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In case you wanted to spend your Saturday evening reading me not knowing much about 5G…
We explore the state of U.S. broadband competition and the likely impact of the fifth-generation (“5G”) wireless technologies. Should cable companies be scared, and if so, how long will it take 5G to upset the competitive balance?
Technology knowledge gaps notwithstanding, I think there’s a lot to consider regarding new communication technologies from an economic standpoint- as you can probably tell from the transcript, the big thing that I find interesting is that existing broadband and, to some degree, wireless companies have higher opportunity costs of offering new technologies since they would at least partially be cannibalizing their existing business. I worry that this slows the pace of technological progress and provides incentives for anticompetitive behavior. This isn’t such a big deal for areas where the existing technology is “good enough,” but it matters more when it affects the incentives to get more households covered with broadband-level infrastructure and access.
On Fridays, we examine a research paper that uses (or fails to use) a clever method to perform causal inference, i.e. to tease out cause and effect.
In case you haven’t been keeping up, I’ll start by noting that Flint, Michigan is still having problems with its water supply. (Technically, residents are being told that the lead prevalence is down to acceptable levels but also that they should keep using bottled water anyway for the next 3 years until the pipes are replaced, which, uh…ok.) We know on a general level that lead consumption is bad, but it’s worth thinking about what specific problems can arise, so here’s a fun synopsis on lead poisoning. Oh, and lead is also being blamed for Legionnaries’ Disease in the area, so there’s that. We’re also learning that the water problems appear to have had significant effects on fertility and infant mortality:
Flint changed its public water source in April 2014, increasing lead exposure. The effects of lead in water on fertility and birth outcomes are not well established. Exploiting variation in the timing of births we find fertility rates decreased by 12%, fetal death rates increased by 58% (a selection effect from a culling of the least healthy fetuses), and overall health at birth decreased (from scarring), compared to other cities in Michigan. Given recent efforts to establish a registry of residents exposed, these results suggests women who miscarried, had a stillbirth or had a newborn with health complications should register.
Woah if true. On principle, I’m talking about this study because I feel like this matter needs attention in order for government officials to be willing to act responsibly. As a data analysis matter, I’m talking about this paper because it illustrates an important component of causal analysis. So let’s take a look at one of the pieces of data that the authors used to reach their conclusion:
Ok, that doesn’t look great. BUT…it’s actually not enough information to conclude that the water itself is causing a problem- maybe there was something else going on more generally that resulted in decreasing fertility at the time. (In other words, maybe we’re falling for the post hoc ergo propter hoc fallacy– it’s not just the title of the second episode of The West Wing!) To do a proper analysis, we need a counterfactual- theoretically, a world where everything is the same except there is no water crisis. In practice, a counterfactual approximation is usually constructed by looking at comparable areas that didn’t undergo the “treatment”- in this case, didn’t have a water crisis.
If you looked closely at the above graph, you may have noticed that I cheated in order to fit my narrative- the graph actually looked like this:
Oh. Yeah, that doesn’t look great either for the people of Flint, but it at least looks better from a data analysis perspective. Economists refer to this sort of analysis as “differences-in-differences”- i.e. a comparison of before-after comparisons. In picture form, something like this:
|Treated (i.e. Flint)||A||B||B-A|
|Untreated (i.e. comparison group)||C||D||D-C|
We can then analyze whether the treatment had an effect by investigating whether the incremental difference of the treatment group (B-A) – (D-C) is different from zero. (If the difference is positive, the treatment had a positive effect and vice versa, assuming that larger outcomes are better.) In order to be more rigorous in the analysis, the next logical step would be to test whether this difference in differences is statistically significantly different from zero. To do this, economists run regressions with various interaction terms that get at this “difference in differences.”
Like I’ve said before, causal analysis generally aims to be as close to the middle-school science project as possible- control group, experimental group, the only difference between the groups being the treatment, and so on. In this case, the causal interpretation of the data presented in the paper rests crucially on whether Flint really is like the comparison group in all ways other than the water supply (or at least those ways that can’t be controlled for). In addition, it’s crucial that the treatment that is being analyzed (lead in the water, in this case) is random, meaning that it doesn’t pop up in response to something about the treatment group that researchers can’t observe/quantify.
