I did an informal survey among undergraduate students. This is not a representative sample of American youth. Before answering the question “How is TikTok affecting your peers?” they had just heard about the TikTok recommendation algorithm. Answers might have been slightly different if they had not been primed to think about the app from a business perspective.
Most of the answers were negative, both among students who use TikTok themselves and especially from students who are staying off of the app. Some answers presented both a positive and a negative reply.
Here is one of the more positive replies:
“TikTok is affecting my peers in a few different ways. On the positive side, people can learn very useful things on the app. On the negative side, it can be very time consuming. I have heard from many friends how they have wasted a lot of their time on TikTok when they could have been doing something more productive.”
Some students emphasized the social aspect:
“TikTok is one of the biggest social platforms amongst my friends and I. When we hangout, we are creating our own TikToks, but when we are apart we are able to share videos with each other. TikTok for me is a big rabbit hole that I find myself spending way too much time on.”
Also, they believe that this platform, more so than the original social networks, allow a new user to break out. “The idea that a normal, average person can post on TikTok and have a likelihood of it becoming viral is what has launched the platform.” I can see how a 20-year-old today would think Twitter is less fun because it is hard for a newcomer to get noticed.
Some students mentioned the addictive aspect of TikTok:
“I see a lot of my peers stay on the app for long periods of time. I can’t count the amount of times people say something about how they didn’t realize they were scrolling for an hour before they looked at the clock.”
“I have three friends back home who are being affected by Tik Tok in the worse way possible. All they do is watch Tik Toks all day and has even affected their sleep schedule cause they can’t put their phone down. It’s hard to see my friends sucked in the rabbit hole.”
“Personally, I have had to set screen time limits for TikTok through my phone’s settings because I can easily spend extended periods of time of the app without even realizing it; and even then, sometimes, I even override the limits I have set in place because I want to see even more content.”
The funniest line award goes to: “I personally hate TikTok and think it is rat poison.”
I wonder how the responses might have differed if I had asked a similar question to college students about TV and video games 20 years ago.
I use Twitter frequently. Maybe I spend more time on it than I should, and I don’t support as many paid media outlets as I might otherwise. Thus, the non-Twitter world is less rich for today’s college students.
For balance, here’s how Big Tech helped me in the past week. I needed to help my son build a model rocket from a kit. Some stranger kind young man had made an excellent YouTube video detailing how to make this rocket. This video really helped me, and the man should get the satisfaction of one more watch on his views count.
Slavery is a bad and we should rid ourselves of it. One of the arguments made by abolitionists before the Civil War in the United States is that slaves make poor workers and therefore it’s not that costly to get rid of slavery. Of course, it doesn’t matter if slaves worked hard or not. Slavery was a moral abomination, regardless. However, it does make it easier to argue that we should direct government resources to fight enslavers when we can make a case that slavery makes the entire country poorer.
On the one hand, there were many non-slave workers and farmers in North America, demonstrating that products including cotton could be produced by free labor. On the other hand, slavery as an institution expanded into the South and the West, presumably because of the economic advantage it gave to slave-owners.
In Slavery and American Economic Development, economist Gavin Wright states that whether or not slaves were as productive as free/wage labor is hard to measure and also is not hugely important. Slavery might have provided wealth to slave owners in the South, but that is only because of the institutional setting that was created explicitly to maintain the slave society.
The American South had some of the best land in the world for growing cotton and cotton became a lucrative export crop thanks to British demand before the Civil War. Before the Civil War, there were some extremely profitable plantations on which slaves worked. It is true that some enslavers became rich and that drives up what appears to be the GDP per capita in the South at the time.
Wright explains that a slave owner living closer to the East Coast was better able to go on an entrepreneurial venture into Alabama to clear a large plantation for cotton farming than a typical free family farmer. The slave owner could obtain large loans and had a future guarantee of workers (thanks to the local laws and police state). So, something like a modern corporation employing free labor could also have accomplished the venture. But, at the time, it was an opportunity that was easier for enslavers to take advantage of. Free farmers also expanded West into the United States, but they tended to move more slowly and focus on subsistence (e.g. wheat for consumption). That is, partly, what Wright means when he speaks of slavery as a “system of property” as opposed to just a “system of production”. That helps explain why slavery was on the rise in the American South.
Wright also examines slavery as a political regime. In a place with many slaves, resources had to be allocated to policing and preventing revolts. It might have been individually rational for a landowner to offer freedom to slaves as a form of compensation for work, but this was disallowed by that broader political environment. So, everyone was somewhat trapped. Most importantly, the slaves were abused and trapped. But the free residents of the South had to live in a stagnant society. Governments did not invest in schooling, even for white children.
Municipalities in the North were booming and attracting free migrants with public investments. These investments set the North on a path to overtake the South economically and demonstrate which system is superior for creating wealth. Wright blames property owners in the South for continuing to fail to vote for good institutions that foster economic growth after the Civil War.
I’m reading a new book Liberty Power by historian Corey Brooks. It is about how abolition was accomplished through American politics. Something that stood out to me in the introduction is that abolitionists claimed that they had been “cancelled” by proslavery dominant powers in Congress. Americans did not like to see someone getting cancelled, and it created sympathy for abolitionists.
