When adapting ESCO, a European labor taxonomy, to the South African "seen" economy, the first step involved asking if occupations in Europe - and the skills associated with them - are similar to those in South Africa. The answers to this were approached from two directions.

First, do occupations with the same titles across Europe and South Africa substantively entail the same tasks and require the same skills? For instance, being a "specialize seller" or "taxi driver" in Europe (and hence in ESCO), rarely involves having to negotiate prices or bargain. However, these are critical skills for such occupations in most developing countries, including South Africa. These gaps in occupational definitions and skills requirements are larger for more informal occupations, especially in a gig economy like South Africa's.

Second, do Europeans and South-Africans refer to the same occupations in the same terms? For example, is someone who conducts data collection called an data collector in both regions? Locals in South Africa use the term "fieldworker" more commonly. ESCO provides space to link "alternative titles" to every occupation it lists, so as to reflect this diversity of names given to the same jobs. It is also translated in every European language and reflects the conceptual diversity of ways languages refers to the same occupations. Expansion of ESCO beyond the EU, particularly in relatively data-poor developing contexts, requires careful groundwork for the new countries and languages.

Tabiya approached answers to these questions in two ways:

1. Using existing databases and taxonomy: We used a combination of Harambee's rich database of employment data, and SA's pre-existing local labor taxonomy in testing methodologies to systematically add South African alternative titles to ESCO.

2. Directly ground truthing the applicability and usability of ESCO for microentrepreneurs: We conducted a survey with young micro-entrepreneurs registered with Harambee in trying to assess whether: i) respondents could readily find their work on a list of ESCO occupations ii) skills under selected ESCO occupations fully captured the skills that a microentrepreneur thinks they have/need.

Alternative titles for occupations allow us to make sure that there is no "one best way" to refer to a job, an informal activity, or a hobby. To make sure every one feels represented on the Harambee platform, we included idiomatic occupation titles, and allow for an adaptative listing of alternative occupation titles.

Why are alternative job titles important?

In any economy, individuals who are otherwise employed to do the same job may be hired under different titles. The ESCO taxonomy attempts to address this concern by providing a list of alternative titles that are associated with the different occupations captured in its database. However, regional variations in job titles may exist, and so while the ESCO list of so-called “alternative titles” or “alternative labels” for occupations may be sufficient for the European labour market, the same cannot necessarily be said for the South African labour market.

Localizing ESCO to the South African Labor Market

In localizing ESCO to South Africa's context, we aimed to identify to what extent ESCO's existing titles and alternative titles captured the jobs done by participants in this labor market. The exploration and subsequent alternative title additions were completed in two blocks of work: 1) a matching exercise using the EJ data entries and ESCO titles 2) a cross-referencing exercise using SA's local labor taxonomy, the Organizing Framework for Occupations (OFO), with ESCO.

1. Matching with EJ Data

To get a sense of the need to add alternative titles for ESCO occupations, we first mapped 500 formal, seen occupations from the EJ data to the ESCO taxonomy. The first attempt was an automated text-matching exercise between job titles and descriptions in the EJ data and the ESCO taxonomy. Given the diversity of job titles and job descriptions on both sides, automating the text-matching proved to be a significant challenge. Consequently, a manual review of Harambee's EJ data needed to be undertaken as an initial proof of concept before large-scale data collection and analysis could be undertaken.

Random samples of EJ entries from both formal sector workers and the microentrepreneurs were analyzed. A preliminary analysis of a random sample of 50 EJ entries from each category was followed by a scaled up analysis of 500 entries from each category. Results were consistent across both subsamples. For brevity, the results presented in the following section are based on the random subsample of size 500.

To match the job descriptions provided to by young job-seekers to ESCO occupations, we manually applied the following rules:

This manual mapping resulted in a total of 279 individual EJ entries being matched to 73 unique ESCO occupation codes. At the occupation level, 51 occupations required no additional alternative titles to be added, with 39 providing exact matches to individuals’ user-provided titles, and 12 providing root-word matches to user-provided titles. The remaining 22 occupations potentially require the addition of alternative titles to ensure cohesiveness between South African occupation titles and ESCO occupation titles.

At the individual-level, we find that 224 individual user-provided occupation titles were matched to ESCO occupation titles or alternative titles via either an exact match or root word match. This accounts for 80.3% of the total matched sample of individuals. In other words, only 19.7% of individuals in the matched sample entered occupation titles that may require the addition of alternative titles to the ESCO database. This information is summarized in Table 5.

