Economic growth: inclusive, green, no-cost.

Contrast: A tale of two sales

Employment, services, and business activities are fragmenting. That makes marketplaces ever more pivotal. Inadequacies of the platforms where most of us can seek opportunity are demonstrable.

 

 

Five-factor markets

Economic activity is increasingly irregular, rather than steady and predictable. That makes markets pivotal. For example: Someone in a job will access the labor market every few years, using job-boards to start discussions with employers. But as people come to rely on gig work, they can be in and out of the market every few hours in search of a next booking. Platforms they use decide what they do, where they go, who they work for, and what they’re paid.

Beyond employment; services, travel, finance, and other parts of the economy are splintering in ways that drive up dependence on marketplaces. But how good are those exchanges? Any forum in which sellers find buyers can be ranked on five fundamental factors.

Advances in authentication, connectivity, displays, fulfillment, network security, interoperability, data mining, and payments have dramatically increased the ability to deliver markets with all 5 factors. But these Modern Markets technologies are distributed unevenly.

To see the difference that’s making, let’s compare two ambitious young women, quarter of a mile apart. It’s Monday morning, both are in search of economic opportunity:

 

Trader Jo

Jo works on the global desk at a bank in the world’s foreign exchange epicenter; London, UK. She believes the US dollar will fall against the Euro today and wants to sell $100m to be bought back later.

USD/EUR is a vast market with buyers meeting sellers on 15 public and many private exchanges plus countless “dark pools”. She uses trading software that accesses all exchanges and many pools. It also proactively searches for emerging trading forums.

Sudden release of a big block of currency might lower prices. So Jo’s system breaks the $100m into random tranches to be automatically placed confusingly around the trading forums.

In case she’s wrong about the dollar’s fall she imposes “stop orders”. If the likely profit is below a threshold, her sales will halt. There’s also a “collar” that hedges smaller losses. It takes her under a minute to set this up. The trades will be secure; a worldwide settlements system guards against counterparty default.

In another few seconds she might further back her insights on the Euro’s rise by authorizing some algorithmic trading around futures derivatives. Or she could set an arbitrage tool to accumulate profits on microsecond delays in USD/EUR movements between competing exchanges. Jo’s systems constantly compare performance of diverse asset classes so she can tweak her positions. The markets she uses are solid and constantly upgraded for her.

 

Sharon in the shadows

Sharon is part of the huge market for hourly labor in Tower Hamlets, London’s poorest borough. A single mother, she’s employed on-demand as a food-server in the Canary Wharf financial district.

That makes her part of a growing, global, world of low-skilled employment; foraging for work from multiple sources, including the informal economy. This world skews towards women and workers of color.

Today Sharon has arranged childcare but was then not called in to serve food. Work from some other source must be found. But she has to keep herself available tomorrow in case the scheduling system used by her primary employer needs her.

She could ring round temp agencies in search of work today. But she’s low margin; they’re not going to put any effort into finding the precise hours she needs.

Thousands of online services claim they can connect Sharon to on-tap employment. Like so many others, she churns through them in search of opportunity. Ride-hailing through Uber was her entry point. But she then did some research, reading about how Uber have been caught slashing pay, misleading work-seekers, distorting the market, withholding data, systematically undermining regulation, and spending heavily to roll back worker rights. She knows that risks of transaction failure can be borne by the drivers.

So, today, Sharon has decided to join the 96% of Uber drivers who exit within 12 months. She’s seen a lot of delivery riders in the pandemic and believes it’s a sector from which she could benefit. Where should she start? Deliveroo, Citysprint, Doordash, Just Eat, Beelivery, City PantryUberEatsGophr, and other platforms each serve a few of the households or businesses booking deliveries in her area. But she can only risk listing in two, maybe three, of these services at a time; if she is off doing a booking for platform A, algorithms running platform B can stop her future work for not being responsive.

 

Platform thicket

Sharon has other talents that could be remunerative; experience in a distribution depot, and as a cleaner. She’s good with pets or children, and cooks regularly. Her school taught the basics of gardening.

Should she be in markets for those skills instead? Who knows? She has no data to inform her search for opportunity.

So, she arbitrarily decides on two delivery markets then one for petcare. But which?  Rover has raised hundreds of millions to slug it out for worldwide dog-walking dominance with rival Wag! who haven’t launched in London yet, but might do so anytime. That would likely involve price subsidies and a marketing blitz that moves her potential buyers out of Rover where she would have built a track record. Sharon gains nothing from these warring labor platforms’ global ambitions. She just needs to sell her range of skills locally.

