This essay is the intellectual backstory of my book, WILD PROBLEMS: A GUIDE TO THE DECISIONS THAT DEFINE US. In Wild Problems, I critique what I call narrow utilitarianism — the day-to-day calculus of pleasure and pain — as a basis for making big life decisions. In the process of writing the book, I realized that utilitarianism is embedded in various ways in how economists think about decision-making at both the societal and individual level. I also realized that the utilitarian approach tends to encourage economists to focus on the measurable at the expense of what is not measurable and that this can lead us badly astray, a theme that is at the heart of Wild Problems. This essay also expands the ideas in the book beyond the focus on individual decision-making and looks at a wide array of policy issues and how our desire for certainty and measurement can corrupt our thinking.
“If your knowledge cannot be measured it is meager and unsatisfactory”
— Lord Kelvin
Let’s give Lord Kelvin his due. Without measurement, progress is impossible. Consider a farmer in early times, long before the advent of modern agriculture. How is he doing? Probably not so well. Hovering on the edge of subsistence, barely able to keep his family and himself fed winter to winter, he is desperate to find ways to improve his annual crop of apples. No matter how he’s doing, he wants to do better. To do better, he must measure. He must count. How many apples did he harvest this year vs. last year? Is his crop going up or going down? Arithmetic is born.
He notices that in places where animals graze, the trees seem to do better compared to where animals do not graze. So he tries to fertilize part of his land with animal manure. Is this a good idea? To find out, he must count the number of apples he gets this year vs last year from the areas he has fertilized. If he is a very wise farmer he will realize that any change in the crop may be due to the something other than just the fertilizer, like a change in rainfall. If he is especially wise, he will compare this year’s crop to the average of the last five years to try to take out the impact of variations in other factors. Statistics is born.
Perhaps this farmer has gone beyond self-sufficiency. He’s not only able to feed himself and his family, but he’s sufficiently productive to grow more apples than his family can eat. He takes his extra apples into town and sells them, using the money to buy other things he cannot make easily for himself. Commerce is born.
Once he interacts with others through buying and selling, he can ask a deeper question. Maybe it’s a mistake to grow apples. By growing apples, he gives up the chance to grow something else. And now economics is born, the idea of trade-offs and opportunity cost. To really know how he’s doing, the farmer needs to know something of what is possible, how he might be doing if he made different choices.
The farmer decides to reduce the size of his apple orchard and replace some of his apple trees with orange trees. He doesn’t know — yet — that orange trees won’t grow well in climates where apple trees grow well. But he plunges ahead and he wonders whether he made a mistake. Does his meager orange harvest make up for the apples he’s no longer growing? To answer the question, he needs to compare apples and oranges.
I apologize for this elaborate joke — comparing apples and oranges is of course, the metaphor for two things that aren’t comparable. The metaphor captures why we have an urge to quantify, to measure. A farmer must implicitly or explicitly compare apples and oranges if he wants to know how he’s doing.
Before arithmetic and accounting, the farmer’s measure of well-being would be loose, vague, and indeterminate. He would have a crude idea of how he was doing by how often his children complained about being hungry. He could look out into the field and eyeball the amount of land covered with apple or orange trees. He could have a feel for how long it takes to harvest different fruits. He might understand something about the inherent variability of some crops relative to others
A lot, a few, some, abundant, cornucopia, meager, decent, pretty good, amazing are all imprecise ways to capture how the farmer is doing. But the desire for certainty and a desire to improve, pushes the farmer toward more precise measures of her harvest. And once she has more than one crop, the problem becomes particularly challenging. Saying you have a lot of apples but not so many oranges isn’t that helpful. A farmer needs a way to compare apples and oranges.
This is especially urgent as the number of crops and the different kinds of livestock get more numerous. In any one year, the harvest of some crops will be higher and some lower. Some crops may be big and unblemished while others are small and barely edible. He may have a growing herd of cattle but because he planted less corn, his chickens may have less to eat and may not be laying as many eggs as before.
How can the farmer wrap his head around this complexity?
Some answers are more helpful than others.
Counting the number of apples, oranges, ears of corn, eggs, chickens or even to go crazy — the number of grains of barley — and so on and adding them up, does give you a measure of success but it’s not an actionable measure because there is no inherent value to such a count — it correlates poorly with anything the farmer actually cares about. Another obvious measure, weight, has the same problem.
A better measure, once there is a market for his produce and livestock, is the dollar value of everything he produces. Multiply the number of ears of corn by the price per ear, the numbers of eggs times the price of an egg and so on. The result is his income. Accounting is born.
The dollar value of each crop is the common denominator that lets you compare apples and oranges. It’s a very good measure because the farmer’s income is crucially correlated with not starving to death, correlated with being able to rise above abject poverty and correlated with being able to hire someone to fix the leaky roof of the farmhouse or send the kids off to school instead of having them work on the farm.
Imagine a chart that has all of the different aspects of the farmer’s output for a year. The chart would not just have the total number of eggs but the number of eggs graded by size and quality. It wouldn’t have just tons of corn, but how many tons were good enough for human consumption and how many were only fit to feed to the cattle. It’s overwhelming.
Income takes all the complexity of the various crops and their myriad characteristics and converts that complexity into a single number. It’s really magic.
The chart would have many different rows and columns and you can always add more rows and columns to capture this or that characteristic of the harvest. And many of the entries in that chart — a subjective measure of the color of the apples from this year to the next, for example — would not be comparable in any way. As life gets more complicated, it gets harder and harder to eyeball the chart from one year to the next and figure out whether you’ve made progress. Measuring income takes the loose, vague, and indeterminate information embodied in the chart and turns it into something precise, accurate, and indispensable — a single number.
If that isn’t magic, what is?
This urge to quantify, to transform information that is loose, vague, and indeterminate into something more precise such as a single number is hard to resist. It answers our desire for certainty and it opens the door to progress. It’s a fantastically powerful tool for management. It has two glorious features. First, it’s simple. In contrast to the chart, you can see it instantly.
There’s a second important feature of a single number that captures the success or failure of this year’s harvest. You can compare it to last year’s number. You can see if it’s bigger or smaller. You can project next year’s number and when next year arrives, you can see if your plans came to fruition.
When you consider buying a house you look at its location and how many bedrooms it has and the size of the kitchen and so on. But every house has irregularities and different shapes. So we usually rely on a single number — the square footage — to figure out which one is bigger. I may care independently about the size of the kitchen because I love to cook (or don’t care about cooking at all), but the square footage sure beats a list of the different rooms and their respective sizes. The ability to boil complexity down to a single number so I can make comparisons is very powerful.
The mathematical name for numbers that describe physical concepts like area is scalar. The origin of the word is the Latin word for ladder, or scala — something that helps you to climb. It’s the same Latin word for scale as in the things that help you measure or the verb as in to scale the highest peaks — to rise.
Scalars make it easy to put things on a single scale, to make them comparable. They simplify complicated things. We are really good as humans at heavier, higher, taller, shorter, bigger, smaller. We are really good at comparing numbers and deciding whether one is bigger, smaller, or the same as the other. 1000 is bigger than 10. 17.3 is bigger than 17.1. Making these comparisons are so easy we never think about it.
