Behavioral Economist at Work

A blog post titled “The Death of Behavioral Economics” went viral this summer. The clickbait headline was widely shared. After Scott Alexander debunked it point-by-point on Astral Codex Ten, no one corrected their previous tweets. I recommend Scott’s blog for the technical stuff. For example, there is an important distinction between saying that loss aversion does not exist versus saying that its underlying cause is the Endowment Effect.

The author of the original death post, Hreha, is angry. Here’s how he describes his experience with behavioral economics.

I’ve run studies looking at its impact in the real world—especially in marketing campaigns.

If you read anything about this body of research, you’ll get the idea that losses are such powerful motivators that they’ll turn otherwise uninterested customers into enthusiastic purchasers.

The truth of the matter is that losses and benefits are equally effective in driving conversion. In fact, in many circumstances, losses are actually *worse* at driving results.

Why?

Because loss-focused messaging often comes across as gimmicky and spammy. It makes you, the advertiser, look desperate. It makes you seem untrustworthy, and trust is the foundation of sales, conversion, and retention.

He’s trying to sell things. I wade through ads every day and, to mix metaphors, beat them off like mosquitoes. Knowing how I feel about sales pitches, I don’t envy Hreha’s position.

I don’t know Hreha. From reading his blog post, I get the impression that he believes he was promised certain big returns by economists. He tried some interventions in a business setting and did not get his desired results or did not make as much money as he was expecting.

According to him, he seeks to turn people into “enthusiastic purchasers” by exploiting loss aversion. What would consumers be losing, if you are trying to sell them something new? I’m not in marketing research so I should probably just not try to comment on those specifics. Now, Hreha claims that all behavioral studies are misleading or useless.

The failure to replicate some results is a big deal, for economics and for psychology. I have seen changes within the experimental community and standards have gotten tougher as a result. If scientists knowingly lied about their results or exaggerated their effect sizes, then they have seriously hurt people like Hreha and me. I am angry at a particular pair of researchers who I will not name. I read their paper and designed an extension of it as a graduate student. I put months of my life into this project and risked a good amount of my meager research budget. It didn’t work for me. I thought I knew what was going to happen in the lab, but I was wrong. Those authors should have written a disclaimer into their paper, as follows:

Disclaimer: Remember, most things don’t work.

I didn’t conclude that all of behavioral research is misleading and that all future studies are pointless. I refined my design by getting rid of what those folks had used and eventually I did get a meaningful paper written and published. This process of iteration is a big part of the practice of science.

The fact that you can’t predict what will happen in a controlled setting seems like a bad reason to abandon behavioral economics. It all got started because theories were put to the test and they failed. We can’t just retreat and say that theories shouldn’t get tested anymore.

I remember meeting a professor at a conference who told me that he doesn’t believe in experimental economics. He had tried an experiment once and it hadn’t turned out the way he wanted. He tried once. His failure to predict what happened should have piqued his curiosity!

There is a difference between behavioral economics and experimental economics. I recommend Vernon Smith’s whole book on that topic, which I quoted from yesterday, for those interested.

The reason we run experiments is that you don’t know what will happen until you try. The good justification for shutting down behavioral studies is if we get so good at predicting what interventions will work that the new data ceases to be informative.

Or, what if you think nudges are not working because people are highly sensible and rational? That would also imply that we can predict what they are going to do, at least in simple situations. So, again, the fact that we are not good at predicting what people are going to do is not a reason to stop the studies.

I posted last week about how economists use the word “behavioral” in conversation. Yesterday, I shared a stinging critique of the behavioral scientist community written by the world’s leading experimental researcher long before the clickbait blog.

Today, I will share a behavioral economics success story. There are lots of papers I could point to. I’m going to use one of my own, so that readers could truly ask me anything it. My paper is called “My reference point, not yours”.

I started with a prediction based on previous behavioral literature. My design depended on the fact that in the first stage of the experiment, people would not maximize expected value. You never know until you run the experiment, but I was pretty confident that the behavioral economics literature was a reliable guide.

Some subjects started the experiment with an endowment of $6. Then they could invest to have an equal chance of either doubling their money (earn $12) or getting $1. To maximize expected value, they should take that gamble. Most people would rather hold on to their endowment of $6 than risk experiencing a loss. It’s just $5. Why should the prospect of losing $5 blind them to the expected value calculation? Because most humans exhibit loss aversion.