From what I’ve read about the history of the Flint water crisis, I feel pretty comfortable ruling out that latter concern- in other words, I really don’t think Flint did anything to invite the water crisis that would also affect fertility. (I guess I also don’t think Flint did anything to invite the water crisis more generally, so there’s that.) As to the former concern, the comparison group is comprised of the 15 largest non-Flint cities in Michigan, so they could be different from Flint in various demographic ways that are not controlled for in the picture above. That said, those differences can be controlled for in the regression analysis, which still does find a significant difference in fertility. On the other hand, it could be the case that some of the women who wanted to have children moved out of Flint to do so for precautionary reasons- we wouldn’t be able to see this easily in the data, and it gives an explanation for why fertility rates could have dropped even if the water wasn’t actually making women infertile. While this explanation is initially plausible, we would also have to believe that the women who stayed in Flint were predisposed to have much sicker babies, since there is also an observed difference in infant mortality between Flint and the comparison cities.
The general idea is as follows: use techniques at your disposal to perform causal analysis as best as possible from a mathematical perspective, try to come up with alternative explanations for your findings, and then try to use the data to rule them out. These last two parts can get interesting, largely because different people think of different alternative explanations. For example, the authors of the paper conjecture that people in Flint might just have decided to have less sex rather than leave the area, and they actually use data from the American Time Use Survey to argue that this is not the case, which I personally find hilarious. Overall, I’m not sure whether to feel happy that we’re doing rigorous analysis or depressed that we’re finding that the water supply has significant negative effects.
Update: I thought about it some more, and I don’t think that women moving out of Flint to have children even comes close to a valid alternative explanation, mainly since the dropoff seems to be right at the time of the water switch even though residents weren’t immediately aware that there was a problem and thus couldn’t have changed their behavior in response. (Furthermore, I guess you would also expect to see an increase in fertility in neighboring areas if this were the case.)
Sooo…I have a minor confession to make- I’ve established a bit of a cottage industry tutoring students in the course I taught while I was in grad school. Not gonna lie, it’s pretty nice to be seen as an advocate as opposed to the thing between the student and the grade that the student wants, in part because students are more willing to admit what they find confusing to me than to their “real” instructors.
As a related project, I figured it would make sense to create videos for the items that students find to be particularly confusing or challenging. The first one is about gains from trade, since we specifically teach that the “price” of a trade has to be right in order to make all parties better off from trade, but we kind of gloss over the fact that a trade also has to be of a size that makes sense for everyone as well.
Also, as a related matter, never say “give me all of your money” when mugging an economist.
“So, like, do you mean only M1 or do I need to hand over M2 as well? Are you only counting items officially recognized as currency or are you demanding all items that could function as money? Technically speaking, fiat money has no intrinsic value so is there any chance I can convince you that this is not worth your time?”
(Don’t get it? See here for a brief explainer.)
On Fridays, we examine a research paper that uses (or fails to use) a clever method to perform causal inference, i.e. to tease out cause and effect.
Disclaimer: I’m kind of stretching the definition of both “causal analysis” and “research paper” here, but I guess you could interpret the analysis as relating to the causal impact of being female.
In case you haven’t heard, Google is the target of a class-action lawsuit based on gender discrimination. (Shocking, I know, given what we know about Silicon Valley more generally. =P) Part of the impetus for the lawsuit is an employee-led effort to collect compensation data that shows that men are paid more than women at the company:
A spreadsheet created by employees to share salary information shows pay for women is falling short of what men make at various levels.
From a data perspective, proving discrimination can be somewhat difficult- for example, we hear the often-quoted “women make 77 cents for every dollar a man makes” statistic, but this in itself doesn’t really tell us anything about discrimination. It could instead be the case that women sort into lower-paying occupations and jobs of their own volition, choose to work fewer hours, and so on. (On the other hand, we can’t rule out the discrimination hypothesis either.)
Ideally, what one would do to look for discrimination would be to compare otherwise equivalent men and women and see whether compensation differences still exist within the matched groups. Mathematically, this is essentially what economists do when they run a regression with “control variables”- variables that suck up the differences that are accounted for by stuff other than gender.
Google employees seem to be up on their applied math, since they put together an analysis so that they could make the following statement:
Based upon its own analysis from January, Google said female employees make 99.7 cents for every dollar a man makes, accounting for factors like location, tenure, job role, level and performance.
