Labour markets in low- and middle- income countries are characterised by a dearth of quality formal jobs (Fields, 2011). This leads to high rates of formal unemployment in some countries, and in others, to large numbers of workers taking insecure jobs with very low pay. In either circumstance, many labour market participants in low and middle income countries struggle to earn decent, steady incomes despite being willing and able to work. This contributes to high rates of poverty and exploitative working arrangements without labour protections.

This challenge has been exacerbated by the 2008 Financial Crisis and the COVID-19 Economic Crisis, two catastrophic global recessions that destroyed economic value in many countries. Moreover, in Sub-Saharan Africa, demographic pressures, rising youth unemployment and the destruction of manufacturing jobs have exacerbated the unfavourable ratio of workers to formal jobs (McKenzie). From 2023-2033, 1.2 billion youth will become working age and 714 million youth will require job opportunities (World Bank, 2023).

Labour Market Frictions in Sub-Saharan Africa

One lever for addressing unemployment is helping firms to increase their hiring of workers from the large pool of available candidates. However, firms are often reluctant to expand hiring, and cite an inability to find suitable candidates. The World Bank reports that about 23% of firms cite workforce skills as a significant constraint to their operations. In some African and Latin American countries, this share rises to 40–60% (World Bank 2023). This is surprising when firms have large applicant pools available to them. Increasingly, academic research is showing that both firms and workers lack good information required to match worker skills to suitable jobs. This leads to workers searching for and applying for jobs inefficiently and firms being unable to find or observe the skills they need when hiring. These frictions reduce firm willingness to hire and reduce the wage offers firms are willing to make because of uncertainty of the quality of hired workers.

Features common to many Sub-Saharan Africa labour markets are suggestive of large frictions that may reduce hiring demand and misdirect job search. Large shares of jobseekers have limited formal work experience and very similar education qualifications. Young jobseekers are particularly likely to have few experiences they can use to demonstrate skills. These constraints limit the ability for jobseekers to signal competency to firms and limit the ability for firms to compare applicants and judge who is best suited for the role. Moreover, job search and migration costs are high, especially relative to incomes. As summarised in Caria & Orkin (2024), studies from Ethiopia, Jordan, South Africa and Uganda find that job search expenses among active jobseekers are at least 16% of total jobseeker expenditure. This reduces the ability for job seekers to search widely, find the jobs that demand their particular abilities and experiences and gain information about the nature of jobs available in the market. Finally, a number of studies document a high prevalence of inaccurate beliefs among jobseekers (Hensel et al. 2024, Abebe et al., 2021). This suggests job seekers lack information about their skills, comparative advantage and prospective earnings.

Spatial frictions exacerbate difficulties for the matching of firms and workers. Studies that document high job search costs in Sub-Saharan Africa show that reducing travel costs by providing for example, transport subsidies or improving travel quality (Donald & Grosset, 2022) can increase job search intensity in the short term (Franklin et al. 2015, Abebe et al., 2021) but cannot entirely overcome frictions arising from weak signalling ability, misdirected search and biased beliefs. Intuitively, this is because searching for more jobs may not translate into matches if effort is focused on applying for jobs that applicants are not well suited for. Consequently, job search subsidies tend to fail to translate increased search into improved employment outcomes.

Overcoming spatial Job fairs that bring together jobseekers with firms looking to recruit (Abebe et al., 2023) have been shown to provide information to both parties, leading to updated, more accurate, beliefs about the job market and pool of candidates. Online job search platforms can play a similar role in easing some costs of search and providing information about prospective employers and jobs for jobseekers (Wheeler et al., 2022., Field et al., 2023, Jones & Sen, 2022). However, a number of studies document no overall effect on employment from encouraging the use of online job platforms, and in some settings, a replication of off-platform negative bias towards socially marginalised groups (Chakravorty et al. 2023, Afridi et al 2023).

Bibliography:

Abebe, G., Caria, A.S., Fafchamps, M., Falco, P., Franklin, S. and Quinn, S., 2021. Anonymity or distance? Job search and labour market exclusion in a growing African city. The Review of Economic Studies, 88(3), pp.1279-1310.

