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Employment Journeys

The Employment Journey (EJ) dataset compiled by Harambee is a valuable source of information about job-seekers' previous experience.

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 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:

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.
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.
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

Note on data cleaning: As evident from Figure/Table 1, majority of individuals in the Harambee EJ data are classified as “unemployed”. Although individuals in this category do still occasionally report a job title, this is non-sensible. As such, we remove all unemployed individuals from our analysis and further remove those individuals whose livelihood type was categorized as “Unspecified”. This leaves a total of approximately 508 000 individual EJ records to analyze, of which 39.2% are in the formal sector, 31.7% in the informal/microenterprise sector, and 29.1% in volunteer work.

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 (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.

Note that this analysis does not account for:

Youth who have a first opportunity and then do not transition to something else. When such gaps are present on a user’s profile, we cannot conclude whether the user became unemployed or simply did not update their SA Youth profile. For this reason, we do not consider them in this analysis.

Youth who gained employment and retained it, without transitioning to anything else. Youth who secure a job and stay in it for many months and years are positive outliers among South Africa’s youth. These youth are not studied in this analysis, however, because there is no transition to report.

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.