On the surface, this seems to suggest that significant gender discrimination just doesn’t show up in the data. BUT…and this is important…this example highlights the difference between doing math and doing data analysis (or, more charitably, data science)- while this conclusion may be mathematically correct, it’s basically a “garbage in, garbage out” use of econometric tools. Simply put, if you’re trying to isolate gender discrimination, you can’t just blindly control for things that themselves are likely the result of gender discrimination! It’d be like looking at the impact of diet on health and using weight as a control variable- sure, you’d get an “all else being equal” sort of result, but it wouldn’t make sense since weight is likely a step in the chain between diet and health outcomes.
In this way, Google tipped its hand quite a bit regarding the particular nature of gender discrimination at the company- if men and women are paid the same once job title and performance reviews are taken into account, then gender discrimination (if it exists) is taking place either by herding women into jobs with different roles/levels or showing anti-female (or pro-male) bias in performance reviews. (Also, if the “levels” have set pay bands, which the article kind of suggests, doesn’t controlling for level largely amount to assuming your conclusion?)
Turns out my suspicions are pretty on point, given the specific claim of the lawsuit:
Exclusive: Women say Google denied them promotions, telling the Guardian they were forced into less prestigious jobs despite qualifications
It’s amazing what you can learn from data IF you look at it properly. In a semi-previous life, I worked as an economic consultant, which basically means that I helped prepare expert testimony to be used in lawsuits involving economic matters. What I wouldn’t give to be the expert witness who gets to offer up a rebuttal to Google’s crap econometrics here.
Update: This is amazing:
Occupation controls are literally the textbook example of how not to measure wage discrimination.
Labor Economists pic.twitter.com/jfIoQCUHKy
— Sally Hudson (@SallyLHudson) September 16, 2017
In case you’re curious, the excerpt is from this book, which I highly recommend.
Look, I get it, negative supply shocks suck. They’re not as good as everyone getting what they want at low prices. Sometimes economists are too flippant about high prices as a rationing mechanism. We’ve been over this. I do feel a little like I’m screaming into the void though, especially when I see, um, interesting takes come from places that should know better:
It helps your customers, which helps your brand.
I’m sorry, come again? Fine, I’ll reserve judgment until I finish reading the article…
Ok done. What the article is saying without being terribly explicit about it is that companies should engage in completely untargeted disaster charity in the form of low prices since it will make customers so happy that they’ll be super loyal afterwards. Maybe it’s just me, but I’m not going to be terribly loyal to a company that made their stuff cheaper so that it sold out before I could get what I needed. To be fair, the article’s recommendation seems to be that companies both lower prices and satisfy whatever level of demand exists at that price, which at best would be very expensive and at worst logistically infeasible.
While I do get that public relations is a thing and that customers aren’t robots, two things still bug me. First, the article asserts that lowering prices and satisfying demand at the lower price would be a low-cost tactic to generate goodwill, but, unless you’re running a zero marginal cost business, it’s really not. (For example, it’s far cheaper to offer free phone service than free plane flights.) Second, it’s far from clear that the rewards in terms of customer loyalty are strong enough to warrant such an investment- in fact, using Jetblue as an example is particularly bad since it’s pretty well known that part of why airline service is so bad is that many customers focus on price to the exclusion of all other considerations. So sure, maybe it would be a nice thing to do, but don’t pretend like it’s long-term profitable without even trying to estimate the costs or benefits.
Moving on…look, I tried to warn you that below equilibrium prices lead to sub-optimal allocation of goods, but you didn’t listen, and now we have this:
Hollywood opened its garages so people in flood-prone areas could park for free in a dry spot. Many of the spots were quickly filled with cars with price tags and no license plates.
I really hope this snaps some people out of their fantasy world where low prices get goods to the nice, deserving but perhaps not high income people. I guess it also highlights the need for some sort of non-price allocation rule if you’re not going to allocate via price- straight-up rationing is generally not great, but in this case perhaps require a license plate or local address? Geez. (Sidenote: This is where my parents live and let’s just say they are not surprised by this outcome.) Come to think of it, this is even an example of the lowering of prices that the HBR article recommends, but I’m not convinced that the City of Hollywood got a whole lot of positive PR in return for its largesse.