Registration with the random three services, then waiting for their approval, takes much of the morning with no indication of whether it will be worthwhile. AmazonFlex, as one example, has made unpaid candidates answer questions on 19 training videos, wait weeks to see if they’re approved, then sit constantly tapping their screens to find out if there is work.

Sharon is selling blind, hoping she’s picked platforms that will have many buyers. But she must also hope her chosen platforms aren’t too dominant. Achieving network effects allows them to seriously cut sellers’ income and boost profits. This has been reported – as examples – at Shipt, LyftDoordash, Instacart, AmazonFlex, and TaskRabbit.

By lunchtime Sharon hasn’t received any bookings. She has no way of knowing it, but algorithms assigning the work might be focused on keeping proven deliverers busy, not taking a risk with new entrants. So, she makes some calls; a friend knows a family restaurant missing a dish washer tonight. The work will be cash-in-hand, which means no market operator retaining 20-30% of her earnings. It also means no protections, possible wage theft, and risk of official detection. Plus she will need to find more childcare.

 

Massively parallel

Much has been written about contrasts in mobility, income, health, and political power between people like Jo and Sharon. Our focus is the quality of markets available for each to pursue their economic aspirations.

Both are selling small units: Jo’s random tranches, Sharon’s hours. Exchanges where Jo sells are five-factor (see above), they work together as a whole; huge numbers of buyers competing for her assets. Rich, actionable, data is generated. The only way in which Sharon’s exchanges interoperate is their joint campaigns to slash labor costs. Facing operationally incompatible platforms, Sharon must chose between them. So she only has exposure to a small sub-set of the buyers who would pay for her skills, and no insights about them.

Jo’s network of exchanges gives her stability; if one fails, activity diverts seamlessly to others. But Sharon could diligently develop a strong track record in a market that suddenly shutters taking her immediate flow of work, track record, and buyer relationships with it. Jo’s across-exchanges software will – of course – factor overheads into each selling decision. That forces markets to compete over the lowest charges. Sharon’s channels to buyers are fighting for investors; they need to constantly drive up what they extract from her earnings.

If Jo is mistaken about the dollar falling today, her losses are capped. If Sharon picks the wrong skill to sell, or offers the right skill in the wrong forum? A lot of unpaid time wasted in registration then waiting for work. And she still has to put food on the table tonight.

Many organizations – government and philanthropic – would like to give Sharon a hand-up with targeted training or support. But beyond broad, often ineffective, job training programs, there’s little they can do. There’s no data, tools, or monitoring to underpin day-to-day interventions that would improve her prospects. In Jo’s world, governments, companies, and speculators regularly intervene in markets with laser-like precision.

 

The closing of markets

The issue here is not who has the latest technology. Sharon is scheduled, monitored, possibly sanctioned, by sophisticated software. But she is not in a market in the sense of sellers advancing by improving, then setting their own rates and parameters, while free to experiment. She has been commoditized within a series of modernized ordering systems.

Bastardized markets are waiting for Sharon in all directions. Perhaps she could start selling stuff? Research will show how small sellers are penalized and charges racked up by Etsy, eBay, Amazon Marketplace and the other big forums.

Her best prospect for a neutral, lowest cost, market is 1990’s style bulletin boards. If that sounds promising, take a look at today’s flexible earning listings on the best known site; Craigslist.

Sharon can strive, innovate, cultivate customers, or try to fill gaps in the market. But with her channels to work rigged, she can’t succeed. As with Jo, her buyers want the efficiencies of new trading tools. But business models for that convenience and cost-cutting have produced Balkanized platforms in which Sharon has negligible options, insights, or individuality. It is a tragic waste of her potential for the economy and recovery.

Jo and Sharon may be at the economic extremes; there was still a core of steady jobs in the middle of most economies pre-Covid. But new trading tools are eating at it from both ends. Platforms for devalued labor cut costs of workers, constantly tempting companies away from taking on the overheads of long-lasting jobs. Meanwhile, Wall Street’s analytics capabilities drive continuous pressure for staffing “efficiencies“.

And charges imposed by gig work markets might help explain why estimates of off-the-books work even in developed countries tally it at 11% the size of GDP.

Our two young women are fictitious. But their divergent marketplaces are a new norm around the world. There are efforts to reign in Wall Street’s outsized profitability.  But they have only marginal impact, rather like the failed attempts to roll back excesses of the “markets” regular people increasingly have to use.

 

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