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HOME > Ann Occup Environ Med > Volume 34; 2022 > Article
Original Article Mental health symptoms among dependent contractors in Korea: a cross-sectional study based on the Fifth Korean Working Condition Survey
Seong-Uk Baek1,2,3orcid, Sung-Shil Lim1,2orcid, Sehyun Yun1,2,4orcid, Won-Tae Lee1,2,4orcid, Min-Seok Kim1,2,4orcid, Jin-Ha Yoon2,4,5orcid, Jong-Uk Won1,2,4orcid
Annals of Occupational and Environmental Medicine 2022;34:e1.
DOI: https://doi.org/10.35371/aoem.2022.34.e1
Published online: February 10, 2022

1Department of Occupational and Environmental Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

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Correspondence: Jong-Uk Won. Department of Occupational and Environmental Medicine, Severance Hospital, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. juwon@yuhs.ac
• Received: November 17, 2021   • Accepted: January 26, 2022

Copyright © 2022 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
    Recently, there has been a call to improve the holistic welfare of dependent contractors (DCs). Thus, our study examined the relationship between DCs and mental health symptoms and how this relationship was modified by age, sex, and income status of workers.
  • Methods
    A total of 27,980 workers from the Fifth Korean Working Conditions Survey are included in our study. The participants who reported having depression or anxiety over the last 12 months are defined those who had mental health symptoms. We performed exact matching for age group and sex, followed by conditional logistic regression with survey weights. Finally, stratified analyses by age, sex and income level were conducted.
  • Results
    DCs were found to be at increased risk of depression/anxiety compared to other workers. The odds ratio (OR) is 1.52 (95% confidence interval [CI]: 1.06–2.17). In the stratified analyses, vulnerable groups were middle-aged (OR [95% CI]: 1.68 [1.10–2.54]), female (OR [95% CI]: 1.85 [1.20–2.84]), and low-income (OR [95% CI]: 3.18 [1.77–5.73]) workers.
  • Conclusions
    Our study's results reinforce those of other studies that show that DCs are at greater risk of experiencing mental health issues than other workers and that and this risk is greater for middle-aged, female, and low-income workers. These results suggest that appropriate policy efforts should be made to improve the psychological well-being of DCs.
  • Keywords: Dependent contractor; Worker in special employment types; Depression; Anxiety
Over the last few decades, the traditional binary classification of employment status as either self-employed or employee has been largely challenged by changes in labor relationships.1 Dependent contractors (DCs), also called dependent self-employed workers or special employment-types workers, provide labor services under a work consignment contract, not an employment contract. Such workers are not protected by labor laws because they are not classified as employees. Thus, there have been increasing calls for protection for DCs recently.2 In response, the International Labor Office created a separate category for DCs in the International Classification of Status in Employment-18.3
According to the Korea National Statistical Office (KNSO), there were nearly 500,000 DCs, accounting for 2.4% of all wage workers and 24.0% of non-typical workers.4 Typical DC occupations include insurance salespersons, visiting teachers, golf caddies, and truck drivers in Korea. However, the recent changes in the economic structure, such as the rise of the gig economy, have contributed to the increasing number of jobs classified as DC.5 The number of DC-related occupations covered by the Industrial Accident Compensation Insurance Act in Korea increased from 4 in 2008 to 14 in 2021, newly including software technicians and platform workers, such as door-to-door deliverers and drivers.6
DCs are known to have low job security, high income volatility, and lack of social protection because they are not protected by labor laws.2,7 Precarious employment harms workers' physical and psychological health such as putting them at an increased risk of work-related injuries, cardiovascular disease, and psychological disorders.8,9 Among the dimensions of precarious employment, employment insecurity has been consistently reported in multiple studies as having an adverse effect on workers' mental health. For instance, temporary/daily work is associated with depressive mood in Korea.10 On the other hand, the effect of exclusion from labor protection law and social security system is left undiscovered, though one previous study found employment insurance can protect psychological health.11 Moreover, a recent systematic analysis on precarious employment mentioned that the income volatility has not yet been addressed among the dimensions of precarious employment.12
Job insecurity can have differential effects by worker's characteristics,13 such as their family responsibilities,14 age,15 and sex.16 However, it is unclear whether job insecurity has differential effects by income status. DCs are often exposed not only to job insecurity but also high level of income volatility because of their performance-based earning system.17 Also, they have been excluded from social security, regulatory protection and labor policy because they are not classified as employees.18,19 Considering income instability and lack of social protection have been addressed as key features of DC, these factors need to be taken into account when investigating the mental health of DC. However, majority of prior studies on precarious employment have mainly focused on employment insecurity, and little is known about the health effects of other characteristics of DC.
Mental disorders of workers affect their psychological well-being and lower their work efficiency and productivity.20 Factors associated with the poor mental health of workers include hazardous working conditions, poor work–life balance, workplace discrimination, and job insecurity.21 However, proper intervention that reduce occupational stress could help protect workers' mental health.22
Most studies on DCs have examined their legal status, while only a few have examined their psychological well-being of DC. Furthermore, to our knowledge, there is no analysis of how the effects of being a DC differs by demographic group. However, precarious work condition has a detrimental effect on mental health,23 so it was hypothesized that DCs are more at risk of poor psychological health than other workers. Therefore, our study investigated the characteristics of DCs, the relationship between DC status and mental health symptoms, and how this relationship changed by age, sex and income level.
Study design and study population
This study examined responses to the Fifth Korean Working Conditions Survey (KWCS), which was conducted from June to September 2017. The Occupational Safety and Health Research Institute (OSHRI) has conducted the KWCS every 5 years since 2006. Participants are at least 15 years old and are selected by multistage, stratified, random sampling. Participants are interviewed individually by an expert interviewer. They are surveyed about their occupation as classified by the Korean Standard Classification of Occupations (KSCO), working environment, work characteristics, sociopsychological characteristics, health, and demographic characteristics. Raw KWCS data is available in both Korean and English upon request (https://www.kosha.or.kr/eoshri/index.do).
The Fifth KWCS had 50,205 participants, but our study only examined wage workers (n = 30,108). DCs are classified as wageworkers by the KWCS. Soldiers (n = 93) and those who did not answer at least one question were excluded from analysis (n = 2,035). Finally, 27,980 participants were included in the current study (Fig. 1).
Fig. 1

