I got an email from Steve Landsburg with the subject line "krugman, me and you." I can't decide whether that counts as the sort of threesome I've always dreamt about...
I get daily emails from The Chronicle of Higher Education newsletter. Today's headline: "Academe Today: Professor Says His University Cares Little About Teaching." I had to stop for a second and confirm that I wasn't in fact reading The Onion.
I am not a macro person (by nature at least- I don’t deal well with severe empirical limitations and unanswered questions I guess). That said, I enjoy this profusely:
What I think I don’t like about basic macroeconomics is that I feel like we (I mean instructors) don’t always do a great job explaining why things work the way that they do. For example, we introduce the concept of gross domestic product, or GDP (but even then are kind of murky on how goods with imported components get counted), and we give the “real” version of GDP a variable, namely Y. We then say that Y can represent aggregate production, expenditure, or income. Ok great- I guess it has to be true that the amount people spend on our stuff has to equal our income, but it would be nice to point that out explicitly. What is less obvious is why it must be true that the amount of stuff we produce has to equal the amount that people spend on the stuff we make- sure, that should be true in equilibrium, but what is stopping an economy from producing a whole bunch of stuff that goes into inventory? As it turns out, the extra output is counted as purchased by the company that made it, so we’re sort of forcing that part of the equality by redefining expenditure a bit. Sure, why not.
You see this all the time in macro, and what it means is that the spending on an economy’s output can be broken down into
Consumption spending (C) – spending by households in an economy
Investment spending (I) – spending by (mostly) businesses in an economy on stuff that makes more stuff
Government purchases (G) – spending by the government
Net Exports (NX, or X – IM) – the difference between foreign spending on domestic goods (exports, X) and domestic spending on foreign goods (imports, IM)
If you’re anything like me, this all makes perfect sense until you get to the net exports part, and then you’re like wait, what? Allow me to summarize a discussion that I think should happen in the classroom much more…
If we think about the different ways that stuff can be produced and consumed, we get something like this:
Since GDP, by definition, represents domestic production (regardless of where stuff is consumed), the area that should count in GDP looks like this:
But let’s think about the other GDP categories for a second- consumption, for example. Consumption represents the purchases by domestic households (other than newly constructed houses, technically speaking), and, if you’re anything like me, some of what you consume is produced in the U.S. and some of it is imported. As a result, the consumption area looks like this:
Therefore, the GDP identity needs to have a correction factor to turn the domestic consumption area (and, by similar logic to some degree, investment and government spending) into the domestic production area. Looking at the picture, it becomes pretty clear that we can do so by adding in exports and subtracting out imports. Funny thing- this is exactly what the net exports category of expenditure does!
Hopefully that helps the expenditure identity actually make sense as opposed to something you just memorize and try not to think too hard about. But it also highlights an important point- taking away imports, in and of itself, doesn’t increase GDP. Now, I get why people might think that, since looking at the basic Y = C + I + G + (X – IM), it certainly seems like Y goes up if you take away the thing that is subtracted out. The problem with this logic, as the pictures above illustrate, is that the IM part is just a correction factor, and you can’t take away a correction factor without also taking away the thing that you’re correcting! In other words, if you’re going to take away IM, you have to reduce, well, mainly C, and maybe some I and G, by a corresponding amount, at least in an accounting sense.
As a result, whenever anyone tells you that limiting or eliminating imports will increase GDP, they are making hidden assumptions about consumption (mainly along the lines that people will just buy domestic stuff instead and nothing else will change) that generally fall under the heading of assuming the conclusion. They are also potentially ignoring the fact that such an increase may not actually increase households’ standard of living if it makes their consumption decrease. (Taking away $100 of my imported stuff isn’t going to magically generate $100 of just as cool stuff for me to purchase from domestic producers- if this were true, I probably wouldn’t have been buying imported stuff in the first place.)
With me so far? Great, you’re farther along than Trump’s economic advisers in an important way. In reality, there are interconnections between the expenditure components that are not shown in the basic Y = C + I + G + NX identity, and these interconnections make it so that you can’t just look at this simple equation to analyze cause and effect.
But of course you can’t model an economy just using the national income accounts identity. Even a freshman at the end of ec 10 knows that trade deficits go hand in hand with capital inflows. So an end to the trade deficit means an end to the capital inflow, which would affect interest rates, which in turn influence consumption and investment.
