What an MBA taught me 2

More importantly than the time and valuation problems mentioned yesterday, the complicated nature of models like the DCF or those in new Keynesian macroeconomics, or what “value” or “money” even is, brings to bear how difficult it is to properly predict the real world on which many of these assumptions are based. This difficulty is known as the knowledge problem.

The knowledge problem

The knowledge problem was popularized by Nobel Prize–winning economists Friedrich Hayek and Milton Friedman. In short, through their many papers and books, they argued that it’s impossible for a person or group of people to obtain or synthesize the information required to make policies that accurately guide the economy, an investment, or even at times a business. That’s why only five out of a hundred VC investments are true successes, or why economists have, as the joke goes, predicted nine out of the last three recessions.

Consider whether policymakers made the right call with COVID lockdowns: Young students lost years of education, workers lost millions of jobs, murder rates rose 30% and “deaths of despair” rose 10–60%, and many other negative outcomes. Or how about disruptions to employers and employees caused by vaccine mandates, a Coasean bargaining problem if there ever were one? These policies obviously save lives, but it doesn’t seem clear if they save lives on net given the other challenges they raise. There are just too many factors and downstream effects for which it’s impossible to account. And those factors are changing literally every day as the market system re-prices values and people change their decisions.

This critique applies to even the brightest minds on the planet. Remember, we’re finite creatures.

I asked about the knowledge problem to a senior adviser to the Federal Reserve Bank of Dallas. He essentially replied that, because of this enormous difficulty, Fed leaders should be humble in their decision making. The Federal Reserve has what’s known as a dual mandate: They use sophisticated models to make policy adjustments that maintain stable inflation and keep unemployment low.

Yet with all their sophistication, senior Fed officials have recently walked back their arguments that the inflation we’re seeing is “transitory.” Why? Not only could the models be inaccurate but policies also take time to make their way through the system into individuals’ decisions. One central banker at the Bank of England put it this way:

One [reason for the difficulty in controlling inflation] is that it takes time for policy to work. A change in interest rates has its peak impact on inflation only after a significant delay—probably eighteen months or more. One implication is that, fully to offset the inflation we’ve seen through the course of this year, monetary authorities would have to have foreseen the various things that have pushed it up (including, for example, the recent problems with gas supplies).

Now that we have more information about how market participants are responding to their monetary policy, their models seems to have made the wrong call, or perhaps the right call but for too long, which has happened many times over the years. Some argue, for instance, that the Great Depression was caused in part by the Fed’s ineffective decision making and limiting the money supply, or that the 2007–2008 financial crisis was caused in part by easy monetary policy and punitive housing regulation rather than merely the greed of investment bankers.

Errors like these are caused partly because the models policymakers build rely on historical data rather than changing data from the future. Prediction is hard, as the saying goes, especially about the future. Another says that past results don’t indicate future performance. This critique of macroeconomic modeling is known as the Lucas critique, which gave rise to new classical economics in the 1970s, but still has some import today as models fail to predict reality.

In short, market participants are dynamic, not static. That is, the millions of variables in these models change as policymakers implement their policies. Not to mention the extraordinary disruptions brought about by entrepreneurs, new technology, and swinging prices every single day.

Some criticize economists for having “physics envy,” since they try to reduce the vagaries of millions of people into simple, elegant formulas that have little semblance to reality. Perhaps more disciplines than just economics suffer from physics envy.

To be clear, I’m not suggesting all regulation, government intervention, or well-intended policies from public or private institutions are bad. I’m only thinking through the enormous difficulty facing these institutions to know what policies actually are best in reality.

Also, I’m aware that many people face daunting challenges, including racism and discrimination, in order to build a life of dignity. We should be superlatively compassionate toward these people and their challenges. You may think that the critique below is skewed toward the political left, but those of you who know me know I’m equally critical of the right. I try to maintain an even balance. It’s just that these topics are very popular right now particularly for those left of center.

To put this post simply, I’m more skeptical of the data underpinning many leaders’ policies.

Climate change

Consider a popular topic like climate change. It doesn’t seem possible to measure precisely how the climate will change over the next 70 years given how much humans affect it, as the UN’s International Panel on Climate Change (IPCC) predicts with their models. How could anyone know what individual people and entrepreneurs, companies, governments, or societies will or will not do over several generations all over the world? Get a glimpse of the difficulty of such a task in this article in The Wall Street Journal.

Even if the models were accurate, the harm done to the economy hardly seems worth the cost: The worst IPCC projections will cause economic damage valued at only 0.05% of GDP by 2090, according to the undersecretary of energy for science during President Obama, which is far less than the trillions policymakers around the world plan to spend with an already indebted budget.

One other thing to keep in mind during this debate is how cheap, reliable sources of energy correlate with economic well-being. That is, as societies emit more carbon, they also grow richer. Why? Because economic growth creates prosperity, but we need energy to grow the economy and power our homes, cars, and businesses. Poor countries, like poor people, simply can’t afford renewable sources of energy at the current rate of technology. It’s too expensive per megawatt hour. Renewables aren’t a panacea for other reasons, including the corruption and exploitation in Congo’s cobalt mining required for batteries, or the production backups for electric grids in case of, for example, a lack of wind, which crushed the UK’s energy markets in September.