Flowchart of selection of study population.

aoem-34-e1-g001.jpg
Main variables
The KWCS classifies DC according to whether the participants' earnings are performance-based or not (“Does your income correspond to how many clients you meet and you provide prizes or services (performance-based earning system)?”). On the other hand, Article 125 of the Industrial Accident Compensation Insurance Act states only insurance salesperson, construction machine operator, visiting teacher, golf caddie, door-to-door deliverer, quick service deliverer, loan solicitor, credit card solicitor, designated driving service worker, door-to-door salesperson, door-to-door rental equipment examiner, electrical and electronic home appliance fitters and repairer, truck driver, and software technician can be classified as DC.9 In our study, among the workers belonging to these 14 designated occupations by the law (n = 1,575), those whose earning was not performance-based were excluded from the DC (n = 788). In other words, those who have (1) performance-based earning system and (2) occupations within the scope of a DC, according to the Industrial Accident Compensation Insurance Act in Korea are classified as DC in our research design (n = 787). Four-digit 6th KSCO codes are used for classification.
Regarding their mental health, participants were asked, “Over the last 12 months, did you have any of the following health problems?” to which they could respond “Yes” or “No” to each problem. Those who responded “Yes” to depression or anxiety were defined as having mental health symptoms.
Covariates
Age, sex, education level, income category, occupation, working hours, shift work, and psychosocial job demands were possible confounding variables that were adjusted for. Age groups were classified as 15–29, 30–39, 40–49, 50–59, and ≥ 60 years old. Income category was classified into tertiles according to monthly income. Education level was classified as having completed middle school or lower, high school, and or college. Occupation was classified as blue-collar work, service and sales work, or white-collar work based on the KSCO. Managers, professionals and related workers, and clerks were grouped as white-collar workers. Service workers and sales workers were classified as service and sales workers. Skilled agricultural, forestry, and fishery workers; craft and related trades workers; plant workers, machine operators, and assemblers; and elementary workers were classified as blue-collar workers. Working hours per week were classified as < 40, 41–52, and ≥ 53. Workers were asked to answer “Does your job involve handling angry clients?”. According to the answer, participants were classified as “Rarely” (never, almost never), “Sometimes” (1/4 of working hours, half of working hours), and “Always” (3/4 of working hours, almost always, always). Participants were also asked to answer “Does your job require that you hide your feelings?”. Participants were classified as “Rarely” (never, rarely), “Sometimes” (sometimes), and “Always” (most of the time, always).
Statistical analysis
First, we performed exact matching for age groups and sex through the package “MatchIt” in R, resulting in 787 DCs being matched with 27,193 other types of wageworkers. None of the participants was excluded by this exact matching procedure. After matching, a χ2 test was used to compare the characteristics of workers by DC status. Conditional logistic regression with survey weights was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Next, we stratified participants by age, sex and income level to examine the different impact of dependent contracting by worker's sociodemographic characteristics. Age stratification was conducted for the < 40 (young), 40–59 (middle-aged), and ≥ 60 (old) groups. For sensitivity analysis, weighted unconditional logistic regression was performed, and the outcomes are presented in the supplementary material. All statistical analyses and visualizations were performed using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria).
Ethics statement
This study was approved by the Institutional Review Board of Severance Hospital (approval number: 4-2021-1303).
Of the 27,980 wageworkers' responses examined in this study, 787 (2.8%) were DCs. The sociodemographic characteristics of the study population are shown in Table 1. Of the DCs, 76.6% of workers were female and 70.9% were service and sales workers. Compared to the control group, DCs were more likely to be exposed to psychological job demands. Of the DCs, 6.0% had mental health symptoms while only 3.6% of other workers did.
Table 1

