Effects of female worker’s salary and health on safety education and compliance in three sectors of the service industry

Abstract

BACKGROUND:

According to a survey of the working population of women by industry, service industries accounted for the majority.

OBJECTIVE:

The effects of female worker’s salary and self-rated health on safety education and compliance in three sectors of the service industry are reported.

METHODS:

A sample of 700 women service workers were surveyed; their age, work experience, salary, self-rated health, safety educational participation, and compliance were recorded.

RESULTS:

The salary of female service workers was directly related to safety educational participation and compliance, as well as the health levels they reported.

CONCLUSIONS:

The results suggest that an increase in the self-rated health and salary of female workers can contribute to enhancing safety educational participation and compliance. Development of educational programs in prevention and safety compliance is expected to contribute to the prevention of industrial accidents in the service sector.

Keywords

Female service worker safety education safety compliance health salary

1 Introduction

According to the economically active population survey of South Korea in 2018, women’s participation stood at 52.9 % [1]. Among female workers in South Korea, 47.3% were employed in business, and the public service, and 27.5% were employed in the wholesale, retail trade, accommodation, and food services. The majority of employed women in South Korea worked in the service industry [1].

In 2016, 56.8 percent of all women participated in the US labor force [2]. Women’s earnings as a proportion of men’s earnings also have grown over time. In 1979, women working full time earned 62 percent of what men earned; in 2016, women’s earnings were 82 percent of men’s. In 2016, women accounted for more than half of all workers within several industry sectors [2]. In South Korea, the economically active population rate of women by age group is an M-shape, which peaks at ages 25–29 years and 45–49 years. Women leave their jobs in their 30s due to childbirth and childcare, but they actively participate to increase household income after reaching their 40s [3]. Non-regular service jobs with poor working conditions and low salary have relatively low barriers to entry [4]. Non-regular workers include part-time workers and other precarious employment groups such as dispatched workers, contract workers, and temporary employees [5]. The salary of non-regular female workers is as low as 62.8% of male wages [1]. Thus, middle-aged female workers in South Korea have a high percentage of non-regular service workers.

Female workers in the service industry have different working characteristics and working conditions than workers in the manufacturing industry, and the approach to securing safety is not straightforward. Furthermore, the non-regular workers are unstable, and competition for jobs is severe, even if the work environment is poor, or the workload is heavy, it is hard for workers to complaint or request improvements. Thus, non-regular workers are more vulnerable to industrial accidents due to poor working conditions, low maintenance, and insufficient safety devices [4 –8]. As of 2017, occupational injuries occurred in the service industry accounted for 36.3% of all occupational injuries [1]. Therefore, it is necessary to investigate the working conditions and safety outcomes for service workers and implement appropriate accident prevention policies.

Workers in service occupations interact withcustomers and deal with complaints [9–10]. Thereis a high rate of female workers performing emotional labor in services and sales [11–12]. Also, non-regular workers are more likely to be exposed to dangerous working conditions and are more likely stressed due to unstable employment, resulting in the low health-related quality of life [13]. Female workers are more vulnerable to stress than male, and female workers are more likely to suffer from depression, headaches, respiratory problems and dizziness than males [14]. Furthermore, female service workers in the non-skilled and low-wage labor market are reported to have low health status [13, 15]. Sales and retail workers experience higher levels of job stress [16], which can negatively affect their perceived health [17]. In other words, the female workers’ salary and perceived health status in the service sector is an important issue that needs attention. Also, workers in theservice industry may often overlook safety-related activities at their workplace [18]. Thus, this study looks at salary level and self-rated health level as independent variables to see their correlation with safety behavior.

Safety behavior is the key to reducing injuries in the workplace and overall injury prevention [19]. Griffin and Neal [20] differentiated two types of safety behavior: compliance and participation. Safety compliance refers to the core activities that individuals need to carry out to maintain workplace safety. These behaviors include adhering to standard work procedures and wearing personal protective equipment [21]. Factors affecting safety compliance are related with worker’s knowledge, trust, money, time, awareness, motivation, effective safety training, safety rules and regulation, personal protective equipment, safety and health officer and formal management systems [22, 23].

Safety participation describes behaviors that do not directly contribute to an individual’s safety but that do help to develop an environment that supports safety and injury prevention. These behaviors include activities such as participating in voluntary safety activities or attending safety meetings [21]. Both safety participation and compliance have been used as components for safety-related performance, and they are essential behaviors for developing a work environment that supports safety [20]. Individuals should be motivated to comply with safe working practices and to participate in safety activities [21]. Safety motivation is related to safety knowledge, and both are related to safety performance [24]. Safety education is the teaching of specific knowledge and skills, that workers need to know if they are to stay safe in any given situation [25, 26]. This study concerns with safety compliance and educational participation. In this study, educational participation includes activities such as participating in voluntary safety educational activities or attending a safety education program.