It also opens the door to mistakes. In “This is Spinal Tap,” guitarist Nigel Tufnel played by Christopher Guest shows Marty DiBergi played by Rob Reiner the special amplifiers that his band uses and points to the knobs that control volume:
Nigel Tufnel: The numbers all go to eleven. Look, right across the board, eleven, eleven, eleven and…
Marty DiBergi: Oh, I see. And most amps go up to ten?
Nigel Tufnel: Exactly.
Marty DiBergi: Does that mean it’s louder? Is it any louder?
Nigel Tufnel: Well, it’s one louder, isn’t it? It’s not ten. You see, most blokes, you know, will be playing at ten. You’re on ten here, all the way up, all the way up, all the way up, you’re on ten on your guitar. Where can you go from there? Where?
Marty DiBergi: I don’t know.
Nigel Tufnel: Nowhere. Exactly. What we do is, if we need that extra push over the cliff, you know what we do?
Marty DiBergi: Put it up to eleven.
Nigel Tufnel: Eleven. Exactly. One louder.
Marty DiBergi: Why don’t you just make ten louder and make ten be the top number and make that a little louder?
Nigel Tufnel: [pause] These go to eleven.
Q.E.D! Christopher Guest looks at Rob Reiner like he’s a moron. A five-year old knows that 11 is bigger than 10. But not all elevens are created equal.
This kind of mistake isn’t the typical one. Go back to the farmer. It’s hard to remember that the single number, income, is not a perfect measure. It’s hard to remember that it’s not the only thing we care about. It’s hard to remember that it’s only an approximation of how we’re doing even if all you care about is material well-being.
Some economists claim you don’t need economic models to understand the world. You just need data. Let the numbers do the talking. But numbers are mute. They only speak with our help. And what we say they are saying, inevitably involves a model of the world. But the model is veiled by the simplicity of the numbers.
In today’s Washington Post, [https://www.washingtonpost.com/local/dc-hunger-report/2020/10/01/1770590c-0337-11eb-8879-7663b816bfa5_story.html] as I write these words, there is a story with the headline:
District’s food insecurity rate estimated to be 16 percent, up from 10.6 percent before pandemic
I don’t know what food insecurity is. The article does not explain it. Presumably it is related to hunger. That it has increased by such a large amount is disturbing but it’s hard to know just how disturbing without knowing what exactly is meant by food insecurity and more importantly, how the measure of 16 percent was determined. I suspect most people read the story and concluded, as the story suggests we should, that food insecurity is up a little over 50% in the nation’s capital. Definitely alarming.
The story does link to a report from the Office of Planning in the Mayor’s office of Washington, DC. I click through and open the report. The first paragraph mentions:
the critical importance of ensuring that every resident in the District of Columbia has access to healthy, affordable, and culturally appropriate food.
Is that the definition of food insecurity? Not quite. That is explained in the second paragraph which reads in its entirety:
“Food insecurity” is a term defined by the U.S. Department of Agriculture that refers to a lack of consistent access to enough food for an active, healthy life.
Food insecurity is definitely a bad thing. Or at least it could be depending on how it is defined. But as this makes clear, it’s a concept that is loose, vague, and indeterminate. Food insecurity has some connection to hunger and malnutrition but measuring it is inevitably a subjective exercise to quantify a subjective concept and give it the patina of an objective measure. When we see a comparison between 16% and 10.6% our brains see something that looks objective.
And is there anything more objective than the decimal point? When last year’s food insecurity measure for Washington DC was calculated, it wasn’t rounded up to 11 or made vague by saying “a little more than 10%.” It was 10.6%. Most jokes about economists are cruel. Perhaps the cruelest is this one:
Q: How do you know macroeconomists have a sense of humor?
A: They use decimal points.
Meaning that the macroeconomics forecaster isn’t content to predict that unemployment will be higher or lower next year or next quarter or next month relative to the present. The forecaster will usually predict the precise number carried out to at least one decimal point to give the forecast the air of precision that sometimes comes with scientific measurement. Decimal points, to quote W.S. Gilbert of Gilbert and Sullivan, “give artistic verisimilitude to an otherwise bald and unconvincing narrative.”
And it turns out, when you dig deeper into the report on food insecurity, the 16 percent number is a projection. The report says that food insecurity is projected to be at least 16 percent. I want to suggest that when you see the headline, your mind decides that there is a reliable objective fact that food insecurity is at least 50% worse than it was before. But that’s not an objective fact. What it really means requires some digging and even some digging may not be enough.
The desire for certainty, the desire to turn a matrix into a scalar, a chart of attributes into a single number, is not just understandable, it’s often the only way to make progress on a problem. If food insecurity is indeed on the rise and policies are put in place to help reduce food insecurity, measurement gives you some chance to know if the policies helped or hurt.
Without measurement, as Lord Kelvin points out, we can’t know if our knowledge is reliable. That’s the upside of measurement. The downside is that inevitably, almost any measurement we use embodies a set of assumptions that are easily forgotten.
I’m about 66 inches from the bottom of my feet to the highest point on my head. I say “about” because precise measurement is difficult. It depends on how I stand and the challenge of measuring anything with perfect precision. But that I am 5’6” is reasonably described as a fact, partly because any deviation from that precision is quite small relative to the correct, unobserved, not easily ascertained exact height I am right this minute on October 2, 2020. It’s also reasonably described as a fact because you as the consumer of that fact have probably used a tape measure or a ruler or yardstick and are very aware of getting an accurate measure of height within say 1/64th of an inch.
The same would be true of the temperature right now in your house or outside. The weather app on my phone says it is 63 degrees Fahrenheit in my house. That is surely not the exact temperature just outside my front door or even 30 feet away from it. There’s no decimal point and the precise temperature will vary whether it is measured over asphalt in the street or in the yard where the grass reacts differently to sunlight. But like height, when I tell you the temperature is 63 that’s close enough for deciding whether to wear a sweater, short-sleeves, or a heavy parka if you are planning on leaving the house.
But is the 16% food insecurity rate for Washington DC a fact? It looks like a fact because it’s a number. Our brains associate numbers with what we call facts, numbers that have standards of measurement that we use or have used often, like measuring height or observing the temperature on a thermometer. But many things that look like objective facts are what we might call scientism — things that have the appearance of science but without the reliability of science. Our brain struggles to keep them apart.
I don’t know whether it is a good idea or a bad idea to try to quantify a subjective concept like hunger, poor access to healthy food, or poor access to affordable food. What I am saying here is that your brain struggles to remember that a number like 16% as a measure of food insecurity is not a simple fact like height or temperature. But your brain, if it’s like mine, is prone to treating it like a simple fact.
The practice of turning a matrix into a scalar is all around us. It’s the air we breathe, the intellectual water we swim in. It’s the human response to information overload. It takes a complex array of reality and turns it into something we can wrap our heads around.
Which movie directed by Rob Reiner is better, “This is Spinal Tap” which IMDB users rate at 7.9 or “When Harry Met Sally” which IMDB users rate at a mere 7.6 as of October 28, 2020? Obviously TISP is a better movie than WHMS, right? Of course not. Better is meaningless here. The higher rating for TISP means one thing and one thing only — the people who rated TISP on average gave it a higher rating than WHMS. That does not tell you what you might really want to know — if you can only watch one tonight, which one will you enjoy more? It certainly doesn’t mean that TISP is a better measure in any objective sense.
