Built to Last? Hopefully Not.

Built to Last? Hopefully Not.

The picture above is of the bridge over the Choluteca River. Honduras, known for its extreme weather, namely hurricanes, commissioned the bridge in 1996 to withstand any storms. A Japanese company competed the bridge in 1998 when Hurricane Mitch tore through the country. Mitch dropped 74 inches of rain, killing 7,000 people, leaving 1.5 million homeless, and causing $2bn of damage. Also Mitch damaged or destroyed nearly all the bridges, but the new Choluteca Bridge survived with minor damage.. Thus it met its requirement, it withstood the worst storms. However, with deluge from Mitch the roads on either end of the bridge had completely vanished, leaving no visible trace of their prior existence. Also, the Choluteca River, which is over 100 metres (300 ft) at the bridge, had carved itself a new channel. It no longer flowed beneath the bridge, which now spanned dry ground.

Roads have since been reconnected to the bridge; however, the moral of the story is that things should not be “built to last,” but “built to adapt.” As I have mentioned before, most people get Darwin evolution wrong when they use the term “Survival of the fittest.” In Darwinian terms, fittest refers to biological reproduction, so what Darwin meant was “Survival of the form that will leave the most copies of itself in successive generations.”

Well, COVID has once more shaken our core processes and what “got us to here, is not going to get us to there.” To survive we have to adapt and change what we do. Due to the failure to understand that this is public health crisis first and foremost, and until we deal with that the rest cannot be fixed, I expect we will be living in a COVID environment for at least another year. So new methods and models have to determined for survival.

In one of my Vistage group meetings this week we shared how we had adapted to the COVID world doing things that we had never considered before. Some of the wonderful new practices were:

  • Enabling piece production to be done at home by providing the employees with the materials and tools and paying the by the piece rather than hourly. The company then did full QA on all pieces when they were picked up. The company found that production levels rose slightly. Employee satisfaction rose too, as people were able to work at home and deal with kids who cannot go to school.
  • Entering new markets to provide COVID related solutions and then expanding within those market to offer more services. These new markets are providing sufficient revenue to make up for those markets damaged by COVID.
  • Training and recruiting people in areas that the company expects will be active soon as a result of COVID. Building a bench of talent to meet new demand.
  • Having weekly video calls with the entire global team where each week one person presents on their passion project within the organization. These calls have lead to great connectivity between employees and departments. Employees now have a better understanding of the challenges faced in other departments, and people are contributing to other’s passion projects across deparments.
  • Restructuring the sales department as the existing sales team was struggling to prospect in the virtual world. Have younger more tech savvy people doing business development work and connecting with prospects. Once the connection is made these relationships are handed over to old sales team.

The above is why Vistage groups are so wonderful! The sharing of ideas that work and those that don’t help everyone.

So what are you doing? Are you waiting for the return to a pre COVID world, aka Waiting for Godot, or are you being proactive to find new ways to drive sales, production, product development and improve internal processes? As a leader, you need to lead the way for your organization so that you will around and relevant in a year. It is hard during times like this, but that is why you are the leader. If you need help, join a Vistage group, help is there.

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Algorithms Once More Run Amok

Algorithms Once More Run Amok

For those who have not been following the disaster in the UK with the GCSE A-level exam results, here is a summary:

The History

  • A-levels are the exams taken in the UK, which determine where students go to college. Most English students receive acceptances from universities that are conditional upon attaining specific A-Level results.
  • Due to COVID, these national exams were canceled, which left students with an uncertain future.
  • In late March 2020, Gavin Williamson, Secretary of State for Education instructed Sally Collier, the head of Ofqual (The Office of Qualifications and Examinations Regulation), to “ensure, as far as is possible, that qualification standards are maintained, and the distribution of grades follows a similar profile to that in previous years”. On 31 March, Williamson issued a ministerial direction under the Children and Learning Act 2009.
  • In August, an algorithm devised by Ofqual computed 82% of ‘A level’ grades. More than 4.6 million GCSEs in England – about 97% of the total – were assigned solely by the algorithm. Teacher rankings were taken into consideration, but not the teacher-assessed grades submitted by schools and colleges.

