There is a code ceiling that prevents career advancement — irrespective of gender or race — because, in an AI-powered organization, junior employees and freelancers rarely interact with other human co-workers. Instead, they are managed by algorithms. As a result, a global, low-paid, algorithmic workforce is emerging. You will increasingly find a gap between top executives and an outer fringe of transient workers, even within organizations. Whether in retail or financial services, logistics or manufacturing, AI-powered organizations are being run by a small cohort of highly paid employees, supported by sophisticated automation and potentially millions of algorithmically managed, low-paid freelancers at the periphery. Job polarization is only part of the problem. What we should really fear is the algorithmic inequality trap that results from these algorithmic feedback loops.
The risks of algorithmic discrimination and bias have received much attention and scrutiny, and rightly so. Yet there is another more insidious side-effect of our increasingly AI-powered society — the systematic inequality created by the changing nature of work itself. We fear a future where robots take our jobs, but what happens when a significant portion of the workforce ends up in algorithmically managed jobs with little future and few possibilities for advancement?
One of the classic tropes of self-made success is the leader who comes from humble beginnings, working their way up from the mailroom, the cash register, or the factory floor. And while doing that is considerably tougher than Hollywood might suggest, bottom-up mobility was at least possible in traditional organizations. Charlie Bell, former CEO of McDonalds, started as a crew member flipping burgers. Mary Barra, chairman and CEO of General Motors, started on the assembly line. Doug McMillon, CEO of Walmart, started in a distribution center.
By comparison, how many Uber drivers do you think will ever have the chance to attain a managerial position at the company, let alone run the ride-sharing giant? How many future top Amazon executives will start their careers by delivering packages or stacking shelves? The billionaire founder and CEO of Instacart may have personally delivered the company’s first order, but how many others will follow in his footsteps?
Here’s the problem: There’s a “code ceiling” that prevents career advancement — irrespective of gender or race — because, in an AI-powered organization, junior employees and freelancers rarely interact with other human co-workers. Instead, they are managed by algorithms.
In this new era of digitally mediated work, there is typically a hierarchical information flow, in which the company decides the information they choose to share with you. Unlike driving a taxi, where there is open radio communication between drivers and the dispatch operator, and among the drivers themselves, when you work for Uber or Lyft, the content of your interactions is the output of an optimization function designed to maximize efficiency and profit.
To be managed algorithmically is to be subject to constant monitoring and surveillance. If you are one of the millions of food delivery workers in China working for Meituan or Ele.me, an algorithm determines how long it should take you to drop off an order, reducing your pay if you fail to meet your deadline. Similarly, employees in Amazon distribution centers are also carefully tracked by algorithms; they must work at “Amazon pace” — described as “somewhere between walking and jogging.”
When you are a gig economy worker, it is not only your AI bosses that should concern you; your co-workers are often also your competition. For example, Chicago residents who live near Amazon’s distribution points and Whole Foods stores reported the strange appearance of smartphones hanging from trees. The reason? Contract delivery drivers were desperate to trump their rivals for job assignments. They believed that hanging their devices near delivery stations would help them game the work allocation algorithm; a smartphone perched in a tree could be the key to getting a $15 delivery route mere seconds before someone else.
Work has been changing over the last few decades. The labor market has grown increasingly polarized, with middle-skill jobs being eroded relative to entry-level, low-skill work, and high-level employment that requires greater skill levels. The Covid-19 crisis has likely accelerated the process. Since 1990, every U.S. recession has been followed by a jobless recovery. This time, as AI, algorithms, and automation reshape the workforce, we may end up with something worse: a K-shaped recovery — where the prospects of those at the top soar, and everyone else sees their fortunes dive.
The new digital divide is a widening gap between workers with access to higher education, leadership mentoring, and job experience — and those without. In my recent book, The Algorithmic Leader, I explore one particularly dire scenario: a class-based divide between the masses who work for algorithms, a privileged professional class who have the skills and capabilities to design and train algorithmic systems, and a small, ultra-wealthy aristocracy, who own the algorithmic platforms that run the world.
A global, low-paid, algorithmic workforce is already emerging. In Latin America, one of the fastest-growing startups is Rappi, a mix of Uber Eats, Instacart, and TaskRabbit. Customers in cities like Bogotá and Mexico City pay about $1 an order or a flat $7 a month. In return, they can access a vast on-demand network of couriers who deliver food, groceries, and just about anything else you want. Amazon has an informal network of delivery people, called Amazon Flex, ready to drop packages right to your door — and soon even hand them to you in the street, place them in your car trunk, or open the door to your house and store your groceries in your fridge.
In his 1930 lecture Economic Possibilities for Our Grandchildren, John Maynard Keynes predicted that by around 2030, the production problem would be solved, and there would be enough of everything for everyone. The catch, however, is that machines would cause technological unemployment. The scenario that Keynes didn’t fully anticipate was our present case of high technological employment, with an accompanying degree of high inequality.
The workforce is changing; so too is the workplace. You will increasingly find a gap between top executives and an outer fringe of transient workers, even within organizations. Whether in retail or financial services, logistics or manufacturing, AI-powered organizations are run by a small cohort of highly paid employees, supported by sophisticated automation and potentially millions of algorithmically managed, low-paid freelancers at the periphery.