Sociodemographic characteristic of study sample by employment type in the Fifth Korean Working Conditions Survey

Characteristics Total (n = 27,980) Dependent contractor p-value
Yes (n = 787) No (n = 27,193)
Mental health symptoms (Depression/Anxiety) 0.001
No 26,949 (96.3) 740 (94.0) 26,209 (96.4)
Yes 1,031 (3.7) 47 (6.0) 984 (3.6)
Age < 0.001
< 30 3,871 (13.8) 28 (3.6) 3,843 (14.1)
30–39 6,399 (22.9) 116 (14.7) 6,283 (23.1)
40–49 7,182 (25.7) 269 (34.2) 6,913 (25.4)
50–59 6,399 (22.9) 313 (39.8) 6,086 (22.4)
≥ 60 4,129 (14.8) 61 (7.8) 4,068 (15.0)
Sex < 0.001
Male 13,402 (47.9) 183 (23.3) 13,219 (48.6)
Female 14,578 (52.1) 604 (76.7) 13,974 (51.4)
Education level < 0.001
Middle school or lower 3,498 (12.5) 38 (4.8) 3,460 (12.7)
High school 9,735 (34.8) 368 (46.8) 9,367 (34.4)
College 14,747 (52.7) 381 (48.4) 14,366 (52.8)
Income category < 0.001
Low 10,845 (38.8) 154 (19.6) 10,691 (39.3)
Middle 8,488 (30.3) 325 (41.3) 8,163 (30.0)
High 8,647 (30.9) 308 (39.1) 8,339 (30.7)
Occupation < 0.001
Blue-collar workers 9,312 (33.3) 118 (15.0) 9,194 (33.8)
Service/Sales worker 7,416 (26.5) 558 (70.9) 6,858 (25.2)
White-collar workers 11,252 (40.2) 111 (14.1) 11,141 (41.0)
Working hour per week 0.006
≤ 40 16,394 (58.6) 502 (63.8) 15,892 (58.4)
41–52 7,646 (27.3) 198 (25.2) 7,448 (27.4)
≥ 53 3,940 (14.1) 87 (11.1) 3,853 (14.2)
Shift work < 0.001
No 24,527 (87.7) 778 (98.9) 23,749 (87.3)
Yes 3,453 (12.3) 9 (1.1) 3,444 (12.7)
Handling angry clients < 0.001
Rarely 21,327 (76.2) 499 (63.4) 20,828 (76.6)
Sometimes 4,912 (17.6) 202 (25.7) 4,710 (17.3)
Always 1,741 (6.2) 86 (10.9) 1,655 (6.1)
Suppressing emotion < 0.001
Rarely 5,911 (21.1) 104 (13.2) 5,807 (21.4)
Sometimes 10,700 (38.2) 258 (32.8) 10,442 (38.4)
Always 11,369 (40.6) 425 (54.0) 10,944 (40.2)
Values are presented as number (%).
The prevalence of mental health symptoms by participant characteristics is presented in Table 2. The prevalence was high among the middle-aged group (40–49 and 50–59) than the other groups, and there was no significant difference between males and females. The proportion of workers with mental problems was high among workers handling angry clients and suppressing their emotion at work.
Table 2

