Economists Do It With Models

Warning: “graphic” content…

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Returns To Scale, Now With More Chocolate…

August 29th, 2016 · No Comments
Econ 101 · Production

Technically, “constant returns to scale” describes a production process where you get exactly twice as much stuff out if you put twice as much stuff in. Economists often argue that at least constant returns to scale should be achievable since, worst case scenario, you could just build a second identical factory next to the first one. As such, I want economic instructors to start using this as their example of constant returns to scale.

Alternatively, you could examine the economics of chocolate more directly. Mmmmmmmmm…

→ No CommentsTags: Econ 101 · Production

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Causal Friday: An Econometrics Review, Now With More Constellations…

August 26th, 2016 · 1 Comment
Causal Friday · Econometrics

Technically, I’m cheating with the “causal Friday” title, since, while regressions do identify associations that exist when controlling for other variables, these associations aren’t always of the causal variety. (This is particularly true when not all relevant factors can be controlled for.) But I choose to not be too persnickety because I think the comic is funny and wanted to share it.

Okay, you should have known better than to believe that I was going to avoid “too persnickety.” Personally, I won’t decide whether I am suspicious of the linear regression until someone tells me whether the slope is statistically significant. Also, if there are multiple explanatory variables that affect an outcome, a scatter plot that only looks at one of them at a time will generally looks like a mess even when all of the variables are individually important. In related news, this is a good opportunity to talk about the distinction between estimated effects (i.e. regression coefficients) and R-squared. (Don’t stop reading if you aren’t super into econometrics, I promise to make this make sense.)

Let’s say an economist is trying to model how much coffee I drink. (In reality, this is not necessary- the regression would just have a really big constant term, but go with me here.) Unfortunately, the only data available to use as an explanatory variable is income. Obviously, there are a lot more factors that affect my coffee consumption than just my income, so it shouldn’t surprise you that if I were to plot coffee consumption as a function of income (where each data point is a month of time, let’s say) I would get something that looks like the scatter plot above.

Let’s say that I’m measuring my income in hundreds of dollars and the estimated slope of the regression line is 0.01. This means that, on average, each hundred dollar increase in income is associated with 0.01 more coffees per month. If the numbers show that this estimate is statistically significant, then it’s pretty unlikely that this association exists in the data by random chance. Let’s also say that the R-squared of the regression is 0.06, like in the picture. This means that changes in my income only explain 6 percent of the variation in my coffee consumption.

My point is that these two conclusions aren’t in conflict with one another- it’s entirely possible for a relationship to both be statistically significant and for it to explain only a small fraction of what is going on. (This happens a lot in finance, actually, and an R-squared of 0.06 wouldn’t generally be seen as a red flag just because there is so much unexplainable noise in the data.) Sure, the result would be more impressive with a higher R-squared, but it’s largely a matter of personal judgment whether explaining, say, 6 percent of a phenomenon is worth talking about. (Not gonna lie- some economics journals vote no on this question.)

That said, I do recommend watching out for a red flag of a slightly different sort- one of the conditions in order for a regression to be valid is that your explanatory variables are uncorrelated with all the relevant stuff that you aren’t controlling for (your error term, in technical terms). In the case of my coffee regression, my result is valid only if my income isn’t correlated with whatever else could be causing variation in my coffee consumption (hours worked, for example). I can tell you personally that that is a lot of stuff.

I’m now tempted to perform a neural net analysis of my coffee consumption in order to see if I could get Rexthor out of it.

→ 1 CommentTags: Causal Friday · Econometrics

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This Has Got To Be The Tweet Of The Day…

August 25th, 2016 · No Comments
Just For Fun


Well played, sir. =P

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More On The Disposition Effect, Now With More Tax-Motivated Selling…

August 25th, 2016 · No Comments
Behavioral Econ · Videos

Tired of the disposition effect yet? This one’s short, I promise- just shows how the incentives for tax-motivated selling of losing stocks change over the year and cloud the disposition effect test statistics.

As usual, you can see the whole behavioral economics playlist here in case you want to catch up or need a review.