I suppose that their calculations might make sense in the simplest Keynesian Cross model, in which investment is exogenously fixed and consumption only depends on income. But that is surely not the right model for analyzing the impact of trade policy over the course of a decade.
(Mankiw provides more detail, but you have to acknowledge that Krugman wins the headline game.) I find it funny that people make it such a big deal when Mankiw and Krugman agree on anything…I mean, they agree on lots of stuff, namely basically everything in their respective textbooks. (Related: I know people who won’t use one of said textbooks bc of bias or whatever, and I find it hilarious since they are functionally identical for the most part.)
The problem is interesting because there is an objectively “right” answer- absent specific circumstances, one dead person is better than five dead people- but psychology, philosophy, ethics, etc. bring in a whole host of other considerations having to do with intention, fate, and so on. Such considerations result in a problem without a correct answer, and these considerations can’t (and probably shouldn’t) be ignored in a society of human beings and not robots.
Because of this unexpected complexity, the trolley problem has spawned a number of extensions, ranging from the even more nerdy…
…to the snarky and political:
There’s even what I will call the economist version, which incorporates opportunity cost/cost of effort as well as a few other factors…
In any case, we seem to be pretty familiar with the “clear efficient answer under some basic assumptions, but fairness and ethical considerations make things complicated” concept. So allow me to present a more accurate economic version of the trolley problem:
You are currently looking at a crisis area. The status quo is that there is a large shortage of Uber drivers to get people out of the area. Do you 1. Do nothing, or 2. Implement surge pricing?
I think this is a situation that we’ve seen before a number of times. Allow me to explain the similarities to the trolley problem:
“Clear Efficient Answer Under Some Basic Assumptions”
Surge pricing is the obvious answer here, under two assumptions: first, that surge pricing gets more drivers to the area, and second, that how much a person is willing to pay for an Uber is an accurate proxy for how important it is to them. Under these assumptions, shortages are smaller (or nonexistent) under surge pricing than they would be otherwise, and cars go to those who need/want them the most. (In case you’re curious, the first assumption seems to have empirical support even though surge pricing doesn’t appear to always get more drivers on the road overall.)
“But Fairness and Ethical Considerations Make Things Complicated”
I can’t really tell you what’s fair- that’s kind of the deal with value judgments- so I will instead report some common themes that I’ve come across. One is that people should have at least a chance to get an item at the “regular” price, and some people view random rationing as more ethical than price-based rationing when extenuating circumstances are present. (I wonder how this would change if pricing were framed as regular prices and discounts rather than surge pricing.) Another is that willingness to pay is a better proxy for wealth than need/want, in which case surge pricing unfairly rations items to rich people. (This may be true in cases of extreme income inequality, but shouldn’t be the case in a market with more uniformly distributed resources, so this view is somewhat of a fact/opinion hybrid.) Yet another that hadn’t even occurred to me (thanks Internet!) is that it’s unethical to use the promise of money to get largely low-income individuals (the Uber drivers) to take on risk of bodily harm, especially when said risk is incurred during the service of higher-income individuals. (See last point, and note that this is the same logic used to justify outlawing kidney donations and such.) Yet another is what Russ Roberts says. You’ll notice that all of the fairness arguments presented except the last one are against surge pricing.
My point in bringing this up isn’t to have a discussion on fairness or convince you of anything in this regard- like I said, you’re more than welcome to subscribe to one of these viewpoints or come up with your own. My point, instead, is to highlight the role that economics can play in conversations about what is best for society. To that end, here are a few points to keep in mind:
Economists can tell you what is efficient under certain assumptions, but they can’t definitively tell you what is fair.
The assumptions used to determine the efficient outcome can and should be examined.
Just because something is a market outcome doesn’t mean it’s fair. (In fact, the existence of market failures implies that market outcomes aren’t even always efficient.)
But so does efficiency- it’s reasonable to ask economists to stay in their lane, but not to discount them entirely.
I guess I could do something similar for economists:
State your assumptions.
At least try to stay in your lane.
When you venture into the philosophical lane, make it clear that you are doing so.
Fairness matters, and people really hate it when you dismiss their value judgments as irrelevant. They’re even likely to reject what you can show them about efficiency if you do so.
Okay? Great- now let’s go have some thoughtful policy discussions.
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.
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.