I was relying on this pattern of behavior in stage 1 of the experiment for the test to be possible in stage 2. The main topic of the paper is whether people can predict what others will do. High endowment people fail to invest in stage 1, so then they predict that most other participants failed to invest. The high endowment people failed to incorporate easily available information about the other participants, which is that starting endowments {1,2,3,4,5,6} were randomly assigned and uniformly distributed. The effect size was large, even when I added in a quiz to test their knowledge that starting endowments are uniformly distributed.

Here’s a chart of my main results.

Investing always maximizes expected value, for everyone. The $1 endowment people think that only a quarter of the other participants fail to invest. The $6 endowment people predict that more than half of other participants fail to invest.

Does this help Mr. Hreha get Americans to buy more stuff at Walmart, for whom he consults? I’m not sure. Sorry.

My results do not directly imply that we need more government interventions or nudge units. One could argue instead that we need is market competition to help people navigate a complex world. The information contained in prices helps us figure out what strangers want, so we don’t have to try to predict their behavior at all.

Here’s the end of my Conclusion

One way to interpret the results of this experiment is that putting yourself in someone else’s shoes is costly. We often speak of it as a moral obligation, especially to consider the plight of those who are worse off than ourselves. Not only do people usually decline to do this for moral reasons, they fail to do it for money. Additionally, this experiment shows that, if people are prompted to think about a specific past experience that someone else had, then mutual understanding is easier to establish.

I’m attempting to establish general purpose laws of behavior. I’ll end with a quote from Scott Alexander’s reply post.

A thoughtful doctor who tailors treatment to a particular patient sounds better (and is better) than one who says “Depression? Take this one all-purpose depression treatment which is the first thing I saw when I typed ‘depression’ into UpToDate”. But you still need medical journals. Having some idea of general-purpose laws is what gives the people making creative solutions something to build upon.

Behavioral Economics Conversation: Cutler and Glaeser

I haven’t written a formal response, yet, to the “behavioral economics is dead” claim going around Twitter. I’m too busy doing my referee reports on behavioral papers to write in depth about why behavioral is not dead. Incidentally, I’m not loving the most recent paper I was sent, so maybe that’s a point in the column of Team Death. I’ll write a few posts intersecting with the arguments being had.

First, I’ll point out two places in a CWT discussion of health and cities where the phrase “behavioral” was used. This is obviously a current conversation. David Cutler probably wouldn’t say that behavioral economics is his field, but here’s how he describes puzzles in decision making over health issues. (bold emphasis mine)

Everything that we know in healthcare is that people have difficulty choosing on the basis of price and quality. It goes back a little bit to some of the behavioral issues that we were talking about, but I think it’s slightly different. If you go to the doctor, and the doctor says you should take medication X, and you go to the pharmacy, and the pharmacy says that’ll be $30, a fair number of people will walk away and say, “I don’t have $30.”

What we would hope they would do is go to their doctor and say, “Doctor, is there any way that there could be a cheaper medicine that might work because $30 is hard for me this month?” In practice, people are extremely uncomfortable doing that. They really don’t like to go to their doctor and say, “Doctor, how do I trade off the money here versus the medicine?”

David Cutler

The previous issues Cutler mentioned had to do with time preference and delayed gratification. The turmoil over dieting alone is evidence that people don’t always make the best decisions.

Here’s the second of two appearances of the word “behavioral”, in response to Tyler’s question about how to make cities healthier.

I certainly join the crowd of economists who have argued that congestion pricing is the best way to deal with urban traffic jams. There’s no reason not to charge people for the social cost of their actions on that. And giving away street space for free is just crazy, especially since we now have technologies that can handle this.

And if we introduce autonomous vehicles without congestion pricing, you have just lowered the cost of sitting in traffic, which means the first-order behavioral response is that more people will sit in traffic, and our congestion will get even worse unless we introduce this from the beginning. So I think pricing is really good.

Ed Glaeser

In the second use of the word, it sounds like an individually-rational decision to sit in your autonomous vehicle and read blogs until your arrive at your destination. Maybe we can use mechanism design to reduce traffic congestion and improve life for all.

