Marina Gorbis's Blog, page 1422

Emotions Are Data, Too

Hardly a day goes by that I don’t meet it, the struggle with emotions at work.

The misunderstood colleague, filled with frustration, attempting not to show it; the executive wondering how to confront her team’s lack of enthusiasm; the student hesitating to confess his affection to a classmate.

It has been two decades since emotional intelligence became a cornerstone of managers’ self-improvement projects. Meditation has broken into the C-suite. Alpha males and females extol the virtues of mindfulness. And still we remain unsure about what to do with emotions at work.

One moment we do not have enough emotion, the next we have too much.  We want work to ignite our passion but we don’t want our passions to affect our judgment. We want cool heads and warm hearts—as long as they remain apart.

The pursuit of passionate equanimity in the office might look like a valid remedy for the consuming pace of business in our day and age. I’d like to argue, however, that it might be a symptom as well—of a work culture that views emotions in ways that keep us struggling with them in the long run.

We have come to regard emotions as assets—precious or toxic as they may be—rather than as data. Therefore we focus on managing them, which often means trying to exploit, diffuse, or sanitize them, far more than staying with them long enough to discern their meaning. And when we do the latter, we usually interpret them as revealing something about their owners alone.

Treating emotions this way, as spillovers of our inner worlds, leaves us with acute, even obsessive awareness of them—and yet limited insight.

Not because we’re neglectful of our emotions, incompetent at managing them, or simply, hopelessly human. Not because emotions are neither always conscious nor easily named. Not just, at least.

It is because our emotions at work are more than echoes of our history, expressions of our virtues and neuroses, or shadows of our longings. While those always play a part, emotions are seldom ours alone.

What you and I feel at work has as much to do with what we are doing, and what others expect of people in our roles—and of someone who looks like us—as it does with our own inner lives.

We readily accept that work shapes how we act and how we see ourselves, that others’ expectations subtly corner us. We rarely think the same may be true of our emotions — even private ones — as well.

But if we play a part at work, more or less willingly, a part more or less fitting with the person we believe we are, why should we not feel that part as well?

What if emotions were another element in our role’s unwritten script, which our history merely prepares us for and our aspirations only make us more willing to perform? What if the assumption that emotions are ours—alone—to mind and tame made us more likely to torment ourselves than to question how that script casts us and who its authors and intended audience are?

Take an energetic executive who was wondering if he had become depressed when I met him, shortly after a big promotion. He had been asked to turn a division around, and had relished the challenge at first.

Months later, however, his reviled predecessor was thriving in another company while he himself was deeply dispirited. Despite his good progress, he could not exorcise a lingering fear of failure with the usual enthusiasm and determination, and worried that it might be catching up with him.

Reflective as he was, he could easily link his fear and shame to certain disappointments of his youth. What he found harder was to see that his feelings also spoke of something broader than his unresolved sense of inadequacy. They reflected the status of his division, whose problems were blamed for everything that threatened the company’s viability in the marketplace.

His well-disguised fears and old sensitivities made him a perfect match for the position, psychologically speaking. They made him more likely to carry the sense of inadequacy on behalf of other executives, who could thus feel blameless for the company’s difficulties, than to challenge the arrangements that evoked it.

Taking a more systemic (and less conformist) view of emotions, as sources of intelligence about the work and culture of our organizations, does not make us any less responsible for them. Quite the contrary, it calls for us to use the insight we gain for more than improving our effectiveness or achieving peace of mind.

How would we go about extracting systemic insight from our emotion? Here are three questions to get us started.

How do we show (which) emotions?

Stop asking whether you show enough emotions. Ask how you show them. We are always expressing emotions, even if we are not talking about them. Particularly when we are not talking about them. There are no emotions we express more than those we are trying to hide, especially from ourselves.

(It’s when we believe that we have no emotions that emotions can most easily have us.)

