Alternative Titles

Including all diverse experiences and skills held by young South African entails ensuring they identify with the titles of occupations provided on our platform.

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.

The ESCO taxonomy exists for all European languages: the same occupations and skills thus have different names. However, problems arise from the fact that the occupations and skills may be called differently depending on the regional variations of the English language. For instance, in South-Africa, a “survey enumerator” may be called “fieldworker”. Therefore, one needs to identify, manually or thanks to algorithmic text processing, alternative titles that are relevant in each regional context.

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 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:

Rule 1: If there was an exact match in the user-provided livelihood title from the EJ data, and either an ESCO occupation title, or an ESCO occupation’s alternative title, then the individual was matched to this ESCO occupation code at the 4.1-digit level.

Rule 2: Where user-provided livelihood titles were etymologically linked to an ESCO title or alternative title by a common “root word” – e.g., the user-provided title of “Teacher assistant” is etymologically linked to the ESCO title “Teaching assistant” through the common root word “teach” – then these individuals were classified as directly mappable to ESCO without the need for an additional alternative title.

Rule 3: Where there was no directly mappable link between user-provided livelihood titles and ESCO occupation titles, individuals were only allocated to a given occupation code if we could be reasonably sure that we had identified the job the individual was trying to communicate through their EJ livelihood data.

Rule 4: Where there was no direct link between user-provided livelihood title and ESCO occupation title, and it was not clear what occupation was being described by the user-provided livelihood title, we opted to not map occupations.

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.

	Number of individuals employed in occupation category	Number of individuals matched exactly	Individuals as % of all matched individuals	Number of occupations	Occupations as % of all matched occupations
Occupation matches exactly to ESCO title or alternative title	94	93	33.3%	39	53.4%
Occupation has a root word match to ESCO title or alternative titles	107	88	31.5%	12	16.4%
Occupation likely requires addition of alternative title	78	43	15.4%	22	30.1%
Total	279	224	80.3%	73	100.0%

Source: Harambee EJ data and own calculation

Note: Although there are 94 individuals who are in occupations with exact matches to ESCO titles or alternative titles, only 93 individuals had exact matches to ESCO titles. This is because one individual described their job as “Youth program – cleaning and helping them with their homework”. This individual has been allocated to occupation “Child care worker”. While this is not an exact match for the individual, their user-provided occupation title is a description rather than a title.

The South African Organizing Framework for Occupations

. 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.

Why we chose to use the OFO instead of the South African Standard Classification of Occupations (SASCO) framework?

As an alternative to the OFO, the other local labor taxonomy available to the team was the South African Standard Classification of Occupations (SASCO) framework, which is based on ISCO and provides information on tasks related to occupations.

The OFO was decidedly better suited for the purpose of this exercise than the SASCO for several reasons:

1. SASCO was last updated in 2012, while the OFO is more regularly updated, the latest being 2021. Time-relevant frameworks are particularly important in dynamic and gig-economy driven labor markets like that in SA.

2. Unlike the OFO, SASCO does not readily provide a list of alternative titles. In trying to develop an exhaustive list of different titles for the same occupation, the team opted for the framework with a larger existing base of alternative titles.

3. Although the national statistics agencies uses the SASCO classification to generate labour market statistics, the South African Department of Higher Education and Training (DHET) notes that SASCO does not provide the level of detail required for skills-related planning and interventions (DHET, 2013). As a result, DHET developed the OFO framework to monitor skills supply and demand in South Africa, which aligns more closely with Tabiya and Harambee's objectives as well.

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

ESCO code	ESCO title	Alternative title 1	Alternative title 2
2146.5	Metallurgist	Aluminium maker
2330.1	Secondary school teacher	Substitute educator
2341.1	Primary school teacher	Substitute educator
2342.1	Early years teacher	Substitute educator
2342.2	Freinet school teacher	Substitute educator
2342.3	Montessori school teacher	Substitute educator
3139.1	Automated assembly line operator	Production assembler
3312.1	Bank Account manager	Universal banker
3341.6	Field survey manager	Fieldwork supervisor
3343.1	Administrative assistant	Office administrator	Chief invigilator
4110.1	Office clerk	Control centre clerk
4212.7	Odds compiler	Fixed odds clerk
4227.2	Survey enumerator	Surveyor	Fieldworker
5142.2	Beauty Salon Attendant	Beauty advisor
5223.6	Shop assistant	Store assistant	Till packer
5230.1	Cashier	Till operator
5312.1	Early years teaching assistant	Educator assistant	ECD assistant
7321.1	Prepress technician	Scanning engineer
7422.7	Telecommunications technician	DSTV installer
9212.4	Livestock worker	Milker

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.

Challenges in merging the OFO and ESCO taxonomy

The OFO and ESCO taxonomies diverge at the 4-digit level. The two frameworks create different categorisations of occupations at the more granular levels. Thus, there is not necessarily a clean one-to-one mapping between .

These discrepancies in how the additional granularity is added meant that the mapping was not a case of simply matching the occupation codes to one another. Therefore, to ensure accurate mapping at these levels, either a string search match, or a manual mapping were required.

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:

If the exact equivalent occupation (same title) exists in ESCO and OFO, a match happens.

If the ESCO title of an occupation is included as an alternative title for an occupation in OFO, then a match happens.

End product: OFO-ESCO merge

The approach finally adopted was manual matching of the remaining OFO occupations to ESCO, through identification of close conceptual equivalents. This work contributes to two downstream functions: First, it allows our partners to build platforms around a localized taxonomy that speaks to young jobseekers, thus improving the functioning of the website. Second, it enriches functionality of Compass, Tabiya's interactive chatbot meant to help young job seekers identify their skills.

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.