What is the EJ data?

Harambee, through SAYouth, regularly collects updated employment information from youth enrolled on their platform. This information, collected over XX years, produces a rich database of activities and occupations held by enrolled youth across time, highlighting the diversity of employment journeys (EJ) among young South African job seekers. For each activity and occupation held by young job-seekers, the database contains titles, a brief job description and additional information on conditions of employment/engagement such as contract duration and permanence, business classification (product/service/both) etc.

What do employment journeys teach us?

Employment experiences of young South African job seekers are a mix of formal, informal, and experiences.

We first look at the distribution of different types of livelihoods in the EJ data. As a first, cut, the data is decoded into three subsets for those who report any occupation or activity:

1. Those who report working for someone else, i.e. as being “employed” or having an “employer” are considered as a sample of the formal employment sector.

2. Those who report working for themselves i.e. "self-employed" are classified as microentrepreneurs and capture a sample of individuals from South Africa’s informal economy.

3. Volunteer work represents unpaid activities reported by jobseekers, and this includes a range of individual or community level unpaid work starting from apprenticeships to community service.

The formal and informal sectors together represent what we are calling the seen economy, while volunteer work and the 57.7% unemployed and unspecified sample represent the unseen economy. Strikingly, among those who have been involved in some work in the past 30 days, informal work and volunteer work make up 60% of cases. This confirms that, in the South African case, recognizing and highlighting skills acquired by young job-seekers in the unseen and informal economies is crucial for inclusivity.

Table 1: Distribution of Harambee EJ data across livelihood types

Source: Harambee EJ Data

Noting data limitations and sample comparability with the SA QLFS

It is important to note that the EJ data, while a rich source of information, is available for a very specific selection of individuals: South African youth who have registered as jobseekers on the Harambee platform, with a disproportionate number of individuals located in and around the province of Gauteng in South Africa (see Table 2). Having an overrepresented urban sample could introduce biases in our analysis, as our assessment of whether ESCO is applicable to our context will be based on livelihood descriptions provided by these individuals.

To avoid such biases, we are making sure that the Harambee platform (version 0.1) allows users to use free text to describe their experiences. Thanks to this, all livelihoods types can be taken into account, and the new information provided can enrich Harambee's list of activities and occupations.

Table 2: Provincial breakdown of youth from Harambee EJ data relative to South African Quarterly Labour Force Survey data

Source: Own calculations using Harambee EJ Data and Statistics South Africa (2023)

Note: 1. The National Youth Commission (1996) defines youth as all individuals between the ages of 15 and 34 (inclusive). Due to the age of majority in South Africa being 18, we restrict our analysis to youth aged between 18 and 34 (inclusive). 2. Numbers from QLFS weighted using sampling weights.

Analyzing job transitions shed light on how moves are influenced by past experiences; impact of job moves on economic stability, mobility and fragility; and employer preferences.

About the data and analysis: The job transition analysis is based on SAYouth placement data of 164,527 unique youth who report multiple opportunities on the platform between 2012 through May 2024. These youth make up 4.3% of the 3.8 million youth who are registered on the platform, and 18.6% of the 884,000 unique youth that report at least one opportunity on the platform (with 1.2 million total opportunities).

The analysis examines the first transitions made by these youth. This means, opportunities are only included if they have a valid start date and a valid opportunity type namely Public Employment Program (PEP), Formal Sector (FS), or Make Your Own Money (MYOM). Among youth who report multiple opportunities, 80% record two so we capture their full journey in this sample. For the remaining 20% with more than two reported opportunities, we examine only the first two.

A total of 329,054 placements in the data have been classified into one of three types of opportunities: PEP, MYOM/microenterprise, and formal sector. Of the three, the sample is PEP-heavy.

The analysis examines opportunities starting as early as 2012, but the majority fall within the 2020-2022 period. For 27% of transitions, the time between start dates of 1st & 2nd opportunities is less than 100 days. For 71% of transitions, this time between is less than 400 days.

Learnings on flows: Youth zigzag across many types of opportunities, but the likelihoods of different transitions are influenced by and in most cases, mirror their previous experiences.

Transitions overall, across PEP, FS and MYOM, are more likely to be within groups (60.3%) than between groups. PEP participants tend to transition to another PEP 71% of the time, and youth in the formal sector tend to transition to another role in the formal sector 54% of the time. Meanwhile, youth in MYOM opportunities are much less likely to transition to another MYOM opportunity (23%) than to the formal sector or PEPs.

• Transitions from/to PEP: PEPs are the most common opportunities, with DBE as the single largest opportunity. 70% of youth in this sample participate in a PEP as a first and/or second opportunity. On net, there is transition out of PEP. However, 54% of all youth in the sample transitioned to a PEP, composed of 71% youth with prior PEP experience, 36% from MYOM and 29% from the formal sector.

• Transitions from/to formal sector: The formal sector is the second most common type of opportunity overall, with 17% of all youth in this sample staying in the formal sector across both opportunities. On net, there is more transition into the formal sector. 32% of all youth in the sample transitioned to the formal sector, composed of 71% youth with prior FS experience, 41% from MYOM and 18% from PEP.

• Transitions from/to formal sector: On net, more youth move into MYOM than moving out of. However, importantly, youth who were engaged in MYOM were nearly twice as likely to transition to the formal sector or PEPs than to a second MYOM opportunity. Of the 14% that transitioned into MYOM, 23% were previously engaged in MYOM, 17% from FS and 11% from PEP.