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.
The number of new jobs is being heralded (example in picture) as disappointing relative to the expectation that we would march steadily back to pre-Covid employment levels. (Ben Casselman is a good Twitter source for the data.)
One of the reasons for a slow recovery is that the Delta variant of Covid hit hard at the end of the summer and people are not getting vaccinated, so the health threat of going out to work and consume did not decline as much as we had expected. Covid was hard on family caregivers, often women. The disruptions to childcare from Covid still are not over. We are seeing a reversal of the massive influx of women into the formal workforce that started in the previous century.
Some people are saying that workers no longer want “dead end jobs,” and there has been a permanent shift in the labor supply, although it is hard to disentangle that from the effect of temporary Covid subsidies.
I am reminded of two very different sources who claimed, before 2019, that what we had in the early 21st century was not sustainable.
First, there were agitators for a $15/hr minimum wage. They marched in the streets while leading Democrats voiced approval. They were pointing out that families in America who depend on $8/hr jobs do not feel like they have a part of the American Dream.
Someone who, I am certain, would be against a federally mandated $15/hr minimum wage was also pointing this out. Tyler Cowen published Average is Over in 2013, when unemployment was still high following the Great Recession. Chapter 1 of Average is Over is called “Work and Wages”. Tyler was concerned that market forces were creating a world where some people have the best jobs that humanity has ever conceived of, by virtue of their compatibility with intelligent machines, while the rest of the workforce is left with jobs that are not so great. At the time, I don’t think people realized how many jobs could be done from the comfort of home or from a hip coffee shop. Covid exposed that. The “not so great” jobs feel especially crappy when you know that people in your city get paid 6 figures to sit at a laptop.
Tyler might have been surprised when unemployment dropped so low in 2019, right after he had written The Complacent Class, which warns us that America isn’t working well for a large group of people.
We are not great at predicting the future.* Some of Tyler’s predictions have come true already, but even he did not try to put a date on things. The point is that maybe the latest job numbers are not as surprising for the reason that the forecasts were more wrong than we thought. Covid has moved us far out of equilibrium, so it is still hard to tell where we are going to land.
Personally, I thought prices at the grocery store, one of the few places you could go in early 2020, would shoot up faster. It seemed to me like we would need to start paying cashiers more as hazard pay.
The longer the disruption from Covid drags on, the more people should be willing to see prices adjust to reflect changing circumstances.
*In one of my experiments, I asked subjects in the role of employers to predict what their employees would do. They failed to predict how strongly employees would respond to wage cuts. We are not great at predicting.
When I moved half-way across the country to take a new job, I had no local support system for my 2-year-old. Putting him in a full-time day care was the plan. I wanted a day care center with a good reputation that is located near work and home. My story, like so many others, includes phone calls and long wait lists. At first, it was hard to understand how I could be willing to pay for a service and it could just not exist.
Opening a large daycare center is risky. Who wants to take that risk? I joked that I’d quit my professor gig and start a daycare in response to the huge demand. Of course, I did not. Fortunately, I don’t live in one of the American counties that lost population over the past ten years.
In Lincoln County, Kan., pop. 2,986, about 40 miles west of Salina, Kan., economic development director Kelly Gourley set out to build the county’s first day-care center not run out of someone’s home. A child-care shortage was making it difficult to work and raise children, she saw. The town’s handful of in-home daycares were the only options, and they tended to come and go.
Ms. Gourley estimated it could cost as much as a half-million dollars to build the facility, and she didn’t think it could weather fluctuations in demand. “In a rural community, you lose one kid and you might be in the red all the sudden,” she said. She shelved the plan and instead is working to increase the supply of in-home caretakers.
Allison Johnson, a 32-year-old nursing home speech pathologist, grew up in Lincoln County and hoped one day to have three children. She no longer thinks that is feasible after she had to wait a year to get an in-home daycare spot when her first child was born. Now she and her husband, who owns a residential-construction business, are trying to figure out how they would juggle having a second child.
Her father, a farmer, watches her son, now 2, when her in-home daycare provider isn’t available. But he and her brother are in their busy season, and “they’re not going to be able to do anything but throw him in the tractor.”
There are attractive economies of scale for day-care centers. This economic fact is part of the reason that young people are leaving rural areas, which in turn makes it harder for rural areas to support services for young families.
There has always been a huge amount of value created at home within families that is not fully captured by GDP. As more childcare is moving to the formal market, we are starting to see just how valuable those services are that used to be provided in the family.
Whatever your views on the matter, it’s not surprising that politicians are talking about subsidized day care.
Allowing for flexibility through policy moves like vouchers and de-regulating in-home daycares is important. Some communities can’t support a day-care center facility, like the one in this article. I think the if you build it they will come philosophy, if applied too widely, would be hugely expensive and not efficient. On the other hand, there could be situations in which more day care would be provided if the local government would take on some of the risk currently faced by entrepreneurs.
Last year I blogged about a service to create your kid’s school Valentine’s cards.
Now, the temperature in Alabama has dropped to a chilly 71 degrees and the pumpkins are out. It’s time for parents to start worrying about who is going to create holiday magic at home.