The Outcome

  • Ofqual’s Direct Centre Performance model used the records of each center (school or college) for the subject assessed. Only after the results of the model’s first use in August 2020, were details of the algorithm released and then only in part.
  • Students at small schools or taking minority subjects, such as are offered at small private schools saw their grades inflated than their teacher predicted. Traditionally, such students have a narrower range of marks, as these schools encourage weaker students to leave.
  • Students at large state schools, sixth-form colleges and FE colleges who have open access policies and historically have educated black and minority ethnic students or vulnerable students saw their results plummet, so the fitted with the historic distribution curve. Nearly 300,000 of the 730,000 A-levels were lower than the teacher assessment this summer.
  • While 49% of entries by students at private schools received an A grade or above, only 22% of students at comprehensive schools received such marks.
  • The fact that students are elite private schools benefited at the expense of those from disadvantaged backgrounds sparked national outrage, including protests.
  • According to some, Ofqual has barred individual pupils from appealing against their grades on academic grounds. Families should not waste time complaining but instead should contact college or university admissions offices to confirm their places in the event of unexpectedly poor grades.
  • At first, the government refused to back down and change the results, but due to the level of protest, it soon backed down.
  • The government announced that official results would be the higher of the algorithm approximation or teacher estimates of how their students would have done. On 19 August, The Universities and Colleges Admissions Service determined that with the change, 15,000 pupils were rejected by their first-choice university on the algorithm generated grades.

What is the problem?

Well, first, there is chaos, as many students are not sure they can get into their first choice universities. For many, the algorithm was just another example of how the UK educational system consistently favors those from elite backgrounds. Statisticians have criticized Ofqual’s algorithm, saying it does not have sufficient data to award grades fairly to most state schools in England, because of wide variations in results within schools and between years. Furthermore, the Royal Statistical Society has called for an urgent review of the statistical procedures used in England and Scotland, to be carried out by the UK Statistics Authority.

However, the deep questions for all of us who aren’t affected by these results are (i) how did the algorithm get it wrong? And (ii) how many other algorithms are messing up our personal and business lives without us knowing.

AI Bias

The category of algorithms known as deep learning is behind the vast majority of AI applications. Deep-learning algorithms seek to find patterns in data. However, these technologies have a significant effect on people’s lives. They can perpetuate injustice in hiring, retail,  insurance, advertising, education, and security and may already be doing so in the criminal legal system, leading to decisions that harm the poor, reinforce racism, and amplify inequality. In addition to articles by MIT and others, Cathy O’Neil laid out these issues in her 2016 book, Weapons of Math Destruction – a must-read for anyone with interest in this area. O’Neil argues that these problematic mathematical tools share three key features; they are:

  1. Opaque – especially those run by private companies who don’t want to share their IP. As a result, no one gets to audit the results.
  2. Unregulated – they do damage with little consequence to important areas of people’s lives; and
  3. Difficult to contest – the users don’t know how they were built so deflect and the providers hide behind their IP.

Also, such systems are scalable, which amplifies any inherent biases to affect increasingly larger populations.

Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (because of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.”

A recent MIT article pointed out that AI bias arises for three reasons:

  1. Framing the problem. In creating a deep-learning model, computer scientists first decide what they want it to achieve. For example, if a credit card company wants to predict a customer’s creditworthiness, how is “creditworthiness” defined? What most credit card companies want are customers who will use the card, make partial payments that never take the entire balance down so that they earn lots of interest. Thus, what they mean by “creditworthiness” is profit maximization. When business reasons define the problem, fairness and discrimination are no longer part of what the model considers. If the algorithm discovers that providing subprime loans is an effective way to maximize profit, it will engage in predatory behavior even if that wasn’t the company’s intention.
  2. Collecting the data. Bias shows up in training data for two reasons: either the data collect is unrepresentative of reality, or it reflects existing prejudices. The first has become apparent recently with face recognition software. Feeding the deep-learning algorithms more photos of light-skinned faces than dark-skinned faces, resulted in a face recognition system that is inevitably worse at recognizing darker-skinned faces. The second case is what Amazon discovered with its internal recruiting tool. Trained with historical hiring decisions that favored men over women, the tool dismissed female candidates, as it had learned to do the same.
  3. Preparing the data. Finally, during the data preparation, the introduction of bias can occur. This stage involves identifying which attributes the algorithm is to consider. Do not confuse this with the problem-framing stage. In the creditworthiness case above, possible “attributes” are the customer’s age, income, or the number of paid-off loans. In the Amazon recruiting tool, an “attribute” could be the candidate’s gender, education level, or years of experience. Choosing the appropriate attributes can significantly influence the model’s prediction accuracy, so this is considered the “art” of deep learning. While the attribute’s impact on accuracy is easy to measure, its impact on the model’s bias is not.