Abebe, G., Caria, S.A., Fafchamps, M., Falco, P., Franklin, S., Quinn, S. and Shilpi, F.J., 2023. Matching frictions and distorted beliefs: Evidence from a job fair experiment (No. 958). working paper.

Abel, M., Burger, R., Carranza, E. and Piraino, P., 2019. Bridging the intention-behavior gap? The effect of plan-making prompts on job search and employment. American Economic Journal: Applied Economics, 11(2), pp.284-301.

Abel, M., Burger, R. and Piraino, P., 2020. The value of reference letters: Experimental Evidence from South Africa. American Economic Journal: Applied Economics, 12(3), pp.40-71.

Afridi, F., Dhillon, A., Roy, S. and Sangwan, N., 2023. Social Networks, Gender Norms and Labor Supply: Experimental Evidence Using a Job Search Platform (No. 677). Competitive Advantage in the Global Economy (CAGE).

Banerjee, A. and Sequeira, S., 2023. Learning by searching: Spatial mismatches and imperfect information in Southern labor markets. Journal of Development Economics, 164, p.103111.

Bertrand, M. and Crépon, B., 2021. Teaching labor laws: Evidence from a randomized control trial in South Africa. American Economic Journal: Applied Economics, 13(4), pp.125-149.

Bhorat, H., Köhler, T. and de Villiers, D. (2023). Can Cash Transfers to the Unemployed Support Economic Activity? Evidence from South Africa. Development Policy Research Unit Working Paper 202301. DPRU, University of Cape Town.

Carranza, E., Garlick, R., Orkin, K. and Rankin, N., 2022. Job search and hiring with limited information about workseekers’ skills. American Economic Review, 112(11), pp.3547-3583.

Chakravorty, B., Bhatiya, A.Y., Imbert, C., Lohnert, M., Panda, P. and Rathelot, R., 2023. Impact of the COVID-19 crisis on India’s rural youth: Evidence from a panel survey and an experiment. World Development, 168, p.106242.

Fields, G.S., 2011. Labor market analysis for developing countries. Labour economics, 18, pp.S16-S22.

Franklin, S., 2015. Location, search costs and youth unemployment: A randomized trial of transport subsidies in Ethiopia.

Jones, S. and Sen, K., 2022. Labour market effects of digital matching platforms: Experimental evidence from sub-Saharan Africa.

Mudiriza, G., De Lannoy, A. (2023). Profile of young people not in employment, education or training (NEET) aged 15-24 years in South Africa: an annual update. Cape Town: Southern Africa Labour and Development Research Unit, University of Cape Town. (SALDRU Working Paper Number 298).

Wheeler, L., Garlick, R., Johnson, E., Shaw, P. and Gargano, M., 2022. LinkedIn (to) job opportunities: Experimental evidence from job readiness training. American Economic Journal: Applied Economics, 14(2), pp.101-125. Hensel, L., Tekleselassie, T., Isphording, I., Radbruch, J. & Witte, M. 2024. Demand for Feedback and Job Search. Working Paper.

Human capital – the collective skills, knowledge, and experiences of an individual – is a significant determinant of economic productivity and income potential. The level of human capital an individual has often directly correlates with their ability to earn an income. It’s also deeply tied to the concept of human agency - the ability to make independent decisions and exert influence over one’s life. Working with our partners, we aim to make visible and usable the human capital of everyone in an economy.

The motivation for our work is described well by a quote from one our key partners, :

Young people in South Africa often lack the resources, networks, education and work experiences needed to be considered for formal employment. But, in the past 12 years, our work at Harambee has taught us that young people have the potential to perform in these jobs if we give them a chance! What if a young person was better able to identify and articulate the skills they have gained outside of the formal economy? What if they could signal skills gained from unpaid work?

Harambee Youth Employment Accelerator, South Africa

Objectives

Organizations that help young people access economic opportunities typically use structured frameworks to describe the universe of opportunities in an economy and the anatomy of each opportunity – the mix of skills, competences, qualifications required.

