I am pleased to have been asked to discuss a paper in an ASHE (American Society of Hispanic Economists) session at the 2022 AEA meeting. Our session is “Hispanics and Finance” on Sunday January 9 at 12:15pm Eastern Time.
The paper is “Neuroeconomics for Development: Eye-Tracking to Understand Migrant Remittances”. Here is a bit about each author. Meeting in person is a benefit that I miss this time, since the meeting is virtual.
Eduardo Nakasone of Michigan State University has several papers on information and communication technologies and agricultural markets. I pondered this sentence from one of his abstracts, “Under certain situations, ICTs can improve rural households’ agricultural production, farm profitability, job opportunities, adoption of healthier practices, and risk management. All these effects have the potential to increase wellbeing and food security in rural areas of developing countries. Several challenges to effectively scaling up the use of ICTs for development remain, however.” His prior work on ICTs is relevant to the paper at hand, which is about how migrants utilize information about remittance tools.
Máximo Torero is the Chief Economist of the Food and Agriculture Organization (FAO). He has worked on development and poverty in many capacities including at the World Bank.
Angelino Viceisza, an associate professor at Spelman College, is doing interesting work at the intersection of Development and Experimental Economics. Here is his 2022 paper (Happy New Year!) published in the Journal of Development Economics.
I am discussing their paper on how migrants choose financial services. The pre-analysis plan is public. Remittance sending is important for migrants and for the entire world economy. The authors remind us that a significant chunk of what migrants earn is “lost” to service fees. The authors are examining how migrants incorporate new information about competitive alternative services.
Some neat aspects of their work:
Their subject pool is migrants who send remittances, recruited in the DC area.
Like most experiments I am used to, the stakes are real and significant.
Not only can they observe which service is selected, but by using eye-tracking they can get a sense of what information was salient or persuasive.
It is potentially a big deal for migrants to compare services more rigorously and switch providers more readily. The internet, as least in theory, makes it easy to find information on transaction fees. Policy makers have even proposed subsidizing websites that compare the fees of money transfer operators (MTOs). The authors are trying to understand how such a website might impact behavior. A basic question is: does information in this format affect behavior? A small change in behavior could have a huge impact on the world economy and recipient countries. Imagine if a country currently receiving a billion dollars in remittances had 1% more next year because migrants switched to a more efficient service. Might it be cheaper to nudge people toward low-fee services than to send foreign aid?
Their experiment will reveal whether people make switches based on new information, and it also helps us start to understand which attributes of MTOs migrants consider. Their design includes a treatment manipulation that sometimes emphasizes either transfer speed or user reviews.
If you have read this far hoping for a summary of their results, I will disappoint. Their paper is not public yet and data is still being analyzed. I can say that migrant subjects do sometimes switch their choice of MTO, based on information, in some circumstances. They are more likely to make a switch when the induced stakes are higher. If you tune into the session tomorrow, you will get to hear a summary of preliminary results by the author (not free to public, requires conference registration).
Most US states require hospitals and other healthcare providers to obtain a “Certificate of Need” (CON) from a state board before they are allowed to open or expand. These laws seem to be one reason why healthcare is often so expensive and hard to find. I’ve written a lot about them, partly because I think they are bad policies that could get repealed if more people knew about them, and partly because so many aspects of them are unstudied.
States vary widely in the specific services or equipment their CON laws target- nursing homes, dialysis clinics, MRIs, et c. One of the most important types of CON law that remained unstudied was CON for psychiatric services. I set out to change this and, with Eleanor Lewin, wrote an article on them just published in the Journal of Mental Health Policy and Economics.
We compare the state of psychiatric care in states with and without CON, and find that psychiatric CON is associated with fewer psychiatric hospitals and beds, and a lower likelihood of those hospitals accepting Medicare.
Together with the existing evidence on CON (which I tried to sum up recently here), this suggests that more states should consider repealing their CON laws and letting doctors and patients, rather than state boards, decide what facilities are “economically necessary”.