Last but not least, apparently the small slice of the world that is the economics profession is heartless. *headdesk* I don’t think I particularly like being referred to as if I’m a quirky zoo animal or something. Unless it’s a panda, hen I’ll allow it. (Also, the article actually says that the voucher idea I presented as a joke earlier is actually a thing. GUYS, I WAS JUST KIDDING, IT’S MOSTLY ABSURD. Mostly.)
price discrimination: n. the action of selling the same product at different prices to different buyers in order to maximize profits.
This is, of course, the definition of price discrimination that I give to my classes. In practice, however, this notion of “same product” isn’t quite as simple as us instructors would have you believe. (Is anything ever, really?) We know that early-bird discounts at restaurants are generally considered price discrimination, as are higher prices for airline tickets purchased at the last minute…but is dinner at 4pm really the “same product” as dinner at 7pm? If you look at what economists file under price discrimination, we could probably expand the definition to “selling different versions of a product at different prices, where the differences in prices largely aren’t driven by cost differences.”
As such, when I was looking into the purchase of a Tesla (I was feeling fancy ok) and saw that the longer-range battery added an extra $3,000 to the price, I remember thinking to myself “heh I bet that’s mostly a price discrimination thing since I’m guessing it doesn’t cost an extra $3,000 to make a better battery,” but I certainly wasn’t expecting this:
Normally the upgrade costs at least $3,000.
Wait, what? First, I guess I should point out that this is a nice thing to do. But…you mean to tell me this whole time you were just sandbagging some of the batteries???? That’s…bold, among other things. I hope the warm fuzzies you get for this gesture outweigh whatever customer fury may be heading in your direction…(personally, I can’t decide whether I would be more irritated if I had or hadn’t paid for the better battery) Granted, even this isn’t “true” price discrimination, narrowly speaking, since the lower-priced Tesla doesn’t come with the same functionality from the driver’s perspective.
People typically aren’t thrilled when they hear the phrase “price discrimination,” since they seem to assume it’s just another fun way for a company to rip them off. Not all of these customers are wrong- it’s entirely possible that some customers pay higher prices than they would otherwise if a company decides to price discriminate. That said, it’s almost always the case that price discrimination results in lower prices for some customers, and it’s even possible that price discrimination results in lower prices for some customers without subjecting any customers to higher prices. Let’s look at a simple example to see how this could be the case:
|Willingness to Pay|
(Assume for simplicity that whatever good this is doesn’t cost anything to produce.) Without price discrimination, the company can either sell 1 unit of the product to Customer 1 and make $10 or sell 2 units (1 to each customer) at $4 each and make $8. Given these numbers, the company isn’t going to be willing to lower the price to sell to Customer 2, since it would have to lower the price to customer 1 as well. But Customer 2 is willing to pay more than it costs to produce the product (i.e. nothing), so this seems inefficient- what if there were a way to give a lower price to Customer 2 without lowering the price to Customer 1? In that case, the company could sell to Customer 1 for $10 and to Customer 2 for $4 and make $14. In this case, price discrimination made the company better off, gave the company the incentive to sell to more people, and didn’t raise the price to anyone. Boom. (As a related observation, I don’t generally see companies jack up their regular prices once they start offering student discounts so…)
Of course, Customer 1 might be annoyed that someone, somewhere is getting a better deal than he is, but I’m not sure what to do about that. This discussion also makes me wonder how many people complain about the possibility of price discrimination even when they’d be in the group to benefit from it. In related news, this might be why I have Uber pick me up at the 7-11 down the street, so next time maybe we’ll talk about how price discrimination doesn’t work if people can fake being in the low willingness-to-pay group.
One of the things I do in order to make my writing as helpful as possible is to try to time topics with associated current events- makes sense to talk about something when it’s on people’s minds, right? By that same logic, I’m a little suspicious (read, a lot suspicious) of people who say that hurricanes are not a time to talk about climate change, especially since warmer ocean temperatures do in fact have an impact on hurricane severity. So let’s talk about climate change, sort of.