Prevalence of mental health symptoms among the study sample

Characteristics Dependent contractor
Yes No
Depression/Anxiety p-value Depression/Anxiety p-value
Yes (n = 40) No (n = 740) Yes (n = 984) No (n = 26,209)
Age 0.519 < 0.001
< 30 2 (7.1) 26 (92.9) 110 (2.9) 3,733 (97.1)
30–39 3 (2.6) 113 (97.4) 182 (2.9) 6,101 (97.1)
40–49 17 (6.3) 252 (93.7) 261 (3.8) 6,652 (96.2)
50–59 22 (7.0) 291 (93.0) 260 (4.3) 5,826 (95.7)
≥ 60 3 (4.9) 58 (95.1) 171 (4.2) 3,897 (95.8)
Sex 0.611 0.854
Male 9 (4.9) 174 (95.1) 475 (3.6) 12,744 (96.4)
Female 38 (6.3) 566 (93.7) 509 (3.6) 13,465 (96.4)
Education level 0.660 < 0.001
Middle school or lower 2 (5.3) 36 (94.7) 171 (4.9) 3,289 (95.1)
High school 25 (6.8) 343 (93.2) 345 (3.7) 9,022 (96.3)
College 20 (5.2) 361 (94.8) 468 (3.3) 13,898 (96.7)
Income category 0.079 0.003
Low 15 (9.7) 139 (90.3) 428 (4.0) 10,263 (96.0)
Middle 15 (4.6) 310 (95.4) 251 (3.1) 7,912 (96.9)
High 17 (5.5) 291 (94.5) 305 (3.7) 8,034 (96.3)
Occupation 0.570 0.021
Blue-collar workers 6 (5.1) 112 (94.9) 356 (3.9) 8,838 (96.1)
Service/Sales workers 32 (5.7) 526 (94.3) 267 (3.9) 6,591 (96.1)
White-collar workers 9 (8.1) 102 (91.9) 361 (3.2) 10,780 (96.8)
Working hours per week 0.491 0.001
≤ 40 31 (6.2) 471 (93.8) 521 (3.3) 15,371 (96.7)
41–52 9 (4.5) 189 (95.5) 296 (4.0) 7,152 (96.0)
≥ 53 7 (8.0) 80 (92.0) 167 (4.3) 3,686 (95.7)
Shift work 0.958 0.020
No 47 (6.0) 731 (94.0) 835 (3.5) 22,914 (96.5)
Yes 0 (0.0) 9 (100.0) 149 (4.3) 3,295 (95.7)
Handling angry clients < 0.001 < 0.001
Rarely 20 (4.0) 479 (96.0) 663 (3.2) 20,165 (96.8)
Sometimes 14 (6.9) 188 (93.1) 214 (4.5) 4,496 (95.5)
Always 13 (15.1) 73 (84.9) 107 (6.4) 1,548 (93.5)
Suppressing emotion 0.001 < 0.001
Rarely 1 (1.0) 103 (99.0) 135 (2.3) 5,672 (97.7)
Sometimes 8 (3.1) 250 (96.9) 340 (3.3) 10,102 (96.7)
Always 38 (8.9) 387 (91.1) 509 (4.6) 10,435 (95.3)
Values are presented as number (%).
The association between DC and mental health symptoms is shown in Table 3. After adjusting for socioeconomic status and working conditions, there was a statistically significant correlation between DC and mental health symptoms (OR [95% CI]: 1.52 [1.06–2.17]). In addition, our fully adjusted Model C shows that long working hours, shift work, handling angry customers, and suppressing one's emotion were significantly correlated with mental health symptoms.
Table 3