This study concerns female worker’s salary and health in the service industries. Two types of safety behavior: safety compliance and educational participation were investigated. The objective was to investigate how female worker’s salary and self-rated health status in the service industry was related to their safety compliance and educational participation.

2 Methods

2.1 Research variables and data collection

We conducted interviews with a sample of female service workers about their safety behaviors. Survey items comprised safety behavior (educational participation, and compliance) and worker-related factors (occupation, age, work experience, number of working days per week, salary, and self-rated health status). Table 1 summarizes the research variables in this study.

Table 1
Definition of research variables

Variable Description

Subject and working conditions

Occupation Cook; Building cleaner; Merchant

Gender Female

Age Years

Work experience Years

Working days per week days

Salary level per month 1: very low, 2: low, 3: moderate, 4: high, 5: very high

Self-rated health level 1: very low, 2: low, 3: moderate, 4: high, 5: very high

Subjective scores for safety

Safety educational participation 1: very low, 2: low, 3: moderate, 4: high, 5: very high

Safety compliance 1: very low, 2: low, 3: moderate, 4: high, 5: very high

Variable	Description
Subject and working conditions
Occupation	Cook; Building cleaner; Merchant
Gender	Female
Age	Years
Work experience	Years
Working days per week	days
Salary level per month	1: very low, 2: low, 3: moderate, 4: high, 5: very high
Self-rated health level	1: very low, 2: low, 3: moderate, 4: high, 5: very high
Subjective scores for safety
Safety educational participation	1: very low, 2: low, 3: moderate, 4: high, 5: very high
Safety compliance	1: very low, 2: low, 3: moderate, 4: high, 5: very high

Participants in three service occupations, where female occupational injuries are reportedly highest among service industries in South Korea were: cooks, building cleaners, and merchants. Cooks range from those who work in small-scale diners to those who work for large catering companies, making food with cooking equipment in a wide variety of kitchens, often for long hours [27, 28]. Building cleaners are typically elderly females and are characterized by having low work intensity but also low wage [29, 30]. A majority of these workers are less qualified women working part-time, with flexible and inconvenient schedules [31]. Merchants are responsible for selling products at small local stores or franchise supermarkets and have a diverse demographic regarding gender and age, as well as employmentstatus [16].

The work experience refers to the duration of employment at the current workplace. The level of salary and self-rated health refers to the level of wages and health that workers reported. Educational participation refers to the degree of worker’s educational participation, for example: “I voluntarily participate in the safety education program that helps to improve knowledge and understanding for workplace safety.” Safety compliance refers to a workers’ subjective assessment of safety compliance or regulations, for example: “I use the correct safety procedures and the necessary safety equipment for carrying out my job.” The salary, self-rated health, educational participation, and safety compliance were measured on a 5-point scale where 1 = very low and 5 = very high.

A total of 700 female workers were recruited from three service occupations. They graduated from secondary school or higher and had basic skills in reading and writing. The sample included 301 cooks, 275 building cleaners, and 124 merchants. The average age of the 700 respondents is 53.54 years (standard deviation 12.55 years), and the average work experience is 5.38 years (standard deviation 5.81 years).

A sample is selected by a stratified random sampling process. A researcher visited service companies in the region to recruit participants from different service sectors, in proportion to the size of the industry. Trained researchers explained and answered questions prospective participants had about any unclear terms or questionnaire items. The study complied with the Korean Statistics Act, and all participants were paid.

2.2 Model and data analysis

To find out the effects of salary and self-rated health on educational participation and safety compliance, we used regression analysis. The regression model was used to examine the impacts of monthly salary (X) and self-rated health (M) on educational participation and safety compliance (Y), respectively. As described by Edwards and Lambert [32], regression analysis follows as (1) regression analysis of Y with X, (2) regression of the mediation variable M with X, and (3) regression analysis of dependent variable Y with X and M.

The regression model of Edwards and Lambert [32] was used to examine the effect of educational participation and self-rated health level on safety compliance. The significance level was 0.05, and a statistical package SPSS 18.0 was used for regression analysis.

3 Results

3.1 Correlation coefficients between working conditions and safety scores

Table 2 shows the correlation coefficients between age, work experience, working days, monthly salary, self-rated health, educational participation, and safety compliance. Table 2 shows that age is significantly correlated with work experience, working days, self-rated health, educational participation, and safety compliance. That is, as age increased, the work experience, working days, educational participation, and safety compliance increased, while the self-rated health score decreased.