Opinions on game theory differ. To most of the public, it’s probably behind a shroud of mystery. To another set of the specialists, it is a natural offshoot of economics. And, finally a 3rd non-exclusive set find it silly and largely useless for real-world applications.
Regardless of the camp to which you claim membership, the Pure Strategy Nash Equilibrium (PSNE) is often misunderstood by students. In short, the PSNE is the set of all player strategy combinations that would cause no player to want to engage in a different strategy. In lay terms, it’s the list of possible choices people can make and find no benefit to changing their mind.
In class, I emphasize to my students that a Nash Equilibrium assumes that a player can control only their own actions and not those of the other players. It takes the opposing player strategies as ‘given’.
This seems simple enough. But students often implicitly suppose that a PSNE does more legwork than it can do. Below is an example of an extensive form game that illustrates a common point of student confusion. There are 2 players who play sequentially. The meaning of the letters is unimportant. If it helps, imagine that you’re playing Mortal Kombat and that Player 1 can jump or crouch. Depending on which he chooses, Player 2 will choose uppercut, block, approach, or distance. Each of the numbers that are listed at the bottom reflect the payoffs for each player that occur with each strategy combination.
Again, a PSNE is any combination of player strategies from which no player wants to deviate, given the strategies of the other players.
Students will often proceed with the following logic:
Player 2 would choose B over U because 3>2.
Player 2 would choose A over D because 4>1.
Player 1 is faced with earning 4 if he chooses J and 3 if he chooses C. So, the PSNE is that player 1 would choose J.
Therefore, the PSNE set of strategies is (J,B).
While students are entirely reasonable in their thinking, what they are doing is not finding a PSNE. First of all, (J,B) doesn’t include all of the possible strategies – it omits the entire right side of the game. How can Player 1 know whether he should change his mind if he doesn’t know what Player 2 is doing? Bottom line: A PSNE requires that *all* strategy combinations are listed.
The mistaken student says ‘Fine’ and writes that the PSNE strategies are (J, BA) and that the payoff is (4,3)*. And it is true that they have found a PSNE. When asked why, they’ll often reiterate their logic that I enumerate above. But, their answer is woefully incomplete. In the logic above, they only identify what Player 2 would choose on the right side of the tree when Player 1 chose C. They entirely neglected whether Player 2 would be willing to choose A or D when Player 1 chooses J. Yes, it is true that neither Player 1 nor Player 2 wants to deviate from (J, BA). But it is also true that neither player wants to deviate from (J, BD). In either case the payoff is (4, 3).
This is where students get upset. “Why would Player 2 be willing to choose D?! That’s irrational. They’d never do that!” But the student is mistaken. Player 2 is willing to choose D – just not when Player 1 chooses C. In other words, Player 2 is indifferent to A or D so long as Player 1 chooses J. In order for each player to decide whether they’d want to deviate strategies given what the other player is doing, we need to identify what the other player is doing! The bottom line: A PSNE requires that neither player wants to deviate given what the other player is doing – Not what the other player would do if one did choose to deviate.
What about when Player 1 chooses C? Then, Player 2 would choose A because 4 is a better payoff than 1. Player 2 doesn’t care whether he chooses U or B because (C, UA) and (C, BA) both provide him the same payoff of 4. We might be tempted to believe that both are PSNE. But they’re not! It’s correct that Player 2 wouldn’t deviate from (C, BA) to become better off. But we must also consider Player 1. Given (C, UA), Player 1 won’t switch to J because his payoff would be 1 rather than 3. Given (C, BA), Player 1 would absolutely deviate from C to J in order to earn 4 rather than 3. So, (C, UA) is a PSNE and (C, BA) is not. The bottom line: Both players must have no incentive to deviate strategies in a PSNE.