It is not always unpleasant emotions that we deny—or hide in plain sight. I know workplaces where aggression is acceptable while needs for comfort and recognition make people uncomfortable. So fighting, for all it is bemoaned, becomes a safer form of intimacy—a way to connect and show that one cares.

Silencing emotions breeds mistrust and loneliness. Acting them out without talking about them safeguards the status quo. Silence makes it harder to recognize, make sense of, and challenge the division of emotional labor, so to speak, that keeps us feeling the same way over and over again.

Who gets to feel what?

Emotions are seldom distributed equally. They are often bundled with certain roles.

Consider hope and despair, confidence and concern, pride and shame, poise and agitation, vocal outrage and silent contempt. The former in each pair is usually assigned to, and expected of, people in powerful and visible roles. The latter is consigned to those in less powerful and visible ones, to nurse on behalf of those who must avoid them.

“Be yourself” and “get a grip” are common ways we are nudged into those places, as both often translate into, “Feel and show more of what I expect you to.”

This runs counter to the common belief that our emotions are what funnel us into different roles, and that by managing those emotions we make ourselves more suitable for certain assignments.  In fact, our roles often elicit our emotions. And we don’t often realize that until, when we move on from one role to the next, the emotions we felt dissipate, only to capture our successors.

Needless to say, such divisions of labor, never explicit but respected by most, do not bode well for problem solving, mutual understanding, and collaboration.

What is the purpose of these emotions (and who benefits from them)?

Assume that which emotions are silenced and which are voiced, and who gets to feel and express what, is neither random nor affected by our character alone.

The heartless CEO, the guilty working mom, the ambitious middle manager, the frazzled assistant. Consider them assignments, albeit unconscious ones.

Then you have a lens to examine what purpose, and whose interests, those assignments may serve—what they enable, what they avert, who they protect—and what everyone, including you, gets out of them.

It may be safety, righteousness, approval, achievement, or relief. It may be the illusion that everyone gets what they deserve rather than what they can afford.

It may be the familiarity, if not comfort, of experiencing what we are used to—within and around us. A sense of knowing our place and what it feels like.

Interpreted that way—tied to ourselves in a role, in context, doing work—emotions can help us learn about and manage more than just ourselves. They give us hints about what keeps us in our place, how we may change places, and even what it might take to change the whole place.

When you find yourself thinking, “Here I go again,” because you sense that you are getting caught up in a familiar pattern, ask where in your past that pattern comes from, what it says about you, and how you may ease its grip. But don’t stop there. That’s only half the work. Ask also what evoked those emotions here, in these circumstances, now.

Unless we use our self-awareness to examine the system more dispassionately, reflection is just another form of withdrawal. Unless we turn our hard-earned equanimity into resolve to change our surroundings as much as ourselves, the struggle with emotions never ends. Any practice to manage them becomes at best a coping mechanism—at worst an instrument of the status quo.

We can’t be saner, or at least freer, until we stop sanitizing emotions. We can’t make workplaces fairer if we lock people into managing them alone.

Yes, emotions are personal. They are just not all about us.

 

When the Board Agrees with the CEO’s Politics, Oversight Suffers

Similarity of political views between CEOs and their boards strengthens directors’ empathy for chief executives and thus weakens their monitoring of CEO performance and compensation, says a team led by Jongsub Lee of the University of Florida. A study of thousands of U.S. firms shows that this political alignment, which is common, also reduces the quality of financial reporting: A small increase in board–CEO “political homophily” leads to a 3% increase in the marginal probability of a firm’s being involved in high-profile corporate fraud. The alignment effects are most pronounced for small boards that frequently interact with the top executive, the researchers say.

Why You Need a Resilience Strategy Now

This past winter was a rough one for big swaths of the United States, with both unusual cold snaps and disruptive snowstorms. General Mills’ CEO recently blamed the winter for less-than-expected earnings, saying that “severe winter weather…disrupted plant operations and logistics…We lost 62 days of production…which hasn’t happened in decades. That would be the result of people not being able to get into work safely or not having inputs arrive.”

