Results matching “"greg mankiw"”

Dan Lakeland has been thinking about taxation curves and the poverty trap.

One theme that comes up a lot when we discuss race and politics in the United States is the way that the concept "race" itself changes over time. For example, nowadays you hear a lot about white voters, but fifty years ago, the central category was occupied by white Protestants. White Catholics were considered a separate category, not black but not fully mainstream white, sort of like Hispanics and Asians today. (This is not to say that today's commentators treat whites as a monolithic bloc, there's still lots of talk about the Catholic vote, but I think that "white" as a category is perceived as having more meaning today as a national political category, compared to how things were thought of than in the mid-twentieth century.)

Another example is the perception of Asian Americans. I was thinking about this topic recently after seeing this offhand comment in a blog by Tom Maguire:

The Japanese are neither brown-skinned nor Muslim nor poor . . .

I'm pretty sure he's right about there being very few Muslim or poor people in Japan. (Not zero in any case but a small fraction of the total population of the country. Apparently Japan has a high rate of relative poverty, though.)

But I always thought the Japanese did have brown skin. I guess people used to say "yellow," but it always seemed more like "tan" to me. I wonder if this is some sort of modern redefinition: if Japanese are honorary whites, then their skin gets lightened too? I'm not suggesting any malign intent here on the part of Maguire, just wondering if there's some relabeling going on implicitly.

Sort of vaguely related to the idea that, until very recently, in the U.S. when people said "Asian" they meant east Asian and not, for example, south Asian. (Consider, for example, HABAW. I think she would've been referred to as HIBAW had she been south-Asian-looking. Especially considering that she had a British accent.)

P.S. That first paragraph above was pretty much of a mess. Probably because it's based on my speculations and not backed by any hard facts.

P.P.S. I can't quite bring myself to post this on 538; it doesn't quite seem of general interest. Scrolling through Maguire's blog, I noticed that he and his commenters don't have a very high impression of Nate, so I don't know what they'd think of my comments here.

P.P.P.S. I encountered Maguire's blog through a typically circuitous internet path, starting with a search offi my own blog to find this graph that had been posted by Greg Mankiw and then going to the source, then to this update, to the main page of that blog, where i scrolled though a few pages of Youtube links until I found this link which caught my eye.

A key principle in applied statistics is that you should be able to connect between the raw data, your model, your methods, and your conclusions.

Unfortunately, this principle isn't often well understood. We've all seen it a zillion times: someone shows you a regression analysis with a counterintuitive result, but then when you ask to see where in the data this is happening, you're told: Don't worry, it's a regression, we controlled for everything. Or you'll see a regression or some other analysis backed up (if you could say that) by a couple of anecdotes. Again, though, you have to put full trust in the statistical analysis, because you can select an anecdote to support almost any point.

It is possible, however, to do better. IIn my own work, I try to link data to results in several ways: most obviously, with scatterplots showing data and fitted models (lots of examples in ARM) but also with graphical model checking. Your model's wrong, you know, and it can be a good idea ot explore the ways it doesn't match the data used to fit it, and to explore the ways it doesn't jibe with other information you have.

Anyway, this was really all just by way of introducing a beautiful little example from Seth Masket on the topic of national unemployment rates and congressional elections. After Masket posted a graph showing zero correlation between unemployment rates and the President's party's losses in midterm elections, Ross Douthat responded skeptically in the New York Times:

In the last 50 years, there's only been one midterm election fought with unemployment above 8 percent, let alone 10. (That would be 1982, when Reagan's Republicans lost 22 House seats.) The sample size of relevant races is way too small to draw any useful generalizations, in other words, and it's better to fall back on common sense . . .

Masket responded:

I agree with you that the lack of historical cases with very high unemployment should give us some humility in predicting next year's election. . . . As it happens, the average midterm seat loss for the president's party over the past sixty years is 22 seats. So if we knew nothing else about next year's election, the Democrats losing 22 House seats would be a reasonable guess. The fact that the one case with unemployment over nine percent (1982) produced precisely the average number of seat losses suggests that unemployment really isn't a factor.

Very nice.

P.S. For another example of the power of combining models with simple numbers, and also on the topic of unemployment rates, see Greg Mankiw's useful discussion of the difficulties of evaluating interventions when n=1:

Updated graph is here.

Also this scary, scary picture. Here I'd prefer to go back a few years on the x-axis. The graph with the forecast pretty much had to start near 2009--that's when the with/without-recovery-plan lines come from. But the historical jobs graph would be much better going back ten years or longer. Sure, you want enough resolution so you can see the trend in the past year, but you also want enough context to have a sense of the fluctuations, so you can see how often it is that 5 million jobs disappear like this.