There are reasons that game theory as a discipline developed beyond the idea of Nash Equilibria and Pure Strategy Nash Equilibria. Simple PSNE identify possible equilibria, but don’t narrow it down from there. PSNE are strong in that they identify the possible equilibria and firmly exclude several other possible strategy combinations and outcomes. But PSNE are weak insofar as they identify equilibria that may not be particularly likely or believable. With PSNE alone, we are left with an uneasy feeling that we are identifying too many possible strategies that we don’t quite think are relevant to real life.
These features motivated the later development of Subgame Perfect Nash Equilibria (SGPNE). Students have a good intuition that something feels not quite right about PSNE. Students anticipate SGPNE as a concept that they think is better at predicting reality. But, in so doing, they try to mistakenly attribute too much to PSNE. They want it to tell them which strategies the players would choose. They’re frustrated that it only tells them when players won’t change their mind.
Regardless of whether you get frustrated by game theory, be sure to have a drink and make toast to John Nash.
*Below is the normal form for anyone who is interested.
The largest hospital system in Rhode Island, Lifespan, is trying to merge with the second-largest hospital system in Rhode Island, Care New England. Next Wednesday I’ll be on a panel discussing the proposed merger, following a panel with the Presidents of the three institutions involved (Lifespan, CNE, and Brown University). I’ll summarize my thoughts here.
Basic economics tells us that if a company with 50% market share buys a company with 25% market share in the same industry, they have strong market power and are likely to use this monopoly position to raise prices.
The real world is often more complicated, especially when it comes to health care, but in this case I think basic economics holds up well. A wealth of empirical evidence, including studies of previous hospital mergers, suggest that reduced hospital competition leads to higher prices without bringing commensurate benefits in quality or efficiency.
I think the Federal Trade Commission will almost certainly challenge the merger, and that they will likely succeed in doing so. The FTC merger guidelines more or less demand it, and current FTC leadership if anything seems to want to be more aggressive than required on antitrust. To me the biggest question is whether they will try to stop the merger entirely, or whether they would allow it to proceed subject to conditions (e.g. spin off one or two hospitals to remain independent)- I’ll be watching with interest and letting you know how it goes.
I was pleased to see yesterday the announcement of a new journal, the Journal of Comments and Replications in Economics. As the name implies, it will publish articles that comment on or attempt to replicate previously published economics papers.
While empirical economics papers have in some ways become more believable over time, it is still rare for anyone to verify whether the results can actually be replicated, and formal comments on potential problems in published papers have actually become less common over time (though Econ Journal Watch has been a good outlet for comments).
The ability to independently verify and replicate findings should be at the core of science. But economists, like most other disciplines, are generally too focused on publishing original work to test whether already-published papers hold up. When we do try to replicate existing work, the results aren’t very encouraging; at best 80% of economics papers replicate.
If we want people to trust and rely on our work, we need to do better than that. The US Department of Defense agrees, and funded a huge project to determine what types of social science research hold up to scrutiny. I’ve been a bit involved in this and hope to sum up some of the results once this semester is over. For now, I’ll just say I’m happy to see the new Journal of Comments and Replications in Economics (and that it is both free and open-access, a rare combo) and I hope this represents one more small step towards economics being a real science.
Yesterday Jeremy pointed out that while the 2021 economics Nobelists have reached various conclusions in their study of labor economics, the prize was really awarded to the methods they developed and used.
Like Jeremy, they think that empirical economic research (that is, research using econometrics) was most quite bad up to the 1980’s; as Ed Leamer put it in his paper “Let’s take the CON out of Econometrics”:
This is a sad and decidedly unscientific state of affairs we find ourselves in. Hardly anyone takes data analyses seriously. Or perhaps more accurately, hardly anyone takes anyone else’s data analyses seriously.
