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## The top marginal income tax rate should be about 65%…

by Mike Kimel

Cross posted at the Presimetrics blog.

To maximize real economic growth in the United States, the top marginal income tax rate should be about 65%, give or take about ten percent. Preposterous, right? Well, it turns out that’s what the data tells us, or would, if we had the ears to listen.

This post will be a bit more complicated than my usual “let’s graph some data” approach, but not by much, and I think the added complexity will be worth it. So here’s what I’m going to do – I’m going to use a statistical tool called “regression analysis” to find the relationship between the growth in real GDP and the top marginal tax rate. If you’re familiar with regressions you can skip ahead a few paragraphs.

Regression analysis (or “running regressions”) is a fairly straightforward and simple technique that is used on a daily basis by economists who work with data, not to mention people in many other professions from financiers to biologists. Because it is so simple and straightforward, a popular form of regression analysis (“ordinary least squares” or “OLS”) regression is even built into popular spreadsheets like Excel.

I think the easiest way to explain OLS is with an example. Say that I have yearly data going back to 1952 for a very small town in Nebraska. That data includes number of votes received by each candidate in elections for the city council, number of people with jobs, and number of city employees convicted of graft. If I believed that the votes incumbents received rose with the number of people jobs and fell with political scandals, I could have OLS return an equation that looks like:

Number of incumbent votes = B0 + B1*employed people + B2*employees convicted of graft

B0, B1, and B2 are numbers, and OLS selects them in such a way as to minimize the sum of squared errors you get when you plug the data you have into the equation. Think of it this way – say the equation returned was this:

Number of incumbent votes in any given year

= 28 + 0.7*employed people – 20*employees convicted of graft

That equation tells us that the number of incumbent votes was equal to 28, regardless of how many people were employed or convicted of graft. (Bear in mind – that first term, the constant term as it is called, sometimes gives nonsensical results by itself and really is best thought of as “making the equation add up.”) The second term (0.7*employed people) tells us that every additional employed person generally adds 0.7 votes. The more people with jobs, the happier voters are, and thus the more likely to vote for the incumbent. Of course, not everyone with a job will be pleased enough to vote for the incumbent. Finally, the last term (- 20*employees convicted of graft) indicates that every time someone in the city government is convicted of graft, incumbents lose 20 votes in upcoming elections due to an increased perception that the city government is lawless.

Now, these numbers: 28, 0.7, and -20 are made up in this example, but they wouldn’t have been arrived at randomly. Instead, remember that together they form an equation. The equation has a very special characteristic, but before I describe that characteristic, remember – this is statistics, and statistics is an attempt to find relationships based on data available. The data available for number of people employed and number convicted of graft – say for the year 1974 – can be plugged into the equation to produce an estimate of the number of votes. That estimate can then be compared to the actual number of votes, and the difference between the two is the model’s error. In fact, there’s an error associated with every single observation (in our example, there’s one observation per year) used to estimate the model. Errors can be positive or negative (the estimate can be higher than the actual or lower), or even zero in some cases.

OLS regression picks values (the 28, 0.7, and -20 in our example) that minimize the sum of all the squared errors. That is, take the error produced each year, square it, and add it to the squared errors for all the other years. The errors are squared so that positive errors and negative errors don’t simply cancel each other out. (Remember, the LS in OLS are for “least squares” – the least squared errors.) You can think of OLS as adjusting each value up or down until it spots the combination that produces the lowest total sum of squared errors. That adjustment up and down is not what is happening, but it is a convenient intuition to have unless and until you are someone who works with statistical tools on a daily basis.

Note that there are forms of regression that are different from OLS, but for the most part, they tend to produce very similar results. Additionally, there are all sorts of other statistical tools, and for the most part, for the sort of problem I described above, they also tend to produce similar outcomes.

I gotta say, after I wrote the paragraphs above, I went looking for a nice, easy representation of the above. The best one I found is this this download of a power point presentation from a textbook by Studenmund. It’s a bit technical for someone whose only exposure to regressions is this post, but slides eight and thirteen might help clarify some of what I wrote above if it isn’t clear. (And having taught statistics for a few years, I can safely say if you’ve never seen this before, it isn’t clear.)

