Sampling design and data sources

This study was carried out in Addis Ababa, the capital city of Ethiopia. A cross-sectional study design was used to assess the e-waste awareness and management of purposely-selected e-waste workers including those who are actively engaged in e-waste buying, selling, dismantling, storing, and transferring activities. The primary data were obtained by using a structured questionnaire. The questionnaire tools were designed to assess socio-economic status of the target groups, the basic e-waste-related awareness, and associated influencing factors, and e-waste management strategies and its determinants among the study groups. Awareness and e-waste management are dependent variables that are influenced by several independent variables. The type of job, work experience, educational status, lack of access to information and concern and newness of the issue are independent variables influencing awareness level of respondents, whereas the specific job type, concern, presence of container, gender, lack of law enforcement, e-waste-related awareness are identified as independent variables for e-waste management.

The data were collected from 5 purposely-selected target groups in: electronic repair and maintenance workers in the city, storekeepers in government institutions, e-waste-sellers, and waste pickers/ dismantlers.

Sample size

Sample size determination was challenging because the exact number of these informal and unregistered groups particularly the movable groups such as the e-waste dismantlers and sellers was impossible. Therefore, for these groups, all possible efforts were exerted to get appropriate number of willing respondents. Three hundred forty-five individuals was included in the survey. Of which, 82 were storekeepers working in health and educational institutions, 100 were repair and maintenance workers randomly selected from 316 registered repairers and maintenance shops, 60 were scavengers and traditional dismantlers (Qorales), and 102 were old electronic and e-waste-sellers. Except for storekeepers, majority of the target groups were obtained from Adis Ketema Sub-city, Merkato, area where the biggest market of the country is located.

Statistical analysis

For the data analysis and visualization, Stata 14 and Minitab 16 software were employed. Pearson’s Chi² test of independence was used to check whether a statistically significant association exists between e-waste awareness and variables that potentially can affect the awareness levels of the respondents. A similar analysis was also done for e-waste management strategies and associated variables. To examine the effect of significant factors on the awareness level of the respondents, ordered logistic regression was used; whilst important factors influencing e-waste management strategies individual e-waste workers was examined by using multinomial logistic regression.

Socio-economic status of the respondents

The response rate of the 344 questionnaires distributed to the study groups was 98.8% (340/345). Table 1 shows details of the socio-economic and emographic characteristics of the study participants. The assessment shows that 91.9% of the repair and maintenance workers, 86.8% of the storekeepers, 63.3% of the dismantlers, and 89.2% of the sellers are above the age of 21. Among the respondents, 81% were male implying that in an informal e-waste-related business, the participation of women is 4 times lower than men do, which may be due to the possible exposure of women to gender-related harassment and high-energy demand nature of the work. Education wise, about 93% of the respondents were at least finished elementary school education, and among them, 32% of them completed diploma and university level education, of which the majority are repairers and storekeepers in university and colleges. The minimum education level among the repairers and the storekeepers is a high school certificate, whilst dismantlers are the least educated group. About 45% of the respondents stayed from 5 to 10 years in the business. This is an indication of the fact that these youngsters start the business at a very young age by dropping their education at elementary or secondary school. The income level of the respondents varies from a minimum of less than 1000 ETB (the majority are waste-pickers and dismantlers) to above 10 000 ETB (the majority are electronic repairers and maintenance workers).

Table 1.

Socio-economic conditions of the respondents.

Awareness of the respondents

Awareness about e-waste-related issues was evaluated using questions focusing on 5 aspects of awareness: definition, human health-related, environment-related, safety-related, and composition-related issues (Table 2). Only 28.5% of the respondents consider old, malfunctioned pieces of electronics and electrical items are part of waste; the rest 72% are not sure or do not believe that these items are part of wastes materials. For 68.9% of the respondents, e-waste has no serious effect on human health; and still, 12.2% are not sure that e-waste has a human health problem. More than 60% of the respondents did not know that e-waste has serious environmental consequences and 70% responded that e-waste does not create any public nuisance. The current poor awareness level of these key role-players of e-waste workers show that e-waste will stay the major public and environmental health risks in the country. A cross-sectional study reported that about 93% of the households at all income levels in Addis Ababa have no idea about the impacts of e-waste on human health and the environment.¹⁷ A study carried out in 2 sub-cities in Addis Ababa reported that about 40% of the respondent households have no any idea about e-waste.¹³

Table 2.

The e-waste-related awareness level of the respondent (n = 340).