Traditional frameworks to measure economic activity and describe labor markets typically overlook significant areas of human capital investment and productivity such as household work for own production (e.g., cooking, cleaning, and childcare), volunteer work, and informal work. These activities form important parts of individual livelihoods and contribute significantly to human capital, but they are often undervalued or ignored because they do not involve direct monetary transactions.

The objective of our work is to make visible and usable the human capital of everyone in an economy. Human capital here is defined as the skills, knowledge, and experience of individuals that can be used to create economic value.

This involves two related efforts:

1. Making visible all economic activity and the skills, knowledge, and experience gained through these activities, including and especially in the traditionally “unseen” parts of an economy (i.e. the informal economy, unpaid household work, etc.).

2. Making usable the skills, knowledge, and experience gained through these activities by organizing and integrating them into traditional frameworks of economic activities.

Outcome: An Inclusive Reference Taxonomy as Basis for Labor Market Intermediation and Analysis

A reference taxonomy offers a common language for understanding, categorizing, and linking different pieces of labor market information. It is a structured system of classification that aids in the interpretation and analysis of vast and diverse data sets in the labor market. Such a taxonomy can be seen as a map of the entire labor market. It describes the universe of jobs in an economy and the anatomy of each job – the mix of skills, competences, qualifications required for the job.

This taxonomy is the foundation for governments, nonprofits, and other actors to provide effective guidance on potential career pathways and other services. The dropdown box below provides an overview of various use cases.

Problem: Traditional Frameworks Overlook Parts of the Economy

Commonly used labor market taxonomies typically overlook many economic activities that generate important skills, knowledge, and experiences; they fail to accurately portrait the often complex and diverse livelihoods of people.

However, several significant areas of human capital investment and productivity fall outside this boundary, such as household work for own production (e.g., cooking, cleaning, and childcare), volunteer work, and informal work. These activities form important parts of individual livelihoods and contribute significantly to human capital for millions of people around the world, but are often undervalued or ignored because they do not involve direct monetary transactions. We call these activities the "unseen economy".

Intuitively, "seen" activities are paid, and thus typically considered as work, whether they are formal or informal, if not illegal. "Unseen" activities include all productive activities, such as cooking for a family member, that are unpaid and thus typically not considered as work. One other way to qualify unseen activities is that they are unpaid activities, that one could pay someone else to do. Therefore, it does not include leisure, as one may not pay someone to enjoy leisure activities for them.

Recognizing and accounting for the human capital in the unseen parts of the economy is vital for accurately representing people's contributions to the economy, and providing a basis for policies that protect and support all forms of work, thereby enhancing individual agency.

Our Approach: Expanding the Map

Our Challenge

As explained , Tabiya's work builds upon widely used taxonomies of skills and competences to ensure that our inclusive taxonomy is compatible with existing intermediation tools used by governments and organisations. However, there exist multiple taxonomies serving as basis for national matching platforms and public policies, among which ISCO (International Standard Classification of Occupations), ESCO, O*NET (Occupation Information Network) or OFO (Organising Framework for Occupations in South Africa).

The challenge when picking a taxonomy is the following. On the one hand, the taxonomy needs to be broad enough to account for the occupations and human capital of job-seekers on the whole African continent and elsewhere. On the other hand, it needs to be adaptable to local contexts, which implies using a language understandable by local users, and encompassing context-specific skills and occupations. Finally, it needs to be open-source and regularly updated, to account for the rapid evolution of labour markets, as well as the integration of new skills and occupations, such as the ones linked to AI or the green economy. Based on these considerations and discussions with our localised partners, ESCO appeared as the most appropriate taxonomy.

The Advantages of ESCO

Accessibility to job-seekers:

1. When it comes to user experience, one of our main concerns is to make sure that demographics who may be traditionally marginalised from labour markets et intermediation solutions can use our inclusive taxonomy. In some contexts, the mastery of official languages, such as English in South Africa, is limited. Compared to O*NET, ESCO relies on skill tags that are easier to understand.

