Economic growth: inclusive, green, no-cost.

A tale of two sales

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It’s worth understanding what Modern Markets can do. Then we see inadequacies of the platforms most of us use.




Five-factor markets

Competing minibus drivers in Kenya, the National Stock Exchange of India, a medieval village green where sheep were sold; any forum in which sellers find buyers can be ranked on five fundamental factors.

Twenty-first century technologies have dramatically increased our ability to deliver markets with all these attributes. But these Modern Markets aren’t universal. To see the difference that’s making, let’s compare two young women quarter of a mile apart. It’s Friday morning, both are in search of economic opportunity.


Trader Jo

Jo works on the trading 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 globally 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.

Before her first cup of tea of the morning she might further back her hunch 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. Her software constantly compares performance of diverse asset classes so she can back her insight or protect against misjudgment. Again, it takes just a few clicks to trigger.


Sharon in the shadows

Sharon is part of the vast 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 global world of low-skilled employment; foraging for work from multiple sources, including the informal economy.

Today she has arranged family 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.


Like so many who now seek odd hours of work, Sharon started driving for Uber. But she did some Googling, reading about how they; will force drivers into long trips and have been caught; misleading work-seekers, slashing pay, distorting the market, withholding data,  systematically undermining regulation and lobbying against worker rights around the world.  She knows that risks of transaction failure are borne largely by the drivers.

So, today, Sharon has decided to join the 80% of Uber drivers who exit the platform within 12 months. She’s seen a lot of branded delivery riders around her area and believes it’s a booming market from which she could benefit. Where does she start? Deliveroo, Citysprint, Doordash, Just Eat, Beelivery, Gophr, and other platforms each serve some of the households or businesses buying delivery services in her neighborhood. But she can only trade in 2-3 of these “gig work” markets at once; if she is off doing a booking for Market A, algorithms running Market B will downgrade her for not being available when needed.


Platform thicket

Sharon has other talents that could be remunerative; experience in a distribution depot and in a care home, she’s good with pets or children and an experienced cook, her school taught the basics of gardening. Should she be in markets for those skills instead? Who knows? She has no meaningful 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. Sharon gains nothing from these warring labor platforms’ global ambitions. She just needs to sell her range of skills locally.

Registration with the three services, then waiting for their approval takes most of the morning with no indication as to whether it will be worthwhile. AmazonFlex for 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 for them. Sharon is selling blind, hoping she’s picked platforms that will have many buyers. But she must also hope her markets aren’t too dominant, that’s when sellers’ income tends to get cut, as has been reported for example at Lyft, Doordash, 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. She makes some calls; a friend knows a family restaurant missing a dish washer this evening. 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 detection by authorities. And she will need to find childcare.


Massively parallel

Much has been said 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 a fragmented asset; Jo’s random currency tranches; Sharon’s hours. But exchanges where Jo sells work together as a seamless whole so her assets are in front of nearly all possible buyers; and rich, actionable, data is generated. Almost the only issue which unites Sharon’s exchanges is opposition to basic protections for their sellers. Forced into a minimal number of trading platform; she only has exposure to a small sub-set of the buyers who would pay for her skills, and no data.

Jo’s network of exchanges gives her stability; if one fails, activity transfers seamlessly to the 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 operators to compete over the lowest charges. Sharon’s channels to market are fighting for investors; they need to constantly drive up their take of her earnings.

If Jo is mistaken about the dollar falling today, her losses are capped. If Sharon picks the wrong skill to sell, or the wrong market to trade it? 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. Beyond broad-brush, often ineffectual, job training programs, there’s little they can do. There’s no data, tools or monitoring to underpin day-to-day interventions to improve her prospects. In Jo’s world; governments, companies and speculators regularly intervene in markets with laser-like precision.


Market farces

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

If Sharon wants a genuine open market she has to put up her own website and try to drive diverse buyers of low-skill labor towards it. Or she could turn to primitive tools like listings websites, adverts on shop noticeboards even newspaper classifieds. If that seems like a promising route for Sharon, browse today’s work offers on the best known of these open markets: Craigslist.


Jo and Sharon are fictitious, but their divergent marketplaces are a new norm around the world. Our point is not that no-one loses money in financial markets. It’s that trading with such precision, safety, convenience and cheapness allows financiers to learn and pursue upside while constantly setting their limits for the downside.

For those being commoditized by ordering systems at the other end of the economic pyramid; there are negligible controls, insights or tools compared to what’s now possible. 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.

There are attempts to roll back excesses of the channels Sharon uses. They have only marginal impact, rather like efforts to reign in Wall Street’s outsized profitability.


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