And I bring this up mainly because This is Spinal Tap is the only movie at IMDB that is rated out of 11. For better or worse, 10 is still the maximum rating you can use. But the average is listed as 7.9 out of 11. I cannot decide if IMDB should have let users give Spinal Tap an eleven.
Let’s return to the farmer. The farmer’s income last year is much closer to an objective measure than the level of food insecurity in Washington, DC. And income is a particularly good scalar because it aligns so nicely with something the farmer cares deeply about — achieving a level of material well-being that could mean the difference between life and death. So measuring the total value of the harvest seems like the perfect way to measure success and solve the apple and oranges problem. Now the farmer can track his performance and know whether he is doing better this year than last year. The concept of income takes a messy complex matrix and converts into a clean, simple scalar that allows the farmer to evaluate progress. But a farmer will still find it challenging to use wisely.
Maybe the farmer starts to get excited when he sees that the value of this year’s harvest exceeds that of last year. He starts to wonder if he can make it even bigger next year. In his ambition, he might forget that some crops have harvests that are highly variable. The dollar value of this year’s harvest doesn’t capture any of the uncertainty surrounding next year. The orange harvest is very sensitive to changes in temperature. The farmer may expand his orange crop in hopes of making more money and forget the downside risk that comes from that decision.
Or in his excitement to expand the size of the harvest, he may forget that devoting more time to making money comes at a cost. That means less time for his family, which isn’t counted in the dollar value of the harvest. Or he may damage his health working longer hours with less sleep. These intangibles are hard to keep in mind. Like the person who looks for the car keys under the streetlight, the farmer may forget what else there is to care about.
What gets measured gets managed. But I’m saying something stronger here. If we are not careful, what gets measured is all we manage. We don’t just pay more attention to what is in the light. We forget what is in the shadows. We forget about the rest of the things that do not get captured in measures we become accustomed to studying and using.
Our desire to quantify complexity seduces us into ignoring things that are less easily measured. We particularly are drawn to scalars — a single number — as a way of summarizing a more complex situation. This phenomenon is an example of what Jerry Muller calls the tyranny of metrics.
David Epstein, in his lovely book, Range, tells the story of the banner that hung on the wall at NASA that captured the mindset of the engineers there: “In God We Trust. All Others Bring Data.” That is a crucial mindset if you want to solve the difficult but ultimately tame problem of launching people into space and bringing them home safely. Sitting in the nosecone of an enormous rocket, you want proof of where you’re heading, not someone’s gut feeling or intuition.
The Challenger launched on a day so cold that there was no data to substantiate or question the reliability of the O-rings for the launch that day. Engineers who felt uneasy about the launch did not feel comfortable speaking up because they had no data. They had information — they knew that the O-rings would be more brittle on a cold day than a warm one. There was also evidence that scorching was worse on cold days than warm ones. But there was no hard data. Did engineers fear being laughed at for making a claim that had no data behind it? The Challenger tragedy launched a lot of soul-searching on these questions about the culture of NASA that may have focused too much on data and less on pieces of information that were only in the shadows.
What is the job of the head of a charity? To enhance and fulfill charity’s mission, whatever that might be. Seems straightforward, but in my experience, heads of charities and private schools often forget their mission and fall prey to maximizing the size of the organization or its budget or some other flawed measure simply because it can be measured. Other factors get forgotten.
Some of this pressure comes from the board — usually people from the business world who come from a world where there is a clear metric of success: profit. The head of the organization becomes focused on measurable measures of success to share with the board. How many people visited the organization’s website? How long did they stay there? Some organizations focus on “engagement,” usually an index of the charity’s interaction with the people they are trying to help. A new video promoting the organizations broader goals is a success if it gets a substantial number of views.
Unfortunately, many of these measures correlate only imperfectly with the ultimate mission of the organization. It’s easy to use advertising to boost the hit counts of a video — paying for your video to show up in a Facebook users feed. Viewer barely notice it before scrolling past it, but it counts as a hit, a hit that had no effect except to annoy the viewer. But the board only hears the hit count number and assumes that the video was a success in spreading awareness of the organization.
Donors to charities suffer from a different form of scalar failure — a scalar being a single number rather than a matrix of different measures. When evaluating a charity, a reasonable idea is to care about how well the charity uses donations to achieve its goals — goals you the donor presumably care about or you wouldn’t be considering donating. But it is difficult and perhaps impossible to measure this idea of bang for the buck — how well a charity will use your dollars. My kingdom for a scalar!
Donors and the charities do have at least one scalar: overhead. Charities are frequently rated on how much that is collected from donors goes to overhead — salaries, rent, electricity, fund-raising — versus how much goes to the cause itself. The lower the overhead, the argument goes, the more that’s available for the cause itself.
It’s not a ridiculous idea. As a donor you don’t want to think that your donations are merely lining the pockets of the overpaid executives of the organization or wasted on staff retreats in pleasant locations. So the proportion of donations that goes to salaries and other forms of overhead is worth knowing. But it’s an absurd measure to use by itself to measure the overall effectiveness of your donation.
A charity that pays poorly and attracts mediocre managers might have low overhead but do a horrible job achieving its core mission. Similarly, a charity that pays high salaries and attracts highly effective people to execute its mission and executes that mission with sublime care and maximal impact may be very worthy of your money.
Do you want a charity that spends a high percentage of its funds on fund-raising or one that’s more spartan? That would seem obvious — don’t waste money on fund-raising. Fund-raising isn’t the actual mission of the charity even though it can sometimes feel that way.
Dan Pallotta has argued eloquently against this single-minded scalar failure of using overhead as a measure of effectiveness. In 2002 his organization’s 3-day bike-a-thons collected $118 million to fight breast cancer. They were able to do that by offering riders an incredible experience which helped motivate them to participate and excel at raising money. But that required resources and a skilled staff to create an unforgettable experience for the riders.
The overhead underlying the $118 million was a seemingly unconscionable 40%. “Only” $71 million was available to fight breast cancer. Embarrassed by the magnitude of the 40%, charities fighting breast cancer decided to stop using Pallotta’s organization and instead put on the bike-a-thons themselves. The next year they collected $60 million less than the year before. Was that a better world?
The debate over the minimum wage is generally over one number — the number of jobs available to low-wage workers after the minimum wage is increased. For decades economists opposed the minimum wage because the dollar gains to workers who kept their jobs and who now have higher earnings was more than offset by the dollar losses to those workers who lost their jobs. For the last 25 years or so, new studies using different data and statistical techniques find either a very small effect on employment or none at all.
Many economists now support the minimum wage arguing that the benefits to those who keep their jobs is large relative to the losses to those who can’t find work. If you point out that some workers will lose their jobs and that they are likely to be those workers with the worst alternatives, supporters of the minimum wage will point out that we should create a safety net for those workers who might struggle to compete with technology in the future, anyway
Ignore which side has the better studies. Ignore whether the minimum wage has a big or a small impact on employment of low-skill workers. Isn’t it strange that the focus is only on working vs. not working and at what income? Very few economists worry about the loss of dignity for workers who are no longer able to support themselves or their family by working. A high minimum wage also makes it easier for the boss to treat their workers badly, with disrespect or worse. The reason is that when the minimum wage is high there are more workers chasing fewer job openings. A high minimum wage effectively creates a reserve army of the unemployed. This raises the cost of quitting to leave an abusive employer — it will be hard to find another job.