Compared with studies in other countries, awareness about the impacts of e-waste on human health and environmental health is extremely low.¹⁸ For example, a cross-sectional study carried out in Malaysia showed that 80% and 67% of the respondents are well aware that e-waste is hazardous and has serious impacts on humans and ecology respectively.¹⁰ A study carried out in Uganda also shows that 64.8% of the consumers had good awareness of human health and environmental effects of e-waste.¹⁹ Several authors reported that most informal e-waste recycling workers are not aware of environmental and health risks and do not know of better practices.^2,9,20,21

Since the majority (92.4%) don’t believe or are not sure that e-waste contains toxic substances and only 17% believe that e-waste may risk their health, the majority of them (70.6%) haven’t ever used any protective wear; and 17.7% sometimes put on some kind of shield on their mouth while working with e-waste. Consequently, only about 17% of the respondents think that mismanagement of e-waste can have some kind of human health risk. About 84.3% of the respondents did not know the presence of the e-waste refurbishment center in Addis Ababa; the rest of them who know or who are not sure are among the dismantlers and storekeepers. This is because some of the dismantlers and storekeepers have direct or indirect contact with the refurbishing company.

Moreover, luckily, 97.4% of the respondents do not believe or are not sure that e-waste contains precious metals such as gold, silver, and platinum; and even those who know the presence of precious metals in their old electronics, none of them have ever dismantled or burnt to extract the metals. In many countries, e-waste recycling or dismantling is aimed to recover precious metals. If e-waste workers aware the presence of precious metals and the skills required to extract these precious metals, the impacts on the human health and environment will be exacerbated. Evidence from other nations clearly show that informal e-waste recycling severely affect the environment.^9,22–24 For instance, in Malaysia, 80% of the respondents know the presence of precious metals in the e-waste and thus e-waste dismantling as a source of the treasury.¹⁰

The awareness about the definition of e-waste, the volume of e-waste they generate, and the risks of e-waste on human health are strongly associated with the specific jobs they are engaged in. To evaluate the overall awareness level of the respondents, the responses were ordered into 3 scales (scores ⩾3 is low; 4-7 = medium; 8-10 = high). Based on this category, 63% of the respondents scored low (answered only 1-3 out of 10 questions); about 26% scored medium (answered 4-7 out of 10 questions) and only 11% of them scored high (answered 8-10 of the questions correctly) (Figure 1). Figure 1 shows the awareness level of the respondents categorized by their job category.

Figure 1.

Awareness level of the respondents by job category.

Respondents vary by their awareness level considerably. About 92% of the dismantlers, 70% of the sellers, 55% of the repair and maintenance workers were found to have poor awareness. At a medium level of awareness, storekeepers, followed by repairers, are better than the others are, and dismantlers scored the lowest. Reports show that about 99% of store-keepers in Addis Ababa don’t have the awareness about the impacts of hazardous substances and they haven’t ever had training on e-waste.¹⁷ At a high level of awareness, sellers followed by repairers are better than the others are because the majority of the repair and maintenance workers are high school and beyond. Findings show that e-waste repairers were better educated and aware than the dismantlers; and the repairers were significantly more aware of the likelihood of health-related risks associated with their jobs.^2,20,21

Factors affecting the awareness

From the array of factors affecting the awareness level of the respondents, 8 of them were selected as the major factors in this study (Table 3). The chi² test showed that there is a significant association between awareness and all the selected factors (P < .01). According to Cramer’s V value, the awareness level of the respondents is strongly associated with the newness of the issue (V = 0.64), relatively strong with lack of concern (V = 0.44), and moderately associated with educational status (V = 0.31), their lack of access to information (0.31), job type (0.28) and income level (0.26).²⁵ However, although the Chi² values are high and the P-value shows a statistically significant relationship, the association between awareness with age and experience is weak. A bivariate analysis conducted shows that age, marital status and educational status have association with the e-waste awareness levels electronic consumers.¹⁹ Another survey carried out in the Pune City of India shows that education and income play positive role on individuals e-waste awareness.¹⁰

Table 3.

The Chi² test for association between awareness level of the respondents and influencing factors.

Ordered logistic regression analysis also showed that the overall effects of all the 6 assessed factors are statistically significant (chi² = 194.7, P < .05) (Table 4). However, only 3 factors, access to information, lack of concern, and the newness of the issue has a strong association with the awareness of the respondents. A unit change in the access to information (shifting from lack of access to getting access) increases the awareness level by 0.8 to the next higher level of awareness. Similarly, a unit change in the newness of the issue (when the issue becomes common), the awareness level increases by 2.85 to the next higher awareness level.

Table 4.

Association of important variables influencing e-waste awareness.