Association between DC status and mental health symptoms

Covariates Model A Model B Model C
Dependent contractor
No Reference Reference Reference
Yes 1.55 (1.10–2.18) 1.58 (1.10–2.26) 1.52 (1.06–2.17)
Education level
Middle school or lower Reference Reference
High school 0.79 (0.60–1.06) 0.72 (0.54–0.96)
College 0.77 (0.55–1.07) 0.70 (0.51–0.98)
Income category
Low Reference Reference
Middle 0.80 (0.65–1.00) 0.76 (0.61–0.94)
High 0.88 (0.70–1.11) 0.86 (0.69–1.08)
Occupation
Blue-collar workers Reference Reference
Service/Sales worker 1.09 (0.88–1.36) 0.95 (0.76–1.18)
White-collar workers 1.01 (0.80–1.29) 1.06 (0.83–1.36)
Working hours per week
≤ 40 Reference
41–52 1.26 (1.05–1.52)
≥ 53 1.40 (1.11–1.77)
Shift work
No Reference
Yes 1.29 (1.02–1.62)
Handling angry clients
Rarely Reference
Sometimes 1.31 (1.08–1.58)
Always 2.00 (1.56–2.55)
Suppressing emotion
Rarely Reference
Sometimes 1.69 (1.30–2.19)
Always 2.12 (1.66–2.71)
Values are presented as odds ratio (95% confidence interval). Statistically significant values are shown in bold type.
Model A: age + sex (matched). Model B: Model A + socio-economic factors (educational level + income category + occupation). Model C: Model B + working conditions (working hours + shift work + handling angry clients + suppressing emotion).
Fig. 2 shows the results of stratified analyses. A statistically significant association was shown in the middle-aged (OR [95% CI]: 1.68 [1.10–2.54]), female (OR [95% CI]: 1.85 [1.20–2.84]), and low-income (OR [95% CI]: 3.18 [1.77–5.73]) workers. Finally, sensitivity analysis was conducted in which weighted unconditional logistic regression analyses were conducted which produced the same association (Supplementary Table 1).
Fig. 2

Results of the stratified analyses of the relationship between dependent contractors and mental health symptoms by age, sex, and income level using the fully adjusted model.

