Takeaways Regarding ESCO
Beyond providing a localised technical answer to Harambee, Tabiya's work in South Africa also allowed us to evaluate the possibility to use the ESCO taxonomy in other national set ups.
Although it is constently improved to include more occupations and skills, the ESCO classification does not yet - and will probably never - cover the full universe of skills and occupations in Europe and, a fortiori, in the world. Working with Harambee and working to build an inclusive taxonomy for the South African youth thus called for solutions to adapt and supplement ESCO. These challenges provided us with valuable insights on reproducing our methodology in other countries.
Internal Inconsistencies in ESCO
One of the main takeaways of our work for Harambee is already that ESCO presents some internal inconsistencies. The ESCO classification results from the work of experts, as described above. This human based methodology has advantages:
It allows to avert a data-mining (CVs, job offers) exercise based on data of unequal quality. Job offers and CVs also notoriously overestimate the skills associated with a given occupation.
Relying on data-mining would have resulted in a taxonomy highlighting the skills that are frequently sought for by employers and displayed by current workers. Therefore, it would tend to invisibilize rare associations of skills and occupations.
However, relying on focus groups also creates consistency issues. For instance, the ESCO research group has highlighed the existence of duplicated skills in the taxonomy. Because there are multiple reference groups, some conceptually similar skills were created for different occupations under different names. More broadly, we faced two issues. First, the decisions rules used to assign skills to occupations are unclear. Second, using a taxonomy like ESCO entails decisions about a tradeoff between the representativity and the inclusivity of the taxonomy.
Unclear decision rules: some anecdotal examples highlight internal inconsistencies in ESCO, that are likely due to unclear decision rules. For instance, "nanny" (5311.1.4) is associated with various skills, among which "cook pastry products", whereas "babysitter" (5311.1.2) also requires to know how to cook various products, but not pastries. There is no a priori reason why that should be the case, as the two occupations are conceptually very close.
This example reveales that the reliance of ESCO on reference groups allows for non-systematic associations of skills with occupations. Although reference groups surely agreed on general rules to decide wether to associate a skill to an occupation or not, this still leaves room for interpretation. Although such inconsistencies do not risk to prevent users of the Harambee platform from picking skills that they have - platform users always have the option to add skills that they think were missed - it risks influencing their pick of skills by modifying the architecture of their choices.
Tradeoff between representativity and inclusivity: Taxonomies like ESCO are meant to impose structure on data (occupations*skills bundles) that is extremely rich and diverse. Although it is not built on measures of the frequency of the co-appearance of each occupation-skill bundles, relying on human decisions and focus groups still reproduces similar issues: rare occupation-skill associations are necessarily overlooked, to avoid the list of skills associated to occupations becoming too long and, therefore, noninformative and useless.
To maximize the representativity and the inclusivity of our taxonomy, while limiting the length of the lists of skills associated witheach occupation, the solution we chose is to keep relying on a "lean" version of ESCO, while allowing platform users to suggest other pre-existing skills when they are assigned a list of skills. When the Harambee platform will go live, we will be able to highlight systamatic occupation-skill associations that are not already included in ESCO, and to potentially decide to enrich our taxonomy.
Do we need to extend ESCO?
The ESCO taxonomy under its current appears to cover most of the "seen" occupations found in the South African labour market, and the same can be said of skills. The data collected among microentrepreneurs suggests that the latter rarely claim to have occupations or skills that are not already covered by ESCO. Moreover, the Harambee platform aims at offering an "other occupation" and "other skill" option. Therefore, collecting data directly from a large number of job-seekers will allow to evaluate the overall representativity of ESCO in the South African labour market.
However, our work with Harambee in South-Africa already convinced us that extending ESCO to include the unseen economy should be at the core of our work, as it allows to build a more inclusive taxonomy. Collecting data about users usage of this part of the taxonomy will allow us to measure the importance of this tool to cover the full range of human capital in the South African labour market.
Adequacy of ESCO for non-European Economies
When it comes seen activities, the microentrepreneurship survey suggests that ESCO satisfactorily covers skills and occupations found in the South African labour market.
The data analysis of the microentrepreneurship survey is still ongoing. Final results will be provided hereby shortly.
As described here, the main challenge to adapt the inclusive taxonomy to the South African context was to ensure that platform users could access skill and occupation titles that resonate with them. Our experience in South Africa has shown that including alternative titles in our taxonomy was necessary for users to navigate the platform.
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