OK. That was a lot of introduction, and I hope some of you are still with me, because now it is going to get really, really cool, plus it is guaranteed to piss off a lot of people. I’m going to use a regression to explain the growth in real GDP from one year to the next using the top marginal tax rate and the top marginal squared. (In other words, explaining the growth in real GDP from 1994 to 1995 using the top marginal rate in 1994 and the top marginal rate in 1994 squared, explaining the growth in real GDP from 1995 to 1996 using the marginal rate in 1995 and the top marginal rate in 1995 squared, etc.) If you aren’t all that familiar with regressions, you might be asking yourself: what’s with the “top marginal rate squared” term? The squared term allows us to capture acceleration or deceleration in the effect that marginal rates have on growth as marginal rates change. Without it, we are implicitly forcing an assumption that the effect of marginal rates on growth are constant, whether marginal rates are five percent or ninety-five percent, and nobody believes that.

Using notation that is just a wee bit different than economists generally use but which guarantees no ambiguity and is easy to put up on a blog, we can write that as:

% change in real GDP, t to t+1 = B0 + B1*tax rate, t + B2*tax rate squared, t

Top marginal tax rates come from the IRS’ Statistics of Income Historical Table 23, and are available going back to 1913. Real GDP can be obtained from the BEA’s National Income and Product Accounts Table 1.1.6, and dates back to 1929. Thus, we have enough data to start our analysis in 1929.

Plugging that into Excel and running a regression gives us the following output:

Figure 1

For the purposes of this post, I’m going to focus only on those pieces of output which I’ve color coded. The blue cells tell us that the equation returned by OLS is this:

% Change in Real GDP, t to t+1 = -0.15 + 0.63*tax rate, t – 0.48* tax rate squared, t

From an intuition point of view, the model tells us that at low tax rates, economic growth increases as tax rates increase. Presumably, in part because taxes allow the government to pay for services that enhance economic growth, and in part because raising tax rates, at least at some levels, actually generates more effort from the private sector. However, the benefits of increasing tax rates slow as tax rates rise, and eventually peak and decrease; tax rates that are too high might be accompanies by government waste and decreased private sector incentives.

The green highlights tell us that each of the pieces of the equation are significant. That is to say, the probability that any of these variables does not have the stated effect on the growth in real GDP is very (very, very) close to zero.

And to the inevitable comment that marginal tax rates aren’t the only thing affecting growth: that is correct. The adjusted R Square, highlighted in orange, provides us with an estimate of the amount of variation in the dependent variable (i.e., the growth rate in Real GDP) that can be explained by the model, here 17.6%. That is – the tax rate and tax rate squared, together (and leaving out everything else) explain about 17.6% of growth. Additional variables can explain a lot more, but we’ll discuss that later.

Meanwhile, if we graph the relationship OLS gives us, it looks like this:

Figure 2

So… what this, er, (if I may be so immodest) “Kimel curve” shows is a peak – a point an optimal tax rate at which economic growth is maximized. And that optimal tax rate is about 67%.

Does it pass the smell test? Well, clearly not if you watch Fox News, read the National Review, or otherwise stick to a story line come what may. But say you pay attention to data?

Well, let’s start with the peak of the Kimel curve, which (in this version of the model) occurs at a tax rate of 67% and a growth rate of 5.85%. Is that reasonable? After all, a 5.85% increase in real GDP is fast. The last time economic growth was at least 5.85% was in the eighties (it happened twice, when the top rate was at 50%). Before that, you have to go back to the late ‘60s, when growth rates were at 70%. It isn’t unreasonable, then, to suggest that growth rates can be substantially faster than they are now at tax rates somewhere between 50% and 70%. (That isn’t to say there weren’t periods – the mid-to-late 70s, for instance, when tax rates were about 70% and growth was mediocre. But statistics is the art of extracting information from many data points, not one-offs.)

What about low tax rates – the graph actually shows growth as being negative. Well… the lowest tax rates observed since growth data has been available have been 24% and 25% from 1929 to 1932… when growth rates were negative.