2. ESCO contains a list of ‘similar titles’ for each occupation – making it easier to search for an occupation from a broad starting point. For instance, "data engineer" is saved as an alternative title for "data scientist", which ensures that one may use the platform by typing out "data engineer".

Highlighting job seekers' skills

1. Contrary to O*NET, ESCO includes "attitudes and values", which are soft-skills missing in O*NET. Therefore, it covers a broader range of skills that are sought after by employers.

2. ESCO is a European Commission project administered by the Directorate General Employment, Social Affairs and Inclusion. This ensures that the taxonomy is frequently updated and will be used by major actors in the long run. On the contrary, the last update of ISCO was made in 2008, and the last update of OFO happened in 2013. Frequent updates of ESCO allow it to include skills and occupations associated with major evolutions in labour markets worldwide, such as the greening of economies and, in the next few years, the predictable growing reliance and AI.

Compatibility with Compass

1. Technically, ESCO is designed to be used by third-party organisations. ESCO is specifically built for others to use and embed the taxonomy in their own technologies.

2. Other developing countries in , and they are contributing to the open-source tools around ESCO which can be leveraged (for instance, ML-classification models which take in free-text job-titles and assign the ESCO occupations).

3. . This means that Tabiya can host this API locally and adjust it as need be without depending on third-party performance when the volumes of requests are scaled up. This also gives Tabiya the ability to edit and adapt the framework freely.

Tabiya creates open-source software, models and standards to help tackle the global youth employment challenge. We foster research, coordination, and harmonization among partners that create learning and career pathways. Our vision is to unlock human capital and empower people in informal and formal labor markets.

We are a non-profit organization that started at the University of Oxford.

Discover Tabiya's Work

If you have any questions or comments, please do not hesitate to reach out via email. You can also create an issue in the Github repository that hosts this documentation.

ESCO vs O*Net

ICATUS Level Choice

As discussed here, the ICATUS taxonomy comprises three levels that represent different levels of disaggregation, namely the major division, division and group levels. For Tabiya’s matching procedure, the group level activities pertaining to the unseen economy are matched to ESCO Occupations. There are a total of 60 ICATUS group level activities that comprise the Framework’s ICATUS input. The group level is chosen, rather than the major division or division level primarily due to the fact that the group level attains the highest level of precision in the ICATUS taxonomy. Thus, we are able to glean the most detail when matching from this level, relative to the major division and division levels.

The challenge, though, has been in the matching of group level activities to ESCO occupations that occasionally define activities and occupations at differing levels of granularity. In particular, we find that there are some instances where an ESCO Occupation comprises more than one ICATUS group level. For instance, the ICATUS group level activities, “Cleaning up after food preparation/meals/snacks'' and “Preparing meals/snacks” are each matched to the ESCO occupation, “kitchen assistant” (defined as “Kitchen assistants assist in the preparation of food and cleaning of the kitchen area.”). Clearly, the occupation “kitchen assistant” is made up of these two ICATUS group level activities. Theoretically, one way to circumvent this would be to match ICATUS activities at the division level, rather than at the group level. However, this method is ultimately deemed too information costly since at the division level, the chance of omitting relatively rare but relevant ESCO occupations (and in turn, skills) from the ICATUS to ESCO match is very high. This does harm to the end user because it limits the eventual list of skills available to represent the work that they do. After discussion between the research team, and the Harambee team, we mutually agreed that the best way to do no harm in this case is to match ICATUS activities to ESCO occupations at the group level.

ESCO occupations as a substep to skills (NLP, Word2Vec etc.)