On one hand, engaging in activities that are not environmentally friendly is not evidence that one doesn’t believe in climate change (though I guess you can’t rule it out either), it’s just evidence of self interest, and it’s why there’s opportunity for centralized coordination regarding how to mitigate the effects of climate change. Here’s more:
If you follow politics news, you’ve probably noticed that there’s a lot of discussion regarding whether our elected leaders “believe in…
On the other hand, fleeing a hurricane zone actually does mean that you don’t believe that a hurricane is fake news, since the self-interest logic doesn’t apply in the same way. Nor does this:
HURRICANE UPDATE FROM MIAMI: LIGHT RAIN; RESIDENTS AT RISK OF DYING FROM BOREDOM
— Ann Coulter (@AnnCoulter) September 10, 2017
I think this is a good time to remind everyone that only costly signals matter. In related news, I could use any help you can give on arguing against this logic:
Never thought I’d be fighting a war against Bayesian updating, but here we are.
One of the main topics in organizational economics (and economics in general I suppose) is the principal-agent problem– i.e. the misalignment of incentives between one party and another party enlisted to do the first party’s bidding. For example, a small-business owner hires an employee to run things and maximize profit for the owner, but a self-interested employee who is paid a fixed salary would likely rather check Facebook then do whatever it is that would be in the owner’s best interest. (Here’s another great empirical example involving real-estate agents.) This isn’t a criticism of the employee, just a descriptive observation, but it is the starting point for an entire line of research on ways to mitigate the inefficiency created by the principal-agent problem- essentially, the economic version of “if you want something done right, do it yourself.”
Most economists focus their attention on various incentive (i.e. pay for performance) schemes, partly because they are interesting and partly because there is a general consensus that, in a lot of situations, simply hiring managers to monitor the workers and make sure that they do their jobs is an overly costly and/or ineffective solution. Think about it- if the employee doesn’t have the proper incentives to work rather than check Facebook, why would the manager have the proper incentives to monitor employees rather than check Facebook? I guess you could hire another manager to monitor the first manager, but that both adds to cost and just shifts the same problem up one more level. In order for the monitoring solution to work, the owner has to be the ultimate monitor, which largely defeats the purpose of hiring others to work on his behalf in the first place.
That said, I think Scott Adams may have stumbled upon a solution to the monitoring problem:
I guess this could work until our robot overlords get sophisticated enough to get on Facebook and start defying orders- so like 5 years maybe? I feel like my Alexa has already has already made significant progress on the latter front at least.
On Fridays, we examine a research paper that uses (or fails to use) a clever method to perform causal inference, i.e. to tease out cause and effect.
Economists Gregory J. Martin and Ali Yurukoglu have a new paper published in the American Economic Review (also available in working paper form here) that shows that the existence of Fox News has a (statistically) significant impact on Republican vote share. Here’s the abstract:
We measure the persuasive effects of slanted news and tastes for like-minded news, exploiting cable channel positions as exogenous shifters of cable news viewership. Channel positions do not correlate with demographics that predict viewership and voting, nor with local satellite viewership. We estimate that Fox News increases Republican vote shares by 0.3 points among viewers induced into watching 2.5 additional minutes per week by variation in position. We then estimate a model of voters who select into watching slanted news, and whose ideologies evolve as a result. We use the model to assess the growth over time of Fox News influence, to quantitatively assess media-driven polarization, and to simulate alternative ideological slanting of news channels.
Ok sure, that’s a lot to unpack, but let’s work through it. I think we can all agree that people who watch Fox News are more likely to vote Republican than others, but on that basis we can’t tell whether Fox News actually causes them to vote Republican, Republican ideology attracts them to Fox News, or something else both causes them to watch Fox News and vote Republican. In an ideal world (at least from a research standpoint), we could run an experiment to examine cause and effect where we take a group of people and randomly choose half of them to sit in front of Fox News for a while (and disallow the other group from watching) while keeping everything else about their lives the same as before. (This might actually be hard if the Fox News group doesn’t watch a lot of TV and goes outside instead, etc.) To my knowledge, no one has tried to do this yet, perhaps because watching Fox News is too hazardous to get IRB clearance. (That said, I will admit I was too lazy to read the lit review of the paper.)
So do researchers just give up? Well, sociologists might. (I kid because I love.) But economists get creative, and one thing they do is try to find an instrumental variable– simply put, a source of randomization. In this case, the researchers asserted that people are more likely to watch a given channel when it has a lower channel number (perhaps the result of the typical channel-surfing process), and they noticed that what channel Fox News is on differs by geography in a fairly random way. (In other words, it’s not correlated with how likely people are to watch fox News, vote Republican, etc.) These two observations together mean that we basically do have a world where some people are randomly subjected to more Fox News than others, and, as it turns out, there is a (negative) relationship between Fox News channel number and Republican vote share.