Job polarization is only part of the problem. What we should really fear is the algorithmic inequality trap that results from feedback loops. Once you are a gig economy worker reliant on assignments meted out by your smartphone, not only are there few opportunities for promotion or development, but other algorithms may further compound your situation. Think of it as a digital poorhouse. With their earnings and work assignments held hostage by market fluctuations, the new AI underclass may be penalized by automated systems that determine access to welfare, lending, insurance, or health care, or that set custodial sentences.
Nevertheless, it is dangerous to seek quick fixes for a problem that has yet to fully manifest, especially if it means grafting 20th-century worker protections onto 21st-century business models. Already, governments and regulators supported by populist platforms are focused on attacking global digital giants. They seek to prevent them from avoiding tax liabilities and are working to regulate their freelance workforce’s labor conditions, to apply restrictions on their collection of data, and even to tax their robots. Some of these ideas have merit. Others are premature, or worse, just political theater.
The longer-term solution to algorithmic inequality will not lie in just taxation and regulation, but rather in our ability to provide an adequate education system for the 21st century. Rebooting education will not be easy. Rather than looking for ways to use AI in teaching, the real question is: How do we teach people to harness machine intelligence in their careers? And how do we teach people to be prepared for a lifetime of constant learning and retraining?
Business leaders have a crucial role to play. Not only should they carve out channels of communication, feedback, and advancement for freelancers at the edge of their organizations, they need to get serious about retraining and community engagement. For example, AT&T is retraining half of its workforce, while Cisco, IBM, Caterpillar, McKinsey, and JPMorgan are offering internships to high school students and are working with local schools to upgrade their teaching curriculums. These are all good initiatives, but more will be needed — not just for social cohesion, but also to ensure the diversity and agility of tomorrow’s workforce.
We need a better plan for the future. Without one, the algorithmic inequality trap will be a story told not in statistics and wealth ratios, but in distress signals — smartphones hanging from trees, tent cities for the homeless, and human couriers scanning the skies for the delivery drones that spell their impending end.
Canadian regulator lifts banks’ capital buffer to record, priming for post-pandemic world
Canada‘s financial regulator raised the amount of capital the country’s biggest lenders must hold to guard against risks to a record 2.5% of risk-weighted assets, from 1% currently, in a surprise move that could pave the way for them to resume dividend increases and share buybacks.
The new measures, which take effect on Oct. 31, is a sign that the economic and market disruptions stemming from the coronavirus pandemic have abated and banks’ capital levels have been resilient, the Office of the Superintendent of Financial Institutions (OSFI) said in a statement.
But the regulator acknowledged that key vulnerabilities, including household and corporate debt levels, as well as asset imbalances caused by steep increase in home prices over the past year, remain.
In a sign of concern about the housing market, OSFI and the Canadian government raised the benchmark to determine the minimum qualifying rate for mortgages, starting June 1.
The increase in the Domestic Stability Buffer (DSB) to the highest possible level raises the Common Equity Tier 1 (CET1) capital – the core bank capital measure – to 10.5% of risk-weighted assets; a 4.5% base level, a “capital conservation buffer” of 2.5%, and a 1% surcharge for systemically important banks, plus the DSB.
The change “gives OSFI more leeway to loosen a restriction down the road, namely the freeze on buybacks and dividend increases,” National Bank Financial Analyst Gabriel Dechaine said.
OSFI felt it was “useful for the banks to understand what our minimal capital expectations are and to give them time to adjust to that… ahead of any lifting of the temporary capital distribution restrictions,” Assistant Superintendent Jamey Hubbs said on a media call.
Even with the higher requirement, Canada‘s six biggest banks would have excess capital of about C$51 billion, dropping from C$82 billion as of April 30, according to Reuters calculations.
That was driven in part by a moratorium on dividend increases and share buybacks imposed by OSFI in March 2020, although a pandemic-driven surge in loan losses has so far failed to materialize.
The Canadian banks index slipped 0.25% in morning trading in Toronto, while the Toronto stock benchmark fell 0.1%.
The increase is the first since the last one announced in December 2019, which did not come into effect as planned in April 2020, as OSFI made an out-of-schedule change https://www.reuters.com/article/canada-mortgages-regulation-idUSL1N2B636J that dropped the rate to 1% in March. It has maintained that level at its twice yearly reviews.
Prior to that, OSFI had raised the required level by 25 basis points at every twice yearly review since it was introduced at 1.5% in June 2018.
($1 = 1.2326 Canadian dollars)
(Reporting By Nichola Saminather; Editing by Marguerita Choy and Jonathan Oatis)
Canada Economic Indicators
The economic indicators used to gauge the performance of an economy and its outlook are the same across most nations. What differs is the relative importance of certain indicators to a specific economy at various points in time (for instance, housing indicators are closely watched when the housing market is booming or slumping), and the bodies or organizations compiling and disseminating these indicators in each nation.