Whether or not you think behavioral economics is dead, economists are going to keep using the word “behavioral” for a long time.

I did a quick Ngram to get a sense of how common the word is, although this does not restrict the search to books about economics. Ngrams are easier to interpret if there is a comparison word. I choose the word “clustering” because it’s also a relatively new technical term. Both words were quite rare before 1930.

If you missed the small discussion about behavioral econ, Mike Munger did a link round-up here. Tomorrow’s post will be Vernon Smith’s view of behavioral economics.

Generous Health Insurance Makes Employees Stay

The idea of “job lock” is well established in the academic literature- employees leave firms that don’t offer health insurance more often than they leave firms that do. But this literature has always measured employer-provided health insurance as a simple binary: either they offer it or they don’t. In fact employers vary widely in the generosity of their plans, both in the quality of the insurance and in how much of the cost is paid by the employer. Some employers pay all of the premiums, some pay none, and most pay part:

Data are from the Current Population Survey, which uses top-coding to protect privacy (values greater than 9997 are reported as 9997)

In an article published last week in Applied Economics Letters, my colleague Michael Mathes and I combine two supplements of the Current Population Survey to test whether employers who contribute more towards health insurance see their employees stay longer. Perhaps not surprisingly, we find that they do. We run lots of regressions to establish this, but this simple fit plot tells the story best:

What we found more surprising was the magnitude of this effect: a thousand dollar increase in employer contributions to health insurance is associated with at least 83 additional days of job tenure, compared to less than 10 additional days for a thousand dollar increase in wages. We conclude that:

For employers trying to increase retention, increasing contributions to health insurance appears to lengthen employee tenure far more than increasing wages by a similar amount.

Why the difference? Probably employees rationally valuing $1000 in untaxed contributions to health insurance above $1000 in taxable wages. Why don’t employers shift more compensation away from wages and toward health insurance, given that employees seem to prefer it? Here I’m less sure, and they could simply be making a mistake, but one possibility is that they worry about increasing their costs as couples whose employers both offer insurance choose the more generous one for a family plan. Another is that while generous health insurance plans are better for retention, higher wages could be better for attracting new employees, who tend to be younger and for whom the salary number could be more salient.

Preferences for Equality and Efficiency

Most people would consider both equality and efficiency to be good. They are “goods” in the sense that more of them makes us happier.  However, in some situations, there is a trade-off between having more equality and getting more efficiency. Extreme income redistribution makes people less productive and therefore lowers overall economic output.

Examining the preferences people have for efficiency and equality is hard to do because the world is complicated. For example, a lot of baggage comes along with real world policy proposals to raise(lower) taxes to do more(less) income redistribution. A voter’s preference for a particular policy could be confounded by their personal feelings toward a particular politician who might have just had a personal scandal.

With Gavin Roberts, I ran an experiment to test whether people would rather get efficiency or equality (paper on SSRN). Something neat that we can do in a controlled lab setting is systematically vary the prices of the goods (see my earlier related post on why it’s neat to do this kind of thing in the lab).

One wants to immediately know, “Which is it? Do people want equality or efficiency?”. If forced to give a short answer, I would say that the evidence points to equality. But overly simplifying the answer is not helpful for making policy. The demand curve for equality slopes down. If the price of equality is too high, then people will not choose it. In our experiment, that price could be in terms of either own income or in group efficiency. We titled our paper “Other People’s Money” because more equality is purchased when the cost comes in terms of other players’ money.

The main task for subjects in our experiment is to choose either an unequal distribution of income between 3 players or to pick a more equal distribution. Given what I said above that people like equality, you might expect that everyone will choose the more equal distribution. However, choosing a more equal distribution comes at a cost. Either subjects will give up some of their own earnings from the experiment or they will lower the total group earnings. As is true in policy, some schemes to reduce inequality are higher cost than others. When the cost is low, we observe many subjects (about half) paying to get more equality. However, when the cost is high, very few subjects choose to buy equality.

This bar graph from our working paper shows some of the average behavior in the experiment, but it does not show the important results about price-sensitivity.

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Results on stability and gift-exchange

Bejarano, Corgnet, and Gómez-Miñambres have a newly published paper on gift-exchange.

