Waste pickers in informal self-employment: Over-worked and on the breadline

ABSTRACT

Waste picking provides between 60 000 and 90 000 informal self-employment opportunities in South Africa. This study investigates the labour market of waste pickers in the Bellville CBD, highlighting their socio-economic vulnerability. Two-thirds had a previous formal sector job for an average three years. Forty per cent of these lost this job due to business layoffs. They earn on average approximately R2 900 per month, with 70% of them earning below the average. This is lower than the average income of R3 645 of the elementary occupation workers in the QLFS 2017 data. For the waste pickers, under-employment likelihood is the highest in the low-income method (26%), followed by the time-based method (24%), and over-qualification method (16%). Waste pickers provide an invaluable service to local authorities. Practitioners and policymakers need to urgently engage in facilitative processes to dignify their working conditions and value the work that they are doing.

KEYWORDS:

• Waste pickers
• under-employment
• South Africa

JEL CODE:

• J01

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The observations of the fieldworkers while standing at the gates of the BBC provide a fascinating example of this interdependence. There was a woman outside the gate who went through the waste pickers’ collectables while they were waiting in the queue to enter the premises. She was buying items such as clothes, kitchen utensils and other household goods from them. She explained she has a shop in the Wesbank informal settlement where she sells it. This emphasises the interconnectedness of waste picking with other activities in the formal and informal economy.

2 An anonymous reviewer of this article, however, made the important point that interactions with working hours must also be considered, e.g. someone who earns less than the threshold because they voluntarily work only 20 h per week. This is an important area for further investigation.

3 The data was collected from BBCs in Bellville and Parow. Technically, these are two different, adjacent suburbs, but the Parow BBC is on the border with Bellville and, in practice, the waste pickers visiting them are from Bellville, and thus only Bellville is mentioned as the area from which the street waste pickers originate.

4 Cape Town falls within a winter rainfall area, hence July is in the middle of the rainy season. We acknowledge the potential seasonal impact on the productivity of the waste pickers. Previous studies established that in Cape Town inclement weather can make collection difficult. Furthermore, BBCs pay less if paper or cardboard boxes are damp for example (Langenhoven & Dyssel 2007). We did our best to account for that by utilising five different measures of daily income (see Table 5).

5 BBC1 and BBC2 were interviewed at different times of day. Theoretically more income could be potentially earned by the end of the day. This could have raised issues of whether the samples represent the sub-populations they intend to if the survey contained questions in term of their activities and income earned on the day of the interview. The key issue in mitigating these risks are the fact that the interviews did not pertain to the income earned from that specific day, but it pertained to various income measures of income earned in a period before the day of the interview. This period is the same for all respondents therefore. In other words, in theory the answers would not have been different had the timing of the interviews been reversed. While we did all we can to mitigate potential systematic income differentials that depends on interview timing, we must acknowledge that another potential problem still exist as pointed out by an anonymous reviewer. Some waste pickers may have ‘permanent’ low-income generating potential, which would systematically include/exclude them from the one BBC or the other permanently. The recall period is not going to solve this, because some high/low earners are not in the sample at all and the pattern of in/exclusion is systematic by being related to the time of day the interview was conducted. The respective sub-samples could therefore still be non-representative and we acknowledge this is a possible limitation of the study.

6 The names of the BBCs were not used for purposes of anonymity.

7 It is possible for a worker to be defined as under-employed under more than one approach.

8 The upper-bound poverty line stands for the expenditure required to meet basic food and non-food needs, with the aid of the cost-of-basic needs approach. In this study, this line is preferred over the lower-bound poverty line, which is derived by assuming that the households sacrifice some basic food needs to meet their non-food requirements. For detailed explanations on how each line is derived, see Statistics South Africa (2015:9).

9 Since, strictly speaking, we are not using panel data to compare the workers’ income across two periods, the results derived by this approach will be shown as supplementary information. That is, the ‘earning less than 125% of poverty line’ income-based approach will be our main focus.

10 The authors fully realise that these results cannot be considered statistically representative of all informal waste pickers in South Africa. This is a particular, targeted sample and the results with regard to the three variables of under-employment could very well differ in other parts of the country, or even within the same city.

11 The question in the survey asked whether they would like to have a full-time job. Further questions probing respondents’ job search activities/strategies were not included as most of them did not have the time to actively seek alternative employment, given the number of hours and days per week that they spent collecting recyclables.

12 While the average daily work hours are nine, upon analysing the data more thoroughly, the standard deviation is fairly high at 3.01. In addition, 32% actually work fewer than eight hours.

13 These bags are mostly pulled manually by the workers along the road to the BBC – which is an arduous task.

14 Figure 5(b) shows the log of usual income distribution as an additional finding, and the result suggests that the distribution is not as right-skewed when compared to Figure 5(a).

15 Using the 2010–17 QLFS data, Table A1 shows that for the informal sector self-employed, under-employment probability was the highest in the low-income approach, while under-employment likelihood was the highest in the over-education approach for formal sector self-employed.

16 In the first model, only the gender is included. Models two to five added being married or not and age are considered. The last four models explored all the demographic variables as well as previous full-time formal work experience. The last model consists of the most extensive model – including all the possible variables. We ran the standard model specification tests. Since the P-values of the specification tests are greater than the conventional alpha values, we accept the null hypothesis that the models have no omitted variables and that they do not suggests any evidence of functional form misspecification.

17 In the sample, for the waster pickers who are married or live together with a partner, 43% travel from home to collect waste, but this proportion is only 30% for unmarried and 25% for divorced or widowed.

18 The authors re-ran the regressions by using income earned from the last time the waste was sold as dependent variable. The results are highly similar to Table 8, except that the age in years explanatory variable also became statistically significant in the first two models.

19 The results are available upon request.