The exercise resulted in a 80.3% match (exact + root-word matches) at the individual level and a 70% match at the occupation level. Table 5 summarizes the findings.

Table 5: Summary of user occupation title match to ESCO occupation title, by occupation group.

Source: Harambee EJ data and own calculation

The South African Organizing Framework for Occupations

In our EJ individuals sample, we find a total of 55 people who do not have exact matches to an ESCO occupation title or alternative title. In a further attempt to match these occupations, we opted to explore using the South African Organizing Framework for Occupations (OFO) as a cross-referencing tool for potential alternative titles that would need to be added to the ESCO framework.

Table 6: List of ESCO occupations with suggested additional alternative titles (based on sample of 500)

Source: Own construction based on Harambee EJ data and European Commission (2022).

The above exercises demonstrated that for the most part, ESCO titles and alternative titles are able to capture most of South Africa’s occupations, and the results can be improved by additionally using the OFO framework.

We then chose to merge the 2021 OFO list of occupations, specialisations and alternate titles to ESCO. Among individuals in our EJ data sample who provided sufficient information to enact a mapping, 89% of these can find their occupation description in some combination of ESCO and OFO titles. This makes a substantive case to use an OFO-ESCO merge as a baseline from which to build our taxonomy for the South African labour market.

The matching from ESCO to OFO was done in two steps. First, straightforward matches were made thanks to a simple matching algorithm the following two rules:

End product: OFO-ESCO merge

The key to maintaining a time-relevant labor taxonomy is to build systems which are consistently taking feedback from users. Using a fixed taxonomy runs the risk of future users not finding occupations and skills because of both new kinds jobs being created and existing jobs being rebranded. To manage these risks, Harambee continues to include a free text option for job descriptions/titles on the platform. This option doubles input and feedback for the research team at Tabiya to continue adding missing occupation titles and skills to the taxonomy.

To assess the applicability of the ESCO taxonomy to the informal micro-entrepreneur economy of South Africa, we conducted a series of primary data collection exercises with Harambee users. The core objective of this data collection was to finally address two key questions:

1 - Can all activities in the informal micro-entrepreneur economy in South Africa be associated to at least one ESCO occupation?

2 - Does the ESCO taxonomy effectively encompass both occupation-specific skills and entrepreneurial skills prevalent in the informal micro-entrepreneur economy?

Activity 1: Focused Group Discussions to identify a relevant list of general entrepreneurial skills

Microentrepreneurs constitute a unique segment within the workforce, as they possess two distinct sets of skills. First, they possess occupation-specific skills directly related to their trade; for instance, a hairdresser possesses skills such as hair washing and styling. Second, they exhibit general entrepreneurial skills required for self-employment or own-account work, such as personal initiative and drive, identifying suppliers, resource management, and understanding customer needs.

Harambee facilitated focused group discussions (FGD) with 18-35 year-old, prior or current informal micro-enterprise owners within their network. These FGDs were designed to identify the second set of generic entrepreneurial skills that might be relevant to microentrepreneurs in South Africa

A sample of 60 participants including replacements was selected, balanced on gender, age group and type of entrepreneurial venture reported. Ten percent people with disabilities were included in the sample. To encourage participation, a circular was disseminated, inviting eligible individuals to join the FGDs, with selected participants offered R450 for their involvement

Six sessions with a total of 33 participants were conducted over three days at the Harambee Braamfontein offices in Johannesburg. Each session featured diverse entrepreneurial activities among participants, ranging from car washing and music production to tutoring, baking, goods selling, and hairdressing. The FGDs were designed as a series of individual exercises combined with group discussions, aiming to gain insights into how young individuals perceive their skills as microentrepreneurs.

Participants received three skill lists from ESCO and EntreComp, indicating skills they had, skills they didn't have but deemed relevant, and irrelevant or unclear skills for entrepreneurs. They then chose the lists, and skills within lists, that resonated most with their work.

The EntreComp list was most preferred, with 16 out of 33 participants (48%) choosing it as the list they resonated with the most. Participants commonly cited the relatability of the EntreComp list as the primary reason for its preference, noting that they could identify with most of the skills on that list. Other positive feedback highlighted its conciseness, clarity, and self-explanatory nature; emphasis on interpersonal skills related to business; and better inclusion of ideas, creativity, and motivation compared to the other lists.