So given we know how the bias in models arises, why is it so hard to fix? There are four main reasons:

  1. Unknown unknowns. During a model’s construction, the influence of bias on the downstream impacts of the data and choices is not known until much later. Once a bias is discovered, retroactively identifying what caused it and how to get rid of it isn’t easy. When the engineers realized the Amazon tool was penalizing female candidates, they reprogrammed it to ignore explicitly gendered words like “women’s.” However, they discovered that the revised system still picked up on implicitly gendered words, namely verbs that were highly correlated with men over women, e.g., “executed” and “captured”—and using that to make its decisions.
  2. Imperfect processes. Bias was not a consideration in the design of many deep learning’s standard practices. Testing of deep-learning models before deployment should provide a perfect opportunity to catch any bias; however, in practice, the data used to test the performance of the model has the same preferences as the data used to train it. Thus, it fails to flag skewed or prejudiced results.
  3. Lack of social context. How computer scientists learn to frame problems isn’t compatible with the best way to think about social issues. According to Andrew Selbst, a postdoc at the Data & Society Research Institute, the problem is the “portability trap.” In computer science, a system that is usable for different tasks in different contexts is excellent, i.e., portable. However, this ignores many social settings. As Selbst said, “You can’t have a system designed in Utah and then applied in Kentucky directly because different communities have different versions of fairness. Or you can’t have a system that you apply for ‘fair’ criminal justice results then applied to employment. How we think about fairness in those contexts is just totally different.”
  4. Definitions of fairness. It is not clear what an absence of bias would look like. However, this is not just an issue for computer science; the question has a long history of debate in philosophy, social science, and law. But in computer science, the concept of fairness must be defined in mathematical terms, like balancing the false positive and false negative rates of a prediction system. What researchers have discovered, there are many different mathematical definitions of fairness that are also mutually exclusive. Does “fairness” mean that the same level of risk should result in the same score regardless of race? It’s impossible to fulfill both definitions at the same time, so at some point, you have to pick one. (For a more in-depth discussion of why click here) While other fields accept that these definitions can change over time, computer science cannot. A fixed definition is required. “By fixing the answer, you’re solving a problem that looks very different than how society tends to think about these issues,” says Selbst.

As the UK A-level exam debacle reminded us, algorithms can’t fix broken systems. When the regulator lost sight of the goal and pushed for standardization above all else, the problem began. When someone approaches you with a tempting AI solution, consider all the ramifications from potential bias because if there is bias in the system, you will bear the responsibility, not the AI program.

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SPACs Anyone?

SPACs Anyone?

Special purpose acquisition companies (or SPACs) are back! They are not new, but become trendy during overvalued financial markets are frothy, e.g., before the last crisis and now. 

Over the last few years, SPACs have raised record amounts. Everyone including hedge fund billionaire Bill Ackman, sports executive Billy Beane of Moneyball fame, former Citigroup dealmaker, Michael Klein, and ex-Blackstone rainmaker Chinh Chus are launching a SPAC, or three. Some of these high-profile investors believe SPACs have shed the dodgy reputation and seek to raise cash in blank check companies, believing they have the unique eye to find under-appreciated businesses that they can bring to the public markets.

This year 28 SPACs have had IPOs, raising $8.9 billion, and at this rate should reach $16.5 billion for the end of the year. If so, it will beat last year’s $13.6 billion and is far ahead of the 2011–2015 average of $1.7 billion.

SPACs have a simple business model:

  • Raise funds from the public markets. 
  • Find a target company with which to merge.
  • On the announcement of the merger, shareholders can either accept stock in the new company or redeem their shares at the original price of the offering.