What about the here and now? The top marginal tax rate now, and for the foreseeable future will be 35%; the model indicates that on average, at a 35% marginal tax rate, real GDP growth will be a mediocre 1.1% a year. Is that at all reasonable? Well, it turns out so far that we’ve observed a top marginal rate of 35% in the real worlds six times, and the average growth rate of real GDP during those years was about 1.4%. Better than the 1.1% the model would have anticipated, but pretty crummy nonetheless.

So, the model tends to do OK on a ballpark basis, but its far from perfect – as noted earlier, it only explains about 17.6% of the change in the growth rate. But what if we improve the model to account for some factors other than tax rates. Does that change the results? Does it, dare I say it, Fox Newsify them? This post is starting to get very long, so I’m going to stick to improvements that lie easily at hand. Here’s a model that fits the data a bit better:

Figure 3

From this output, we can see that this version of the Kimel curve (I do like the sound of that!!) explains 36% of the variation in growth rate we observe, making it twice as explanatory as the previous one. The optimal top marginal tax rate, according to this version, is about 64%.

As to other features of the model – it indicates that the economy will generally grow faster following increases in government spending, and will grow more slowly in the year following a tax increase. Note what this last bit implies – optimal tax rates are probably somewhat north of 60%, but in any given year you can boost them in the short term with a tax cut. However, keep the tax rates at the new “lower, tax cut level” and if that level is too far from the optimum it will really cost the economy a lot. Consider an analogy – steroids apparently help a lot of athletes perform better in the short run, but the cost in terms of the athlete’s health is tremendous. Finally, this particular version of the model indicates that on average, growth rates have been faster under Democratic administrations than under Republican administrations. (To pre-empt the usual complaint that comes up every time I point that out, insisting that Nixon was just like Clinton in your mind is not the point here. The point is that in every presidential election at least since 1920, the candidate most in favor of lower taxes, less regulation and generally more pro-business and less pro-social policy has been the Republican candidate.)

Anyway, this post is starting to get way too lengthy, so I’ll write more on this topic in the next few posts. For instance, I’d like to focus on the post-WW2 period, and I’m going to see if I can search out some international data as well. But to recap – based on the simple models provided above, it seems that the optimal top marginal tax rate is somewhere around 30 percentage points greater than the current top marginal rate. The recent agreement to keep the top marginal rate where it is will cost us all through slower economic growth.

As always, if you want my spreadsheet, drop me a line. I’m at my name, with a period between the mike and my last name, all at gmail.com.

It occurs to me that I should probably explain why I used taxes at time t to explain growth from t to t+1, rather than using taxes at time t+1. (E.g., taxes in 1974 are used to explain growth from 1974 to 1975, and not to explain growth from 1973 to 1974.) Some might argue, after all, that that taxes affect growth that year, and not in the following year. There are several reasons I made the choice I did:

1. When changes to the tax code affecting a given year are made, they are typically made well after the start of the year they affect.

2. Most people don’t settle up on taxes owed in one year until the next year. (Taxes are due in April.)

3. Causation – I wanted to make sure I did not set up a model explaining tax rates using growth rather than the other way around.

4. It works better. For giggles, before I wrote this line, I checked. The fit is actually better, and the significance of the explanatory variables is a bit higher the way I did it.

## A Simple Explanation for a Strange Paradox: Why the US Economy Grew Faster When Tax Rates Were High, and Grew Slower When Tax Rates Were

by Mike Kimel

A Simple Explanation for a Strange Paradox: Why the US Economy Grew Faster When Tax Rates Were High, and Grew Slower When Tax Rates Were Low
Cross posted at the Presimetrics blog.

If you are familiar with my writing, you know that for years I have been covering the proverbial non-barking dog: the textbook relationship between taxes and economic growth, namely that higher marginal rates make the economy grow more slowly, is not borne out in real world US data.

Sure, there are a whole raft of academic studies that claim to show just that, but all of them, without fail, rely on rather heroic assumptions, and most of them throw in cherry picked data sets to boot. Leaving out those simple assumptions tends to produce empirical results that fail to abide by the most basic economic theory. This is true for data at the national level and at the state and local level.