It wasn’t just one company, though; the whole economy was slowed by the extremes and volatility we faced.

The disruption to operations and supply chains is real and costly, and all signs point to increasing threats as weather gets more volatile, driven in large part by climate change. The science is getting clearer that we’ll see more extreme hurricanes, droughts, floods, and even snowstorms – more moisture in the atmosphere means bigger downfalls of all kinds.

The latest report to confirm these issues are not some theoretical model to debate, but reality today, came on Tuesday from the quadrennial U.S. National Climate Assessment. The 840-page tome did not bury the lede and declared in the first sentence, “Climate change, once considered an issue for a distant future, has moved firmly into the present.”

Of course, all weather isn’t necessarily tied directly to climate change – like with the recent tornadoes that swept through the American Midwest – but no matter what you believe the cause, extreme weather will play an increasing role in our lives and economies. Nobody can predict exactly what might go wrong, but we can say with near 100% confidence that something will.

So let’s consider what a company can do in a world that’s volatile, uncertain, complex, and ambiguous – that’s “VUCA” for short, a military term that’s been adopted by business. Here’s a review of the five core components of resilient systems, which I pulled together for my new book, The Big Pivot, based in part on two other important works: Nassim Taleb’s Antifragile: Things That Gain from Disorder and Resilience: Why Things Bounce Back, by Andrew Zolli and Ann Marie Healy.

1. Diversity. A company is clearly more at risk if it has just one major product, service, technology, key supplier, or other core element. In the 2011 Thailand floods, both hard drive makers and auto giants realized that having a sole key component made in one place made for a fragile system (Toyota took a $1.5 billion hit to earnings). While companies don’t often share the details of their supply chain strategy publicly, you can bet these companies have built more diverse options for sourcing key inputs.

2. Redundancy and buffers. Taleb uses the natural world as a model for this principle: “Layers of redundancy are the central risk management property of natural systems,” he writes, pointing out how many of our biological systems have doubles (like lungs) or backups. Our business systems need leeway for extremes as well. A few days ago, for example, the Obama Administration announced a plan to stockpile a million barrels of gasoline in the northeast specifically to avoid the shortages that plagued New England after Hurricane Sandy.

This is all smart strategy, but the challenge for business specifically is that companies don’t like keeping two of anything – that’s not lean or (seemingly) efficient. It’s a fine line for sure, but having multiple pathways to get key inputs, for example, might have saved General Mills –  and the hard drive and car companies –  lots of money. It might have actually generated increased revenue as well, if it meant operating while competitors couldn’t. As Taleb says, “redundancy seems like a waste if nothing unusual happens. Except that something unusual happens – usually.”

3. A love/hate relationship with risk. It’s a paradoxical idea, but one way to build resilience, or antifragility, is to keep the vast majority of the business as safe as possible, but then take big risks – ones that may pay off 10-fold or more – with a smaller part of the business.

Think of the famous idea from Clayton Christensen of trying to disrupt or cannibalize your own business before someone else does. Imagine setting up a skunk works to identify major risks to the business stemming from resource constraints or climate change – and then lean into those risks and come up with products and services that avoid them and challenge the core business (for example, a car company investing in car sharing programs which consumers use to save money, but also reduce material and energy use dramatically).

4. Fast feedback and failure. If you’re going to take some risks to, ironically, make us less risky, you need to drop what isn’t working quickly. To be more responsive, companies need better data on resource use and climate risks up and down the value chain. So invest in capturing information and building real-time systems.

5. Modular and distributed design. If some part of a system fails, it would be great if it didn’t bring down the rest of it. A tree branch hit a power line in Ohio in August 2003, causing cascading failures across a highly connected U.S. grid, and 50 million people in the northeast lost power (including me, my wife, and our 11 day-old child in Connecticut – we were not in a resilient mood).

