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.

Vernon Smith on Behavioral in 2008

Like last week, this post is adjacent to the internet chattering over whether behavioral economics is “dead”.

Vernon Smith wrote a book Rationality in Economics that came out in 2008. I’m going to pull some quotes from that book that I think are relevant. This is not an attempt to summarize the main point of the book.

I began developing and applying experimental economics methods to the study of behavior and market performance in the 1950s and 1960s…

Preface, pg xiii

Repetitive or real-time action in incomplete information environments is an operating skill different from modeling based on the “given” information postulated to drive the economic environment that one seeks to understand in the sense of equilibrium, optimality, and welfare. This decision skill is based on a deep human capacity to acquire tacit knowledge that defies all but fragmentary articulation in natural or written language.

Preface, pg xv

I think that improved understanding of various forms of ecological rationality will be born of a far better appreciation that most of human knowledge of “how,” as opposed to knowledge of “that,” depends heavily on autonomic functions of the brain. Human sociality leads to much unconscious learning in which the rules and norms of our socioeconomic skills are learned with little specific instructions… Humans are not “thinking machines” in the sense that we always rely on self-aware cognitive processes…

Introduction, pg 5, emphasis his

Research in economic psychology[footnote 6] has prominently reported examples where “fairness” and other considerations are said to contradict the rationality assumptions… Footnote 6: I will use the term “economic psychology” generally to refer to cognitive psychology as it has been applied to economics questions, and to a third subfield of experimental methods in economics and recently product-differentiated as “behavioral economics”… Behavioral economists have made a cottage industry of showing that SSSM assumptions seem to apply almost nowhere… their research program has been a candidly deliberate search “Identifying the ways in which behavior differs from the standard model…”

Introduction, pg 22, italics mine

Vernon Smith doesn’t always like the direction of the behavioral economics literature as a whole, however he agrees in the book that humans don’t always behave rationally. Chapter 6 has the very un-fuzzy title FCC Spectrum Auctions and Combinatorial Designs. Here’s an example of the way Vernon uses the word behavioral, which I offer like I did last week as an example of how “behavioral” is never going away.

I will provide a brief review of the theoretical issues and some… experimental findings that bear most directly on the conceptual and behavioral foundation of the FCC design problem.

Chapter 6, pg 116

Unfortunately, the popular press… has often interpreted the contributions of Kahneman as proving that people are “irrational,” in the popular sense of stupid… In the Nobel interview, Kahneman seems clearly to be uncomfortable with this popular interpretation and is trying to correct it.

Chapter 7, pg 150

Chapter 7 is about loss aversion and fairness and any other “behavioral” phenomenon of interest. I recommend anyone who is following the current conversation to read all of Chapter 7 for yourself. Vernon sees the best in all whenever possible, despite being annoyed that certain academics have used a tool he developed to make points that he believes are wrong. He forges a way forward for everyone in this book.

Experiments help us understand how human beings who are prone to error can arrive at good outcomes when they are working within good/effective institutions.

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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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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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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Talking about redistribution in the lab

I am grateful to Yang Zhou for inviting me to talk about a working paper (with Gavin Roberts) on Friday. Yang told me that this audience is not familiar with lab experiments, so I’m going to take a few minutes out of my time to set the stage for my research.

There is a new book out, Causal Inference by Scott Cunningham, that is the talk of #EconTwitter (Cunningham, 2021). The book is 500 pages of dense prose and code. Here is a review saying that Cunningham left out many key things that a practitioner would need to know. Causal inference from naturally occurring data is hard!

Lab experiments bring something important to the research community. Lab experiments give the researcher a lot of control, which is why they are particularly useful for causal inference  (Samek, 2019).

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