Maybe these effects are small. Or maybe their importance to human well-being is small. But they are rarely if ever mentioned in any discussion of the merits of the minimum wage. These effects, if real, are virtually impossible to quantify. Dignity isn’t in the data set.
This is one of the first mistakes we can make with a scalar — we forget that it only captures part of what we care about. The other mistake we make is that a measure isn’t the thing itself.
How is manufacturing doing in the United States? It’s doing badly — manufacturing employment has fallen pretty steadily in the United States over the last 70 years. Or is manufacturing doing well? Manufacturing output has risen steadily over the last 70 years. Rising productivity is one way to understand these seemingly inconsistent claims. Because of technology, we don’t need as many workers to produce the same level of output as before. And if workers are sufficiently more productive, you can even have higher levels of output and fewer workers. Think of robots on an automobile assembly line assisted by human counterparts. You just don’t need as many workers as you once did. General Motors which once employed almost a million workers now has fewer than 200,000 but manages to produce many more cars than they once did.
I told this story for many years. Manufacturing in the United States isn’t being hollowed out. It’s thriving, just not as a source of employment. Because we need fewer workers to make manufactured goods, other industries can do better using those workers who would have worked in factories. The story is indeed supported by the data. But the data isn’t as clear cut as I once thought.
What, after all, is manufacturing output? Manufacturing output is incredibly diverse — screws, bed frames, F-35 fighter planes, computer chips. It’s a classic apples and oranges problem. From year to year, these things all increase or decrease at different rates. What if America produces fewer cars but more airplanes? Or what if America produces fewer airplane but more nuts and bolts. Has manufacturing output gone up or down? Just like the farmer I discussed in part 1, you don’t want to use the total number of pieces or the weight of the output as a measure of total output. You need some measure of manufacturing output that allows you to compare screws and jet planes.
One measure that people use is “value-added,” the extra dollar value created in America in the manufacturing sector. So not the dollar value of the airplane but the extra dollar value of the airplane above and beyond the raw materials used as inputs and corrected for inflation.
Between 2000 and 2010, US manufacturing employment fell by about 1/3. An enormous decrease over a single decade. At the end of that decade was the Great Recession. But even with that recession, value-added in manufacturing rose by 15% over the same time period.
Maybe the US isn’t being hollowed out. We’re not just a service economy. The manufacturing sector is indeed thriving. And it’s more than thriving. It’s creating more output with fewer workers — a sign of increasing productivity.
It’s hard to remember that the measure is just a measure. It’s not the thing itself. There is no such thing as a pile of all the things we call manufactured output that we can actually measure. There is instead a constructed index, value-added output corrected for inflation, that is an attempt to capture the health of an incredibly diverse sector. The thing itself is a matrix. Reducing it to a scalar can misinform.
As Susan Houseman has pointed out, only one manufacturing sector thrived between 2000 and 2010: computers. Exclude computers and manufacturing output fell between 2000 and 2010. The scalar called manufacturing output is just a measure and in this case, a measure that poorly represents the complexity of the thing itself. I don’t think America is being hollowed out. I don’t think we should worry at all about the size of the manufacturing sector as a measure of economic health. But converting the complex matrix of manufacturing into a scalar, masks what has actually been happening in manufacturing.
The urge to quantify is crucial for assessing progress in a complex world. Converting complexity into a single number allows us to wrap our head around a situation that might otherwise overwhelm us. But the real seduction takes place in decision-making. Almost every human decision is multi-faceted with multiple pluses and minuses even when there is no uncertainty about those pluses and minuses.
Having finished college, you are offered a job in Austin and a job in Boston. Which is a better choice? The answer will depend on your personal preferences, your tastes surrounding a multitude of factors — climate, your love of seafood or dislike of it, where your friends and family or located, what kind of live music you like, how you feel about a small apartment or a large one, and so on. Neither city will dominate in every category. So how do you compare them?
Many people struggle to make such comparisons and such choices. Similarly, almost all government policy like the minimum wage result in benefits to some people and costs to others. Faced with these tradeoffs, how are we to make good personal and good political decisions? Is there a way to find the right thing to do? If only we could boil the complexity down to a single number and cut through the Gordian knot of complexity.
About 200 years ago, there was a man who found a way to boil all that complexity into a single number hoping to take all the guesswork out of personal decision-making and public policy. His modest goal was to revolutionize morality, public policy, and how to live well. His modest goal was to become the Isaac Newton of morality, to make morality as scientific as calculus or physics.
It’s hard for us to imagine how the work of Isaac Newton electrified intellectual life in the aftermath of his publishing Philosophiæ Naturalis Principia Mathematica in 1687, a work he revised in 1713 and 1726. The epitaph that Alexander Pope wrote for him captures some of the awe that must have been felt at the time:
Nature and Nature’s laws lay hid from sight
God said, “Let Newton be!” and all was light
The mysterious heavens where stars and planets danced weren’t just a random collection of lights dotting the night-time sky. The heavens were held together by a hidden force, gravity, that Newton illuminated using the language of mathematics. Nobel laureate Vernon Smith has argued that Adam Smith — who lectured on astronomy and was born just four years before Newton’s death — was heavily influenced by Newton in thinking about unseen forces that helped create order in the social and commercial realms.
Another thinker born in the 18th century observed that the world was still waiting for a Newton to come along and make morality as transparent and scientific as the complexity of the heavens. What is the right and thing for an individual to do? What should legislators and policy makers do to make the world a better place? What legislation improves our lives and what makes it worse? Wouldn’t it be wonderful if simple rules like those that Newton discovered about gravity for example, could be found for moral decision-making? Even better, might it be possible to discover a calculus of happiness that could guide our actions?
That man was Jeremy Bentham. Bentham’s goal was to take something utterly subjective, as loose, vague, and indeterminate as happiness or morality and make them as precise, accurate, and indispensable as Newton’s physics. He failed utterly. And that could have been the end of the story. Instead, Bentham’s vision of human behavior and good public policy still influences the way all of us, including most of the economics profession, think about human behavior, rational decision-making, and public policy.
In 1823, Jeremy Bentham published An Introduction to the Principles of Morals and Legislation. Here is the first paragraph:
Nature has placed mankind under the governance of two sovereign masters, pain and pleasure. It is for them alone to point out what we ought to do, as well as to determine what we shall do. On the one hand the standard of right and wrong, on the other the chain of causes and effects, are fastened to their throne. They govern us in all we do, in all we say, in all we think: every effort we can make to throw off our subjection, will serve but to demonstrate and confirm it. In words a man may pretend to abjure their empire: but in reality he will remain subject to it all the while. The principle of utility recognizes this subjection, and assumes it for the foundation of that system, the object of which is to rear the fabric of felicity by the hands of reason and of law. Systems which attempt to question it, deal in sounds instead of sense, in caprice instead of reason, in darkness instead of light.
Let me translate.
Human beings care about two things — pain and pleasure. Pain and pleasure determine how we actually behave and how we ought to behave. In other words, pain and pleasure are the key to understanding human behavior and morality. We might pretend to care about other things, but we’re only deluding ourselves. This, says Bentham, leads us to the principle of utility which is all you need to know to rationally decide how to behave and what policies should be put in place. What is the principle of utility?