Thus, the SPAC is a deconstructed IPO with a very short roadshow. However, as the target is negotiating with a few SPACs to attract the highest bidder, there is an argument that most SPACs overpay for the target reducing shareholder returns. To the SPAC investor, it’s a subpar money market fund with a Kinder Surprise Egg-style option attached: invest, and for the cost of tying up your capital for a while, you have the opportunity to get… something.

SPAC advocates have three arguments for SPACs over IPOs.

They are cheaper than traditional IPOs. Also, they avoid the “IPO pop.” However, as Matt Levine noted,

“Compared to an IPO, the SPAC is much less risky for the company: You sign a deal with one person (the SPAC sponsor) for a fixed amount of money (what’s in the SPAC pool ) at a negotiated price, and then you sign and announce the deal, and it probably gets done. With an IPO, you announce the deal before negotiating the size or price, and you don’t know if anyone will go for it until after you’ve announced it and started marketing it. Things could go wrong in embarrassing public fashion.
***
The SPAC structure is less risky for the company than an IPO, which means that it’s riskier for the SPAC (than just buying shares in a regular IPO would be), which means that the SPAC should be compensated by getting an even bigger discount than regular IPO investors.”

Thus while cheaper, it comes at greater cost to investors.

SPACs are quicker.
SPACs advocates argue that the traditional IPO process is too slow and prevents people from taking advantage of opportunities as they arise. A company undertaking an IPO would spend months working with the Securities and Exchange Commission to finalize a prospectus that detailed its financial information and operations. SPACs dramatically shorten that process. However, as a recent article put it, “The SPAC is the Vegas wedding chapel of liquidity events; it seems like an urgently good idea at the time, but it doesn’t always turn out that way.” However, as we consider the craving for speed, it is worth reflecting that the regulations are there to ensure high standards of “IPOs” as WeWork demonstrated.  As a result, the IPOs minimizes a financial meltdown for many rather than the original backers.

A Rebellion Against the Investment Bankers
Founders, companies, and investors are rebelling against the investment banks and their high fees and see SPACs as a way to minimize them. However, if no cash needs to be raised by the target company, a direct listing offers a low-cost alternative that provides the original investors with liquidity and a market. 

However, SPACs also have a dodgy reputation.

An analysis of 145 SPACs organized between 2015 and 2019 by the Financial Times shows that two-thirds are trading below $10 per share, the standard IPO issue price. About a third have not found a target, and less than half are still trading. This poor performance is not just limited to novice investors, the private equity firm, TPG, has three SPACs, none of which are trading above $10.20.

The poor performance record of these SPACs may be a reminder that when Wall Street is pushing a new product, financiers inevitably find a way to shift the risk on to ordinary investors.

However, while SPACs have improved some, most ordinary investors forget that these vehicles hold out the prospect of great riches for Wall Street’s finest, and their advisers. Otherwise, they wouldn’t do them. As Warren Buffett so aptly put it, “If you’ve been playing poker for half an hour and you still don’t know who the patsy is, you’re the patsy.”

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The Future of the Gig Economy

The Future of the Gig Economy

On Monday, a court in California rejected Uber, and Lyft claims that they didn’t need to treat drivers as employees under AB5, a state law passed in 2019. Under AB5, there are three criteria a company must deem to meet to consider workers, independent contractors, namely:

  1. Weighing the employer’s control over how the work is performed;
  2. Whether the services are within the normal course of business; and
  3. Whether the workers have an independently established role.

Uber CEO, Dara Khosrowshahi, has said that if the court doesn’t reconsider, Uber will shut down its services from the state. This strategy seems to follow Travis Kalanick’s approach – If you own the market, the regulators will have to placate you. Following that strategy should bode well for Uber as the Customer always wins. If Uber were to close down Californian operations, there would a massive cry from both customers and drivers, which could force the state to back down. How this unfolds will be observed by other states, e.g., New Jersey, Massachusetts, and Connecticut, which are using the same standard to determine who is and isn’t a gig worker, and New York and Illinois are considering similar legislation.

Gig work describes independent contractors, remote work, part-time employment, and temporary employment. If someone works for a platform like Fiverr, Uber, or Lyft, then they are part of the gig economy.