Carbon capture is another possible solution, but a recent article in Fortune notes, “‘Carbon capture facilities are expensive and energy-intensive to run, while transporting carbon to a storage site—such as a dried-up oil field—is a logistical and regulatory nightmare that few businesses are willing to pay for.’ … the total volume of CO2 captured over all of 2020 adds up to less than half a week’s worth of the world’s CO2 emissions.” And for years carbon credits and offsets have been plagued by fraud and corruption.

A little unrelated, but interestingly, Tesla was founded in 2003, but this year is the first year it’s profitable aside from selling carbon credits to other automakers; one could argue that the government has subsidized Elon Musk’s enormous wealth.

Some argue, as does one book called The Moral Case for Fossil Fuels,† that switching to renewable energy before the technology is cheap enough could deeply harm the world’s poor since it would create more barriers to exit poverty. Another argues that the science supporting many climate change initiatives is simply Unsettled.†

I cite these resources to say that there are many different, intelligent sides to important issues like climate change, and each side has its pros and cons. There are simply trade-offs that lofty rhetoric often fails to take seriously.

Inequality and DEI

One of the biggest lessons I’ve learned, particularly in classes focused on valuation, is that we need to compare apples with apples. Consider inequality, a major driver for diversity, equity, and inclusion (DEI) initiatives. Many proponents argue that the rich are growing richer at the expense of the poor. They claim, for example, that the top 10% of household income far outpaces other deciles, pointing to charts like the one below from The New York Times.

Yet they fail to note how those households change over time. People move in and out of their homes, which would dramatically change what comprises that household income. In fact, as the people in those households grow richer, they often move out on their own, like many immigrant families do, which reduces overall household income because there are now fewer people contributing to the household. The result is exactly the opposite of what those charts and the common rhetoric tell you: Household income may decrease as people grow wealthier.

Additionally, Thomas Sowell pointed out that, in 2016, there were 19 million more people in the top 20% household income category than the bottom 20%, which would certainly create a higher income in that category than in the households at the bottom.

An analyst or journalist should also note how purchasing power has changed over time, since the goods of today are much different than the goods of twenty, forty, or a hundred years ago. Consider how much cheaper and better quality washing machines, refrigerators, TVs, and other durable goods have become over the decades, to say nothing of new inventions like smartphones and computers. Of course, economists use figures like the Consumer Price Index (CPI) and other inflation-adjusted measures to estimate the changes in real wages. But these are imprecise, changing instruments that distort how wealthy we all really are.

Additionally, an analyst also needs to observe how taxes and transfers factor into how we calculate equality. By some measures, income inequality is lower today than it has been in years past. Consider, too, that Americans are the most generous people in the world, and that wealthy individuals give more in terms of money and volunteer hours, and pay most of the government’s taxes.

So, to better analyze the issue, it would be better to use income per capita. Yet even using that indicator fails to describe that people are, in reality, human beings and not statistical categories. Their incomes change over time, they inherit or lose wealth, they buy or sell their homes, they get promotions or layoffs, they get married or divorced, they have children, they go back to school, and so on. They also respond to policies like taxation, housing regulation, and more.

In other words, people are not static blocks of wood to be manipulated by models. The charts show only statistical categories; they don’t follow individual, flesh-and-blood human beings. If you did that kind of analysis, you’d find, as one writer did in The New York Times, that more than half the US population will be in the top 10% of income earners at one point in their lives.

Still further, per capita income data needs to be controlled for industry, education, experience, age, skills, hours worked, marital and family status, and other variables. When you do that, the picture becomes much blurrier. The gender wage gap virtually disappears, and in some roles, women outearn men, even without controlling for variables.

Moreover, the industry component is something to take seriously: In 2016, women sought over 84% of healthcare bachelor’s degrees but only 19% of computer and information sciences degrees. Obviously, the latter has higher incomes than the former. One could argue that culture has dictated those choices, but perhaps individual agency has some import too. People make their own decisions based on changing life circumstances such as having children, getting married, moving, and retiring.

Another similar chart people use, like the one below, shows how corporate after-tax profits as a share of GDP have increased over time. A Marxist critique would argue that this means corporations and bankers are greedy, that they’re not paying their employees because they’re exploiting labor for capital. But what happens when you do similar adjustments like those above, or for inflation, or for changes in industry, geography, tax policy, capital investments and depreciation, and more?

For example, in contrast with past decades, the companies in the S&P 500 now have valuations heavily weighted toward intangible assets like intellectual property and software rather than tangible assets like property, plant, and equipment, which leads to higher profitability. Maybe structural issues in the economy like these contribute to the outcomes in this chart without any nefarious motives. At any rate, higher profitability isn’t clearly a bad thing. They can afford to invest, pay their employees, and reduce costs transferred to consumers. Perhaps it means today’s companies are more efficiently allocating capital, which is a good thing.