Creating the final list

Based on the FGD findings, EntreComp was a clear starting point for entrepreneurial skills. However, the primary objective of the data collection exercise was to assess the relevance of the ESCO framework in this domain. So a hybrid approach was adopted in formulating the final list: i) each skill in the EntreComp list was manually matched to the closest skill in the ESCO framework; ii) this list was then further enriched by incorporating skills from the ‘retail entrepreneur’ ESCO occupation. The final list included ### skills, collectively serving as the list of potential entrepreneurial skills for the primary data collection.

Activity 2: Collecting descriptions of micro-entrepreneurial activities via an online survey

The second step of our work with Harambee was about deepening our insights into income-generating activities undertaken by young South Africans.

To do so, we developed a short online survey for individuals within the SA Youth network. The primary aim of the survey was to obtain accurate descriptions of the main micro-entrepreneurship activities individuals engaged in to earn income in the past 30 days. To achieve this, we included the following question:

"Tell us about the main way you make money. What things or services do you sell? Write a short sentence or two explaining what you do"

We also collected current contact details, the number of income-generating activities, time allocation across different activities, and a brief title for the respondents' primary activity.

5.2.3 Matching descriptions of micro-entrepreneurial activities to ESCO occupations

The next step was to match individual's descriptions of activities from the viable responses with existing ESCO occupations. One option would be to perform this matching exercise manually, similar to the process for the formal economy. However, there were over 3,300 individual descriptions and 3,008 potential ESCO occupations to match these to. Not only would this be significantly labor-intensive, these methods are also highly prone to human-bias and error.

[do we have anything that discusses the risks of carrying over small errors/biases in framework or taxonomy development that can have large consequences in the long run?]

To streamline the process and mitigate the risk of bias, we opted for a two-part, sequential matching procedure leveraging artificial intelligence (Ai). In the first phase, we designed an algorithm capable of identifying up to three relevant ESCO occupations for each provided description. In the second phase, we reviewed these matches manually to evaluate the accuracy of these matches. Adjustments were made if a match was deemed inadequate or if we believed that an apparent occupation match had been overlooked.

In total, 3,236 individuals (99%) were successfully matched to at least one potential ESCO occupation.

Distribution of occupation matched to respondents' answers

Activity 3: Assessing relevance of ESCO occupations and skills for informal entrepreneurs via a phone survey

Finally, we evaluated the suitability of the ESCO taxonomy for informal microentrepreneurs. We developed a phone survey with three main objectives:

1. Objective 1: Verify if the ESCO occupations accurately represent individuals' work and if they would willingly identify themselves with these labels.

   • Evaluation: Participants were presented with each matched ESCO label and its description. They were then asked to consider whether these applied to their main way of making money.

2. Objective 2: Evaluate the relevance of the ESCO skills associated with each matched occupation

   • Evaluation: We randomly selected five essential knowledge and skills and and competences tags associated with each occupation, maintaining proportional allocation between knowledge and skills/competencies tags.

3. Objective 3: Assess the relevance of the identified list of general entrepreneurial skills.

   • Evaluation: Five general entrepreneurial skills identified during the previous process were randomly selected for each participant.

The complete questionnaire can be found here

For each skill or knowledge tag, we designed several questions in order to determine its relevance to individuals’ work. These included whether individuals considered the skill as essential or optional, their perceived proficiency in the skill, their perception of how well other entrepreneurs in similar roles perform the skill, the importance of the skill to their own work, its importance to other entrepreneurs engaged in similar activities, and the perceived importance of the skill in a formal sector job akin to their work. We also incorporated a section aimed at identifying potential skill omissions from the ESCO taxonomy for informal micro-entrepreneurs. This section prompted participants to indicate if there were any other skills that were essential for their work that we had not inquired about.

Results of the Phone Survey

The results of the micro-entrepreneurship survey seem to corroborate our approach, especially by highlighting the absence of systematic bias regarding own skill evaluation between different subgroups in the data. The full results of the survey can be found here.

Importantly, young South African on average consider that they are more skilled than the rest of job-seekers. This is positive, as it means that they are confident in their own skills.

Contrary to what one may expect, the data does not highlight any statistically significant difference in the estimation of respondents' own skills between males and females. If anything, it shows that female respondents tend to deem their own skills as more advanced, compared to their male counterparts.

Finally, there is no statistically significant difference between respondents' own skill evaluation based on whether or not they matriculated (finished high school).