These principles alone may not make for resilience in a hotter, scarcer, more open world, but they go a long way. And they point toward one key pathway for managing – and even thriving – in a VUCA world: renewables.

Companies (and homes) that generate their own onsite energy will be able to literally weather storms better than competitors. Not all the technologies we need to do this well are in place – like building-scale energy storage at a reasonable cost – but we’re getting there. And during the day, companies with their own solar panels can operate after the storm has passed, even if the grid is down.

Nobody can prepare for every possible outcome. Randomness, of course, is a prime element of our new business reality. But we can build systems that are better prepared than they are now. And, sure, it’s a challenge to value resilience: How much is your business damaged by a breakdown in your supply chain, or a threat to your ability to operate? How much will it cost all of us if we let the drivers of deep volatility, like climate change, go unchecked?

It’s not easy to say, but let’s avoid finding out.

Time Is a Company’s Most Valuable Resource

Michael Mankins, partner at Bain & Company, on how to get the most out of meetings. For more, read the article, Your Scarcest Resource.

Download this podcast

A Simple Nuance that Produces Great Strategy Discussions

All too often strategy meetings devolve into pitched battles over who is right and who is wrong about the company’s future direction. How can you reshape the discussion to produce collaboration rather than discord?

The key is to switch the fundamental question you consider from what is true to what would have to be true.

What is true provokes arguments, causes proponents of a possibility to dig in, and minimizes the collaborative exploration of ideas. Let’s imagine you put forward a possibility for a strategic direction and, upon hearing the idea, I focus on what I think is true. With this mindset, it is quite likely that I won’t be confident that your idea is valid and I’ll probably start by saying something like “I don’t think that will work,” words that will instantly turn the meeting into a battlefield. When I then raise an alternative strategic direction, you, smarting at my treatment of your idea, will be equally dismissive of me. And so on, back and forth.

If instead we can focus from the outset on what would have to be true, the conversation can head in the direction of collaboration and mutual exploration of ideas. How? If an idea or possibility strikes you as less than compelling, resist the urge to declare it “not true.” Instead, ask yourself, what would have to be true for me to feel that is a great option? If you identify the features that would have to be true, you can explore whether those really hold true and learn something about that possibility. The process of exploration may well help you modify and enhance the best idea currently in your head.

In addition, taking this approach will likely have the effect of convincing other members of the team to explore your option in similar fashion – and they will build on yours just as you have built on theirs. It does take two to tango in life and if you refuse to battle from the outset, it is likely that those around you will avoid that behavior too.

It is not as though no deaths occur in this process. But the ideas kill themselves rather than get killed by someone. When everybody comes to agreement as to what would have to be true for an option to be a great choice, the group can determine what tests it would have to conduct to determine whether those things hold true or not. Note that the tests are not any one person’s tests but rather those of the group as a whole.

When the group carries out those tests, they will either confirm the validity of the idea or the test results will show the option to be wanting. In the latter case, the option is killed by its own failure to hold up to the tests, not by the arguments of one member of the team. This is a productive process, one that builds group cohesion and collaboration — and keeps the battleground at bay.

How to Hire More Top Performers

How much is your top salesperson worth? Your star engineer? Your best marketer? Everyone knows that some people get better results than others. But the best aren’t just a little better than the rest — they’re typically a lot better. Bain & Company’s research, discussed here, suggests that top performers are roughly four times as productive as average performers. Sometimes the difference is far greater. For example, the best sales associate at Nordstrom sells at least eight times as much as the average sales associate at other department stores.

Given the sizable differences between the best and the rest, a company with a higher percentage of top performers will naturally tend to outdo its rivals. The reason is that it has higher human capital productivity (HCP), which we know correlates closely with financial results. Raw talent isn’t the only determinant of HCP, but if you don’t have the “A” performers you need, none of the other factors will make much difference. So improving your overall talent level is the first step toward higher HCP.