aoem-34-e1-g002.jpg
Our current study suggests that working as a DC has a negative effect on mental health. The OR for working as a DC was still significantly elevated after adjustment for age, sex, and factors associated with socioeconomic status and psychological working conditions. Furthermore, this association was shown to differ by age group, sex, and income level.
Our results show that DCs accounted for 2.8% of all wageworkers. However, other studies using KWCS data reported that 6.2% and 7.3% of all wageworkers were classified as DCs. This considerable difference was a product of how DCs were defined. The KWCS classifies DCs according to whether the participant's earnings are performance-based or not, but this definition can overestimate the number of DCs because some regular employees are compensated according to incentive systems that work in similar ways.24 Therefore, we limited DCs to those who could be classified as DCs by law and who were compensated according to a performance-based earnings system. Our proportion of DCs was similar to that calculated by the KNSO of 2.5% in 2017.25
Unlike other wageworkers, female and service and sales workers constitute the majority of DCs, which was consistent with a previous study that showed that 71% of DCs were female.18 These findings show that there was occupational gender segregation in Korea such that female are overrepresented in non-standard and service and sales jobs.26 The high proportion of DCs accounted for by service and sales workers explains why DCs are more exposed to psychological hazards than other types of workers. Service and sales workers are more likely to face angry customers and hide their emotions, which are causes of depression and anxiety.27
Our findings correspond with those of a previous Korean study, which found a significant relationship between being a DC and mental health problems.24 Although low incomes can cause mental health issues, DCs have relatively high incomes, so income is not the likely cause of their mental health problems. However, there are several possible factors that may explain this relationship.
First, in terms of employment relationships, a high level of insecurity was reported among DCs compared to regular employees.28 Job insecurity has been found to have a negative impact on mental health in several longitudinal studies and a review article,23,29 indirectly by causing financial difficulties and directly by causing depression and anxiety.30 Thus, employment insecurity has been addressed as a key factor in other research on DCs' sleep and mental health problems.24,31
The second factor is DCs' income instability. Income instability is negatively correlated with psychological health.32,33,34 DCs' earnings are performance-based, so they are more volatile than those of other types of workers.17 For example, during the coronavirus disease 2019 (COVID-19) pandemic, DC and freelance workers suffered the greatest declines in income.35
Third, due to their ambiguous legal position, DCs have not been classified as employee so that they were in a situation of lack of legal protection or social security. Lack of right and social protection could have a negative impact on the mental health of workers.11,36 Mostly, DCs work independently and adequate safety risks and protocols are not often provided or shared with them.37 Thus this lack of safety information made DCs more vulnerable to job stress as well as occupational accidents.19 In addition, DC has been excluded from legal policy to protect psychological health of customer-facing workers until recently.38 It has been shown that DCs are less provided with a manual on emotional expression while they are more frequently exposed to emotional labor,37 putting them at higher risk of depression.39 Moreover, employment insurance of Korea has not covered DC until recently. Concerning employment insurance plays an important role in mitigating financial hardship and thus results in better mental health,11 the lack of social security could negatively affect DC's psychological well-being.
Our stratified analysis showed that the mental health symptoms of middle-aged DCs were significantly affected by their employment type. This result was similar to that of previous studies that showed that middle-aged workers had stronger reactions to job insecurity because they have more family obligation.14,15 Specifically, middle-aged workers often have more responsibility to support their parents and children. This explanation is supported by the facts that job insecurity is significantly correlated with poor mental health for workers who have children40 or are heads of household41 but nor for other workers, regardless of gender.
Regarding sex differences only female workers' mental health symptoms were correlated with DC status. The results of studies examining how precarious employment correlates with mental health by sex are inconsistent. Some studies have a significant correlation between these 2 variables only in male workers,16,42 others only in female workers,43,44 and others in both male and female workers.45 Explanations for these results often refer to cultural characteristics. Our study's result contradicts the result that would be expected by traditional perceptions of gender roles in which men are would be more strongly affected by employment status than women because they perceive themselves as breadwinners and women are less job-oriented. Rather, our finding can be supported by the fact that job insecurity significantly intensifies depression in workers with high levels of domestic responsibilities.44 This assumption is supported by a recent interactive study that showed that job insecurity had a significantly strong effect on depression among workers with high domestic responsibility.46 Thus, in the case of women, psychological stress from insecurity of work may be more severe when the work-family balance is disrupted due to high household workloads. Furthermore, in case of female DC, they often encounter angry customers or suppress emotion while they have not been properly protected from hazardous psychological environment by labor protection policies.38
Finally, there was a significant effect of DC status on poor mental health in low-income workers. Workers with low incomes are more vulnerable to the detrimental impacts of job insecurity and income volatility than workers with higher incomes.14,32,42 This vulnerability would likely be increased by the legal status of DCs in Korea who have not been covered by unemployment benefits because they were considered to be self-employed,47 and still have less coverage than that of regular employees. Unemployment benefits mitigate the financial hardships of unemployment, making perceptions of it less stressful, which in turn improves mental health.11 Thus, low-income DCs know that they will have fewer unemployment benefits, making the effects of precarious employment more serious.
Concerning that service and sales workers are overrepresented in female and middle-aged workers while blue-collar workers in male and old workers, the results of stratified analyses should be interpreted with caution. Even though our model adjusted for psychological job demands and occupations, considerable difference in occupational distribution according to age and sex groups could have influenced our findings. Also, unlike employees, DC provides services through a work consignment contract rather than an employment contract. Therefore, to compare job insecurities between these 2 different contract types, more detailed survey item will be needed in further studies.
Our study had some limitations. The first limitation was that our study was cross-sectional. Longitudinal research could identify causal associations between DC status and mental health symptoms. The second limitation was that we did not define DCs to include occupations that are socially but not legally recognized as DCs, such as caregivers and telemarketers. Therefore, this study's results may not be generalized. The third limitation was that the health outcomes were self-reported, so more reliable and valid measurements of depressive or anxiety symptoms would be required in further studies.
Nonetheless, our study does have notable strengths. First, this study reconfirmed the findings observed in prior studies even after applying strict criteria for DC classification. Next, while previous study on mental health of DC in Korea24 did not consider psychological working conditions, our study found that DC status was consistently associated with increased depression/anxiety even after adjusting for workers' psychosocial job demands. Finally, our research is the first to suggest that the influence of DC status can vary with age, sex and income level.
Our study's results reinforce those of other studies that found that DCs are frequently exposed to psychological hazards and that working as a DC itself is correlated with poor mental health. In addition, our study's results showed that these relationships are stronger among middle-aged, female, and low-income DCs. Appropriate policies should be implemented to improve DCs' psychological well-being.
The authors would like to thank the survey participants and Occupational Safety and Health Research Institute (OSHRI) for providing raw Korean Working Conditions Survey (KWCS) data. The paper's contents are solely the responsibility of the authors and do not necessarily represent the official views of OSHRI.

CI

confidence interval

DC

dependent contractor

KNSO

Korea National Statistical Office

KSCO

Korean Standard Classification of Occupations

KWCS

Korean Working Conditions Survey

OR

odds ratio

OSHRI

Occupational Safety and Health Research Institute

Competing interests: The authors declare that they have no competing interests.

Author contributions:

  • Conceptualization: Baek SU, Won JU.

  • Data curation: Baek SU.

  • Formal analysis: Baek SU, Lim SS.