The sequel is already assured of box-office success, so now's the time to start thinking about what's gonna be in volume 3. Here are a few models that Levitt and Dubner could consider, in no particular order:

I don't really think this one is of general interest so I'll put it all below the jump . . .

Greg Mankiw links to an article that illustrates the challenges of interpreting raw numbers causally. This would really be a great example for your introductory statistics or economics classes, because the article, by Robert Book, starts off by identifying a statistical error and then goes on to make a nearly identical error of its own! Fun stuff.

Greg Mankiw writes:

The next time you hear someone cavalierly point to international comparisons in life expectancy as evidence against the U.S. healthcare system, you should be ready to explain how schlocky that argument really is.

He points to the following claim by Gary Becker:

National differences in life expectancies are a highly imperfect indicator of the effectiveness of health delivery systems.for example, life styles are important contributors to health, and the US fares poorly on many life style indicators, such as incidence of overweight and obese men, women, and teenagers. To get around such problems, some analysts compare not life expectancies but survival rates from different diseases. The US health system tends to look pretty good on these comparisons.

Becker cites a study that finds that the U.S. does better than Europe in cancer survival rates and in the availability of hip and knee replacements and cataract surgery.

It makes a lot of sense to think of health as multidimensional, so that some countries can do better in life expectancy while others do better in hip replacements and cancer survival.

But I disagree with Mankiw's claim that it's "schlocky" to compare life expectancy. If the U.S. really is spending lots more per person on health care and really getting less in life expectancy compared to other countries . . . that seems like relevant information.

Greg Mankiw looked up the Consumer Reports of ratings of car companies and found:

Dead last was Chrysler. CU recommended zero percent of the Chrysler vehicles they tested. That's right--zero. Second to last was General Motors. CU recommended 17 percent of GM models. By contrast, most other companies had half or more of their models get the thumbs up. Honda was the top ranked brand; CU recommended 95 percent of its models.

Mankiw writes:

Is it any surprise that Chrysler and GM are now in the process of going out of business? From the perspective of the Consumer Reports advice, it looks like their business model was to count on the ignorance of the buying public about the quality of their products. Their bankruptcy should perhaps be viewed as a success of the market system.

This makes sense to me, but I wonder if it explains too much. Presumably these companies have been making crappy cars for awhile. How did the companies stay alive so long? In all seriousness, perhaps the market system would've been more successful had it shut down those companies 10 or 15 years ago.

Beyond this is the principal-agent problem, or moral hazard, or whatever it's called, by which the people who make the decisions to make crappy cars are probably not actually going broke themselves: the companies might fall apart, but they'll do OK, I assume. So I can see how the companies could stay alive for awhile, living off their assets and their ability to borrow money. I just don't completely see it as a "success of the market" that they've been hanging on so long when the low quality of their products has been public knowledge.

Here.

P.S. I added a note explaining why Mankiw's reasoning may make more sense than I thought at first.

P.P.S. Update here.

Greg Mankiw reports on an article by Betsey Stevenson and Justin Wolfers that finds:

By many objective measures the lives of women in the United States have improved over the past 35 years, yet we show that measures of subjective well-being indicate that women's happiness has declined both absolutely and relative to men. . . . Relative declines in female happiness have eroded a gender gap in happiness in which women in the 1970s typically reported higher subjective well-being than did men. . . .

Mankiw concludes: "It sounds like either the women's movement was a mistake or subjective happiness is not the right objective." The bit about the women's movement doesn't make sense to me--this reasoning seems to contradict the point Mankiw made a few days ago about the difficulty of making inferences based on n=1.

If I had to make a quick guess, I would've gone with the hypothesis of economic stress combined with the difficulty of having a job and taking care of the kids, but Stevenson and Wolfers discuss this issue (see pages numbered 15 and 17 and Table 3 of the linked article) and show that the data don't particularly support this hypothesis.

Getting back to Mankiw's comment: Setting aside the line about the women's movement--who knows, maybe the women's movement was a mistake, it's hard to say with n=1 what might have happened in its absence--I think he's right that subjective happiness is not an "objective." People have written about this: you don't become happy by aiming for happiness as an objective, you become happy by doing things that make you happy (or, just by being the kind of person who's happy in any case). It's an interesting issue, but I'm not sure how this is relevant to the Stevenson and Wolfers study.

P.S. If I were Betsey Stevenson, I might be a little unhappy that Mankiw referred to the authors unalphabetically as Wolfers and Stevenson!

P.P.S. Mankiw has fixed this and put the authors in the correct order.

Greg Mankiw has a nice little discussion of the difficulty of evaluating the effects of interventions in the n=1 setting:

As Mankiw points out, the bad news about the unemployment rate is bad news with or without the recovery plan and thus--although it certainly seems to knock down the predictions shown in that graph--it does not provide much information on the causal effect of the fiscal stimulus. Especially given that the graph comes from a report released in early January, before anyone knew what would end up being included in the final version of the stimulus plan.