While there has been tremendous growth in the gig economy over the last few years, most of it is unskilled work such as driving, delivering, and doing simple errands. A vibrant gig economy for knowledge workers — engineers, consultants, management executives — has not yet materialized. 

 

The Stats and Facts of the Gig Economy

  • From nothing in 2014, 57.3MM people performed gig work in the U.S. in 2019.
  • 20% of gig workers earned over $100k in 2019.
  • 85% of gig workers make less than $500 a month from a single side-job.
  • 40% of gig workers had medical insurance.
  • 39% of gig workers have no retirement savings.
  • 55% of Baby Boomer gig workers have medical insurance.
  • 59% of gig workers are satisfied with their financial situation.
  • 95% of gig workers say flexibility is essential.
  • 19% of gig workers would want a traditional job.
  • The main reason for gig work: 18 – 29-year-olds: Need extra money, Gen X: Need money to make ends meet, Men: “Love being their own boss.”
  • Only 35% of America’s gig workers are female.
  • Almost half of all millennials use online gig economy platforms to find work.
  • The total gig income is almost $1 trillion.
  • On average, gig workers earn 58% less than full-time employees.

Where are the gig workers?

Source: Statista

As can be seen from the above, the Gig economy is significant, is here to stay, and absorbing more jobs and professions. So what are the issues? 

 

Pros of the Gig Economy

  1. Workers have more flexibility. As noted above, 95% of such workers say flexibility is essential. They use this flexibility to gain the work-life balance they need. 
  2. Gig workers like their independence. As note above, men identified the main reason for gig work is that they “Loved being their own boss.” Gig workers are not micromanaged and can enhance the quality of work for some.
  3. Gig workers have access to a greater variety of employment opportunities. Freelances often stay in control of their assignment, deciding whether or not to accept a client. They are not stuck with the same monotonous tasks every day.
  4. Some gig workers work from home, so they have zero commuting costs. Before COVID, this could save $30 to $50 a week commuting to work.
  5. Lowers companies’ costs. Businesses only have to pay for the labor they receive and don’t have to pay for equipment. Besides, they don’t have to provide benefits or pay state unemployment taxes, and in many cases don’t offer space.
  6. It allows companies to scale quickly. Startup and small companies find gig workers enable them to scale at a lower cost promptly. They can race to meet market demand and budget restrictions.
  7. Workers don’t need to cater to the company culture. Gig workers have their own culture. They don’t have to be involved in team-building exercises, corporate rallies, or pander to office politics.
  8. It is suitable for the creative worker. The variety of work enables some workers to explore their full creativity, increasing the quality of work.

 