  • Investigation: Baek SU, Yun S, Lee WT, Kim MS.

  • Supervision: Lim SS, Yoon JH, Won JU.

  • Visualization: Baek SU.

  • Writing - original draft: Baek SU.

  • Writing - review & editing: Lim SS, Yun S, Lee WT, Kim MS, Yoon JH, Won JU.

Supplementary Table 1

Association of dependent contractor and mental health symptoms by weighted unconditional logistic regression
aoem-34-e1-s001.xls
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Figure & Data

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      Mental health symptoms among dependent contractors in Korea: a cross-sectional study based on the Fifth Korean Working Condition Survey
      Ann Occup Environ Med. 2022;34:e1  Published online February 10, 2022
      DOI: https://doi.org/10.35371/aoem.2022.34.e1
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    Mental health symptoms among dependent contractors in Korea: a cross-sectional study based on the Fifth Korean Working Condition Survey
    Image Image
    Fig. 1 Flowchart of selection of study population.
    Fig. 2 Results of the stratified analyses of the relationship between dependent contractors and mental health symptoms by age, sex, and income level using the fully adjusted model.

    Fig. 1

    Fig. 2

    Mental health symptoms among dependent contractors in Korea: a cross-sectional study based on the Fifth Korean Working Condition Survey
    CharacteristicsTotal (n = 27,980)Dependent contractorp-value
    Yes (n = 787)No (n = 27,193)
    Mental health symptoms (Depression/Anxiety)0.001
    No26,949 (96.3)740 (94.0)26,209 (96.4)
    Yes1,031 (3.7)47 (6.0)984 (3.6)
    Age< 0.001
    < 303,871 (13.8)28 (3.6)3,843 (14.1)
    30–396,399 (22.9)116 (14.7)6,283 (23.1)
    40–497,182 (25.7)269 (34.2)6,913 (25.4)
    50–596,399 (22.9)313 (39.8)6,086 (22.4)
    ≥ 604,129 (14.8)61 (7.8)4,068 (15.0)
    Sex< 0.001
    Male13,402 (47.9)183 (23.3)13,219 (48.6)
    Female14,578 (52.1)604 (76.7)13,974 (51.4)
    Education level< 0.001
    Middle school or lower3,498 (12.5)38 (4.8)3,460 (12.7)
    High school9,735 (34.8)368 (46.8)9,367 (34.4)
    College14,747 (52.7)381 (48.4)14,366 (52.8)
    Income category< 0.001
    Low10,845 (38.8)154 (19.6)10,691 (39.3)
    Middle8,488 (30.3)325 (41.3)8,163 (30.0)
    High8,647 (30.9)308 (39.1)8,339 (30.7)
    Occupation< 0.001
    Blue-collar workers9,312 (33.3)118 (15.0)9,194 (33.8)
    Service/Sales worker7,416 (26.5)558 (70.9)6,858 (25.2)
    White-collar workers11,252 (40.2)111 (14.1)11,141 (41.0)
    Working hour per week0.006
    ≤ 4016,394 (58.6)502 (63.8)15,892 (58.4)
    41–527,646 (27.3)198 (25.2)7,448 (27.4)
    ≥ 533,940 (14.1)87 (11.1)3,853 (14.2)
    Shift work< 0.001
    No24,527 (87.7)778 (98.9)23,749 (87.3)
    Yes3,453 (12.3)9 (1.1)3,444 (12.7)
    Handling angry clients< 0.001
    Rarely21,327 (76.2)499 (63.4)20,828 (76.6)
    Sometimes4,912 (17.6)202 (25.7)4,710 (17.3)
    Always1,741 (6.2)86 (10.9)1,655 (6.1)
    Suppressing emotion< 0.001
    Rarely5,911 (21.1)104 (13.2)5,807 (21.4)
    Sometimes10,700 (38.2)258 (32.8)10,442 (38.4)
    Always11,369 (40.6)425 (54.0)10,944 (40.2)
    CharacteristicsDependent contractor
    YesNo
    Depression/Anxietyp-valueDepression/Anxietyp-value
    Yes (n = 40)No (n = 740)Yes (n = 984)No (n = 26,209)
    Age0.519< 0.001
    < 302 (7.1)26 (92.9)110 (2.9)3,733 (97.1)