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On The Increasingly Unfortunate Economics Of Peanut Allergies…

August 24th, 2016 · 5 Comments
Buyer Beware · Econ 101 · Markets · Policy

So here’s a decent and concise overview of the latest issue to be taking over the internet:

Have a peanut allergy? Chance are you’re about to spend a lot more on EpiPens
by Emma Hinchliffe

Over the past few weeks, EpiPens have slowly overtaken the news. So what is going on with them exactly?

Source: Mashable


I feel like we’ve talked about this before as it relates to this guy, who apparently has some feelings about economists:


I don’t think he understands that economists are one of the groups likely most sympathetic to his ideals of profit maximization. Anyway, the general narrative is “company buys product, decides to make money from product, raises price of product,” and I would like to proceed by addressing one of Senator Sanders’ specific points:


This is where I point out that just because someone doesn’t like the reason doesn’t mean that the reason doesn’t exist. (As you’ll see in a bit, however, I pretty much agree with the spirit of what Sanders is saying.) In this case, the reason is some combination of inelastic demand and monopoly power– if consumers aren’t price sensitive (in this case because the product is a necessity) and they don’t perceive substitutes as being available (in this case one technically exists but often isn’t viewed as an acceptable substitute, so forgive me for not opining on the evils of patent protection at the moment), a producer can increase profit by increasing price. Sure, it will usually lose some customers, but the additional profit from customers that remain will more than make up for it. (In fact, the Lerner Index shows that markup over cost is inversely related to price elasticity of demand.)

Now, Bernie’s a smart guy, so I’m pretty sure that he knows this. He probably also knows that, even when producers are always maximizing profit, not all price increases are a result of cost increases. They could be the result of cost increases, but they also could be the result of an increase in demand for the product, at least in the short run. (That said, individuals do tend to view the former as fair and the latter as unfair.) In this case, there does seem to be an increase in demand for EpiPens over time, but what has more likely transpired is that the producer has shifted its values over time to focus more on pure profit maximization and less on keeping prices “fair.”

The somewhat uncomfortable reality is that, unless I’m interpreting price-gouging laws wayyyyyy too narrowly, the producer is within its rights to increase the price of its output, regardless of whether or not its costs have changed. (I feel like this point gets lost since companies often pay lip service to government to try to justify price changes in order to try to avoid future increases in regulation.) In fact, there’s even somewhat of an expectation coming from investors and capital markets that companies act so as to maximize profit. (While investors do seem to be increasing their emphasis on fairness and good corporate citizenship, the notion of a fiduciary responsibility to shareholders does still exist.)

I’m not saying that this outcome is right, either from an ethical or an efficiency standpoint, just that it shouldn’t be surprising. Again, the uncomfortable reality is that we rely on companies being “nice” as far as necessities are concerned in many cases, and recently we’ve gotten smacked with examples that show how fragile that trust can be. I’m also not saying that regulation is easy- how do you decide whether a product is truly a necessity? How do you decide whether a market is sufficiently competitive so as to solve its own problems? How do you regulate price or remove barriers to entry without destroying the incentives to innovate or keep costs down? (Economists have some models for regulating natural monopolies such as this, but they’re not perfect solutions.) I do think, however, that it’s a little unfair for policymakers to resort to shaming companies that are operating within the legal framework that they created because they kicked the regulatory can down the road (but, ironically yet likely justifiably, regulated enough to create the problem in the first place) rather than addressing what is an easily foreseeable issue from an economic standpoint.

→ 5 CommentsTags: Buyer Beware · Econ 101 · Markets · Policy

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More On The Disposition Effect, Now With Econometric Issues…

August 24th, 2016 · No Comments
Behavioral Econ · Videos

Last time, we discussed the main result that shows evidence for the disposition effect- i.e. the bias toward selling winning stocks and against selling losing stocks. What I didn’t tell you at the time was that the calculation suffers from somewhat of an econometric problem, so I make up for my oversimplification and discuss that here.

As usual, you can see the whole behavioral economics playlist here in case you want to catch up or need a review.

→ No CommentsTags: Behavioral Econ · Videos