By the principle of utility is meant that principle which approves or disapproves of every action whatsoever, according to the tendency it appears to have to augment or diminish the happiness of the party whose interest is in question: or, what is the same thing in other words, to promote or to oppose that happiness. I say of every action whatsoever, and therefore not only of every action of a private individual, but of every measure of government.
By utility is meant that property in any object, whereby it tends to produce benefit, advantage, pleasure, good, or happiness, (all this in the present case comes to the same thing) or (what comes again to the same thing) to prevent the happening of mischief, pain, evil, or unhappiness to the party whose interest is considered: if that party be the community in general, then the happiness of the community: if a particular individual, then the happiness of that individual.
Translation: things that give you happiness or pleasure or satisfaction are good. Things that cause pain are bad. Bentham proposes to use the word “utility” to summarize the good that results from an action or policy. If you want to know whether an action or legislation is good or bad, add up the impacts of that action on the happiness of all the people who are affected. Good actions have positive scores. Bad actions, negative scores. And if you have to choose between two actions that both yield positive scores, choose the one with the highest score.
On the surface, this makes a lot of sense. When we are faced with a decision, we care about which is the best choice. Surely in making that choice, we would want to know how it is going to make us feel. And it seems natural to extend this logic to the larger community when we think about policy choices. Shouldn’t we choose policies that lead to “benefit, advantage, pleasure, good, or happiness?”
Bentham then rolls ups his philosophical sleeves and gets to work. He lists 14 kinds of pleasure including the pleasures of wealth, skill, amity [friendship], a good name, power, the senses [food, sex, watching a beautiful sunset] and so on. And he lists 12 kinds of pain: privation, awkwardness, imagination, a bad reputation and so on.
Bentham then explains that the value of happiness depends in turn on six variables:
- Its intensity.
- Its duration.
- Its certainty or uncertainty.
- Its propinquity or remoteness.
- Its fecundity, or the chance it has of being followed by sensations of the same kind: that is, pleasures, if it be a pleasure: pains, if it be a pain.
- Its purity, or the chance it has of not being followed by sensations of the opposite kind: that is, pains, if it be a pleasure: pleasures, if it be a pain.
The goal of public policy, Bentham argues famously, is to generate the greatest good for the greatest number of people. So just add everything up, the way the farmer adds up the value of his harvest:
Sum up all the values of all the pleasures on the one side, and those of all the pains on the other. The balance, if it be on the side of pleasure, will give the good tendency of the act upon the whole, with respect to the interests of that individual person; if on the side of pain, the bad tendency of it upon the whole.
Take an account of the number of persons whose interests appear to be concerned; and repeat the above process with respect to each.
And so utilitarianism is born. Bentham’s project is a bold one. It’s a three-fer: a guide to living well, a guide on how to live morally, and a guide to how to legislate and govern. He’s telling us as individuals how to get the most out of life for ourselves while taking account of the impact on others. And he’s telling policy-makers how to do the right thing as well.
On the surface, who could argue against it? When we evaluate our choices, personal or political, we should look to the consequences. And the consequences we care about are the how those consequences affect our well-being. Rulers and legislators should support and implement policies that have the biggest improvement on people’s well-being and the more people we can help, the better. The goal of public policy is to maximize the total well-being of the citizenry. The greatest good for the greatest number is the aim.
Bentham didn’t just spawn utilitarianism. He spawned the way economists and others look at how people make decisions. Underlying Bentham’s framework is an assumption that our day-to-day decisions should serve a goal: the maximization of pleasure over pain. It’s the ultimate solution to the apples and oranges problem — it reduces everything to a common denominator. After all, pain is just a negative pleasure. So all we have to do is subtract the pain the pleasure to get the full effect of any personal or policy choice.
Underlying Bentham’s approach is the power of reason and the importance of rationality. We don’t want to stumble around in the dark making mistakes, doing things that are bad for us or that reduce or well-being. We don’t want to choose randomly or make choices based on tradition or old habits and biases we’ve accumulated in life. In Bentham’s view, to live well is to live rationally, toting up the costs and benefits of our decisions and policies to make sure they are the best they can be.
Modern economists take a similar approach to human behavior. As Bentham urged, the economist assumes that people make their choices in life to maximize their utility where utility is just as Bentham described it — some measure of happiness, benefit, satisfaction. In Bentham’s view and in modern economics, life is something like a day at a giant amusement park where you have a fixed amount of money to spend on a finite number of rides. You seek out the rides that you enjoy and avoid the ones you don’t. You even ride some over and over again as long as the pleasure you get from them remains higher than the pleasure you might get from riding a different ride for the first time.
What determines which rides you like and which ones you don’t? The economics assumes that everyone has certain likes and dislikes and that they vary across individuals. What floats your boat may scuttle mine. I like anchovies, foreign films with subtitles, sitting on the beach reading for hours, meditation, and the Marx Brothers. For you, any one of these could be the equivalent of fingernails on a blackboard. Economists can even imagine there are people who like the sound of fingernails on a blackboard.
Life becomes an engineering or calculus problem. What to do and how to do it is a problem of reason and calculation. The goal is to get the most out of your finite income, given the pleasure (or pain) you experience from the different experiences or products or services you can choose from.
I would argue that there are indeed parts of life where we are utilitarian in this way. We seek out people whose company we enjoy and try to avoid people whose company we do not enjoy. I will pay a premium for an incredibly comfortable pair of shoes if I enjoy them sufficiently more than a bargain version. But as I argue in my book, Wild Problems, to see all of a life as a problem to be solved akin to a calculus problem (which is literally how economists teach graduates and undergraduates) is to misunderstand both how we actually behave and even how we should behave. If we follow the mainstream economist’s advice about rational choice and how to get the most out of life, we will miss crucial parts of the human experience. Rationality, at least as defined in economics textbooks and models, is overrated.
I will simply point out now that there are parts of life where it’s not all about me and what I can milk from every encounter, experience, or transaction. In a good marriage or friendship, you don’t keep score — you don’t make an attempt to measure whether you are getting your share of the pleasure above and beyond the pain. I’m not talking about being miserable — that’s a different situation. I’m saying that you shouldn’t act the way economists describe the rational pursuit of utility — trying to maximize the utility you get from the encounter.
Much of life is more like a dance floor than a dance competition. In a dance competition, it’s about being the best dancer, about attracting attention to yourself and winning prizes or respect for being better than those around you. In a dance competition it would be normal to strive to do as well as possible, to maximize your place in the rankings relative to the other dancers.
But on a dance floor, there are rules of conduct where you must subdue your own self-interest if you are to be a member in good standing of the culture around you. It can’t be all about you. You must be careful not to bang into the other dancers. To do that, you must observe the moves and positions of the other dancers and find a way for your own self-expression and that of your partner, to mesh with the movements of others. You might choose to sublimate your own status in the name of making your partner shine in his or her gracefulness or expression.
On the dance floor and in life, there is virtue in perceiving and then following the norms of the environment you are in, even when those norms are not consistent with your own goals or direct well-being. This may mean avoiding some environments and favoring others. Norms emerge and evolve to the extent that they allow for individuals to enjoy themselves and express themselves. But at any one moment, that enjoyment may be imperfect.
A good economist understands that it’s not all about individual well-being and pleasure vs. pain. Community matters. Friendship and family matter. They more than matter — many people would argue that they dwarf the pleasure we get from the material objects and short-term physical comforts of our daily lives.