Obviously, there is a no direct link between Fox News channel number and voting patterns, and instead the hypothesis is that channel number impacts viewing time, which in urn affects the votes. Kind of fancy econometrics stuff enables the researchers to isolate the part of watching Fox News that is essentially random and then determine the impact of that random part on voting. They estimate that this impact is 0.3 percentage points in vote share as a result of a random extra 2.5 minutes per week of Fox News watching. (for example, 55.3% to 55.6% voting Republican) A few things to note:
I’m a little conflicted here- on one hand, given that Fox News is heavy on the misinformation, it’s pretty depressing to learn that it actually shapes ideology and actions. On the other hand, math is SO COOL.
(Sidenote: If you think this sort of think is neat, you can see a whole talk about it here.)
It’s hurricane season, so you know what that means- a plethora of articles econsplaining that actually price gouging is awesome and people’s silly little non-econ brains just don’t get it. Ok fine, they’re not ALL that bad…some of them are more “ok fine it’s not great but can you think of anything better and what we actually do is worse.”
I took a stab at it, and I tried to veer into why people hate the concept of price gouging:
Every time a natural disaster or other crisis situation hits, we see two things: one, people outraged that the things they want to buy have…
In terms of feedback I’ve gotten, two things surprised me a little:
1. Some people, even economist people, tried to argue that income in itself doesn’t change willingness to pay. *looks at randomly chosen textbook* So I see here that income is a determinant of demand, and, for normal goods at least, increases in income (ceteris paribus even!) lead to increases in demand, i.e. a shift to the right of the demand curve. Looking at this diagram, I’d have to do some serious mental gymnastics to argue that this increase in demand can’t be equivalently stated as an increase in willingness to pay (i.e. a vertical shift).
2. Some people tried to argue that there’s no better system, which fine, I do kind of agree with, but upon further reflection I HAVE AN IDEA. Here goes- we give each person the same number of tokens when they’re born, and the total price of an item will be the sum of the money price and the token price. Now we disallow price gouging with the money price but we let the token part of the price adjust- you want that generator in short supply? Pay MSRP, but token up. Boom. Ok, maybe this is why economists don’t always make great policymakers, but you know you’re intrigued.
Until the country gets behind my genius token scheme, I guess we’re kind of stuck with what we have in terms of crappy but socially acceptable rationing mechanisms. Luckily, people are sometimes better than economic models given them credit for:
This lady's father is on oxygen. She broke down when realizing the last generator was taken. This man insisted she take his. God bless them! pic.twitter.com/nCRJXTXEmm
— Ryan Fournier (@RyanAFournier) September 8, 2017
(Sidenote: If this people are better than economists think thing interests you, I have a whole talk about it here.)
I feel like this is something that most social scientists can relate to:
I mean, that’s pretty much how this came about:
Seriously though, I do think a lot about how we could make economics more visually interesting than repeated supply and demand diagrams, but it’s hard…for example, I tried to implement a “CSI: Regression Analysis” parody (as proposed by Charles Wheelan in Naked Statistics)- the script was pretty straightforward, but when I got people together to act it out we were like “heh, maybe add some sort of white board and write random stuff on it I guess?” I know, you’ve never thought of that before.
The end product wasn’t terrible, but visual suggestions are certainly welcome. Unless the suggestion is “tables of regression coefficients,” then just GFY.
Jeff Sessions made me angry, so I wrote a thing:
Today I learned from Jeff Sessions that DREAMers are taking jobs away from real Americans. No wait, that’s not right- today, Jeff Sessions…
It’s easy to dismiss people who defend ending DACA as racist or whatever, but it’s entirely possible that well meaning people believe that immigrants are taking their jobs, since it’s superficially plausible. If this is the case, focusing on humanitarian arguments isn’t entirely helpful, since the reaction is basically “I agree with you on principle but jobs…” People are entitled to their own opinions, but I get super worried when those opinions are based on (perhaps deliberate) misinformation.
For those of you who *do* want the multiple regressions, unlike what I wrote in the link above, maybe check this out. Or this. (There’s more out there, but I figured that could at least get you started.)
Earlier this summer, four economists released a working paper suggesting that part of the decline in male labor-force participation can be attributed to the increased quality of video games:
Young men are working less. Some economists think it’s because they’re home playing video games.