Angrist and Pischke argue that the field is in much better shape today:
empirical researchers in economics have increasingly looked to the ideal of a randomized experiment to justify causal inference. In applied micro fields such as development, education, environmental economics, health, labor, and public finance, researchers seek real experiments where feasible, and useful natural experiments if real experiments seem (at least for a time) infeasible. In either case, a hallmark of contemporary applied microeconometrics is a conceptual framework that highlights specific sources of variation. These studies can be said to be design based in that they give the research design underlying any sort of study the attention it would command in a real experiment.
The econometric methods that feature most prominently in quasi-experimental studies are instrumental variables, regression discontinuity methods, and differences-in-differences-style policy analysis
Our field still has big problems: the replication crisis looms, and the credibility revolution’s focus on the experimental ideal leads economists to avoid important questions that can’t be answered by natural experiments. But I do think that the average empirical economics paper today is much more credible than one from 1980, and that the 3 Nobelists are part of the reason why, so cheers to them.
Josh Hendrickson and Brian Albrecht have a Substack called Economic Forces that is a source of economics news and examples. We have linked to EF before at EWED.
Albrecht just published an op-ed titled “Behavioral Economics Is Fine. Just Keep It Away from Our Kids”. I’ll to respond to this, just as I responded to that other blog. I think the group of people who are pitting themselves against “behavioral economics” is small. They might even think of themselves as a minority embattled against the mainstream. So, why bother responding? That’s what blogs are good for.
I agree with Albrecht’s main point. The first thing an undergraduate should learn in economics classes is the classic theory of supply and demand. Even in its simplest form, the idea that demand curves slope down and supply curves slope up is powerful and important.*
Albrecht points out that there are some results that have been published in the behavioral economics literature that turned out not to replicate or, in the recent case of Dan Ariely, might be fraudulent. Then he makes a jump from there by calling the behavioral field of inquiry a “fad”. That’s not accurate. (See Scott Alexander on Ariely and related complaints.)
In his op-ed, Albrecht names the asset bubble as a faddish behavioral idea. Vernon Smith (with Suchanek and Williams) published “Bubbles, Crashes and Endogenous Expectations in Experimental Spot Asset Markets” in Econometrica in 1988. Bubbles have been replicated all around the world many times. There is no doubt in anyone’s mind that the “dot com” bubble had an element of speculation that became irrational at a certain point. This is not a niche topic or a very rare occurrence. Bubbles are observed in the lab and out in the naturally occurring economy.
Should we start undergrads on bubbles before explaining the normal function of capital markets? No. Lots of people think that stock markets generally work well, communicate reliable information, and should be allowed to function with minimal regulation. Behavioral Finance is usually right where it should be in the college curriculum, which is to be offered as an upper-division elective class for finance and economics majors. I am not going to do research on this, but I looked up courses at Cornell, and there it is: Behavioral Economics is one of many advanced elective classes offered for economics students. I don’t know how they teach ECON101 at Cornell, but it would seem like they are binning most of the behavioral content into later optional courses.
In a social media exchange, Albrecht pointed me to one of the posts by Hendrickson on how they handle the situations where it seems like economic forces are not explaining everything. Currently, for example, it seems like the labor market is not clearing right now because firms want to hire but wages are not rising. The quantity supplied seems lower than the quantity demanded at the market wage. Hendrickson claims that this market condition is temporary. He says that firms are cleverly paying bonuses to attract workers so that they won’t have to lower wages in the future when conditions return to normal post-Covid. This would be a perfect time to discuss downward nominal wage rigidity, a pervasive behavioral phenomenon.** It has been studied extensively in lab settings. Nominal wage rigidity has implications for monetary policy. Wage rigidity might be a “temporary” thing, but it helps to explain unemployment. Some of the research done by behavioral economists in this area follow the Akerlof 1982 paper on the gift exchange model. It was published 40 years ago by a Nobel prize winner and cited extensively.*** The seminal lab study of that theory is Fehr et al. 1993. There have been hundreds of replications of the main result that people will trade out of equilibrium due to positive reciprocity.
