Burnout! Houston We Have a Problem

Burnout! Houston We Have a Problem

For most of us in an office environment, it is now over five months since we vacated our office and began working from home. While some companies are seeking to have employees return, many are pushing that back until sometime in 2021. Also, in some areas with children returning to “virtual” school,” work from home will continue for a while.

While the data shows that overall productivity is up, what is becoming apparent is that few are taking vacations since COVID hit. According to a recent survey by the global online employment platform Monster, 59% of employees are taking less time off than usual, and 42% of those working from home are not planning to take any time off to decompress. SAP internal data shows employee vacation usage is 4% vs. 24% for the same period last year. For many employees, a combination of cancellation of events, summer camp closures, risks from travel, and minimal ability to travel internationally has led to a deferment of vacations. 

However, fewer vacation increases the risk of employee burnout. The recent Monster survey revealed that 69% of employees are experiencing burnout symptoms while working from home, an increase of 20% since a similar study in early May. In addition to the burnout, financial anxiety is also causing mental health issues.

The World Health Organization has updated its definition of burnout from a stress syndrome to “a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed.” Three symptoms characterize burnout:

  1. feelings of energy depletion or exhaustion;
  2. increased mental distance from one’s job or negative feelings toward one’s career; and
  3. reduced professional efficacy.

The damage employee burnout can do to an organization is very real. Individual employee burnout reduces productivity. Also, as employees begin to show symptoms of burnout, they transfer their stress (and workload) to others–and the burnout spreads. 

With so few employees taking a vacation, issues of what to do are arising.

  • Give employees more days off during the year, e.g., a Friday a month.
  • Make employees take staycations? 

The odd day off used to be a great break, but working from home, it is just the same as being at the office. So the rest and recharging that it used to offer are no longer there.

Encouraging employees to take staycations may sound good for their mental wellbeing. However, according to an HR Consultant, “The type of staycation where you don’t travel, but you stay home and forget all things work-related for a week feels different when you are working from home. [ The staycation ] is not by choice, and there is a lot of fear, trepidation, and isolation involved. If you don’t have enough space to have a completely separate work from home space, your staycation will feel like you just took a pillow and blanket into your office.”

Finally, some are taking vacations, but not turning off during that time. Since we can all work virtually, they are just continuing to work but at the vacation spot rather than at their home. This type of vacation defeats the purpose and results in the break being ineffective at reducing stress and burnout.

Another issue that is arising is what to do with all the unused vacation time. Many companies that have a use it or lose it policy may find that people lose it during these uncertain times, but that probably increases the risk of burnout. Another large set of companies are revisiting their employee policies to allow for unused vacations to roll over into 2021 so that when things allow for holidays, employees can use them. Right now though 2021 may not be long enough and rolled over vacation is a liability carried on the balance sheet.

Like many things during COVID, the situation is fluid, and flexibility is critical. First, though, find a way to reduce burnout and get your employees downtime. Then you can figure out what to do with vacations.

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Where Exactly Are We Headed?

Where Exactly Are We Headed?

What is happening out there? Airbnb confidentially filed for its IPO last week. In the spring, the company laid off 2,000 employees and was negotiating over the terms of two fundraising deals totaling $2 billion in debt and equity.

However, total consumer spending on Airbnb in July was 22% higher than in the same period last year, according to Edison Trends. According to the company, it surpassed 1 million bookings on a single day that same month, led by an increase in stays at nearby destinations.

So, are we returning to normal? I would answer no, but there is hope. Normal is a long way away as people are still scared and want to social distance. However, given we can only take so much of staring at the same four walls, we are heading on vacation. Those vacations may not be the ones of pre-COVID days with cruises, trips abroad, or all-inclusive resort, but booking a house for just our family or close friends that we trust, works! Thus as the company reported, bookings are up for close to home destinations, basic economic substitution.

Along with other reports, consumer spending has increased during the pandemic, and I put that down to the fact that we are not spending as much on other things, e.g., commuting, sports, and meals out. However, will that spending last? Last week the Labor Department reported first-time jobless claims increased to 1.1 million, and it was the 22nd consecutive week claims exceeded those during the worst week of the Great Recession. On the positive side, the total number of Americans collecting unemployment fell from 15.5 million to 14.8 million, the lowest since early April. This data goes to show that the recovery will not be quick and a V-curve.

Where exactly are we headed, I am not sure. I hear lots of talk of continued layoffs ahead with about Wells Fargo and Boeing announcing more cuts as well as many smaller companies planning layoff. There is a sense of uncertainty over Q4 2020 and Q1 2021, and expect many are taking a wait and see approach. However, with school restarting, albeit in a confused manner, the Federal Unemployment Benefits in unchartered waters, and Congress in gridlock, there is a lot of confusion out there.

However, as an old Keynesian, the amount of stimulus that the government has poured into the economy is why we are experiencing a robust recovery to date. According to economic theory, in a world of excess capacity and mass unemployment, a combination of vast government borrowing with monetary expansion will not fuel inflation until most of the excess capacity is exhausted, which is where we are now. A Keynesian fiscal stimulus financed with negative real interest rates will boost private consumption and investment and should generate above-trend economic growth. Before the cry of “Crowding Out,” arises from many as I heard during the Great Recession, where all indications showed none. Currently, with central banks worldwide committing to financing this Keynesian stimulus with zero or negative interest rates for years ahead, there is no risk that public borrowing will crowd out private investment.

Thus, will this Keynesian stimulus lead to a healthier and longer growth economy? I would put that down to two factors.

  1. As always, public health. The sooner we adopt and proactive, data, and science-driven approach to the COVID crisis, the sooner we return to a functioning economy. Cases are rising again in Europe, which indicates that this is a marathon and not a sprint. I know for many, it already feels like a marathon, but the more apt analogy is the British in September 1939 saying, “It’ll all be over by Christmas!”
  2. The Stimulus. The actions by the Fed and the Congress, through the CARES Act, have injected substantial stimulus into the economy. However, as these have ended, we will have to observe to see what happens. As in the Great Recession, Congress stopped the stimulus too soon, for political reasons, which lead to a much weaker recovery than there should have been. Hopefully, this time, they will put the country first and give the economy what it needs to recover.

A lot of economists are arguing that the stock market is pricing in continuous stimuli for the economy, and if Congress fails to deliver the will, a market correction to accompany the economic contraction.

For those gnashing their teeth and anguishing over a Keynesian expansion, it is worth remembering that the 20 years of broadly Keynesian macroeconomic policy in place from 1946 until the late 1960s saw the most robust economic growth and productivity advances ever recorded. At the same time, we experienced generally moderate inflation and almost continuous bull markets in equities, property, and other real-value assets.

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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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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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