Cons of the Gig Economy

  1. Less flexibility than thought. While many experienced gig workers, e.g., graphic designers can pick and choose clients and projects, many of the ordinary gig workers don’t have that luxury. Several online platforms use variable pay, rating systems, and notifications to push people to accept specific jobs and work certain hours. These practices, called algorithmic management, limit the flexibility of work. 
  2. No benefits. Most gig workers do not receive any benefits, regardless if they are working 40+ hours a week. Thus they are responsible for their health insurance and retirement contributions, as is seen above. COVID has shown the problems with this. Many gig workers suddenly have no income, but cannot claim unemployment benefits. Furthermore, with a public health crisis, few having health insurance slows the solving of the crisis. Many who don’t have insurance will not seek treatment as they cannot afford it or cannot take time off work to get it, and second those that do, face bankruptcy because they cannot pay for whatever treatment they have.
  3. More taxes. Since gig workers do have tax withheld, they have to pay income tax and self-employment taxes from their pay, requiring them to withhold 25+% for those purposes. Also, concerning self-employment taxes, gig workers pay both the employee and employer taxes, unlike full-time workers. Finally, gig workers have to manage cash flow as they have to file taxes quarterly, so they will have to save enough to make those payments.
  4. Gig workers considered lazy. Since many gig workers work from home, many will consider them lazy and nonproductive, which causes social issues.
  5. Gig workers have high levels of isolation. Gig workers miss the social element of work, discussions around the water cooler, chats with teammates, office events. Lack of the office environment leads to isolation, which can cause mental health issues, as we are seeking with the COVID work from home and suicided rates.
  6. Gig workers drive more. While gig workers don’t have to commute daily, they end up driving more during a week as they move to pick up and deliver projects or from work site to work site. This increase in driving increases costs and stress.
  7. More stress for gig workers. Full-time workers know that their employment position is relatively secure. Although layoffs can happen, the risk of such a circumstance is relatively low for the average person. Gig workers have several different stresses. They are always looking out for the next job. They have to be prepared for changes to occur in their current assignments, including being let go in the middle of a task. Thus, their income is never really 100% secure.
  8. Companies may find their workers are not as reliable. This lack of reliability is a result of workers not adopting the corporate culture. Since gig workers are external, they don’t have to adapt to the corporate culture, and cultural fit is not a hiring requirement. Furthermore, since they are interested in their well being over the corporations, thus, while they will work, they may not work as hard, do what the organization considers “right,” and go the extra mile. Since happy employees lead to satisfied customers, this will reduce customer satisfaction if they interact with customers.
  9. Gig workers must continuously up-level their skills. Gig workers must continue to up-level their skills to get better because new gig specialists enter this field every day. The levels of industry knowledge that they receive must increase if they want to keep receiving contracts or employment offers. Thus, gig workers have to build in continuing education into their schedules. While this will keep them relevant, it is time that they are not earning.
  10. Gig workers need to budget for vacations. Gig workers don’t get paid time off, so they need to budget for the fact that while on vacation, they will be spending money and not earning money. Furthermore, many have to give one to three months of advance notice to clients that they will not be available. Finally, if a job comes up as they are about to leave, they may not want to turn it down as they could lose a client.
  11. It takes longer to build a depth of experience. In an organization, employees are often put on cross-functional teams to address issues. As a result, they create networks and knowledge across many areas of the organization. Gig workers are siloed and don’t build any skills or experience outside their narrow field, limiting their development and earnings potential.
  12. Us and Them. Many companies have large numbers of gig workers, e.g., Google has more gig workers than full-time workers. Since gig workers are not part of the organization, earn less, and have less opportunity to work in cross-functional areas, they are considered fungible and, in many organizations, are considered less than employees, creating an Us v. Them environment. Furthermore, most gig workers perform specific tasks, not the “package.” The package is more complex and requires working and information sharing across the organization, which gets harder for gig workers who don’t where the information lies due to their lace of organizational knowledge.
  13. State Tax Revenue. While gig workers have to pay FICA taxes, they are not obliged to pay state unemployment taxes. A study found that if Uber and Lyft had classified its drivers as employees in California, between 2014 and 2018, they would have paid $418 million into the California unemployment insurance fund.

 

Race to the bottom

A trend that has picked up during COIVD is the race to the bottom. As many people found themselves jobless, they applied for gig jobs. Upwork has seen a 50% increase in freelancer sign-ups since the pandemic began. Talkdesk, which launched a gig economy platform, witnessed 10,000 new applications within ten days. Instacart hired 300,000 additional workers in a month at the beginning of the COVID crisis and, in April, announced it would add 250,000 more.

On platforms like Upwork and Fiverr, many freelancers are already feeling the pinch. With a large number of new workers, given the laws of supply and demand, many gig workers are now vying for freelance work and pitching their services for lower rates to make money.

Now there are too many would-be workers to make the gig economy viable for many of them, and this may be irreversible as companies adapt to the reality of a global recession. By keeping headcounts low, companies will drive more desperate people into the gig economy, expanding the potential labor pool for jobs and driving down the prices that workers can command. 

 

The Recovery

The pandemic has shown that as business recovers, digital economy companies will benefit from their ability to be more flexible than other industries in certain aspects. COVID will probably intensify investors” growing scrutiny of the path to profitability for companies, making them increasingly wary of startups” strategies that favor scale before profitability. Thus gig workers will be vital in meeting this metric.

Also, many governments are now leveraging gig work to fast-track recovery. Having seen how digital platforms have contributed to crisis management and recovery using their nimble operations, flexible thinking, and technological prowess to get things done quickly, India and China are promoting gig work to drive growth in the economy. 