Over 250 years ago, Adam Smith wrote in The Theory of Moral Sentiments that the “chief part of happiness arises from the consciousness of being beloved.” Good economists (and even bad economists who are decent human beings) know this and will tell you that of course, our satisfactions in life come from more than stuff and from more than income. A good economist surely recognizes that a feeling of belonging to a community or contributing time to a meaningful cause or being respected and loved by those around us are not just part of what we care about but perhaps the chief part.
Economists know this, but these deeper satisfactions are often forgotten because they are harder to measure and observe than the more tangible aspects of life — income, and the purchases we make with that income — the car we drive, the house we live in, and the gadgets we play with. And so in economics textbooks and in economics classes, well-being is maximized subject to the constraints not of norms or virtue or morality but subject to the finite nature of income. Economists know that life is about more than stuff but in the textbooks and in the equations, utility is determined by how much stuff we have. Economists know that money is not the only thing we wish we had more of. Our times is scarce and economists surely know that at some point, time away from our families to acquire more stuff may not make us happier. But these deeper concerns and connections are left out of the diagrams we typically draw on the board in class for our students.
To take a banal example, a bad economist will explain that when you are invited to someone’s house for dinner, rather than giving your host a $20 bottle of wine, give them a $20 bill. Obviously, says the bad economist, the $20 bill is better than the wine. This gives the host the freedom to buy something that they like even more than the wine. So at worst, the $20 bill allows them to receive the same utility they would have received from the wine but gives them the chance to do even better by buying something they like even more. So if you care about your host, bring money, not wine. And maybe give them $21 to compensate for the inconvenience of having to put the wine in the shopping cart themselves.
Tell this to a normal human being and you will get a look of puzzlement. Surely you are trying to be funny. Surely, no one really thinks bringing money to one’s host is a good idea. Yet a Yale University professor, Joel Waldfogel, actually measured what he called the “inefficiency” of Christmas and other holidays where millions of people give gifts instead of money, gifts that are often worth something less to the recipient than the money it cost to buy those gifts. Giving gifts at Christmas or birthdays or any time, in this view, is irrational — you are missing an opportunity to make the recipient even happier. And for gifts the recipient actually dislikes, you are lowering their happiness in absolute terms. Give money!
Waldfogel’s paper was published in the American Economic Review, the flagship journal of the American Economic Association, and arguably the most prestigious journal in the field. Waldfogel estimated that the cost of this cultural “error” in 1992 was between $4 billion and $13 billion. This is not a measure of the money that is wasted directly or lost. It is an attempt to measure the dollar value of the lost utility or happiness or satisfaction that people would have been able to enjoy had they been able to use the money spent on their gift to buy something they valued more highly. The dollar measure of that increased enjoyment is what Waldfogel is trying to measure. How he manages to do that is quite clever. But I don’t think it’s meaningful.
I won’t elaborate here on the perverse misunderstanding this promotes of human interaction. It’s a perfect example of utilitarianism run amok — adding up apples and oranges across people while ignoring the benefits of affection people receive from knowing that someone has taken the care to try to find them a gift they would enjoy, possibly even a gift they would not otherwise have bought for themselves simply because they didn’t know about it. That it’s the thought that counts goes unmeasured in the economist’s calculation.
Outside of a few creative practitioners like Gary Becker, economists have had little or nothing to say about the decisions people make as parents or how they behave in religious communities, or how they decide whether to vote — a decision economists constantly decry as irrational — or why gift-giving is a central feature of human societies from time immemorial and persists even after economists point out its “irrationality.” For the most part, economists focus not surprisingly on our behavior as humans in the marketplace — our behavior as consumers, workers, employers, or investors.
And because economists take preferences as given, there is nothing in the economist’s view of humanity that who we are today need not be who we are tomorrow. In worldview of the mainstream economist, there is little scope for what the philosopher Agnes Callard calls aspiration.
All of this would be fine except that economists easily make judgments about broader life outcomes well beyond the marketplace. Should the minimum wage be increased? Is free trade good for America? Should we allow driverless vehicles that might lead to millions of taxi drivers and truck drivers being out of work? What kind of schools reforms make students better off? These are all questions that go well beyond the material, the commercial, and the easily measurable. Do economists have anything definitive, anything precise, accurate, and indispensable to say about these questions?
Jeremy Bentham certainly thought that someone should have something to say about these questions. He argued that when policy-makers try to evaluate a policy, all they need to take account of is the pleasure and the pain. The benefits and costs.
Alas, there’s a catch to Bentham’s vision. Bentham offers no way to quantify the value of pleasure and pain. While the duration of pleasure or pain could imaginably be quantified, Bentham’s very first category, intensity, which is the heart of the matter, can’t be measured even by the person who experiences it. There are no units to conceive of the measurement.
In the early days of economics, some economists conceived a measure of utility — utils. But where is the util to be found? Where is the utilometer to sense it, measure it, and show it in the printout? Pleasure and pain can’t be quantified. Bentham’s edifice is a castle made of sand, an exercise in description and classification rather than a working tool for an individual seeking happiness or a legislator seeking a guide for designing good public policy.
And if utility cannot be measured in practice, if the value of pleasure and pain cannot be computed, what can Bentham’s famous adage — the greatest good for the greatest number — possibly mean?
Suppose you have two tickets to the musical Hamilton and you invite me to come with you, your treat. Alas, I’ve already seen it and unknown to you, it’s my 25th wedding anniversary, so I turn you down and take my wife to dinner at an elegant restaurant. You find a different friend and have a great time. Who had more happiness from the evening, me or you?
The right answer is I have no idea. No one does. Not me. Not you. And certainly not a philosopher king economist looking on from the outside. No amount of data on the frequency of our smiles during our two very different experiences can answer the question. It’s a meaningless question that falls outside the purview of science or social science.
Or try this variation. You can’t find anyone to go with you so you watch Hamilton alone. You discover you don’t like hip-hop musicals. Given the hype and the expectations, you leave the theater badly disappointed. I, meanwhile, enjoy my anniversary dinner tremendously. The food’s spectacular. The wine, divine. My wife and I spend the evening in rapturous conversation savoring our marriage and each other.
Did my pleasure outweigh your pain? Again, a totally meaningless question. It’s apples and oranges all the way down. I was delighted to discover that Bentham himself used fruit to illustrate the difficulty:
’Tis vain to talk of adding quantities which after the addition will continue to be as distinct as they were before; one man’s happiness will never be another man’s happiness: a gain to one man is no gain to another: you might as well pretend to add 20 apples to 20 pears.
Bentham himself was deeply frustrated by this practical difficulty. My kingdom for a scalar. But how?
Modern utilitarians argue that in some cases, the benefits are so much greater than the costs that measurement is irrelevant. Imagine a policy that doubles the income of every poor person in the United States but as a result, Jeff Bezos has to pay an extra dollar for a cup of coffee. Surely the gains to all those poor people outweigh the trivial cost to Bezos who already has so much.
Or consider Peter Singer’s example — a child is drowning as you walk to work. Surely the gains from saving the child outweigh the cost of ruining your fancy shoes and being a few minutes late. Surely, the ethical choice is save the child.