The reason that ICATUS activities are matched to ESCO occupations, and then to skills as opposed to a match from ICATUS activities immediately to skills requires a summary of the conceptual roadmap taken to ultimately come to this decision. Initially, since the goal is to assign skills to activities, the most intuitive course of action seemed to be to directly match ICATUS activities to ESCO skills. In order to do this, Natural Language Processing (NLP) techniques were adopted. In particular, to compare ICATUS activities to ESCO skills, a technique called Word2Vec was adopted. Word2Vec is an algorithm used in NLP that uses word embeddings to assign numerical values to a given word in relation to the surrounding context of this word which is informed by the relationship that this given word has with other words in a dictionary used to train the algorithm (Mikolov et al., 2013). After several attempts to match ESCO skills directly to ICATUS activities, NLP techniques failed due the incompatibility in nature between ICATUS/ESCO as inputs and the required input for the algorithm to function optimally. Since ICATUS comprised short phrases pertaining to activities rather than single words as the ESCO taxonomy does for skills, Word2Vec could not optimally reconcile these taxonomies to produce a meaningful skills match. From this arose a precision-reproducibility tradeoff. On the one hand, NLP methods allow for near perfect reproducibility but score low on the precision scale in this context. A decision was taken to prioritize precision throughout the conceptualizing of the Tabiya Framework. Thus, the prevailing methodology that uses ESCO occupations as a crosswalk to skills proved to strike the precision-reproducibility scale optimally for our purposes. That is, the manual assignment of ESCO occupations to ICATUS activities to derive skills associated with unseen economy proved to score high on the precision scale and high enough on reproducibility scale, since this work was independently checked. Hence, the manual matching procedure prevailed for the Tabiya Framework’s creation.

Our Mission

Tabiya is a research organization that develops open-source technology and practical solutions to connect jobseekers with opportunities. Our vision is one of inclusive labor markets that empower people by recognizing their formal and informal skills.

Tabiya, fom Arabic طبيعة “essence”, is a chess opening position that serves as the starting point for many possible subsequent moves. It alludes to the starting point from which jobseekers may navigate the many different paths through labor markets. It may also be translated as “talent”, alluding to the human capital that young people are looking to build, protect, and utilize.

Our objective is to create software, models, and standards that help governments, nonprofits, grassroots organizations, education and training providers, and employers in low- and middle-income countries (LMICs) harness the promise of data and AI to create more equitable and more efficient labor markets while navigating the global challenges of digitalization and decarbonization.

We do not aim to deploy these technologies directly, but act as a convening and incubation platform for other partners.

Our Guiding Principles

Our work is based on the following four guiding principles:

1. Public goods instead of walled gardens: Rich private sector ecosystems for labor market intermediation are emerging in low-and middle-income countries, but with little to no integration, coordination, and harmonization of data. LMIC governments and non-profits find it difficult to tap into private sector ecosystems for analytics or build public good solutions for their labor markets. Off-the-shelf labor market information systems and AI-based analytics are exclusively geared at high-income country labor markets and are prohibitively expensive for LMIC applications.

2. Standardization to foster integration, coordination, and harmonization: Different labor market actors need to speak the same “language” – they need to use a common reference terminology for the labor market. This reference terminology needs to describe the universe of jobs in an economy and the anatomy of each job – the mix of skills, competences, qualifications required for the job. Such frameworks either do not exist for LMIC labor markets or are frequently outdated. A standardized framework is a key requirement to harness data across actors for large-scale data analysis and AI-based applications. At the same time, a taxonomy that does not reflect livelihoods in LMICs risks exacerbating existing inequities and raises concerns about algorithmic fairness.

3. Rigorous evidence and research on equity, efficiency, or algorithmic fairness: There is an emerging research agenda on the promise and perils of technology and AI for labor market intermediation in high-income countries, but challenges in LMICs are not well understood.

4. A neutral platform for open discussion, exchange, and learning: Coordination and knowledge exchange on the promise and perils of technology for labor markets among public and private employment service experts and policymakers from LMICs is lacking. With our roots at the University of Oxford, we provide a neutral platform to make recent advances in data and AI-based technologies more widely available while rigorously scrutinizing algorithmic biases.

The Global Youth Employment Challenge

Youth employment is already a pressing global challenge, with about 75 million young people unemployed and 280 million people not in employment, education, or training. In the next 10 years, more than 1 billion young people will enter the labor force, 700 million in Africa alone.