 

So What

Gig work is here to stay, and it will be vital for economic recovery. We must realize; however, that gig workers are not just drivers and delivery people. Presently many occupations are effectively gig jobs, e.g., journalists. However, the issues laid out above are massive and require addressing for gig work to be successful for all. Otherwise, we continue our move to a Hunger Games society.

As Dara Khosrowshahi recently said in an oped in The New York Times, we need to redefine “workers” and have new laws rather than the binary system today. We need to address the reality that this is how millions of people earn some or all of their livings. As a result, we need to ensure that:

  • Gig workers can move easily from job to job;
  • Gig workers receive healthcare benefits. During a pandemic, we have realized that public health is an essential requirement, and the U.S.healthcare system is not designed to meet it. While ObamaCare causes a visceral reaction for many, much of the thinking behind it was to enable workers to have health insurance not tied to employment. While it may not work well, it needs improving to meet this need for now and the future, not removal and reversion to the past.
  • Gig workers receive retirement benefits. Our outdated IRA and 401ks need to restructuring so that gig workers can more easily contribute to them. Today a quarter of Americans have no retirement savings.
  • Gig workers need to receive unemployment insurance of some form so that when we have a second COVID like situation, millions are not driven into financial distress due to lack of protections.
  • Companies need to contribute towards these benefits so that it doesn’t fall on those who can least afford them, or bore by all taxpayers.
  • Changes the balance of power structure between gig workers and corporations in negotiating rates. Our current system encourages a race to the bottom that is detrimental to the gig workers and beneficial to the shareholders. This system reinforces the current U.S. system or rewarding capital at the expense of labor and winnowing out the middle class. It needs addressing, but that will be complex and not easy.
  • Companies need to develop better ways of onboarding gig workers to remove the Us v. Them situation. Also, even though these workers are task-specific, smart organizations will start to ensure that gig workers share their core values to ensure that they get better results from them and can use them for the “package.”

If we can address these issues, then the economy should be able to recover quicker and have a flexible, practical framework for future growth while protecting many. If not, the migration to a Hunger Games society will continue, creating more problems and social unrest. However, I believe we up to the task.

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Well, we have been working from home now for nearly five months and probably will continue to do so for another year.

So what is the verdict? 

 

The Good News

Many leaders anticipated that employee performance would significantly deteriorate. Given that numerous studies over the years had shown that falls in productivity accompanied a significant change, their expectations were not unfounded. However, a Harvard Business School survey indicated that workers adjusted to working virtually far quicker than expected. In many cases, workers felt they were as productive as they were before. According to one employee, “I’m able to get everything accomplished just like before, and I think everyone else is finding they can too.” This quick return to productivity is remarkable.

Not to say the changes didn’t cause problems. Job satisfaction and engagement fell sharply after two weeks of working virtually. However, by the end of the second month, both measures had recovered dramatically. As another employee put it, “It took some time to get used to it and for things to go right. It was a learning process.”

The improvements in satisfaction and engagement resulted from organizations finding the right balance between meeting and work time. There were issues with too many virtual meetings, leaving no time to do the actual work. Also, people had to get comfortable with communicating over Zoom, Skype, Teams, etc. 

 

Work-Life Balance

The biggest issue was figuring out how to manage work-life balance, basically turning off work at home. According to research, managers who cannot “see” their direct reports often struggle to trust that their employees are working. With such doubts, managers sometimes start to develop the unreasonable expectations that team members be available at all times, ultimately disrupting their work-home balance and causing more job stress.

Data provided by Humanyze from email, chat, and calendar systems across a global technology company supported the HBR survey results. The data revealed that the workday significantly increased at the beginning of all-virtual work. Immediately after the lockdown began, about 50% of employees could maintain at workday of 10 hours or less, compared to 80% pre-COVID. While workday time has started to trend back to pre-lockdown levels, workdays are still 10% to 20% longer on average.

Furthermore, since all-virtual work began, employee stress, negative emotions, and task-related conflict have all been steadily falling; each is down at least 10%. Also, employees have experienced an approximately 10% improvement in self-efficacy and their capacity to pay attention to their work. Employees now say they are “falling into a consistent routine,” “forming a pattern [of work time and breaks] with my coworkers,” and “learning what makes me the most productive and how I can best manage my time and energy.”