    30–393 (2.6)113 (97.4)182 (2.9)6,101 (97.1)
    40–4917 (6.3)252 (93.7)261 (3.8)6,652 (96.2)
    50–5922 (7.0)291 (93.0)260 (4.3)5,826 (95.7)
    ≥ 603 (4.9)58 (95.1)171 (4.2)3,897 (95.8)
    Sex0.6110.854
    Male9 (4.9)174 (95.1)475 (3.6)12,744 (96.4)
    Female38 (6.3)566 (93.7)509 (3.6)13,465 (96.4)
    Education level0.660< 0.001
    Middle school or lower2 (5.3)36 (94.7)171 (4.9)3,289 (95.1)
    High school25 (6.8)343 (93.2)345 (3.7)9,022 (96.3)
    College20 (5.2)361 (94.8)468 (3.3)13,898 (96.7)
    Income category0.0790.003
    Low15 (9.7)139 (90.3)428 (4.0)10,263 (96.0)
    Middle15 (4.6)310 (95.4)251 (3.1)7,912 (96.9)
    High17 (5.5)291 (94.5)305 (3.7)8,034 (96.3)
    Occupation0.5700.021
    Blue-collar workers6 (5.1)112 (94.9)356 (3.9)8,838 (96.1)
    Service/Sales workers32 (5.7)526 (94.3)267 (3.9)6,591 (96.1)
    White-collar workers9 (8.1)102 (91.9)361 (3.2)10,780 (96.8)
    Working hours per week0.4910.001
    ≤ 4031 (6.2)471 (93.8)521 (3.3)15,371 (96.7)
    41–529 (4.5)189 (95.5)296 (4.0)7,152 (96.0)
    ≥ 537 (8.0)80 (92.0)167 (4.3)3,686 (95.7)
    Shift work0.9580.020
    No47 (6.0)731 (94.0)835 (3.5)22,914 (96.5)
    Yes0 (0.0)9 (100.0)149 (4.3)3,295 (95.7)
    Handling angry clients< 0.001< 0.001
    Rarely20 (4.0)479 (96.0)663 (3.2)20,165 (96.8)
    Sometimes14 (6.9)188 (93.1)214 (4.5)4,496 (95.5)
    Always13 (15.1)73 (84.9)107 (6.4)1,548 (93.5)
    Suppressing emotion0.001< 0.001
    Rarely1 (1.0)103 (99.0)135 (2.3)5,672 (97.7)
    Sometimes8 (3.1)250 (96.9)340 (3.3)10,102 (96.7)
    Always38 (8.9)387 (91.1)509 (4.6)10,435 (95.3)
    CovariatesModel AModel BModel C
    Dependent contractor
    NoReferenceReferenceReference
    Yes 1.55 (1.10–2.18) 1.58 (1.10–2.26) 1.52 (1.06–2.17)
    Education level
    Middle school or lowerReferenceReference
    High school0.79 (0.60–1.06) 0.72 (0.54–0.96)
    College0.77 (0.55–1.07) 0.70 (0.51–0.98)
    Income category
    LowReferenceReference
    Middle0.80 (0.65–1.00) 0.76 (0.61–0.94)
    High0.88 (0.70–1.11)0.86 (0.69–1.08)
    Occupation
    Blue-collar workersReferenceReference
    Service/Sales worker1.09 (0.88–1.36)0.95 (0.76–1.18)
    White-collar workers1.01 (0.80–1.29)1.06 (0.83–1.36)
    Working hours per week
    ≤ 40Reference
    41–52 1.26 (1.05–1.52)
    ≥ 53 1.40 (1.11–1.77)
    Shift work
    NoReference
    Yes 1.29 (1.02–1.62)
    Handling angry clients
    RarelyReference
    Sometimes 1.31 (1.08–1.58)
    Always 2.00 (1.56–2.55)
    Suppressing emotion
    RarelyReference
    Sometimes 1.69 (1.30–2.19)
    Always 2.12 (1.66–2.71)
    Table 1 Sociodemographic characteristic of study sample by employment type in the Fifth Korean Working Conditions Survey

    Values are presented as number (%).

    Table 2 Prevalence of mental health symptoms among the study sample

    Values are presented as number (%).

    Table 3 Association between DC status and mental health symptoms

    Values are presented as odds ratio (95% confidence interval). Statistically significant values are shown in bold type.

    Model A: age + sex (matched). Model B: Model A + socio-economic factors (educational level + income category + occupation). Model C: Model B + working conditions (working hours + shift work + handling angry clients + suppressing emotion).

    Table 1

    Table 2

    Table 3

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