Importantly, inequality as a concept doesn’t tell you anything really meaningful about how the people in these statistical categories actually live. One of the main ways economists measure societal inequality is the Gini coefficient or Gini index. However, the US has nearly the same Gini index (41.41) as the Democratic Republic of the Congo (42.1), one of the poorest countries in the world. Would you rather live in the US or in the DRC?

It’s also not clear that DEI initiatives will make equity any better, and there’s evidence to suggest that they may make matters worse. Consider the 2007–2008 financial crisis: Politicians forced banks to lower their lending standards to create “affordable” housing along racial and income criteria, but this only distorted lending and valuation models, spurring the crisis years later and deeply harming the people these policies were intended to help. For example, to expand affordable housing, Fannie and Freddie, key players in the crisis, were created by the government to securitize mortgage backed securities, the very assets politicians later blamed for creating the crisis. Of course, banks took on more risk, but whether they were the chicken or the egg is hard to know. In fact, according to a new video from The New York Times, progressive cities’ policies have actually made poor people worse off in housing affordability, which is the exact opposite result of their good intentions.

Or consider the student loan and tuition crises because there should be “no student left behind.” Or consider rising medical costs because “healthcare is a human right.” Or consider other debates around minimum wages reducing jobs or the ineffectiveness of the War on Poverty or the War on Drugs.

Further, most DEI statistics comprise blunt categories like White, Black, Asian, and Hispanic. Of course, people don’t fit neatly into these categories. We’re composed of myriad ethnicities, cultures, and backgrounds containing endless differences in values as well as intellectual, social, and physical talents. If you break down income by more detailed ethnicities using Census data for income per capita here, you’d find that Americans of Western European descent actually have lower incomes than those from many other developing economies. Kenyan Americans, for example, have higher incomes than American Americans, as do Americans from Barbados, Algeria, Cameroon, Egypt, Palestine, Syria, Armenia, and others. Jewish Americans, some of the most persecuted of all people (even still today), are at the very top of this list of household income, followed by Indian Americans and Taiwanese Americans. Besides, Asian Americans outperform White Americans on almost every economic scale, from incomes to test scores, rates for poverty, drugs, crime, divorce, and others. Of course, this data excludes controls for some of the variables regarding income inequality mentioned above, but it still runs counter to common narratives. A critical review of Sowell’s book in The New York Times discusses some of these arguments; his other books like Discrimination and Disparities contain many more.

What’s more, DEI initiatives are applied only in specific sectors like academics and business. What about sports or music? For example, this year’s Olympics US men’s basketball team was 100% Black. The rowing team was 100% White. NFL kickers are nearly 100% White. Most rappers are Black, most country artists are White, and most K-Pop stars are Korean. Should DEI initiatives apply there too?

Further still, the very nature of the planet and our history as a species living on it evokes wild inequalities in endless ways. Rain falls on one side of a mountain in orders of magnitude more than the other, creating arid soil on the “shadow side” that’s unfit for farming, reducing health and wealth over time for the people that live there. Communities living in mountainous areas are much less well off in terms of health and wealth than those living near water because of trade, access to ports, and other factors.

Some ethnicities like Jews throughout history have arguably developed better financial skills, Germans better engineering skills, English better textile skills, Chinese better manufacturing skills, and so on. Economists call this effect agglomeration, where economic activity seems to coalesce around a certain people group or location. Consider that most of the world’s carpets are made in Dalton, Georgia; or most of the world’s technology activity is based in Silicon Valley; or finance in New York, London, or Hong Kong; cork production in Portugal; rubber in Southeast Asia; and so on ad infinitum.

Even further, consider that my older brother and I grew up in very similar conditions and cultures with very similar genetics. Yet his income has always far exceeded mine because he went to a top engineering school (Georgia Tech), majored in engineering, and worked as an engineer, while I went to a mid-tier state school (Georgia Southern), majored in writing, and worked in marketing and media. There’s nothing nefarious about this result; it’s simply a matter of market prices and our life decisions: I didn’t like math at the time and he did, and the market rewarded that decision.

Because of this thinking, I’ve concluded that the world is simply unequal and difficult to interpret for infinite reasons, and many DEI initiatives involve a never-ending search for equality that is merely a mirage. It’s better to let market processes bring the results we hope to see in the world because they decentralize power, corruption, and bias in our decisions, and they coordinate and incentivize market participants based on more objective criteria like price and profit. In other words, people are able to retain their autonomy and largely get the ability to be treated based not on the color of their skin but on the content of their character.

To be fair, I’m not an expert in any one of the issues mentioned above. It’s true that just because I don’t know something doesn’t mean someone else is equally ignorant. I’m simply growing skeptical that articles with bombastic headlines touting crises and solutions even begin to scratch the surface of the enormous complexity of these topics. Yet business school has taught me how to think about them in more detail by analyzing some of the calculations that go into them. Again, I’ve learned that this is a finite world, and that someone, somewhere, at some point has to pay for our grand visions. And that cost-benefit analysis may not make much sense.

Tomorrow, we’ll look at the biases and incentives we face while making decisions.

Notes

† Disclosure: I have not read this book, but I have read about it.