Table 2
Correlation coefficients between safety-related factors

Variables Work experience Working days Salary level⁺ Self-rated health⁺ Educational participation⁺ Safety compliance⁺

Age (yrs) 0.253^* 0.259^* –0.068 –0.176^* 00.221^* 0.125^*

Work experience (yrs) 0.195^* 000.159^* –0.037 –0.007 0.021

Working days 000.262^* –0.051 0.040 0.061

Salary level 00.082^* 00.110^* 0.094^*

Self-rated health 00.130^* 0.206^*

Educational participation 0.296^*

Variables	Work experience	Working days	Salary level⁺	Self-rated health⁺	Educational participation⁺	Safety compliance⁺
Age (yrs)	0.253^*	0.259^*	–0.068	–0.176^*	00.221^*	0.125^*
Work experience (yrs)		0.195^*	000.159^*	–0.037	–0.007	0.021
Working days			000.262^*	–0.051	0.040	0.061
Salary level				00.082^*	00.110^*	0.094^*
Self-rated health					00.130^*	0.206^*
Educational participation						0.296^*

^*Significant at significance level 0.05.

Table 2 shows that monthly salary increased with the length of work experience and the number of working days. If the monthly salary was high, the level of self-rated health was also high.

Educational participation was positively correlated with age, monthly salary or self-rated health scores. As age, monthly salary, and self-rated health score increased, educational participation increased. Also, as the age, monthly salary, or self-rated health scores increased, the level of safety compliance increased.

3.2 Effects of salary and health level on educational participation

Figure 1 shows the regression model for estimating the effect of monthly salary levels (independent variable) and self-rated health scores (mediation variable) on educational participation (dependent variable). The arrows designate the estimation coefficient used in the regression equation.

Fig.1

Regression model of salary and health level on educational participation. (^*Significant at significance level 0.05).

Table 3 shows the regression model of the effect of monthly salary and self-rated health on educational participation. Regression analysis showed that workers with higher monthly salaries indicated higher educational participation. Also, workers with higher salaries reported higher self-rated health scores. Workers with higher self-rated health scores had higher educational participation. In other words, the salary level directly affected the educational participation, and also indirectly influenced educational participation through self-rated health levels. These results suggest that worker’s educational participation increased as salary levels increased.

Table 3

Effects of salary and health level on participation for education

Description	Health level (M) = f(X)		Educational participation (Y) = f(X)		Educational participation (Y) = f(X,M)
	B	p	B	p	B	p
(Constant)	3.388	0.001^*	3.388	0.001^*	2.693	0.001^*
Salary level (X)	0.056	0.030^*	0.127	0.004^*	0.116	0.008^*
Health level (M)					0.205	0.001^*
Regression model	m = 3.388 + 0.056x		y = 3.388 + 0.127x		y = 2.693 + 0.116x + 0.205m
Statistics for model	F = 4.732		F = 8.577		F = 9.611
	p = 0.030^*		p = 0.004^*		p < 0.001^*

^* Significant at significance level 0.05.

3.3 Effects of salary and health level on safety compliance

Figure 2 shows the regression model for estimating the effect of monthly salary (independent variable) and self-rated health (mediation variable) on safety compliance (dependent variable).

Fig.2

Regression model of salary and health level on safety compliance. (^*Significant at significance level 0.05).

Table 4 shows the regression model of the effect of monthly salary and self-rated health on safety compliance. Regression analysis shows that a higher monthly salary indicated higher safety compliance. Also, workers with higher salaries reported higher self-rated health scores. Workers with higher self-rated health scores demonstrated higher levels of safety compliance. In other words, the level of salary was directly related to safety compliance and indirectly influenced safety compliance through self-rated health levels.

Table 4

Effects of salary and health level on safety compliance

Description	Health level (M) = f(X)		Safety compliance (Y) = f(X)		Safety compliance (Y) = f(X,M)
	B	p	B	p	B	p
(Constant)	3.388	0.001^*	3.781	0.001^*	3.076	0.001^*
Salary level (X)	0.056	0.030^*	0.067	0.013^*	0.056	0.036^*
Health level (M)					0.208	0.001^*
Regression model	m = 3.388 + 0.056x		y = 3.781 + 0.067x		y = 3.076 + 0.056x + 0.208m
Statistics for model	F = 4.732		F = 6.271		F = 17.784
	p = 0.030^*		p = 0.013^*		p < 0.001^*

^*Significant at significance level 0.05.

3.4 Effects of educational participation and health level on safety compliance

Figure 3 shows the regression model for estimating the effect of educational participation (independent variable) and self-rated health (mediation variable) on safety compliance (dependent variable).

Fig.3

Regression model of educational participation and health level on safety compliance. (^*Significant at significance level 0.05).