I think most people would agree that you should save the child and that Jeff Bezos should willingly pay more for coffee if he could help millions of people. And happy people would be happy to legislate these solutions — taxing Bezos a small amount if the proceeds could be spent in a way to lift millions out of poverty and taxing people who are well off with jobs and nice shoes if it can save the lives of children. But these two examples are reductio ad absurdums. What they actually imply for harder questions in the real world is much murkier.
Let’s take the Bezos example. The utilitarian argument is that the world is a better place if a nearly trivial cost paid by Bezos can lift millions out of poverty. The argument is that the pain endured by Bezos is smaller than the pleasure received by millions of people improving their material well-being. You probably agree and I do, too. But what are you willing to do to Bezos to help millions? Take half of his income and his wealth? Take more than that and turn him into a pauper? Destroy his ability to create Amazon? Torture him on pay-per-view TV along with his extended family?
Somewhere in there, you would probably draw a line and say that such a policy makes the world a better place but more than that makes the world a worse place. Some would draw the line at the very beginning — no one has a right to harm someone in order to help others. Others might never draw the line arguing that hurting one person no matter how grotesquely is worth it if millions can be lifted out of poverty. And still others might be content to say say they don’t know what it means to make the world a better place. It’s too complicated.
But the promise of Bentham, the promise of utilitarianism is that the question of where to draw the line can be drawn objectively, scientifically, precisely. That promise cannot be fulfilled. Of course if you have a choice between saving ten lives instead of one life, everything else held constant, then save ten lives. But that’s an apples and apples problem. Most of the world where the tough questions are inevitably ubiquitous is apples and oranges. No calculus is available for those challenges.
I’m not saying data is irrelevant. When you are deciding how to spend your charitable contributions, try to find out what the impact is on the people you are trying to help. Find out how many will be helped. The amount of good and how many are helped are both relevant. This is what is admirable about the Effective Altruism movement — charity should help people not merely signal virtue or bring comfort to the giver.
If you are a legislator trying to decide how to vote on the minimum wage, you will surely want some measure of how many people will be helped and how many might lose their jobs even if you know that these scalars cannot easily be combined. But don’t pretend that all the costs and benefits can be reduced to a scalar or that the things we cannot measure are unimportant.
When I ask you which movie you’d rather watch tonight, This is Spinal Tap or When Harry Met Sally, you can probably come to a decision. In the economist’s world, we pretend that you weigh the prospective pain and pleasure from watching each movie, or perhaps the expected pain and pleasure from watching each movie, and come to a decision. Is that what people literally do when choosing a movie? It may not be a bad approximation. But that does not imply that we can determine which movie is better for the family if three members vote for Spinal Tap and only two vote for Harry Met Sally.
Majority rule is one way to deal with the apples and oranges problem. But there is nothing utilitarian about using that method. It leads to a decision as long as the number of family members is an odd number and everyone votes. But that does not imply that somehow the family is better off watching one movie rather than another.
You can’t even argue that it would be a better world if the family split up and three of the family members watched Spinal Tap while the other two went into another room to watch the movie they prefer. Maybe something that can’t be quantified is lost when the family splits up in this way. It’s not a question that can be answered by philosophy or social science decisively. Each family deals with these challenges in its own way. Those solutions inevitably take account in some way of what members prefer and how intensely. But there’s no Benthamite arithmetic in the literal sense of adding up pain and pleasure to measure the gains or losses to the family from choosing one movie over another.
The apples and oranges problem has bedeviled economists ever since they decided to weigh in on public policy. Let’s return to the question of increasing the minimum wage. Millions of poor workers get a raise. But what if that means that the poorest, least-skilled workers possibly can’t find work, can’t provide for themselves through their own efforts, can’t provide for their family, can’t have dignity, and face a lifetime of dependency.
Can you even make the case for raising the minimum wage even if all it does is ruin the life of one worker, let alone the thousands or hundreds of thousands who might actually lose their jobs or who struggle to find one in the first place? Sure, you can count or at least estimate the dollar gains in higher salary to the workers who keep their jobs. Can you weigh that with a clean conscience against the loss of dignity of that one worker?
You might say yes. Someone else will say no. Dostoevsky would say no, I think, based on a similar example in the Brothers Karamazov. Ursula Le Guin would say no based on her story, “The Ones Who Walk Away from Omelas.” You might say yes. And if you have to make that decision whether to raise the minimum wage, certainly the number of workers who are helped and hurt would matter. But it’s not a scientific assessment. It’s a judgement that is inevitably loose, vague, and indeterminate. What I have been trying to say is that if you are not careful, you may come to ignore the intangible gains and losses in dignity and pride that cannot be quantified.
But even if you remember those intangible factors, people will differ as to whether raising the minimum wage is good for the poor or good for the country. That is not a scientific question amenable to measurement. But if all you economists can say is that some people win and some people lose, what’s the fun of being an economist?
Thomas Sowell likes to say, quoting, I think, George Stigler — that the essence of economics is understanding there are no solutions only tradeoffs. Any intervention comes with costs and benefits that cannot be easily compared in order to come to a judgment about what is best.
Harry Truman longed for a one-armed economist because then he wouldn’t be able to say “but on the other hand” because there’d be no other hand. In other words, Truman wanted to turn an economist from someone prone to loose, vague, and indeterminate observations into someone who is precise, accurate, and most importantly, indispensable. But that’s like saying I’m having trouble deciding between moving to Austin and moving to Boston. Which is going to be better for me? There’s no straightforward answer.
An economist who has two arms and can only focus on tradeoffs becomes a mere functionary in the palace of the king. The one-armed economist can be the power behind the throne.
Here is how it’s done. Like Waldfogel’s analysis of gift-giving, you want to be able to convert the intangible factors like how much pleasure a sweater gives you, into some dollar measure. That will let you combine apples and oranges.
Consider one small piece of the North American Free Trade Agreement that allowed brooms from Mexico to come into America without tariffs. Allowing that competition will lower the price of brooms in the United States and destroy some or all American broom-making companies.
By importing brooms from Mexico, 100 million broom buyers gain a dollar, the savings per broom from lower prices now that brooms from Mexico are allowed without tariffs. As a result let’s say a thousand American broom makers will lose their jobs. Profitable American businesses will disappear and the towns where those businesses are located will struggle. How do you weight those changes? In theory, you can convert the losses into a dollar amount. Would you want to just add them all up to decide whether to rid of tariffs on brooms?
I reject this calculus. As would almost anyone who isn’t trained as an economist. Are you serious? Ruin the lives of a few thousand workers so 100,000,000 Americans can gain a dollar? Why would you ever thing that adding up the dollars lost and gained is the right way to make this decision? Why would you implicitly assume that 100,000,000 people gaining a dollar is equivalent to say a thousand people losing their income and the pride they get from their work?
Despite this point, I still think free trade is good idea. Why I think that’s the case is a different story — see my book, The Choice: A Fable of Free Trade and Protectionism. But briefly — I am in favor of free trade because the broom story misses other intangible benefits from open borders that are even harder to quantify but very important — the ability of each generation to shape life according to the dreams and skills of that generation. But I don’t pretend that’s a scientific judgment, even though there are dollar-denominated calculations that show that the gains from trade that can be quantified exceed the losses.