How can a company raise its skill level—and in particular, how can it increase its proportion of top talent? Our research and experience at Bain suggests three keys.

Assess your talent pool. You can’t know the magnitude of the task you’re facing until you know exactly where your human-capital strengths and weaknesses lie. AllianceBernstein (AB), a $3 billion asset management company based in Manhattan, rates each of its 3,700 employees every year on both performance and potential. The senior team at AB spends several days together each year cross-calibrating the two sets of ratings across the entire company, so it always knows where its top performers are. One caution: performance and potential both matter, but performance should count for a lot more. Performance is real; it can be measured objectively. Potential is always subjective, and may never be realized. So pay close attention to your high performers. If they’re a tiny fraction of your overall talent pool, you know you have a problem.

Control your pipeline. Whenever possible, avoid relying on executive search firms as the primary source of new talent. A company looking for more A players needs to know first-hand about the talent that is available, and it needs to do its own recruiting. One well-known Silicon Valley firm relied heavily on headhunters to find engineers for many years. The result? It got engineering candidates who couldn’t land jobs at Apple, Google, Facebook, and other A-list companies in the valley. Only the arrival of a new CEO led to a change in the policy — and an eventual uptick in the company’s talent base and performance.

Have only high-performers conduct the interviews. It’s a sad truth about human beings: B and C players can’t always identify top performers, and may feel threatened by them if they do. Average performers look for congeniality, the ability of an interviewee to fit in. They don’t always look for — and they may not favor — someone who seems likely to raise the bar or make them look bad. One other tip: involve as many line managers in the interviews you can. Many jobs these days are highly technical, from software development to running a copper mine. Interviewers from HR aren’t usually capable of judging someone’s technical skills.

A company, like a sports team or a symphony orchestra, has to do a lot of things right to reach the pinnacle of performance. But if it doesn’t have a healthy share of those exceptional individuals who do so much better than everybody else, then it’s out before the race even begins.

Rethinking Big Data to Give Consumers More Control

The White House recently published two new reports on Big Data and privacy (here and here). The reports outline six policy recommendations, including new legislation to define consumer rights regarding how online activity data is gathered and used.

Not surprisingly, the findings are already viewed “warily in Silicon Valley, where companies see it as the start of government efforts to regulate how they can profit from the data they collect from email and web surfing habits,” according to The New York Times.

Yet by rethinking the use of personal data from the ground up, entrepreneurs can mine new opportunities for experimentation and innovation. This involves focusing on three goals. The first is to design products that shift control of personal data toward consumers (and away from data-brokers and gray markets). The second is to empower people with personal analytics tools to understand the information being collected about them and safeguard their privacy. The third is to create economic value by helping consumers make personal decisions more effectively.

These interlinked objectives aim to give consumers more say over their data and more insight through personal analytics. Here are a few examples of where and how this is already happening:

Making Open Data personally relevant. Over the past few years, once walled-off stocks of governmental big data have been released in the US and UK as part of Open Data, Open Government, and Smart Disclosure initiatives. This trend includes discreetly releasing personal information on-demand to citizens in machine readable formats.

Take the Veteran’s Administration’s Blue Button initiative, which aims to keep patients engaged with their health data outside of hospitals and doctors’ offices. To test commercial opportunities in this environment, a number of entrepreneurs are creating personal analytics tools to help citizens make better decisions about health providers, disease management, and even personal finances related to healthcare.

One prototype enables a patient to use her data to understand the exact dollar amount her policy will cover for a specific procedure. Another aggregates a patient’s healthcare spending data from disparate sources to improve personal decision-making and planning for healthcare spending.

Because of strict HIPAA (medical record) privacy laws, the personal data is not shared with outside commercial interests or data brokers. These tools are producing an intentional and disruptive shift by putting the power of the data in the hands of the patient.