Table 5 shows the regression model of the effect of educational participation and self-rated health on safety compliance. Regression analysis shows that workers with higher educational participation reported higher levels of safety compliance. Also, workers with higher educational participation reported higher self-rated health levels. Workers with higher self-rated health scores reported higher safety compliance. These results suggest that the safety compliance of female service workers increased as self-rated health scores increased.

Table 5

Effects of educational participation and health level on safety compliance

Description	Health level (M) = f(X)		Safety compliance (Y) = f(X)		Safety compliance (Y) = f(X,M)
	B	p	B	p	B	p
(Constant)	3.222	0.001^*	3.251	0.001^*	2.679	0.001^*
Educational participation (X)	0.077	0.001^*	0.183	0.001^*	0.169	0.001^*
Health level (M)					0.178	0.001^*
Regression model	m = 3.222 + 0.077x		y = 3.251 + 0.183x		y = 2.679 + 0.169x + 0.178m
Statistics for model	F = 11.975		F = 67.136		F = 45.890
	p = 0.001^*		p < 0.001^*		p < 0.001^*

^* Significant at significance level 0.05.

4 Conclusion and discussion

With the center of the economy shifting from manufacturing to service, a wide range of new service occupations have emerged, and the number of workers working in the service sector has steadily increased [33]. Particularly within in the Republic of Korea, the tertiarization of the economy has led to a growth of professional jobs that require specialized knowledge as well as minimum wage jobs that rely on completing repetitive and sometimes risky tasks that do not require a specialized set of skills [34]. Kalleberg [35] has addressed the rise of “precarious workers,” characterizing those working under uncertain, unpredictable and risky jobs, as a contemporary challenge in our global economy. This uncertain and changeable nature of work makes it more difficult to ensure the safety of workers in the service sector compared to their counterparts in manufacturing.

We examined the effect of female worker’s salary and levels of self-rated health on educational participation and safety compliance in three sectors of the service industry. According to correlational analysis, the salary of a female worker was found to be proportional to the work experience and working days. Self-rated health scores declined with age, and workers with higher salaries reported higher self-rated health scores. Regression analysis showed that salary was directly related to educational participation/safety compliance, and indirectly to perceived health levels. Also, educational participation was directly related to safety compliance and also indirectly to perceived health levels.

Since non-regular female workers are largely part-time contract employees, it is difficult to establish rules and procedures for safety and to accumulate knowledge of industrial safety. In particular, employers using non-regular workers tend not to emphasize training or allow time for it [36]. Therefore, safety devices for ensuring worker safety are likely lacking or are not adequately implemented, making non-regular workers more vulnerable to industrial accidents than regular workers. According to previous studies, non-regular female workers are associated with lower salaries and less safety [5, 18]. They often encounter inferior working conditions that are detrimental to their health. Also, they had less access to preventive health care. Thus, they tend to report poor self-rated health [5].

In this study, safety educational participation in female service workers was related to safety compliance. Individuals should be motivated to comply with safe working practices and to participate in safety activities [19]. Also, safety motivation is related to safety knowledge. The act of participating in safety activities can lead to a further increase in safety motivation [5], and both are related to safety performance [24]. These results suggest that appropriate compensation systems can lead to increases in educational participation and the safety compliance for female workers in the service sector. It is necessary to give workers the necessary time and resources to participate in the program. Also, worker participation is vital to the success of safety and health programs, and all workers at a service worksite should participate, including those employed by contractors, subcontractors, and temporary staffing agencies [26].

There are some limitations of this study that readers need to consider when interpreting the results. First, the results may not transfer to different cultures or countries with different economic and social structure. Second, the three types of jobs are not an exhaustive list of all female service industry jobs. Work-life balance was related to worker’s gender, age, working sector, occupation, and employment type [37]. Also, work-life balance was significantly related to various health outcomes. Third, educational background is known as an important factor in deciding worker’s incomes, and higher educational attainment is correlated with better health [5]. However, this study did not include work-life balance and educational background in the research variables. Therefore, further research including these variables is required.

Nevertheless, results suggest that it is crucial not only to improve the physical working environment but also to improve overall health levels of female service workers. It is also necessary to develop friendly working conditions by flexibly adjusting or reducing working hours and increasing breaks for elderly workers in particular, who make up a significant proportion of the female workforce. Preventive behavior of workers is a major determinant of occupational health and safety performance of an organization [38]. Results of this study contribute to deriving the essential data necessary for establishing effective preventive policies by examining the work environment, the degree of educational participation, and compliance with safety regulations. Greater knowledge in this area is expected to contribute to the prevention of industrial accidents and higher levels of safety for female workers in the service sector.

Conflict of interest

None to report.

Footnotes

Acknowledgments

This research was financially supported by Hansung University.

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