Nate Silver and Greg Mankiw have an interesting exchange about the use of exogenous instruments to estimate causal effects. Unfortunately, the subject is macroeconomics, a topic on which I know next to nothing beyond what I learned in Mr. Cutlip's econ class in 11th grade. But I think it is, in Greg's phrase, "a teachable moment" on the subject of causal inference.

Greg summarizes the exchange pretty well, although I think he's missing a key point.

Nate noticed a newspaper article where Greg related research by Christina and David Romer on the effects of "exogenous" tax cuts on the economy. Nate writes:

The type of tax cut that Romer and Romer think falls into this category is what they call an "exogenous" tax cut -- one designed not to counter business cycles, but rather a "spontaneous" tax cut under relatively healthy economic circumstances.

This is very much not the type of tax cut that we are contemplating right now. Instead, what is being contemplated is a countercyclical action in an unhealthy economy designed to return the economy to normal growth. Romer and Romer are not all that keen on this type of tax cut; in fact, they argue that such "countercyclical fiscal policy is not achieving its intended purpose" . . .

Greg repiies:

Why did the Romers focus on exogenous policy changes? The reason is that these are the only changes that can be used to reliability identify the effects of tax policy. . . . The Romers focus on exogenous tax changes for the same reason doctors conduct randomized drugs trials--not because they are interested in randomization as a prescriptive tool, but because randomization solves a statistical identification problem.

And now here are my thoughts, again with full recognition that I can really only comment on the statistical issues here, not the economics.

First, Greg is right that it is generally considered desirable or even optimal to estimate treatment effects using randomized experiments or exogenous implementations (but see here for an opposite view from James Heckman), even when the ultimate goal is to understand how the intervention works in the wild, so to speak.

But there is the potential for treatment interactions--that is, a treatment might be more effective in some conditions than in others. There's lots of evidence for treatment interactions in various settings, ranging from education to job training. And this is what Nate is talking about. Again, without attempting to comment on the economics, the treatment effect could vary enough that Nate could be right about the direct relevance of the Romers' study of exogenous tax changes.

To put it another way, Greg is talking about identifiability and Nate is talking about generalizability.

Greg writes, "I usually don't respond to blogosphere commentary on my work because, after all, time is scarce." But since he's had time to respond once, perhaps he'll be able to respond again and clarify this issue. (I think my time is particularly non-scarce since I'm responding to blogosphere commentary on somebody else's work!) In any case, I like the idea of shifting the debate to a discussion of treatment interactions since then it might be more possible to resolve this on a technical level. Perhaps a teachable moment for me as well as for others.

Not to pick on Greg Mankiw, but is he saying that it was a good thing that the stock market rose 50% during the two years he was in the Council of Economic Advisors? I'm not at all trying to blame him for what happened, but in retrospect wouldn't one want to regret the big climb in the stock market that preceded the fall? This is completely out of my area of expertise so I defer to others on this. It looks like Mankiw is making a joke of some kind but I don't know enough about the background to really understand the point. (Probably this is the same reaction that many readers to this blog get when I make a statistics joke.)

Mankiw calculates that McCain's tax plan would tax him at a marginal rate of 83%, while Obama's would tax his marginal dollar at 93%. He concludes:

The bottom line: If you are one of those people out there trying to induce me [Mankiw] to do some work for you, there is a good chance I will turn you down. And the likelihood will go up after President Obama puts his tax plan in place. I expect to spend more time playing with my kids. They will be poorer when they grow up, but perhaps they will have a few more happy memories.

I don't quite follow Mankiw's reasoning on the marginal tax rates, except I do get his point that his marginal dollars are all ultimately going to his kids--none of it will be spent in his lifetime, so in that sense he's talking about different varieties of an estate tax.

I'm more interested in the decision implications.

To start with, it does sound like Mankiw's kids are already well provided for, and, although I'm sure they'd disagree with me on this, it's not clear that they would benefit from having more money in the bank when their parents are gone.

So, from that point of view, the question is why Mankiw isn't already spending more time playing with his kids? I can't speak for him, but for me, I have to say that it can be fun to work (or even to write blog entries). But, more than that, I feel a sense of obligation to get things done. At some level, getting paid is part of the motivation, but in any particular example I'm not quite sure how it fits in. I do lots of work things that pay me $0; I think they're important, so I do them.

On the other hand, if I really, really didn't need the money, I could set my salary to $0 and spend the money on extra postdocs. That would be pretty cool but I can't really live on $0 and keep my current lifestyle.

For Mankiw, I'm not sure; maybe he makes enough from his textbooks that he doesn't need much of his academic salary and could possibly do more by converting it into postdocs and research assistants. Or maybe he already has more research assistants than he knows what to do with; I don't know. But his division of waking hours into "working" or "playing with kids" is, I would guess, not very sensitive to the marginal tax rate.