Using APIs to wrest control of your data. A new wave of opportunities has emerged in crafting analytical products that leverage APIs (“application programming interfaces” are specifications on how software programs should interact) to help users gain more control of and insight from their data.

Facebook allows users to download their personal data sets, which include more than 70 different categories of information, from friends to facial recognition data to every IP address you’ve logged onto with your Facebook account. Nevertheless, this effort at transparency can leave users with an overwhelming amount of information and no way to make sense of it all.

A powerful solution to this problem is Wolfram|Alpha’s Personal Analytics app, which uses the Facebook API to help users look for insights in their Facebook data and networks. Users can discover and visualize unexpected patterns in their usage behaviors, like correlations between posting frequency and historical weather patterns. It also helps users quantify how life changes, like starting a new job, affect social network usage.

The upshot is a shift in power and control back toward the user, who can get compelling answers to questions like, “Am I spending too much time on Facebook during weekends and sunny days?”

Users have control over their data in other fundamental ways as well. For instance, the tool follows HIPAA privacy guidelines and is set up to delete all personally identifiable data after one hour; however, users can opt-in to become anonymized “data donors” to support the research efforts.

Developing personalized choice engines. A third opportunity involves creating “mashup” services combining the two areas described above: governments making citizen data available in machine readable formats and social sites making personal data available to users via new apps that leverage APIs. This combination of open government data and social learning works differently than many of the apps you use now, which may rely solely on voluntary user reviews or marketing data.

For example, Work+ is a mobile app that links New York City data (information on libraries and coffee shop seats) with Foursquare (location and mapping data) to help independent and traveling workers find a suitable workspace on a particular day.

Rather than relying on chance to find an available table with WiFi and an electric outlet, this personalized “choice engine” allows you to be analytical in selecting the right spot and in tracking time to prepare for a nearby client meeting.

Say you want to grab dinner after the client meeting. Don’t Eat At___ is another mobile tool that links Foursquare’s location capabilities with New York City restaurant inspection data. The personal choice engine pings you with just-in-time notifications when you check into an eatery that has been flagged for violations or shut down. The power of Open Data and personal data analytics on behavior (entering a flagged restaurant) is put entirely in the hands of the user to facilitate an informed “no-go” decision. It works almost as the inverse of a location-based marketing offer, which might’ve incentivized you to go to the same restaurant. With Don’t Eat At___, the analytical asymmetry favors the consumer rather than the anonymous marketer.

Don’t Eat At___ also has stringent privacy policies in place and collects the absolute minimum amount of information necessary to improve a specific “go or no-go” decision. For instance, when you disconnect, Don’t Eat At____ deletes the two tidbits of personal information it collected — your Foursquare ID and mobile number.

The realization that we’re only on the brink of this new phase of technology adoption should accelerate the drive to radically rethink personal data, as suggested in the White House reports. And soon, most data will be generated by sensors rather than by smartphones and PCs. Analysts estimate there will be 20-30 billion data-generating sensors by 2020, many of which will gather personal data from our homes, cars, and wearables. This Internet of Things increases the need for entrepreneurial solutions that safeguard privacy while empowering consumers to make analytical decisions that improve their health and everyday lives.

This Just In: Sunspots and Planets Shown to Predict Stock Market

Struck by the large number of studies showing that bumps and dips in the stock market can be predicted by such factors as interest rates, credit spreads, dividend yields, consumer sentiment, and cold weather—despite economics Nobel laureate Robert C. Merton’s description of any attempts to estimate assets’ expected return as a “fool’s errand”—Robert Novy-Marx of the University of Rochester set out to see what other market “predictors” he could find. Using the same rigorous statistical methods that underpin all those studies, he found several such predictors, including sunspots and the relative positions of the planets Mercury and Venus in the sky, he writes in a straight-faced article in the prestigious Journal of Financial Economics. (In a footnote, he hints that the weakness in such “prediction” findings is the standard assumption that there’s a simple linear relationship, consistent over time, between these random portents and stock returns.)