You can also see a non-technical summary of the paper as part of the NBER digest. Conceptually at least, this makes sense- better leisure activities increase the opportunity cost of working, which decreases the net benefit of working, and generally we do less of something when it’s perceived as less beneficial. As the article notes, this could lead to an increase in happiness for the non-working men (though perhaps not for those around them!) despite lower work hours/income if they like the video games more than they like what else they could get with the income from working. (In other words, the video game choice, if it exists, isn’t necessarily irrational.)
Not surprisingly, some people have their doubts about this finding, and there is additional disagreement about the social and policy implications of such a conclusion. I think I fall into this camp to some degree, but additional evidence makes it difficult for me to ignore the link between video games and male labor-force participation at a more general level:
I assume, like any good capitalist, he’s now living off of the interest on all those gold coins he’s collected.
Every once in a while, I get contacted to answer questions and/or provide quotes for articles, news programs, etc. (Nothing will ever be as neat as the feature in Grazia though.) The latest was an inquiry from a very nice fellow named Chris Taylor, who is the money guy for Reuters. Here’s the article, and we’ll get to the larger context afterwards:
In a volatile market, boring investments can be pretty darn sexy. That is why investors have plowed more than $4 trillion into exchange-traded funds, according to London-based research firm ETFGI.
Okay, what actually happened was basically this:
Chris: Can I ask you some questions about leveraged ETFs?
Me: Oh hell no.
Chris: Oh sorry to disturb you.
Me: Nono, that’s my reaction to leveraged ETF, not to you. I HAVE THOUGHTS- fire away.
Here’s the overall Q&A in case you’re curious:
Q: To what do you attribute the rise of these ‘leveraged’ ETFs, offering 2x or 3x returns?
A: I think once ETFs became a thing this was a logical next step. The purpose of the basic ETF is to mimic participation in a mutual fund, either passive or active, with less logistical overhead. (In other words, i can go buy an ETF easier than I can set up an account with Fidelity, and so on.) One follow-on effect of this is that it’s now possible for smaller investors to bet on various indices and funds by trading their ETFs. (Larger investors generally do this directly rather than going through the ETFs.) Similarly, leveraged ETFs mimic behavior that already exists among large investors (though more hedge funds and such rather than traditional asset managers) but make it feasible for smaller investors to essentially do the same thing (and, in at least some cases, at lower expense). If I had to speculate, I would say that the increased sophistication of ETFs has followed changes in the strategies and behaviors of large investors.
I think a persistent low interest rate environment has also encouraged this, since investors are looking for more and more creative ways to eke out nominal returns.
Q: Can or should they be a part of individual portfolios, or are they more for day traders?
A: Leveraged ETFs are almost exclusively for investors looking to trade frequently and “time the market,” etc. One reason for this is almost purely mathematical- many of the leveraged ETF are constructed to magnify short-term (eg. daily) ups and downs but don’t necessarily magnify longer term returns in the same way. (To think about this, consider that a 1% increase followed by a 1% decrease looks approximately the same as a 2% increase followed by a 2% decrease, but there are important differences as illustrated below. Put simply, percentage returns are not additive, so things get weird if it’s the percentage returns that are being magnified by the ETF.) If I was looking for a long-term investment, I don’t see the appeal of a product with higher daily volatility unless it also confers at least proportional higher average long-term returns (and even then…).
Another reason that leveraged ETFs are unattractive from a long-term perspective is that they generally have higher fees than more traditional ETFs and similar products. For those specifically looking to, um, leverage the leveraged ETFs, this is not so bad since they are usually cheaper than explicitly borrowing and such to execute what the ETF is doing for you, but the fees only make sense if you are specifically looking for the behavior of the particular product.
I guess I’m speaking here to how they *should* work, not how they actually work, so it’s entirely possible that misguided long-term investors do hold them. Oops.
Q: Most people are not great at investing — mistiming market, trading on emotion, trading too frequently – so do these products appeal to our worst instincts?
A: *does quick Google search* Yes? I would add that most people are not great at investing in a stock-picking sense even when they are trading less frequently. (This isn’t a statement on people so much as it is one on market efficiency.) I think leveraged ETFs appeal to the same characteristics, but the magnification of returns could make it so a (risk loving) person who wouldn’t bother trading normal ETFs and such could be tempted to play around with the leveraged versions. A good analogue of this situation is stocks versus stock options- the (figurative, not literal borrowing) leverage might make trading options appealing for some individuals who wouldn’t be interested in trading the underlying stocks.