Time-use surveys worldwide aim to capture how individuals spend their day. The International Classification of Activities for Time Uses Statistics (ICATUS) is an international standard that provides a framework and classification for these time-use activities. The taxonomy encompasses a comprehensive set of activities, including those considered typically productive such as doing a job or working for pay, as well as unproductive activities like sleeping or watching television.

The ICATUS taxonomy offers a more inclusive approach to defining what is productive. It encompasses activities that fall outside the production boundary set by the System of National Accounts. These include services rendered without pay for own and others' final use, such as cleaning one’s own dwellings or cooking one’s own food. These activities are considered productive insofar that they have paid equivalents (for instance, a professional cleaner or a cook). Therefore, they engage human capital and enable human capital formation.

We identify four types of such activities in ICATUS, which we call “unseen” activities.

1. Unpaid domestic services for household and family members (Div. 3)

2. Unpaid caregiving services for household and family members (Div. 4)

3. Unpaid direct volunteering for other households (Div. 51)

4. Unpaid community- and organization-based volunteering (Div. 52)

Highlighting this human capital in the South African first entails uncovering through which activities it might be formed. The South African time use survey from 2011 is the latest national wide source of data on how South Africans spend their time on the daily. When it comes to unseen activities, caring for someone in one’s household and maintaining one’s household takes up 19,9% of young South African’s time (15-34 years old). On the other hand, community service for non-household members such as volunteering (at Church, for instance) takes up 0,4% of young South Africans’ time.

At 33% as of 2024 Q1, South Africa has the highest unemployment rate in the world, with the rates doubling (61%) for youth aged 15-24. This is an unequal crisis on multiple dimensions. Rates are overwhelmingly higher among the Black African population (36%), followed a far second colored population (23%). Women face a 13% higher likelihood of unemployment than men.

Over three million young people in South Africa are not in employment, education or training (NEET). The same patterns of unequal outcomes persist: young South Africans who are not in employment, education or training comprise of a majority black (88%) urban (59%) and women (54%). These individual who are NEET are particularly vulnerable to long spells of unemployment and insecure work because they do not possess traditional qualifications and experiences that are observed by firms to determine skills and experiences.

More than 80% of the youth who are NEET have never been employed and have been searching for employment for more than 1 year while 20% have been searching for 5+ years. Low enrolment rates in tertiary education (25.2% as of 2021), is a key factor behind this widespread and persistent unemployability of youth. The lack of tertiary education is further compounded by the apartheid legacy of legal discrimination and segregation that generated large wealth and spatial inequalities across the country. This means, for many of these already disadvantaged jobseekers, available jobs are also located prohibitively far away.

Together, these factors characterize a labor market facing a vast disconnect between labor demand and labor supply sides. On the supply side, a large pool of jobseekers struggle to signal their skills to prospective employers and face inherently high search costs due to socioeconomic and spatial inequalities. On the demand side, firms appear uncertain about the quality of the applicants they attract with many reporting that the skills they are interested in are not emphasized or tested within the education system. This in turn leads to firms relying on trial-and-error recruiting tendencies, creating insecure entry level jobs. Experimental research found firms more willing to hire when given legal advice on how to fire entry-level workers.

Inaccurate beliefs about skills lead to high prevalence of misdirected job search. Job search support interventions have proven useful in improving employment outcomes. Studies have tested and found positive results for job search support through 1) behavioral encouragement interventions such as planning support, social support; 2) better utilization of job search platforms such as training jobseekers in how to use LinkedIn and 3) endorsement mechanisms such as provision of reference letters and low cost skill testing and certification to job seekers. When implemented at scale, better job search infrastructure has the potential to bridge the gap between what firms need versus what jobseekers can offer through increased visibility of the labour market and pathways within it for both sides.

Spatial mismatches between jobs and jobseekers, combined with high search costs, further deepen inaccurate beliefs about the job market among youth. Overly optimistic beliefs about the job market lead to young jobseekers under searching and holding out for "better jobs". On one hand, transport subsidies and cash transfers have been found to increase job search activity, but these search efforts do not necessarily translate to better employment outcomes on average. One study found a negative information shock to such subsidies leading to lower reservation wages and acceptance of lower paying jobs.

Further readings:

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.

Beam, E.A. and Quimbo, S., 2023. The Impact of Short-Term Employment for Low-Income Youth: Experimental Evidence from the Philippines. Review of Economics and Statistics, 105(6), pp.1379-1393.

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.

Branson, N., DeLannoy, A., & Kahn, A. (2019). Exploring the transitions and well-being of young people who leave school before completing secondary education in South Africa. Working Paper Series Number 244, NIDS Discussion Paper 2019/11 Version 1.

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.

Hardy, M. and McCasland, J., 2023. Are small firms labor constrained? experimental evidence from ghana. American Economic Journal: Applied Economics, 15(2), pp.253-284.

ILO., 202. The impact of COVID-19 on the informal economy in Africa and the related policy responses.

ILO., 2023. African youth face pressing challenges in the transition from youth to work.

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

Kiss, A., Garlick, R., Orkin, K. and Hensel, L., 2023. Jobseekers’ beliefs about comparative advantage and (mis) directed search. Available at SSRN 4593303.

Loiacono, F. and Silva Vargas, M., 2023. Improving access to labor markets for refugees: Evidence from Uganda.

McKenzie, D., 2017. How effective are active labor market policies in developing countries? a critical review of recent evidence. The World Bank Research Observer, 32(2), pp.127-154.

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.

World Bank (2023), “Skills and Workforce Development,” worldbank.org. Hensel, L., Tekleselassie, T., Isphording, I., Radbruch, J. & Witte, M. 2024. Demand for Feedback and Job Search. Working Paper.