Midsized Firms Can Survive a Cash Crisis

Operational meltdowns can devour a midsized company’s cash. Without adequate outside capital, a financial hemorrhage can escalate to a liquidity crash, an ugly moment when no one gets paid. At such a time, company leaders must relentlessly focus on one thing: unearthing cash and holding onto it. Not growth, or even profits.

When start-ups run out of money, they often solve the problem with credit cards or a quick infusion of venture capital. But midsized companies typically need millions of dollars to survive. If they don’t receive it, hundreds of employees and their families suffer, as does the larger ecosystem of customers and suppliers. Large companies rarely face this growth killer since most of them maintain deep cash reserves, have access to the financial markets, and possess the financial discipline to react long before a crash.

Midsized companies need to be far more cash-conscious — even penurious — when their markets go sour. The story of MBH Architects, a northern California firm that designs retail stores, conveys an important lesson on how to survive such a cash crisis. And crisis it was in 2008, the same year the U.S. economy (especially the real estate market) fell off the end of the table.

Dennis Heath and John McNulty founded the Alameda, Calif., firm in 1989. By 2007, they had a staff of 205 and revenue of $26 million. Feeling flush about their current and future prospects, they moved the firm to a nicer headquarters, borrowing $3 million to spiff it up. Then, the great downturn hit. Retailers halted construction on new stores and shuttered underperforming ones. By the end of 2009, MBH’s revenue had nosedived 83% and Heath and McNulty had to lay off three-quarters of their staff.

Nonetheless, the company found a way to survive and even bounce back (more on that later) by taking four actions:

Keeping an emergency cash hoard. Owners of midsized businesses should build a financial cushion outside the company in case the coffers start running dry. The founders of MBH Architects had done this for two years before the Great Recession hit. In 2006 and 2007, the partners earned big bonuses. Rather than spending on yachts and second homes, they saved most of those earnings for a day they hoped would never come. But when it did, they were able to loan MBH a combined $1 million during the firm’s liquidity crisis in December 2008. “Without that money, we’d have been gone,” said firm controller, Oli Mellows.

Mastering the numbers. Many midsized companies maintain woefully inadequate general ledger information. To use a different company example, one growing financial services firm’s new CFO learned this in 2004. The financial reports his CEO showed him looked rosy: Revenue had doubled to $200 million and profits were $20 million. But nonetheless, the company was low on cash. The CFO couldn’t understand why. After three months of pre-audit investigation, he found material errors in the financial statements and revenue recognition problems. Cash revenues were indeed $200 million for the year, but the correct GAAP net revenues were only $30 million and profit was $2 million. He delivered a financial restatement to lenders and investors and created a detailed monthly and three-year forecasting model based on historical data. That enabled the company to find a new lender who refinanced $25 million in bank debt. It also helped the company fund a key acquisition. Four years later, the firm reached $400 million in legitimate revenue and strong profitability.

Cutting fast and precisely. To keep a finger on the financial pulse of the business, MBH Architects’ founders kept one eye on a key metric: the utilization of its people (i.e., the number of hours billed to clients as a proportion of the total number of available billing resources). As utilization dropped, Heath and McNulty made painful staff cuts and put all bonuses on hold.

But a company in the grips of a liquidity crisis must also consider what it might need in a better economic climate. Top management must be very careful about how they downsize the organization. MBH Architects’ Heath and McNulty closed down branch offices, reduced expenses significantly and closed an entire floor of their main office to cut the heating and lighting bill. Some of the staff – including Heath and McNulty — even did janitorial work to keep the bill collectors from the door. But to make sure they didn’t jettison the employees they would need once the economy improved, Heath, McNulty, the other partners, and controller Mellows put all 113 employees’ names on index cards and pinned them up on a conference room wall. They asked: “Who do we need in order to rebuild the firm?” They identified their 50 best people and laid off the remainder.