Living means making decisions with imperfect information. But Covid provides many examples of how people and institutions are often still bad at this. A few common errors:
Imperfect evidence = perfect evidence. “Studies show Asprin prevents Covid”. OK, were the studies any good? Did any other studies find otherwise?
Imperfect evidence = “no evidence” or “evidence against”. In early 2020, major institutions like the WHO said “masks don’t work” when they meant “there are no large randomized controlled trials on the effectiveness of masks”
Imperfect evidence = don’t do it until you’re sure Inaction is a choice, and often a bad one. If the costs of action are low and the potential benefits of action high, you might want to do it anyway. Think masks in 2020 when the evidence for them was mediocre, or perhaps Vitamin D now.
Imperfect evidence = do it, we have to do something Even in a pandemic, it is possible to over-react if the costs are high enough and/or the evidence of benefits bad enough (possibly lockdowns, definitely taking up smoking)
Any intro microeconomics class will explain the importance of weighing both costs and benefits. But how do we know what the costs and benefits are? For many everyday purchases they are usually obvious, but in other situations like medical treatments and public policies they aren’t, particularly the benefits. We have to estimate the benefits using evidence of varying quality. This creates more dimensions of tradeoffs- do you choose something with good evidence for its benefits, but high cost? Or something with worse evidence but lower costs? Graphing this properly should take at least 3 dimensions, but to keep things simple lets assume we know what the costs are, and combine benefits and evidence into a single axis called “good evidence of substantial benefit”. This yields a graph like:
Applied to Covid strategies, this yields a graph something like this:
This is not medical advice- I say this not merely as a legal disclaimer, but because my real point is the idea that we should weigh both evidence quality and costs, NOT that my estimates of the evidence quality or costs of particular strategies are better than yours
Judging the strength of the evidence for various strategies is inherently difficult, and might go beyond simply evaluating the strength of published research. But when evaluating empirical studies on Covid, my general outlook on the evidence is:
Dear reader, perhaps this is all obvious to you, and indeed the idea of adjusting your evidence threshold based on the cost of an intervention goes back at least to the beginnings of modern statistics in deciding how to brew Guinness. But common sense isn’t always so common, and this is my attempt to summarize it in a few pictures.
In my Labor Economics class, I do a lecture on empirical work and the minimum wage, starting with Card & Kreuger (1993). I’m going to quickly tack on the new working paper by Clemens & Strain “The Heterogeneous Effects of Large and Small Minimum Wage Changes: Evidence over the Short and Medium Run Using a Pre-Analysis Plan”.
The results, as summarized in the second half of their abstract are:
relatively large minimum wage increases reduced employment rates among low-skilled individuals by just over 2.5 percentage points. Our estimates of the effects of relatively small minimum wage increases vary across data sets and specifications but are, on average, both economically and statistically indistinguishable from zero. We estimate that medium-run effects exceed short-run effects and that the elasticity of employment with respect to the minimum wage is substantially more negative for large minimum wage increases than for small increases.
The variation in the data comes from choices by states to raise the minimum wage.
A number of states legislated and began to enact minimum wage changes that varied substantially in their magnitude. … The past decade thus provided a suitable opportunity to study the medium-run effects of both moderate minimum wage changes and historically large minimum wage changes.
We divide states into four groups designed to track several plausibly relevant differences in their minimum wage regimes. The first group consists of states that enacted no minimum wage changes between January 2013 and the later years of our sample. The second group consists of states that enacted minimum wage changes due to prior legislation that calls for indexing the minimum wage for inflation. The third and fourth groups consist of states that have enacted minimum wage changes through relatively recent legislation. We divide the latter set of states into two groups based on the size of their minimum wage changes and based on how early in our sample they passed the underlying legislation.
The “large” increase group includes states that enacted considerable change. New York and California “have legislated pathways to a $15 minimum wage, the full increase to which firms are responding exceed 60 log points in total.” Data comes from the American Community Survey (ACS) and the Current Population Survey (CPS).
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 what 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.
Economist Writing Every Day 