2,824 2,607 (92.3) 217 (7.7) 6,114 5,443 (89.0) 671 (11.0)
       200–299 5,114 4,614 (90.2) 501 (9.8) 5,442 4,640 (85.3) 802 (14.7)
       300–399 5,471 4,916 (89.8) 555 (10.2) 1,698 1,455 (85.7) 243 (14.3)
       ≥400 5,089 4,551 (89.4) 538 (10.6) 777 648 (83.4) 129 (16.6)
      Occupational type <0.01 0.018
       White collar 8,346 7,583 (90.9) 763 (9.1) 7,206 6,177 (85.7) 1,028 (14.3)
       Pink collar 1,945 1,816 (93.4) 129 (6.6) 3,630 3,201 (88.2) 429 (11.8)
       Blue collar 8,207 7,288 (88.8) 919 (11.2) 3,196 2,807 (87.8) 389 (12.2)
      Company size 0.154 <0.001
       <50 10,508 9,540 (90.8) 967 (9.2) 10,084 8,865 (87.9) 1,219 (12.1)
       50–299 3,822 3,404 (89.1) 417 (10.9) 2,364 1,993 (84.3) 371 (15.7)
       ≥300 4,169 3,743 (89.8) 426 (10.2) 1,584 1,328 (83.8) 256 (16.2)
      Shift work <0.01 <0.001
       No 16,329 14,795 (90.6) 1,533 (9.4) 12,898 11,288 (87.5) 1,610 (12.5)
       Yes 2,170 1,892 (87.2) 278 (12.8) 1,133 897 (79.2) 236 (20.8)
      Working hours (hours/week) <0.001 <0.001
       <40 2,121 1,975 (93.1) 146 (6.9) 4,023 3,637 (90.4) 386 (9.6)
       40–52 14,866 13,442 (90.4) 1,424 (9.6) 9,421 8,068 (85.6) 1,354 (14.4)
       >52 1,512 1,271 (84.1) 241 (15.9) 587 481 (81.9) 106 (18.1)
      Commute time (minutes/day) <0.001 <0.001
       ≤60 14,557 13,235 (90.9) 1,322 (9.1) 12,176 10,678 (87.7) 1,499 (12.3)
       61–120 3,318 2,919 (88.0) 400 (12.0) 1,580 1,326 (83.9) 254 (16.1)
       ≥121 624 535 (85.7) 89 (14.3) 276 183 (66.2) 93 (33.8)
      Variable Male Female
      Total No absenteeism Absenteeism p-valuea Total No absenteeism Absenteeism p-valuea
      Age (years) 0.109 0.246
       20–29 2,563 2,470 (96.3) 94 (3.7) 2,846 2,735 (96.1) 111 (3.9)
       30–39 4,565 4,384 (96.0) 181 (4.0) 2,814 2,676 (95.1) 138 (4.9)
       40–49 4,839 4,621 (95.5) 218 (4.5) 3,206 3,070 (95.8) 136 (4.2)
       50–59 4,024 3,894 (96.8) 129 (3.2) 3,110 2,978 (95.7) 132 (4.3)
       ≥60 2,507 2,447 (97.6) 60 (2.4) 2,056 1,997 (97.1) 59 (2.9)
      Self-rated health status <0.001 <0.001
       Good 13,594 13,283 (97.7) 311 (2.3) 9,960 9,692 (97.3) 268 (2.7)
       Bad 4,905 4,533 (92.4) 372 (7.6) 4,072 3,764 (92.4) 308 (7.6)
      Education 0.976 <0.01
       Middle school or below 1,162 1,120 (96.4) 42 (3.6) 1,559 1,528 (98.0) 31 (2.0)
       High school 5,973 5,755 (96.4) 218 (3.6) 4,466 4,285 (96.0) 181 (4.0)
       College or above 11,364 10,941 (96.3) 423 (3.7) 8,007 7,643 (95.5) 364 (4.5)
      Income (10,000 KRW/month) 0.051 <0.001
       <200 2,824 2,743 (97.1) 82 (3.6) 6,114 5,941 (97.2) 173 (2.8)
       200–299 5,114 4,876 (95.3) 238 (4.7) 5,442 5,182 (95.2) 260 (4.8)
       300–399 5,471 5,280 (96.5) 191 (3.5) 1,698 1,612 (94.9) 86 (5.1)
       ≥400 5,089 4,917 (96.6) 171 (3.4) 777 720 (92.7) 57 (7.3)
      Occupational type 0.080 0.012
       White collar 8,346 8,053 (96.5) 293 (3.5) 7,206 6,862 (95.2) 344 (4.8)
       Pink collar 1,945 1,895 (97.4) 50 (2.6) 3,630 3,507 (96.6) 123 (3.4)
       Blue collar 8,207 7,869 (95.9) 339 (4.1) 3,196 3,087 (96.6) 109 (3.4)
      Company size 0.595 <0.001