Over the next decades, digitalization and decarbonization will further transform livelihoods in low- and high-income countries alike. Jobs in various sectors of the economy might disappear or change dramatically while new economic opportunities will be created elsewhere. Investing into human capital and formulating policy responses to digitalization and decarbonization will require evidence, and targeted advice and recommendations to empower jobseekers in their journeys through these changing labor markets.

Academic research has additionally identified a range of labor market frictions that undermine the efficient allocation of workers to jobs in LMICs. Millions of youths in low-income countries face a “slippery job ladder”: As they try to climb up to better-paying jobs, they frequently fall back into low-wage work, informal self-employment, or unemployment. This unproductive churn traps them in poverty and exacerbates inequalities. Lack of information is one explanation for why the ladder is so slippery – jobseekers often don’t know how to best use their skills, and firms don’t know where to find the best workers – and that improving information can improve jobseeker outcomes.

Improved data and AI systems promise to tackle these challenges but remain inaccessible and unaffordable to stakeholders in LMICs. Proprietary commercial solutions dominate the market but raise concerns about limited interoperability and contractual lock-ins (“walled gardens”). Where these tools are used, they often exclude large parts of the population in the thriving and diverse informal sector. This exacerbates existing, often strongly gendered, inequities.

Context: Improving labor markets with technology can only be a small puzzle piece of the global youth employment challenge

Online job matching platforms have emerged as popular tool for connecting employers and job seekers in an increasingly digital world. And indeed, there is now robust evidence that alleviating search and matching frictions by improving the flow of information between employers and jobseekers can improve labour market outcomes.

At the same time, it is important to note that information frictions only represent a very small part of the global youth employment challenge. While we believe that technology – if used well – can help overcome these frictions, they address merely the surface of deeper, structural issues – most importantly the lack of economic growth, insufficient job creation, and deeply rooted social and economic exclusion of specific groups. This broader economic context can render even the most efficient job matching platforms ineffective.

Technology can thus only be part of a broader approach to solving global youth employment and promoting economic inclusion. It requires broad ecosystems of private sector, government, and civil society working together to tackle structural barriers on both sides of the market. Tabiya aims to contribute to these ecosystems with digital public goods that others can build on.

We are currently developing the Open Taxonomy Platform, which lets partners flexibly adapt our reference taxonomy in a transparent way. The platform also provides access to all taxonomies through an API, which allows users to query the platform for information on specific occupations or tasks.

The platform is currently under active open-source development. You can follow along and contribute on Github. If you would like to learn more, please get in touch.

Tabiya's work on the inclusive livelihoods taxonomy can be divided in two streams of work. First, making sure that the "seen" part of the pre-existing taxonomies such as ISCO and ESCO fit local contexts. Second, broadening existing taxonomies so that they include the unseen part of the economy, namely the activities that are typically not considered as productive and the skills that are associated with them. The first stream of work is thoroughly described for .

Motivation of the Methodology

The challenge is the following: Tabiya aims at creating an inclusive taxonomy of livelihoods, while ensuring that this taxonomy is compatible with existing ones such as ISCO or ESCO. Indeed, this allows to ensure that the inclusive taxonomy can be used by institutions that rely on widely-used taxonomies. Along with our partners, we chose to base our work on the European Skills, Competencies, Qualifications and Occupations (ESCO) taxonomy, for reasons highlighted here. Consequently, Tabiya's work entails evaluating the inclusiveness of the existing ESCO taxonomy, to then improve this inclusiveness.

This work's intellectual underpinning builds upon the "Counting Women's Work" literature, that aims at assigning a monetary value to the tasks done by women, especially in their households. Instead of focusing on monetary values, Tabiya aims to highlight the human capital gained from these tasks. Moreover, Tabiya's work covers all job-seekers, and not just women, although depending on local contexts they may de facto represent the majority of job-seekers.

