Quality work on the fringes

Training

A platform that is finding each person work at the precise times they need can seamlessly incorporate upskilling. That integrates data, responsiveness, and best uses of everyone’s time.

 

 

 

 

Data-driven training

Empowering gig workers involves maximizing their individual options: for day-to-day work and progression. Learning can become uniquely precise with a CEDAH (Central Database of Available Hours). Queries such as “What percentage of carpet layers booked last week within 5 miles of City Hall were also qualified as shoplifters and how did their payrates compare to electricians?” can be answered instantly.

A crucial datapoint is utilization; the ratio of hours offered to hours worked. If you have 20 hours you want work this week and get 10 booked, you have 50% utilization.

Utilization can be measured across any combination of types of work, geographiy, and work-seeker characteristics. (“What was the utilization of call center agents younger than 30 in the east of our city over the last 6 months?”) Transitioning people from low-utilization skills to those that are in demand can become uniquely precise. Data about each individual is also newly granular. Key metrics include reliability (what percentage of bookings they confirmed did they complete, evidenced by timesheet data?), times of availability, and travel area. If funding is available to move van-drivers (low utilization locally) into truck driving (high utilization) workers with demonstrable reliability who are in the right area and want to work at the most needed times of day and days of the week can be targeted instantly.

System outputs will also identify strugglers. Who is not getting booked? Not moving up from minimum wage despite proving their reliability? Are there people who could be unskilled but live  in the wrong part of town and need a minibus service to open up new opportunity?

 

Support for emerging models of micro-education

Not everyone can accommodate a one- or two-year college degree. Formal worker retraining has been criticized as generally ineffectual. Learning can be more effective when interspersed with related work. A range of forces are moving towards “bite-sized learning”.

This concept can be built into a comprehensive hourly labor platform, effortlessly offering facilities like:

  • Digital badges: These can be applied and verified manually in a CEDAH of course. Or badges could be imported as they are awarded to workers in other systems. But it could also award them automatically using, as one example, a rule that “when a person completes at least 10 bookings at a minimum three stores at least half of which involve being on checkout, award them a ‘retail ready’ badge”. An industry federation might set up individual ebadges like this with members making them a requirement for higher level temporary work.
  • Building block modules: A candidate seeking perhaps a career as a pilot could be prioritized for entry level bookings at a local airport that maintains a pool of top-up baggage handlers, meet-and-greeters, or aircraft cleaners. Having proved reliability as an on-call general cleaner they might merit vetting for airside cleaning and from there progress, through bookings at times they were available to work, to membership in more highly trained pools of workers to be called in as needed.
  • Micro-apprenticeships: A business with fluctuating labor needs such as an event operator could undertake to continuously train, perhaps 50 provenly reliable local work-seekers who are then prioritized for that company’s bookings at peak times. The platform could monitor progress.

 

New on-ramps

Many irregular workers won’t factor educational possibilities into their thinking. Planning is hard for individuals currently trapped in survival work that commoditizes and cheapens them. A publicly run, market could push tentacles of opportunity deep into the local economy. As people come into the new market, its tools for upskilling can start work for each of them.

These options can go beyond online courses to a range of cost-effective, real-world, possibilities to tempt people who are not natural technology natives. Some specific capabilities:

  1. Ringfenced markets: The system can run sub-markets each under the control of a specialist group that sets its own rules. One example: a market for the labor of people prone to anxiety who need a support worker to accompany them for each booking. A local charity might establish a list of aware businesses then allow them to book from a pool of accompanied workers at times they were ready to accommodate someone with special needs. As each person works, their aptitude and interests can be used to automatically identify new opportunities.
  2. Early starts: High schools might use the above functionality to run a labor market, perhaps confined to Saturday daytime hours, for students 14-16. A cluster of approved households and businesses would be given access with each student free to offer hours of their choosing, keep their earnings, and build a track record before possibly transitioning, work-ready, into the wider market at 16. Likewise, a sub-market to build employability for the formerly incarcerated could allow prisoners a few months from release to start working for businesses in the market while still inside. They might for instance do intricate packaging work for a distribution business, so they move into work outside with a verified track record and first employer relationship.
  3. Peer support: Using market data, a body such as a workforce board could identify a pool of perhaps 100 reliable flexible workers with customer facing skills to be inducted as “Peer Navigators”. Any work-seeker who is unconfident or lacks basic technology skills could be given 10 one-hour sessions with a navigator. The system ensures they meet at home or a public place for structured sessions culminating in the client’s first booking through the system with the navigator booked alongside them for support. A pro-forma filled in at the end of each session within the system by the navigator tracks progress. This is an alternative to support offered by nine-to-five staff in, often remote, government offices.
  4. Investment models: In a truly advanced hourly labor market any credible body should be able to launch an intervention. As an example, a charity might decide to allocate an initial $25,000 to add to earnings of workers tagged as single parents in a deprived area doing paid housework up to a maximum $100 per candidate per week. With the right functionality, a labor market platform can easily target, administer and audit this. It should attract activity out of the informal economy into workforce services.
  5. Specialist intermediaries: Commercial labor platforms focus on maximizing profit extraction and can rarely accommodate intermediaries who act as employer-of-record, insurer and overseer of workers. A public platform can put these bodies at the heart of the operation. Some may be specifically aimed at target groups, for example a support group for migrant workers that recognizes many of them need to work around family commitments. By using the public platform as one of their services, the group could enforce its protections and business model while being part of a wider market.