       <50 10,508 10,098 (96.1) 410 (3.9) 10,084 9,749 (96.7) 334 (3.3)
       50–299 3,822 3,690 (96.6) 131 (3.4) 2,364 2,225 (94.1) 139 (5.9)
       ≥300 4,169 4,027 (96.6) 141 (3.4) 1,584 1,481 (93.5) 102 (6.5)
      Shift work 0.944 0.437
       No 16,329 15,725 (96.3) 604 (3.7) 12,898 12,375 (95.9) 523 (4.1)
       Yes 2,170 2,092 (96.4) 79 (3.6) 1,133 1,081 (95.4) 53 (4.6)
      Working hours (hours/week) <0.01 <0.001
       <40 2,121 2,055 (96.9) 66 (3.1) 4,023 3,931 (97.7) 92 (2.3)
       40–52 14,866 14,336 (96.4) 530 (3.6) 9,421 8,964 (95.1) 456 (4.9)
       >52 1,512 1,426 (94.3) 87 (5.7) 587 560 (95.4) 27 (4.6)
      Commute time (minutes/day) 0.036 <0.001
       ≤60 14,557 14,060 (96.6) 496 (3.4) 12,176 11,726 (96.3) 451 (3.7)
       61–120 3,318 3,172 (95.6) 146 (4.4) 1,580 1,498 (94.8) 82 (5.2)
       ≥121 624 583 (93.5) 40 (6.5) 276 233 (84.3) 43 (15.7)
      Baseline probability (%) Long commute probability (%) Absolute difference (%p)
      Male
       Presenteeism
        Crude 9.08 14.31 5.23
         Model 1a 9.21 12.33 3.12
         Model 2b 9.17 12.84 3.67
       Absenteeism
        Crude 3.41 6.46 3.05
         Model 1a 3.46 5.62 2.16
         Model 2b 3.44 5.84 2.40
      Female
       Presenteeism
        Crude 12.31 33.85 21.54
         Model 1a 12.51 26.22 13.71
         Model 2b 12.56 25.66 13.10
       Absenteeism
        Crude 3.70 15.67 11.97
         Model 1a 3.80 11.04 7.24
         Model 2b 3.84 10.30 6.46
      Table 1. General and occupational characteristics of the study population

      Values are presented as numbers (%).

      Numbers reflect weighted frequencies rounded to the nearest whole number.

      Table 2. General and occupational characteristics of the participants according to presenteeism

      Values are presented as numbers (%).

      All numbers reflect weighted frequencies rounded to the nearest whole number.

      χ2 test for categorical variables.

      Table 3. General and occupational characteristics of the participants according to absenteeism

      Values are presented as numbers (%).

      All numbers reflect weighted frequencies rounded to the nearest whole number.

      χ2 test for categorical variables.

      Table 4. Adjusted predicted probability of presenteeism and absenteeism

      Baseline probability: predicted probability in the ≤60 minutes commute group; Long commute probability: predicted probability in the ≥121 minutes commute group; Absolute difference: absolute difference between these two probabilities (percentage points).

      Adjusted for age, self-rated health status, education, and income (10,000 KRW/month);

      Adjusted for age, self-rated health status, education, income (10,000 KRW/month), occupational type, company size, shift work, and working hours (hours/week).

      Table 1.

      Table 2.

      Table 3.

      Table 4.

      Ann Occup Environ Med : Annals of Occupational and Environmental Medicine
      © Korean Society of Occupational & Environmental Medicine.
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