Abstract: We extend Akerlof’s (1982) gift-exchange model to the case in which reference wages respond to changes in economic conditions. Our model shows that these changes spur disagreements between workers and employers regarding the reference wage. These disagreements tend to weaken the gift-exchange relationship, thus reducing production levels and wages. We find support for these predictions in a controlled yet realistic workplace environment. Our work also sheds light on several stylized facts regarding employment relationships, such as the increased intensity of labor conflicts when economic conditions are unstable.

Next, I will provide some background on gift-exchange and experiments.

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Editing: You Figure It Out

If you want to change how a field works, you have a few options. You can do what you want to see more of, but you are only one person, and perhaps not the one best equipped to make things better. Or you can encourage others to work differently- but why would they listen to you?

Academics often serve as peer reviewers for the work of others. If a reviewer recommends that a paper be rejected, it usually is; if you recommend specific minor changes they usually get made. But you can’t really tell people that they should work on a totally different topic. Journal editors for the most part simply have a scaled-up version of the powers of peer reviewers to steer the field. But unlike reviewers, their positions are public and fairly long-lasting. This means they can credibly say “this is the sort of work I’d like to see more of- if you do this kind of work, there’s a good chance I’ll publish it”.

This is part of why I’ve been hoping to be a journal editor some day, and why I’m excited to be guest-editing for the first time: a special issue on Health Economics and Insurance for the Journal of Risk and Financial Management. The description notes:

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Sympathy and Predicting Behavior

Part One of The Theory of Moral Sentiments by Adam Smith is called “Of the Propriety of Action”.  Smith argues that we naturally share the emotions and to a certain extent the physical sensations that we witness in others. “Sympathy” is a term Smith used for the feeling of moral sentiments.

In Section One, Chapter Five, Smith writes

In all such cases, that there may be some correspondence of sentiments between the spectator and the person principally concerned, the spectator must, first of all, endeavour … to put himself in the situation of the other, and to bring home to himself every little circumstance of distress which can possibly occur to the sufferer. He must adopt the whole case of his companion with all its minutest incidents; and strive to render as perfect as possible, that imaginary change of situation upon which his sympathy is founded.

After all this, however, the emotions of the spectator will still be very apt to fall short of the violence of what is felt by the sufferer. Mankind, though naturally sympathetic, never conceive, for what has befallen another… That imaginary change of situation, upon which their sympathy is founded, is but momentary. The thought of their own safety… continually intrudes itself upon them…  

The modern word “empathy” is the capacity to step into the shoes of another person and feel their pain or joy from within the other person’s frame of reference.

Adam Smith suggests that if we hear a neighbor just experienced the death of a loved one, then we can briefly experience some sadness on their account. The more we put ourselves in their shoes, the more sadness we can experience on their behalf.

We usually think of it as a nice thing to have empathy for others. It can also be instrumental to be able to think through the perspective of another person, in order to predict what they will do next. In practical dealings, it is an economic advantage to make accurate predictions about future behavior.

If I work backward through my 2020 paper “My Reference Point, Not Yours”, then I can start by saying that people can sometimes predict what others will do.

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How Will Rich Country Fertility Ever Get Back Above Replacement?

For population to be steady or rising, the average women needs to have at least two kids. In almost every rich country- including the United States, all of Europe, and all of East Asia- this isn’t happening. In the extreme case of South Korea, where total fertility averages about one child per woman, the population will fall by half each generation. If this were to go on for 10 generations, South Korea would go from a country of 50 million people- larger than any US state- to one of 50 thousand people, far smaller than any US state. This sounds crazy and I don’t expect it will actually happen- but I can’t say what exactly will stop it from happening.

Global population growth has fallen from a peak of 2.1% per year to the current 1%, and is expected to fall to 0 by 2100. The remaining population growth will happen in poor countries, then stop for the same reasons it did in rich countries- the demographic transition from poverty, argicultural work, and high infant mortality to high incomes, high education, and low infant mortality. As the graph below shows, higher income is an incredibly strong predictor of low fertility- and so if economic growth continues, we should expect fertility to continue falling. But where does it stop?