Also significant, is the way the platform could seamlessly interface into modernized public assistance as a way of bringing people on the labor market fringes into its services.

Administration for lifelong learning

At present, poor quality matching, and the problems of scheduling study or work, both drag on those needing personalized employment and training. An advanced labor market could offer tools like:

  1. Use of unengaged time: If someone is available to work from 2PM to 6PM today and has not received a booking by 1PM they could set a rule that those hours are released for other activity. Options include volunteering, networking, receiving support, or joining a class. If thousands of work-seekers are studying, for example, to be Community Health Workers a key module of their course could be offered every afternoon for two weeks. That should allow everyone to attend while maintaining paid work as first priority. The system can tally who didn’t show of course.
  2. Tailored earn-as-you-learn: Most community college students have to work while studying. In a broad publicly run hourly labor market there would be more capacity to align external work with the ever-changing needs of related businesses. Students of hotel management, for example, could become a pool of top-up waitstaff, receptionists, kitchen porters and room attendants for a range of local accommodation providers. Each works day-to-day hours of their choosing.
  3. Fluid faculty: A college could maintain a pool of qualified trainers who are booked as students are assembled for each class by the system. And, of course, those trainers could each be offered multiple other types of work through the platform when not required by the college.
  4. Study buddies: Students who are paired to work with each other can have the system identify when they are working on the same shift or in proximity to each other. Meet-ups can then automatically be arranged around the diarized work and study commitments of both.
  5. Work credits: If periods of employment are made part of the requirements for course completion, the labor market platform can verify them as it would for digital badging (above).

 

New efficiencies in funding

The data and tools in an advanced market can track every last cent of pay, charge, and overhead. It is all visible to authorized users with links down to individual timesheets, bookings, and users. Pockets of system data could be opened to any organization that might fund training to ensure their needs were met in the local labor market.

So, as example, a manufacturer reliant on top-up staff at peak times that was ordering painting machinery from Germany might put 20 of their consistently re-booked pool of inducted top-up operatives through a course in elementary German. To ensure the funder reaped the benefits they could require those workers to commit to being available for them as a priority, ahead of other businesses.

Charitable interventions could also support progression. A philanthropy may decide it would pay for retraining of scaffolders if housebuilding took a downturn. System data would reveal the best retraining options for each person based on their preferred pathway, patterns of availability, and area.

There is an intriguing possibility around public training allocations such as Pell Grants. Could they be allowed to be used to build careers for those who must work irregularly to support themselves? As an example, a 19 year old seeking a career in nursing might currently be working in fast food. If he was booked for that work through the publicly run platform it would allow him to prove his reliability. That could unlock funds for the vetting needed to also start doing undemanding homecare bookings (assisting a more experienced caregivers on bookings involving moving a client for instance).

Continuing to build a record of reliable work, he might then qualify for a publicly funded eldercare course while the system works to get him medical receptionist or ward orderly bookings en route to a first nursing certificate. This set of steppingstones – work intermingled with learning modules – could span 5 years or more. But if his reliability earned him government funding for each rung on the ladder it would be more finely tuned to his evolving needs than a block grant to fund a one- or two-year course with little flexibility in timings, locations, or outcomes.

If publicly run advanced markets for hourly labor achieve deep penetration, individualized patterns of life-long learning could be woven from a variety of schemes, and funding sources. The student in each case may not have full time work or study but is constantly moving upwards in line with their capabilities and willingness to work. As their skills grow, they can be increasingly selective about the work they will accept.

 

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