Making matters more uncomfortable (and thus explaining all the heroic assumptions and cherry picking of data in the academic literature) is that the correlations between tax rates and economic growth are actually positive. That is to say, it isn’t only that we do not observe any relationship between tax rates and economic growth, in general it turns out that faster economic growth accompanies higher tax rates, not lower ones, and doesn’t take fancy footwork to show that. A few simple graphs and that’s that.

Now, obviously I sound like a lunatic writing this because it goes so far against the grain, but a) I’ve been happy to make my spreadsheets available to any and all comers, and b) others have gotten the same results on their own. Being right in ways that are easily checkable mitigates my being crazy (or a liar, for that matter), but it doesn’t change the uncomfortable fact that data requires a lot of torture before conforming to theory. And yet, that’s the road most economists seem to take, which explains why economics today is as useless as it is. It also speaks poorly of economists. The better approach is come up with theory that fits the facts rather than the other way around.

I’ve tried a few times to explain the relationship that I’ve pointed out so many times, but I never came up with anything that felt quite right. I think I have it now, and it’s very, very simple. Here goes.

Assumptions:
1. Economic actors react to incentives more or less rationally. (Feel free to assume “rational expectations” if you have some attachment to the current state of affairs in macro, but it won’t change results much.)
2. There is a government that collects taxes on income. (Note – In a nod to the libertarian folks, we don’t even have to assume anything about what the government does with the taxes. Whether the government burns the money it collects in a bonfire, or uses it to fund road building and control epidemics more efficiently than the private sector can won’t change the basic conclusions of the model.)
3. People want to maximize their more or less smoothed lifetime consumption of stuff plus holdings of wealth. More or less smoothed lifetime consumption means that if given the choice between more lifetime consumption occurring, with the proviso that it happens all at once, or a bit less lifetime consumption that occurs a bit more smoothly over time, they will generally prefer the latter. Stuff means physical and intangible items. People also like holding wealth at any given time, even if they don’t plan to ever spend that wealth, because wealth provides safety, security, and prestige, and for some, the possibility of passing on some bequest.

(If the first two look familiar, they were among 8 assumptions I used last week in an attempt to get where I’m going this time around. Note that I added two words to the second assumption. More on last week’s post later.)

Due to assumptions 1 and 3, people will want to minimize their tax burden at any given time subject provided it doesn’t decrease their lifetime consumption of stuff plus holdings of wealth. Put another way – all else being equal, peoples’ incentive to avoid/evade taxes is higher when tax rates are higher, and that incentive decreases when tax rates go down. Additionally, most people’s behavior, frankly, is not affected by “normal” changes to tax rates; raise or lower the tax rates of someone getting a W-2 and they can’t exactly change the amount of work they do as a result. However, there are some people, most of whom have high actual or potential incomes and/or a relatively large amount of wealth, for whom things are different. For these people, some not insignificant amount of their income in any year comes from “investments” or from the sort of activities for which paychecks can be dialed up or down relatively easily. (I assume none of this is controversial.)

Now, consider the plight of a person who makes a not insignificant amount of their income in any year comes from “investments” or from the sort of activities for which paychecks can be dialed up or down relatively easily, and who wants to reduce their tax burden this year in a way that won’t reduce their total more or less smoothed lifetime consumption of stuff and holdings of wealth. How do they do that? Well, a good accountant can come up with a myriad of ways, but in the end, there’s really one method that reigns supreme, and that is reinvesting the proceeds of one’s income-generating activities back into those income-generating activities. (i.e., reinvest in the business.) But ceteris paribus, reinvesting in the business… generates more income in the future, which is to say, it leads to faster economic growth.

To restate, higher tax rates increase in the incentives to reduce one’s taxable income by investing more in future growth.

A couple acknowledgements if I may. First, I would like to thank the commenters on my last post at the Presimetrics and Angry Bear blogs, as well as Steve Roth for their insights as they really helped me frame this in my mind.

Also, I cannot believe it took me this long to realize this. My wife and I are certainly not subject to the highest tax rate, and yet this is a strategy we follow. At the moment, we are able to live comfortably on my income. As a result, proceeds from the business my wife runs get plowed back into the business. This reduces our tax burden, and not incidentally, increases our expected future income.