2019 TFR from Population Reference Bureau vs 2019 PPP-adjusted GDP Per Capita fron World Bank

Some have theorized a “J-curve” relationship, where once incomes get high enough, fertility will start rising again. You can see this idea in “Stage 5” of Max Roser’s picture of the demopgraphic transition here:

This makes sense to me in theory. As countries get richer, desired fertility (the number of kids each woman wants to have) has fallen, but realized fertility (the number of kids each woman actually has) has fallen faster. In a typical rich country women would like to have 2-2.5 kids, but actually ends up having about 1.5. There are many reasons for this, but some are clearly economic- the high cost of goods and services that are desired by rich-country parents, like child care, education, and spacious housing near high-paying jobs. Perhaps in a rich enough country all these could be obtained with a single income (maybe even from a part-time job). But it seems we aren’t there yet. Even zooming in on higher-income countries, higher incomes still seem to lead to lower fertility.

TFR vs GDP Per Capita in countries with GDP Per Capita over 30k/yr

The only rich countries with fertility above replacement are Panama and the Seychelles (barely meeting my 30k/yr definition of rich), Kuwait (right at replacement with 2.2 kids per woman), and Israel- the biggest outlier, with 3 children per woman at a 42k/yr GDP. This hints that pro-fertility religious culture could be one way to stay at or above replacement. But in most countries, rising wealth seems to drive a decline in religiousity along with fertility. Will this trend eventually come to Israel? Or will it reverse in other countries, as more “pro-fertility” beliefs and cultures (religious or otherwise) get selected for?

To do one more crazy extrapolation like the disappearance of South Korea, the number of Mormons is currently growing by over 50% per generation from a base of 6 million while the rest of the US is shrinking. If these trends continue (and setting aside immigration), in at most 10 generations the US will be majority-Mormon. Again, I don’t actually expect this, but I don’t know whether it will be falling Mormon fertility, non-Mormon fertility somehow rising back above replacement, or something else entirely that changes our path.

What would a secular pro-fertility culture look like? For my generation, I see two big things that make people hold back from having kids: a desire to consume experiences like travel and nightlife that are harder with kids, and demanding careers. I see more potential for change on the career front. Remote work means that more quality jobs will be available outside of expensive city centers. Remote work, along with other technological and cultural changes, could make it easier to work part-time or to re-enter the work force after a break. Improving educational productivity so that getting better-education doesn’t have to mean more years of school would be a game-changer; in the short run I think people will spend even more time in school but I see green shoots on the horizon.

Looking within the US, we are just beginning to see what looks like the “J-curve” happening. Since about the year 2000, women with advanced degrees began to have more children than those with only undergraduate education (though still fewer than those with no college, and still below replacement):

From Hazan and Zoabi 2015, “Do Highly Educated Women Choose Smaller Families?”

We see a similar change with income. In 1980 women from richer households clearly had fewer children, but by 2010 this is no longer true:

Fertility of married white women, from Bar et al. 2018, “Why did rich families increase their fertility? Inequality and marketization of child care”

The authors of the papers that produced the two graphs above argue that this change is due to “marketization”, the increasing ability to spend money to get childcare and other goods and services that make it easier to take care of kids. If this is true, it could bode well for getting back to replacement- markets first figure out how to make more excellent daycare and kid-related gadgets, then figure out how to make them cheap enough for wide adoption.

Does Cohabitation Predict Divorce?

My article, coauthored with Sarah Kerrigan and published last week, tries to answer the question. In short, the answer seems to be yes- cohabitation before marriage is associated with a 4.6 percentage point increase in the rate of marital dissolution. This is in line with much of the previous literature, which notes one big exception- choosing right (or getting lucky) the first time: “cohabitation had a significant negative association with marital stability, except when the cohabitation was with the eventual marriage partner”.

But we found some even more interesting facts while digging through the National Survey of Family Growth.

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John Duffy Experiments and Crypto

John Duffy and Daniela Puzzello published a paper in 2014 on adopting fiat money. I think of that paper when I hear the ever-more-frequent discussions of crypto currencies around me. To research the topic, I went to John Duffy’s website. There I found a May 2021 working paper about adopting new currencies in which they directly reference crypto. Before explaining that interesting new paper, first I will summarize the 2014 paper “Gift Exchange versus Monetary Exchange.”

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