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What is ‘a job’? Pt 2: Lessons from the data

Mar 4, 2024 | Lessons Learned (the Hard Way)

By Jared Adema and Chris Maclay

We explained how we chose to ‘count jobs’ based on:
* Inclusion criteria: the minimum standards for a platform to consider a job as  ‘created’ or ‘improved’.
* Exclusion criteria: While the above helps us to define what is ‘in’, we also need to consider what is ‘out’. We ringfence which jobs we believe we have contributed to.
* Hidden hierarchies: In some cases, there may be hidden jobs created beyond platform data e.g. one carpentry workshop is listed on a platform, but 10 individuals could be working in that shop.

To define thresholds for monthly income for ‘good enough’, we considered two major considerations for inclusion criteria

* Income level (per month)
Equal to or more than the minimum wage in the country of operation, with consideration for geography within the country.
For part-time work, the hourly rate must be commensurate with the hourly rate of minimum wage in that country. We don’t consider anything below $30/month as sufficient to count as a job, regardless of the number of hours worked.
We look at net income by accounting for the lost costs of delivering e.g. net profit for entrepreneurs, and considering the cost of fuel/car in ride-hailing.
We consider the contribution to the overall livelihood portfolio to judge the right levels of income (for example, $30 part-time work could be valuable to someone otherwise unemployed or unskilled, but would not be a valuable contribution to someone already with a business generating $1,000 a month).

*Timeframe / Regularity
We choose a suitable regularity of income before we count the job. Initially, we considered this to be 3 months of consecutive income above our threshold, or three months in six months if the work was particularly “lumpy”

Lessons from the data

In the last blog, we mentioned that we would keep updating our model based on findings from our work. Having recently completed a process of collecting and analyzing data from over 40,000 users from 13 platforms, we’ve learned that:

  • While a lot of users earn from the platforms, only a small proportion hit our thresholds of ‘what good looks like’. This validates the importance of having a higher bar than just ‘anyone earning income’.
  • Most gig platform users earned intermittent income – this means that they’re not earning consistently each month, but instead experiencing fluctuations in the regularity of the months with earnings. We recognized that our ‘rule of thumb’ for regularity of income (earning the same amount each month for three months in a row) therefore does not accurately represent how most young people engage with platforms.

We heard similar findings from qualitative conversations to better understand this phenomenon. We learned that the reasons for this were both at the user level and business model level:

  • User reasons…

“… during the festive season [December] people leave Kampala and go to their villages….”

‘as users grow their businesses, they actually order bigger amounts less regularly as they’re not consistently managing cash flow’

“I used to just do it on the weekends, but then I got a bit busy [with an internship] so I have less time for it”

“I have three jobs… Personally, I prefer to have multiple jobs – it’s more fulfilling.”

“In 12 months you’d maybe be busy for four months. But the pay is good – in those four months, it could sustain you for the year”

  • … and business reasons.

“Even for us as a business, we do not onboard new riders during [festive season] because the chances of churning are higher” 

“[For a creator platform] you earn 70%+ of your year’s income the month after you drop an album. After that, it tails off”

“[The nature of our work means work for users is irregular] Towards the end of last year, we had one [project] that was specific for fish traders and they were way not near urban areas… We do have gigs in Kenya, but they may not be in your locality… [We advise users that] When the gigs come, do as many as you can, make as much money as you can”

We found that users might be earning our target income thresholds, but not with our target regularity. While our threshold for ‘what is a job’ was based on earning the same amount each month for three months (say, $50/month), we saw that people were earning that threshold over three or six months (ie. $150 or $300 over six months), but just not concurrently. In other words, they could be earning our target income levels but not with our target regularity. We categorized these patterns we were seeing:

  • One of the most common phenomena we saw was people earning income intensively over a shorter defined period but not regularly.

In other words, three month’s worth of threshold income earned within a period of six months was the most common pattern of participation on platforms:

What does this practically mean for us?

We concluded that three months’ worth of target income in six months is the best-fit new rule of thumb. We believe that this would offer the best combination of: 

  • Platform utilization patterns: Recognition of the intermittency of how users engage with platforms 
  • Balancing of intermittency with regularity: At the same time, taking into consideration a half-year duration of engagement that keeps everything within a time limit scope and ensures a ‘density’ of income

Caveats and areas for further exploration

Based on insights from BCG’s piece on Unlocking the Potential of the Gig Economy in India, (thanks for flagging Rahil from Accion Venture Lab!), it could be that this distribution varies based on the personas of users, the nature of the industry, and the types of platforms that were disproportionately represented in our portfolio. Specifically, it could be that the platforms we’re working with are particularly targeting younger youth or students who come and go, or those providing a secondary income for the households rather than primary. This will be an area for subsequent exploration.

Henry Ford said, “Anyone who keeps learning stays young.” We still have more to learn of subtleties of the data as the dataset expands, including:

  • Do these trends continue over time?
  • What are the motivating factors for how users engage with platforms?
  • How long do users stay with a platform before churning?
  • What is the variation by various BCG’s users ‘personas’ for the African continent (or do we need country-by-country personas)?


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