E-waste management practices

The common management strategy of e-waste includes reuse, regulated recycling, material recovery, incineration, and landfilling.^26,27 In this study, none of the groups employs neither of these methods. The e-waste management among the study groups was limited to unsafe disposal, unsafe storage, and transfer to other users, implying that there is no proper management of e-waste at all. E-waste management in Africa is dominated by thriving informal sector collectors and recyclers in most countries; contrary to many developed nations, both organized take-back systems and license provisions for sorting and dismantling e-waste exist.⁴ Generally, the e-waste management method of the study groups is 50% unsafe disposal, 36% unsafe storage, and 14% transfer by selling as second-hand item or give out to somebody or organization (Table 5).

Table 5.

E-waste management among the respondents.

About 85% of the electronic repair and maintenance workers and 78% of the traditional dismantlers mix with any other solid wastes and dispose to the local garbage collection site or arbitrarily throw to the surrounding area, which may or may not be taken by street sweepers. This is the reflection of the actual solid waste management practice in the community. No respondents have separate garbage containers for solid waste segregation. An assessment made on waste management in Addis Ababa University reported that recycling, re-using donating and reselling the e-waste has not been practiced in the university.²⁸

Storing e-waste for unlimited time is very common among e-waste-sellers and institutions; however, a study showed that about 99% of the institutions in Addis Ababa don’t have proper storage facilities.¹⁷ According to a study carried out on e-waste management strategies in Ambo Town government educational institutes, the main reason of the poor waste management is the absence of legislations specifically dealing with e-waste.²⁹ The same author reported that the major reason for storing e-waste for unlimited time is the government order not to take any action except donation. A study carried out in Addis Ababa showed that 94% of every broken old electronics and electrical equipment (including those which are returned from repairers because of the high level of damage) hoping that it would benefit them/have value in the future.¹⁷ The same author also showed that 96% of the low and middle-income households are not willing to dispose or give free any of their e-waste indicating the high potential for the informal market¹⁷; if they do not sell, they store it for an unknown time.

To evaluate whether there is a relationship between job type and their management means, Pearson’s chi² test was done. The analysis showed that there is a statistically significant association between their job type and management systems (chi² (6) = 139, P < .005, V = 0.45). Cramer’s V (V = 0.45) shows a strong association between their job type and the e-waste management strategies.

Factors affecting the management of e-waste

Management of e-waste among the study groups is affected by several factors including job type, awareness level, educational status, gender, experience, absence of law enforcement, and presence of trash can (Table 6). Although the high Pearson’s Chi² and the P-value show that management of e-waste is significantly associated with all the assessed factors, the effect strength of the variables varies. For example, the Cramer’s V of the test, 0.56 shows a relatively strong association between e-waste management types and their job specification (Table 6). All the rest factors are associated with management, Cramer’s V lies between 0.21 and 0.36 indicating moderate association with all the rest factors.²⁵ Lack of awareness and cautionary information on the management operations associated with e-waste can pose a potential threat to human health and the environment.¹⁸

Table 6.

Factors affecting the management of e-waste.

Contribution levels of the factors to the poor e-waste management

To evaluate the magnitude of each factor, multinomial logistic regression was carried out (Table 7). From the 3 categories of outcomes, the model take unsafe disposal (1) as the base outcome; unsafe storage as an outcome 2, and transfer as outcome 3. For outcome 2, the analysis showed that unsafe storage was significantly affected by male (b = −1.3, se = 0.47, P < .05) education status (b = −0.55, se = 0.19, P < .05), experience level b = 3.3, se = 0.4, P < .05) presence of trash can (b = −2, se = 0.37, P < .05), absence of law enforcement (b = 1.3, se = 0.41, P < .05) and working conditions (b = 0.77, se = 0.31, P < .05). For example, the log-odds of practicing unsafe storage relative to unsafe disposal for males is predicted to be −1.32 points less than that of females. The negative slope suggests that fewer males practice unsafe storage and more males practice unsafe disposal as compared to females. For outcome 3, the analysis showed that transfer e-waste was significantly affected by job (b = −0.82, se = 0.32, P < .05), gender (−2.6, se = 0.54, P < .05), educational status (b = 0.7, se = 0.25, P < .05) and lack of concern (b = −3.4, se = 1.1, P < .05). For example, the log-odds of transferring e-waste as a means of management relative to unsafe disposal for lack of concern is predicted to be ‒3.4 times less compared to unsafe disposal. The negative slope suggests that lack of concern is a stronger factor to unsafe disposal as compared to e-waste transfer as a means of e-waste management.

Table 7.

Association of different variables at each categories of e-waste management.