Greg Mankiw writes,

Cass Sunstein and Justin Wolfers say we don't really know whether or not capital punishment deters crime.

Maybe so, but it does solve the problem of recidivism.

He links to a news article that refers to an excellent article by Wolfers and Donohue. But I don't think Mankiw is correct about capital punishment solving recidivism. A key aspect of the death penalty in the U.S. is how rare it is for prisoners to actually be executed. I don't see how you solve the problem of recidivism by executing on the order of a hundred people a year. And, given that already our best estimate is that a person who is sentenced to death has a two-thirds chance of having that sentence reversed by a higher court, it's hard for me to believe that the rate of executions can be increased very much.

Greg Mankiw links to this article by Peter Lawrence on the mismeasurement of science. Lawrence writes:

Modern science, particularly biomedicine, is being damaged by attempts to measure the quantity and quality of research. Scientists are ranked according to these measures, a ranking that impacts on funding of grants, competition for posts and promotion. The measures seemed, at first rather harmless, but, like cuckoos in a nest, they have grown into monsters that threaten science itself. . . .

The journals are evaluated according to impact factors, and scientists and departments assessed according to the impact factors of the journals they publish in. Consequently, over the last twenty years a scientist's primary aim has been downgraded from doing science to producing papers and contriving to get them into the "best" journals they can. Now there is a new trend: the idea is to rank scientists by the numbers of citations their papers receive. Consequently, I predict that citation-fishing and citation-bartering will become major pursuits. . . .

I have a few scattered thoughts on this. First, it is kind of funny to rate a paper based on the citation counts of the journal where it appears. But I guess that might make sense for a fresh new paper that hasn't had a chance to get cited. (An exception would be this paper, which got cited a lot before it ever was published, but that was an unusual case due to unforeseen delays with a journal that was just starting up.)

Second, I think it makes sense to separate two concerns: (a) criticisms of citation indexes for evaluating existing research, and (b) concerns about incentives that will distort future research. I expect that there is a high correlation between the quality of scientists' research programs and their citation counts. But I could see problems heading into the future.

Another issue is citation inflation. At least if you check on Google Scholar, you'll find that citations have increased lots just in the past few months--I assume they've either added new journals to the database or gotten better at linking citations to articles.

You also have to be careful comparing researchers in different fields. Biology and computer science get lots more citations than statistics.

Finally, I don't quite understand Mankiw's response. He links to Lawrence's article, which would seem to indicate some agreement with it, but then he also links to a list which puts him at #6 among all economists. These views aren't necessarily inconsistent--it's possible for a measurement system to be crappy but to still give reasonable results in individual cases--but it's not clear to me what Mankiw's actual views are. In particular, as an economist he might have some view of the importance of these rules as incentives. (The list itself is interesting to look at. I've heard of only a few people on the list, so I can't be sure, but it looks like you have to go down to #66 to find a woman. (Not that things would be much different in a list of statisticians.) I have to say, though, the idea of such a list is pretty unpleasant to me. I mean, how would it feel to be, say, #124 on the list? Would it be frustrating to be so low, or just cool to be on the list at all?

This is some mix of political science and sociology, I'm not quite sure which...

From Greg Mankiw I saw this newspaper article by Steven Pinker, "In defense of dangerous ideas: In every age, taboo questions raise our blood pressure and threaten moral panic. But we cannot be afraid to answer them":

Do women, on average, have a different profile of aptitudes and emotions than men?

Were the events in the Bible fictitious -- not just the miracles, but those involving kings and empires?

Has the state of the environment improved in the last 50 years?

Greg Mankiw and Tyler Cowen point to the release of this book by Bryan Caplan, so it might be worth pointing to my discussion of an earlier version of the book that he showed me when I visited his university in 2005. I don't like the title (unsuprisingly, since I wrote a paper called Voting as a rational choice), but Caplan's book is interesting.

My full comments are here, and here's the short version:

I noticed a link by Tyler Cowen:

A few days ago Paul Krugman argued (Times Select, or here is a Mark Thoma summary) that it matters a great deal which political party rules in the United States. Republicans tend to bring gilded ages, Democrats tend to bring greater income equality.

Cowen gives some discussion and links to other comments by Andrew Samwick, Greg Mankiw, and Matthew Yglesias, along with this overview by Brad DeLong.

Anyway, I think all these people should take a look at Larry Bartels's recent paper on income, voting, and the economy. Here's Larry's graph:

I won't repeat my summary of Larry's paper here and my further comments here except to say that, yes, sample size issues are a concern but Larry has a coherent and interesting story. Definitely worth looking at if you're interested in the topic, whatever your political perspective.