Here are the 12 key economic indicators for Canada, the world’s 10th-largest economy:1
Statistics Canada, a national agency, publishes growth statistics on the Canadian economy on monthly and quarterly bases. The report shows the real gross domestic product (GDP) for the overall economy and broken down by industry. It is an accurate monthly/quarterly status report on the Canadian economy and each industry within it.2
Employment Change and Unemployment
Key data on the Canadian employment market, such as the net change in employment, the unemployment rate, and participation rate, is contained in the monthly Labour Force Survey, released by Statistics Canada. The report contains a wealth of information about the Canadian job market, categorized by the demographic, class of worker (private sector employee, public sector employee, self-employed), industry, and province.3
Consumer Price Index
Statistics Canada releases a monthly report on the consumer price index (CPI) that measures inflation at the consumer level. The index is constructed by comparing changes over time in a fixed basket of goods and services purchased by consumers. The report shows the change in CPI monthly and over the past 12 months, on an overall and core (excluding food and energy prices) basis.4
International Merchandise Trade
This monthly report from Statistics Canada shows the nation’s imports and exports, as well as the net merchandise trade surplus or deficit. The report also compares the most current data with that for the preceding month. Exports and imports are shown by product category, and also for Canada’s top ten trading partners.5
Teranet – National Bank House Price Index
This composite index of house prices across Canada was developed by Teranet and the National Bank of Canada and represents average home prices in Canada’s six largest metropolitan areas. A monthly report shows the change in the index monthly and over the past 12 months, as well as monthly and 12-month changes in Canada’s six and 11 largest metropolitan areas.6
RBC Manufacturing Purchasing Managers’ Index – PMI
Released on the first business day of each month, this indicator of trends in the Canadian manufacturing sector was launched in June 2011 by Royal Bank of Canada, in association with Markit and the Purchasing Management Association of Canada. RBC PMI readings above 50 signal expansion as compared to the previous month, while readings below 50 signal contraction. The monthly survey also tracks other information pertinent to the manufacturing sector, such as changes in output, new orders, employment, inventories, prices, and supplier delivery times.7
The Conference Board’s Consumer Confidence Index
The Conference Board of Canada’s Index of Consumer Confidence measures consumers’ levels of optimism in the state of the economy. It is a crucial indicator of near-term sales for consumer product companies in Canada, as well as an indicator of the outlook for the broad economy since consumer demand comprises such a significant part of it. The index is constructed on the basis of responses to four questions by a random sampling of Canadian households. Survey participants are asked how they view their households’ current and expected financial positions, their short-term employment outlook, and whether now is a good time to make a major purchase.8
Ivey Purchasing Managers Index – PMI
An index prepared by the Ivey Business School at Western University, the Ivey PMI measures the monthly variation in economic activity, as indicated by a panel of purchasing managers across Canada. It is based on responses by these purchasing managers to a single question: “Were your purchases last month in dollars higher, the same, or lower than in the previous month?” An index reading below 50 shows a decrease; a reading above 50 shows an increase. Panel members indicate changes in their organization’s activity over five broad categories: purchases, employment, inventories, supplier deliveries, and prices.9
Canada Mortgage and Housing Corporation (CMHC) issues a monthly report on the sixth working day of every month, showing the previous month’s new residential construction activity. The data is presented by region, province, census metropolitan area, and dwelling type (single-detached or multiple-unit). The indicator is an important gauge of the state of the Canadian housing market.10
This key indicator of housing activity is compiled by the Canadian Real Estate Association (CREA) and is based on the number of home sales processed through the MLS (Multiple Listing Service) Systems of real estate boards and associations in Canada. The monthly report from the CREA shows the change in home sales across Canada, as well as for major markets, from month to month. The report also includes other important housing-related information, such as the change (as a percentage) in newly listed homes, the national sales-to-new listings ratio, months of housing inventory, the change in the MLS Home Price Index, and the national average price for homes sold within the month.11
Statistics Canada releases a monthly report on retail sales activity across Canada, with changes shown on month-over-month and year-over-year bases. The headline number shows the percentage change in national retail sales on a dollar basis; the percentage change in volume terms is also shown. The retail sales figures are shown by industry and for each province or territory, and provide insights into Canadian consumer spending.12
The building permits survey conducted monthly by Statistics Canada collects data on the value of permits issued by Canadian municipalities for residential and non-residential buildings, as well as the number of residential dwellings authorized. Since building permit issuance is one of the very first steps in the process of construction, the aggregate building permits data are very useful as a leading indicator for assessing the state of the construction industry.13
The Bottom Line
The 12 economic indicators briefly described above show the health of key aspects of Canada’s economy: consumer spending, housing, manufacturing, employment, inflation, external trade, and economic growth. Taken together, they provide a comprehensive picture of the state of the Canadian economy.
Canada adds jobs for fourth straight month in May
Canada added 101,600 jobs in May, the fourth consecutive month of gains, led by hiring in the education and health services sector as well as in professional and business services, a report from payroll services provider ADP showed on Thursday.
The April data was revised to show 101,300 jobs were gained, rather than an increase of 351,300. The report, which is derived from ADP’s payrolls data, measures the change in total nonfarm payroll employment each month on a seasonally-adjusted basis.
(Reporting by Fergal Smith; Editing by Alex Richardson)