From CEOs down, many saw benefits from working from home, including a reduction in travel. One CEO hoped that this had put an end to the ‘fly across the country for a one-hour meeting’ expectation forever.” Overall employees reported that they had:

  • More focus time
  • Shorter meetings
  • More flexible time with family; and
  • No loss over missing the daily commute.

 

Challenges for Managers

While the research has shown an increase in the workday and performance returning or above pre lockdown levels, not all management attitudes reflect this. Different HBR research suggested that of managers:

  • 56% are not confident in their ability to manage remotely. Their beliefs about remote employees’ performance reflect their lack of confidence in their ability to manage their reports.
  • 60% agreed or were uncertain that remote workers usually perform worse than those who work in an office. 
  • 58% questioned whether remote workers could remain motivated over time.

The research further shows that:

  • Men are more likely to have negative attitudes to remote working and mistrust their own employees’ competence.
  • Those in non-managerial/non-professional roles had lower self-efficacy for managing remote workers, more negative attitudes, and greater mistrust. 
  • Younger managers are more likely to lack self-efficacy for leading remote workers.

With COVID, many employees are facing increased stress, especially for those with compromised finances or families requiring care. Thus some are struggling to perform at their pre lockdown levels. The concern is that this will create a negative feedback loop in which manager mistrust leads to micromanagement, which then leads to drops in employee motivation, further impairing productivity.

Therefore, managers at all levels need support and training, so that management quality will improve, which will improve remote workers’ wellbeing and performance. This support for managers must be a critical focus of senior leadership if employee performance is to remain high as companies move forward with possibly another year of work from home. 

 

What Else Could Go Wrong

With Google now saying that employees can remain virtual till summer 2021 and others following suit, should we all go virtual were we can?

A key issue with a virtual workforce is the loss of unplanned interactions that lead to significant outcomes. In physical offices, people who don’t usually work with each other to connect accidentally, and that interaction sparks new ideas. While the HBR study found employees increased their communication with close collaborators by 40%, contact with other colleagues fell by 10%. Also there less schmoozing and small talk among virtual workers, which has shown to lead to lower levels of trust.

In addition to a lack of spontaneity, three other issues face organizations in a virtual world that undermine organizational health.

Onboarding new employees
Research has shown that great onboarding involves two sets of activities:

  • Exposing new employees to “how things are done around here” by indoctrinating them into the company’s vision, history, processes, and culture; and
  • Allowing them to apply their signature strengths and express their genuine selves.

While the first has been adapted relatively well to a virtual world, the second is much harder. To achieve the second requires numerous in-depth interactions, and existing employees are accustomed to having those in person.

Weak Ties
These are the shallow or peripheral relationships among members of an organization who don’t work closely with each other but have nonetheless connected over time. Weak ties play an important role in organizational performance, including innovation, raising or maintaining product and service quality, and attaining project milestones. If people cannot interact face to face and just connecting through Zoom and document sharing, weak ties are under threat.

Fostering relationships
It is hard to foster relationships in a virtual environment outside your direct team. Furthermore, with everyone working from home, companies are finding more-limited value in rotational programs, cohort-based training programs, or even cross-functional staffing assignments. While the return from such programs is hard to identify at present, it is an investment that has a significant ROI in the long term. Long-term relationships that once sprang from such shared experiences are undoubtedly at risk and weaken the organization. 

 

What Happens as Some Return

A possible reason identified for the continued performance of virtual employees is that everyone experienced simultaneously, while in prior studies, only some had been working from home. If this is right, then the problem may return when office work resumes.

Presently, those who have returned to the office are a minority, comprising those who find it most challenging to work at home. However, as more people go back to the office for various reasons, those who remain at home may start to feel isolated, affecting performance.

A key reason for employees to return is paranoia. The pandemic has helped managers identity those who are indispensable and who are not. For those deemed non-indispensable, there is the concern that lack of sight will result in termination or offers of early retirement. As more people reason the same way, the pressures to return will grow. Once those back at work reaches a critical mass, the rest may be obliged to follow suit.

For the moment, it is waiting and see.

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