Q: If investors are determined to buy them, any strategies for making sure they don’t get carried away and assume too much risk?
A: Are you making trading our own assets your full time job? No? Then stay away. And even then… I guess you said determined to buy, so…don’t be fooled by perceived diversification. You can have a leveraged ETF that tracks the S&P500, fr example, and it looks like you’re diversified over 500 stocks, but I would argue that this isn’t enough when leverage is involved. It’s incredibly important to diversify across the leveraged ETFs and other investments, or, perhaps even better, to specifically hedge the leveraged ETF position, though this mitigates the effects of the leverage to some degree. (If you don’t believe me, consider that the companies that sell you the leveraged ETFs specifically hedge their exposure to you!).
Update: Finance friend makes one of my points more clearly than I did:
*works through arithmetic* So a 25% increase would be turned into a 50% increase with a 2x leveraged fund, hence 100 to 150, then a 20% decrease would be turned into a 40% decrease, hence 150 to 90, i.e. putting you below where you started even though the underlying index went back to its original point. Similarly, a 25% decrease would be turned into a 50% decrease, so 100 to 50, then a 33% increase would be turned into a 66% increase, so 50 to 83. In both cases, the value of the underlying index went back to its original point but the value of the leveraged ETF did not. (In general, holding a leveraged ETF erodes returns when markets are volatile and amplifies them when markets are steadily increasing or decreasing.) This could be different, however, with leveraged ETfs that are in some way tracking the underlying price rather than the return, so read the prospectus! Or just stay away.
So I’m not entirely clear on what The Onion is making fun of here…
WASHINGTON– According to eyewitness accounts from around the country, the nation’s middle class suddenly and mysteriously reappeared Tuesday with baffled citizens providing chilling reports of thousands upon thousands of financially stable individuals pouring onto factory floors, taking up positions along assembly lines, and resuming their former blue-collar jobs without any warning whatsoever.
…but I have a couple hypotheses:
1. (more obvious) If polls are to be believed, the economy immediately improved once Trump got elected. Yes, this seems absurd if taken literally, but I guess this is what the concept of “animal spirits” is supposed to be all about. If enough people believe that Trump will be good for the economy and act accordingly, it kind of becomes a self-fulfilling prophecy. But it still doesn’t happen immediately so come on, people. (Also, it doesn’t mean that the old-timey jobs will come roaring back.)
2. (more nuanced) This article was published right after the Bureau of Labor Statistics released its monthly “jobs report”, which showed that the unemployment rate increased even though there was an increase in the number of jobs in the economy. This seems weird, but it’s what happens when people enter the labor force (i..e either start working or start looking for work) and not all of them immediately find jobs. I initially thought that the reappearance of the middle class was supposed to be this increase in the labor force, but I could be overthinking things.
Rivalry in consumption: the degree to which one person consuming a good makes it more difficult for another person to fully consume the same unit of the good
Usage: A good generally exhibits high or low rivalry in consumption.
Examples: A fireworks display exhibits low rivalry in consumption. An ice cream cone exhibits high rivalry in consumption. (Alternate phrasing: An ice-cream cone exhibits rivalry in consumption.)
THIS VIDEO IS SACRED… pic.twitter.com/o7ivZtJf8x
— Life on Earth (@planetepics) September 1, 2017
Related terms: congestible goods (goods that go from low to high rivalry in consumption when they get crowded)
Hi everyone! If you’ve been following this site, you’ve noticed that it’s changed its look recently. So here’s the deal- I finally had enough trouble with my old hosting provider that it was worth my effort to navigate the wonderful world of setting up an Apache-server-enabled instance on AWS. (It’s not a trivial process, but the documentation got me through it without incident, in case you’re curious.) As such, I took the opportunity to embark on a redesign, especially since a number of external links were deprecated in the old version. (This is a nice way of saying that the site I used to write for went kaput.) It’s clearly still a work in progress, but it’s getting there- I don’t think I’m going to go back and restore all of the old blog posts, but over time I will re-post the things that I think are worth seeing again. I’ll also try to be better at blogging and not just ranting on Twitter, I promise.