Maintaining borrowing capacity. If the CFO has a realistic budget, it’s much easier for a midsized company to borrow money. With the $1 million they had saved for their rainy day, MBH’s founders were the first financial backstop. That meant that “patient money” was invested in the firm. Ideally, borrowing should take the form of raising one’s line of credit with the bank every year (especially the good years). It’s far easier to negotiate with your bank for a higher line from a position of financial strength than from one of weakness.

When money gets tight, drawing on the line is better than running your cash down to nothing; that’s the purpose of your line of credit. If you need more money than you have available on the bank credit line, consider outside money in exchange for equity. But be very careful to whom you sell stock. Since most midsized companies are privately held, your new cash needs mean turning to private equity or other private investors. Make sure the payback expectations of your investors are in line with yours. PE firms typically sell portfolio companies in three to five years. VC funds can have 10-year horizons in exchange for an outsized return. If you can’t provide the return an investor wants in the timeframe he wants, things are likely to get ugly. In such cases, don’t take the money.

The MBH Architects story has a happy ending. Due to its fiscal prudence (and a rebounding economy), by 2012, the company was nearly the size it had been in 2007. (2012 revenue was 84% of 2007 revenue.) Profits were actually higher than in 2007. Heath and McNulty had survived the liquidity crash to live another day.

Low-Skilled Workers Everywhere Are Getting Squeezed

The supply chain for German cars is significantly more globalized than it was a decade and a half ago. In 1995, 79% of the value created in the process of manufacturing a German automobile was captured by domestic firms and workers; by 2008, German companies and workers captured only 66%.

The difference is explained by the phenomenon of “production fragmentation,” in which different firms (and countries) specialize in producing different parts of a final good. German car companies still assemble the final product, but the value chain leading up to that last step has become more fragmented in recent years, and the role of foreign firms has increased.

A recent paper from the Journal of Economic Perspectives looks at the phenomenon of fragmentation in manufacturing and seeks to determine who is capturing value in today’s more global supply chains, as well as who is getting left behind.

To answer those questions, the authors look at the share of value in manufacturing captured by high-skilled workers (those with a college degree or equivalent), medium-skilled workers (high school degree), low-skilled workers (less than high school), and capital. (The latter category includes machinery, factories, and other physical capital, as well as natural resources, intellectual capital like patents, and returns to financial capital.)

This method of accounting looks at the sale price of a product and subtracts the cost of raw materials and other inputs. What is left over is the financial value created by the production process, which is split up between wages paid to workers, and returns to capital.

The paper reveals a shift in who captured value from 1995 to 2008, away from low- and medium-skilled workers and toward high-skilled labor and capital. That trend is visible in the context of German car manufacturing:

Globalization, and the resulting fragmentation of supply chains, hasn’t just shifted the geography of production. It has meaningfully changed the returns to various stages of the production process. As the paper explains it:

Production processes in manufacturing have increasingly fragmented across national borders, and the change in their factor content was clearly biased towards high-skilled labor and capital. This pattern was not only found for activities carried out in high-income countries, but also in emerging economies.

It’s no secret that in developed economies, high-skilled workers are leaving their low-skilled counterparts behind. More interesting is the dynamic in emerging economies. Despite the fact that low-cost labor is a source of competitive advantage for many of these economies, the share of value captured by low-skilled workers has decreased in these countries as well:

The bulk of value in emerging market manufacturing is captured by factory owners, financiers, and the like, because these investments are in shorter supply than is the low-skilled labor that complements them. But while capital’s share of value captured did increase between 1995 and 2008, on a percentage-basis it was high- and medium-skilled labor that increased the most.

Behind all of this, the authors argue, is “a pervasive process of technological change that is biased towards the use of skilled labor and capital.”

Less skilled workers in developed nations may be losing jobs to those in developing ones, but ultimately both groups face the same challenge. No matter where a product is assembled, high-skilled labor and capital reap the highest returns.