Making Visible the "Unseen Economy"

For the “unseen economy,” i.e. activities outside of the specific SNA production boundary, we rely on an existing classification of all time-uses and tasks one may gain human capital from. Therefore, our works builds on the International Classification of Activities for Time Use Statistics (ICATUS) taxonomy, which lists all time uses someone may have throughout the day. ICATUS represents the internationally applicable classifications of activities that people engage in during their 24-hour days. Countries and regions, including South Africa, Ethiopia, and Ghana, have used either this ICATUS framework in their national data collection efforts.

ICATUS is made up of three levels. The first digit level of disaggregation (highest level of aggregation) is called the major division, the second digit level is called the division level, and the third digit level, which is the most granular level, is called the group level. We first start by comparing ICATUS with the System of National Account, to define the boundaries of the "seen" and the "unseen" economies.

Based on the System of National Account, we thus consider the following ICATUS major division to encompass the unseen economy:

Within these divisions, we exclude the following activities:

Matching unseen ICATUS activities to the ESCO framework

Once ICATUS activities have been associated with the "unseen" part of the economy, the challenge is to make sure the unseen part of our inclusive taxonomy follow the same structure as the existing ESCO taxonomy. Namely, we need to assign skills to the ICATUS activities forming the unseen part of the economy. Therefore, Tabiya conceptualises a framework that links the most granular level of unseen economy ICATUS activities (3-digit level) to a set of non-exhaustive candidate ESCO skills and knowledge tags per ICATUS Activity.

ICATUS 3 digit level activities comprise more specific activities that lie within ICATUS Divisions Three, Four, and Five. For example, “Preparing meals/snacks'' is a Group Level Activity that falls within the Division 3 of ICATUS, namely Unpaid domestic services for household and family members. In addition, at the Group Level, ICATUS includes a definition of each activity, and a non-exhaustive list of the tasks that each activity includes and does not include. Finally, ICATUS presents at least one example of each Group Level Activity.

The ESCO taxonomy is made up of a set of occupations at level five or lower that are derived from the four ISCO-08 occupations hierarchy. For the purpose of the Tabiya Framework, we use only ESCO occupations at level five or lower for our analysis, since this provides the desirable granularity and metadata to adequately link ESCO to ICATUS. The ESCO taxonomy provides a brief description of each occupation it comprises, the occupation’s alternative labels as well as access to regulatory information pertaining to the occupation. What makes the ESCO taxonomy particularly desirable are the skills and competences it assigns to each occupation at level five or lower. These skills and competences include a predetermined list of essential and optional skills and competences, and a list of essential and optional knowledge required to perform a given occupation.

ICATUS Group Level activities are used as one of the inputs for the matching procedure entailed in the Tabiya framework. ESCO occupations and skills, competences and knowledge (skills, competences and knowledge will be referred to as skills henceforth for simplicity) are used as the other input for the matching procedure. In particular, each ICATUS Group Level activity is manually matched to at most four ESCO occupations by a team of researchers. ICATUS, even at the Group Level, is more broad than ESCO occupations and so, to ensure comprehensiveness in the Tabiya matching procedure, up to four ESCO occupations were potentially matched to each ICATUS Group Level activity. Since the ESCO taxonomy assigns skills to occupations, these ESCO-assigned skills then form the initial list of candidate assigned skills per ICATUS Group Level activity.

The assignment of ESCO occupations to ICATUS activities is based on the relative similarity definitions between ICATUS Group Level activities, and ESCO occupations. Comparing definitions of ICATUS activities to ESCO occupations is relatively easy to do amongst a team of researchers, since activities and occupations are similar in nature, and in turn, in the manner that each of these are defined. Once at most four ESCO occupations are matched to each ICATUS Group Level activity by a team of at least two researchers, these matches are then checked by another independent researcher. Throughout this process, there were no instances where the independent researcher disputed an occupation without mutual agreement from the pair of researchers who initially made the match. This system is adopted to ensure that person-specific biases do not prevail during the matching process. This notwithstanding, while we endeavor to minimize biases and human error through this process, we cannot completely rule out the possibility that other (group level) biases and errors may have occurred throughout this process. Over time, as Tabiya accumulates more data and learns from this, the framework will use these learnings to improve and ultimately, not succumb to any currently prevailing biases and errors.