The dollar gains and losses aren’t irrelevant. But such a calculation ignores everything that can’t be put into dollars and more importantly assumes that a dollar is the same to everyone. A million people who get a dollar’s worth of benefit are then the same as a thousand people who lost $1000. But that doesn’t make any sense unless your only goal is to quantify something that’s hard to quantify. Such a calculation assumes that there is something called “society” apart from the individuals who make up that group, something called society that absorbs the gains and losses in total and becomes better or worse off accordingly.
There is a sense in which this makes sense, and economists use it relentlessly. If the gains are larger than the losses in dollar terms, that means it’s possible to share gains with the losers, compensating them for their losses. If the gains are bigger than the losses, then there will still be enough left over for the winners to enjoy. So everyone is at least as well off as they were before and no one is worse off.
That the losers are in fact not compensated in practice becomes a mere footnote. All that matters is the “net gains to society” — the dollar sum of gains and losses. But this calculus is morally bankrupt, even when compensation does take place. It ignores the problem that we all value money differently. It implies that offering a homeless person $10,000 to appear on television to be publicly tortured is worthwhile as long as the promoter of the event can sell ads that make at least $10,000 in profit.
Plus, politicians aren’t Benthamites. They don’t just care about maximizing gain over pain for the society as a whole and then compensating losers from policies they approve. People with little or no political power tend to be ignored in assessing the virtue of legislation while people with political power tend to get what they want.
But what about life and death? Happiness, pain, pleasure may be ambiguously measured. But death is surely a good thing to avoid, right? Yet here we are in 2020 in the middle of the coronavirus pandemic with people saying that to prevent death, we must close the schools, the bars, the restaurants, places of worship, stadiums, arenas, and so on. It’s a good argument. Death is a bad thing. But so is the despair of not being able to pay your rent. So is the loss of skills from a wasted year of school on Zoom for a ten year old child.
That’s the ultimate apples and oranges problem. How do you weigh them? You can easily hold both of these complex ideas in your mind — the deaths of people on the one hand and the human losses from lost employment, lost dignity, shattered dreams from business started and shuttered, social skills and educational development lost due to elementary school being held via Zoom, on the other. But you can’t get to bigger vs. smaller as our brains would like to and thereby objectively decide which is more important.
You can try. One common response to this incomparability is to use the common denominator of death. Skeptics of lockdown or sheltering-in-place in response to the pandemic pointed to an increased suicide from lost work and social connection, or deaths from cancer treatments missed in the face of fear. But this solution, tempting as it is, misses all the losses from a reduced quality of life when there are are few or any restaurants, bars, concerts, schools, religious services, and so on.
Economists actually pretended to add up these costs and benefits arguing that sheltering-in-place was a good idea. It may indeed have been. But putting numbers on the costs and benefits don’t easily justify that assessment. Back in March of 2020, at the very beginning of the pandemic in the United States, Luigi Zingales of the University of Chicago argued that sheltering-in-place could save 7.2 million lives (including people who would be able to get to the hospital for their cancer treatment and the like). [https://promarket.org/2020/03/13/captured-western-governments-are-failing-the-coronavirus-test/].
He argued that a single life saved was worth $14.5 million. Try not to laugh at the decimal point being used for something that is inherently unmeasurable. A dollar value for a life lost is often used by economists and policy analysts. The number is based on the amount people forego to avoid the risk of death. It is a measure of the value people place on reducing their risk of death. As a result it is highly sensitive to how much money people have. For that reason alone, it is worrisome conceptually but let’s pretend you can actually measure the value of lost life in dollar terms.
Then Zingales tries to correct this number for the fact that saving the life of a 20 year old is not as “valuable” as saving the life of an 80 year old. Because most of the lives saved from stemming the pandemic would be old people, he reduced that per-person number of $14.5 million by 37%, the factor the EPA uses when they try to evaluate saving the lives of elderly people.
Zingales concluded that the potential gain to the United States of avoiding 7.2 million mostly elderly people dying later than via Covid was $65 trillion. And because the GDP of the US was a mere $21.3 trillion, putting in the economy in the deepest of freezes from sheltering in place so that economic output was zero meant to him that three years of zero economic activity would pay for itself in the value of lives saved.
I’m a big fan of Luigi Zingales. I’ve learned a lot from him. But in this case, I think he’s off the mark. Not because 7.2 million lives saved in the United States is off the mark. And not because $14.5 million is a truly imprecise measure of the value of life that doesn’t deserve a decimal point. And not even because 37% is a subjective shot in the dark at making the value of an elderly life saved relative to a younger life an objective measure. I think it’s wrong because the lost dollar amount of GDP doesn’t capture the cost of putting the economy in the deepest of freezes where there is no economic activity. Forget the failure to measure despair and lost meaning from people sheltering in place for three years. If such a freeze led to a dictatorship in the United States, would it be worth it? Foregone GDP just doesn’t begin to capture what would be lost were we to freeze the economy for three years.
To be fair to Luigi, he might have felt this was just a quick back of the envelope calculation to dramatize the costs of letting the disease spread unimpeded. But I think that calculus omits so many things in the name of creating a single number ($65 trillion) to be compared to another single number — foregone GDP — that it is an exercise in faux scientific measurement.
Let’s make it even simpler. Should a frail 80 year old grandmother attend the wedding of her grandchild if it increases her chance of getting Covid and dying from it? Forget the potential for masks and social distancing and even nearly-perfect tests for the virus to reduce the risk to the grandmother. Suppose it is indeed very dangerous. What is the right thing for her to do? Would you tell that grandmother, whose life could end any minute from causes other than Covid that she is making a mistake to risk her life in order to celebrate with people she loves and might never see again?
You might argue that that grandmother should be free to risk her own life, but surely she should not do anything that might jeopardize the lives of others. Yet when people took to the streets to protest the death of George Floyd, crowding close together both with and without masks and surely spreading the disease more widely, mainly commentators and politicians refused to censure those efforts. And correctly so. How do you weigh justice vs. an increase in cased of Covid? You might think those protestors were ineffective. Or that if effective, their impact wasn’t sufficient to justify increasing the incidence of the virus. But would you really pretend to be able to measure the value of lives saved against injustice? (And yes, it’s tempting to argue that the goal of the protests is to save even more lives by reducing deaths at the hands of the police. But even if more lives would be lost from the increased cases of Covid, it can still be worthwhile to protest.)
Bentham steered us wrong. And by us, I mean the economics profession and the world. Public policy cannot be decided scientifically. Morality is not a form of applied calculus. Not all costs and benefits can be measured and those that can be measured are measured imperfectly, usually in dollar terms. Such calculations can inform decision-making, but they are rarely decisive in and of themselves. If we are not careful, we forget about the factors we cannot quantify, and we forget that some measures aren’t what we actually care about.
Sometimes quantifying a subset of factors can lead to overconfidence about how well we have mastered the problem we are trying to improve. We forget about what is in the shadows and assume we have an answer when all we really have is information. More data is only helpful when we remember the limits of what we are measuring.
But I think Bentham steered us wrong in a deeper way, and that is his attempt to convince us that a life well-lived is the accumulation of pleasure and the avoidance of pain, even when we broaden this calculus of happiness to include pleasures like friendship and pride in our reputation as Bentham did. A life well-lived, as I argue in my book, Wild Problems, is not a calculus equation to be solved. Too much of what matters to us, is outside of any imaginable mathematics, real or imagined.