You can pay someone to do this. An enterprising local has already posted this in a neighborhood group.
Creating holiday magic is a wonderful thing. There are huge positive externalities to even a simple string of lights around your front door. I love it. Creating the magic is also a lot of work. As someone who is forever swamped at work and has already booked three weekend work trips for Fall 2021, my willingness to pay for this service is positive. (I can’t afford this particular service, nor do I need my Christmas tree to look like the one in her picture.)
The rich have always had extra hands to manage their estates. I have a feeling that the percent of households for which this might be a paid service is expanding. There are women in the comments asking this crew to come over.
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).
Someone wrote a story about my life. It’s a report from The Verge called “File Not Found: A generation that grew up with Google is forcing professors to rethink their lesson plans”.
When I started teaching an advanced data analytics class to undergraduates in 2017, I noticed that some of them did not know how to locate files on a PC. Something that is unavoidable in data analytics is getting software to access data from a storage device. It’s not “programming” nor is it “predictive analytics”, but you can’t get far without it. You need to know what directory to point the software to, meaning that you need to know what directory contains the data file.
As the article says
the concept of file folders and directories, essential to previous generations’ understanding of computers, is gibberish to many modern students. It’s the idea that a modern computer doesn’t just save a file in an infinite expanse; it saves it in the “Downloads” folder, the “Desktop” folder, or the “Documents” folder, all of which live within “This PC,” and each of which might have folders nested within them, too. It’s an idea that’s likely intuitive to any computer user who remembers the floppy disk.
I am a long-time PC user. Navigating File Explorer is about as instinctive as drinking a glass of water for me. The so-called digital natives of Gen Z have been glued to mobile device screens that shield them from learning anything about computers.
Not everyone needs to know how computers work. I myself only know the layer that I was forced to learn.
My Dad, to whom I owe so much, kept a Commodore 64 in a closet in our house. About once a year, he would try to entice me into learning how to use it. I remember screwing up my 9-year-old eyes and trying to care. Care, I could not. It’s hard to force yourself to do extra work without a clear goal. The Verge article explains
But it may also be that in an age where every conceivable user interface includes a search function, young people have never needed folders or directories for the tasks they do. The first internet search engines were used around 1990, but features like Windows Search and Spotlight on macOS are both products of the early 2000s. Most of 2017’s college freshmen were born in the very late ‘90s. They were in elementary school when the iPhone debuted; they’re around the same age as Google. While many of today’s professors grew up without search functions on their phones and computers, today’s students increasingly don’t remember a world without them.
One area in which I do minimum archiving is my email. I rely heavily on the search function. I could spend time creating email folders, but I’m not going to put in the time unless I’m forced to.
Here’s where the “problem” lies:
The primary issue is that the code researchers write, run at the command line, needs to be told exactly how to access the files it’s working with — it can’t search for those files on its own. Some programming languages have search functions, but they’re difficult to implement and not commonly used. It’s in the programming lessons where STEM professors, across fields, are encountering problems.
Regardless of source, the consequence is clear. STEM educators are increasingly taking on dual roles: those of instructors not only in their field of expertise but in computer fundamentals as well.
Personally, I don’t mind taking on that dual role. I didn’t learn to program until I really wanted to. The only reason I wanted to was that I had discovered economics. I wanted to be able to participate in social science research. Let these STEM or business courses be the motivation for students to learn to use computers as tools instead of just for entertainment.
Allen Downey wrote a great blog on this topic back in 2018 that is more practical for teachers than the Verge report. He argues that learning to program will be harder for the 20-year-olds of today than it was for “us” (old people as defined by entering college before 2016). He recommends a few practical strategies, while acknowledging that there is “pain” somewhere along the process. He thinks it is sometimes appropriate to delay that pain by using browser-based programming interfaces, in the beginning.
I gave my students a break from pain this week with a little in-browser game that you can play at https://www.brainpop.com/games/blocklymaze/ They got 10 minutes to forget about file paths, and then it was back to the hard work.
I have found that a lot of students need individual attention for this step – the finding a file in their hard drive. I only have to do that once per student. Students pick the system up quickly. File Explorer is a pretty user-friendly mechanism. Everyone just has to have a first time. Sometimes, Zoomers just need a real person who cares about them to come along and say, “The file you downloaded exists on this machine.”
One way around this problem is to reference data that lives on the internet instead of in a local machine. If you are working through the examples in Scott Cunningham’s new book Causal Inference, here’s a piece of the code he provides to import data from his public repository into R.
The nice thing about referencing data that is freely available online is that the same line of code will work on every machine as long as the student is connected to the internet.
As more and more of life moves into the cloud, technologists might increasingly be pointing programs to a web address instead of the /Downloads folder on their local machine. Nevertheless, the kids need to have a better sense of where files are stored. He or she who can understand file architecture is going to get paid a lot more than their peers who only know who to poke and scroll on a smartphone.
There is a future scenario in which AI does most of the programming for us. When AI can fetch files for us, then File Explorer may seem obsolete. But I worry about a world in which fewer and fewer humans know where their information is stored.
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 