Once the ICATUS to ESCO occupations match is done and verified, the next stage of the Tabiya framework formulation can commence. By design, the ESCO taxonomy pre-assigns skills to each ESCO occupation at disaggregation level five and below. Thus, we exploit this structure for the Tabiya framework. In particular, once ICATUS activities are matched to ESCO Occupations, we adopt the pre-assigned ESCO skills as the first set of candidate skills assigned to each unseen economy ICATUS activity. Once duplicate skills are removed, each ICATUS activity now has a comprehensive list of skills assigned to it. The procedure is shown below:

Reducing the Candidate List of Skills

The taxonomy obtained following this method is hardly usable. Indeed, each ICATUS activity ended up being associated with numerous skills. In order to provide a manageable list of skills to job seekers on the Harambee platform 0.1, the team decided to reduce this tentative list of skills. Reducing this also allowed to addressed the transferability and signalling issues inherent to the unseen economy. To do so, an approach based on a panel of professionals involved in the South-African labour market and in intermediation instances (among which Harambee) was chosen. The panel approach is meant to gain context-specific insights from a wide range of stakeholders. This would make it possible to compensate holders' biases pertaining to the transferability and credibility of skills. In order to test this approach, we first implemented a panel at the scale of Harambee.

For the panel to happen efficiently (in a time limit of 5 hours for the pilot panel), the Tabiya team proceeded with a first round of skill selection in order to delete skills that were deemed obviously not consistent with the definition and description of ICATUS activities. To do so, the team followed 4 easily applicable rules.

Isolating Knowledges:

The ESCO classification distinguishes between "skills" - that all describe an action - and "knowledges". For instance, the occupation "cook" is associated with the skill "use cooking techniques" and the knowledge "cooking technique". As a first step, we chose to isolate knowledges and to mainly focus on skills. However, we chose to let the panel decide to keep certain knowledges when they brought important new informations. For instance, "common children's diseases" may be deemed an essentio

Deleting irrelevant skills:

In ICATUS, each activity is associated with a definition, and explicit list of tasks included in the activty, a list of tasks that are not included, and one or more examples. ESCO is built similarly. However, IACTUS activities are typically not only broader, but conceptually different from ESCO occupations. For instance, "Budgeting, planning, organizing duties and activities in the household" is not a formal sector activity that is included in ESCO. Therefore, to assign ESCO skills to this ICATUS activity, it was matched to "Office clerk", "Accountant", "Bed and breakfast operator", three ESCO occupations that encompass all tasks involved in "Budgeting, planning, organizing duties and activities in the household". The issue is that they encompass more. For instance, because of "bed and breakfast operator", the skill "serve beverages" endded up being associated to "Budgeting, planning, organizing duties and activities in the household", even though serving atsks are not included in the description of the ICATUS activity. When we came across such cases, we decided to exclude the skills that had been unduly associated to the ICATUS activities.

Deleting skills that are deemed too formal: some of the skills associated with ESCO occupations directly refer to situations or tasks only imaginable in the seen economy, whether it is formal or informal. For instance, "maintain customer service" cannot be appropriately associated with "serving meals and snacks", as it describes serving meals and snacks to one's own children/family members.

Deleting redundant skills: ESCO occupations are typically associated with numerous skills, and each ICATUS activity was associated with mutltiple ESCO occupations by Tabiya's team. The lists of skills from each ESCO activity was added to a list of skills for each ICATUS actity, with deletion of skills that appear multiple time (where we only kept one appearance). Therefore, the temptative lists of skills associated to each ICATUS activty contain skills tht may be deemed redundant. For instance, "Outdoor cleaning" was associated with both "prune plants" and "prune hedges and trees". We considered that asociating both skills to "outdoor cleaning" did not bring new information.

This makes up the generalisable unseen economy framework methodology. From this candidate list of skills associated with each unseen economy ICATUS activity, differing contexts can begin to localize this framework.