## Why the Economy Stubbornly Insists on Growing More Slowly When Taxes are Lower

by Mike Kimel

An Economic Theory That Uses Micro Forces to Explain Macro Outcomes: Why the Economy Stubbornly Insists on Growing More Slowly When Taxes are Lower

Cross-posted at the Presimetrics blog.

I’ve been writing for years about the fact that a basic piece of economic theory does not apply to real world US data: unless one engages in the sort of assumptions that can justify eating ceramic plates as a cure for leprosy, there is simply no evidence that lower taxes lead to the good stuff we’ve been led to believe over non-cherry picked data sets. Recent examples include this look at the effect top federal marginal rates on various measures of growth, this look at the effect of top federal marginal rates on tax revenues, a different look at federal marginal rates and growth, and this look using state tax levels. I’ve also shown that effective tax rates also have fail to cooperate with theory when looking over the length of presidential administrations – examples include myriad posts and Presimetrics, the book I wrote with Michael Kanell.

I think the reason a lot of people have trouble accepting this is that they see some sort of conflict between this macro fact and and what seems to be a self-evident micro truth – if tax rates get high enough, people will work less. Now, such micro-macro conflicts have existed in the past, and are certainly aren’t unique to economics. One obvious example we all live with is that to each of us, from where we’re standing, the Earth does a pretty good job of appearing to be flat, and yet we know that its actually round(ish). For most applications, from running a marathon to building a house to making toast, assuming that the earth is flat doesn’t hurt, and even simplifies matters. That is to say, for most applications facing critters roughly our physical size, a flat earth is a good model. On the other hand, we’d be much impoverished by sticking to that model at all times, as we’d lose out on satellites, our understanding of weather and geology, a great deal of transoceanic shipping, and Australia.

The same thing is true when it comes to the economy – failing to understand and account for the dichotomy between micro and macro truths is harmful. It has cost us, all 6.8 billion of us, economic growth and wealth, which is to say, it has cost us in quality and length of life. But nobody is trying to explain that dichotomy, in part because so few people see it. There is a profession that should be trying to explain this dichotomy, and that is the economic theorists. However, they seem to be pretending the data isn’t there, so waiting around them to explain it means more loss of quality and length of life. So let me take a crack at it.

In addition to explaining the real world reasonably well, a good theory, in my opinion, should not rely on crazy assumptions. After all, a theory that doesn’t make any sense simply isn’t going to get used even in the unlikely event that it works. So I came up with a theory that relies on only a few assumptions, all of which are sane and which hew pretty close to the real world. My assumptions are these:

1. Economic actors react to incentives more or less rationally. (Feel free to assume “rational expectations” if you have some attachment to the current state of affairs in macro, but it won’t change results much.)
1a. The probability that an economic agent will choose to do any work is inversely related the tax rate. At 100% tax on income, work drops, but not to zero – many of us do some charity work, after all, for which we aren’t compensated at all. On the other hand, not everyone is going to work even if tax rates drop to 0%.
2. Economic actors do not have perfect information about the economy, and are not homogeneous. They have different skillsets and different size, and that limits their opportunities at any given time. On the other hand, some economic actors are sufficiently similar to other economic actors that they could occupy similar economic niches, albeit they wouldn’t necessarily produce identical output.
3. Economic actors come in different sizes. Small players cannot compete with large players on economies of scale. (I get really irritated with the oft-repeated assumption that everyone is the same size, or that any unemployed person can walk into a bank and borrow \$1.2 billion to build a chip fab.)
4. Economic actors are at least somewhat risk averse.
5. Many parts of the economy are characterized by economies of scale. At some point those economies of scale may reverse themselves, but economic actors rarely work at points where the diseconomies of scale have become strong.
6. Many parts of the economy are characterized by lumpiness. If an economic player is into hot dog stands, for instance, it can buy one hot dog stand, or two, or three, but it can’t buy 2.7183 hot dog stands.
7. Among the the pieces of the economy characterized by economies of scale and lumpiness are tax evasion/avoidance, which economic actors will engage in due to assumption number 1. That is to say, \$1,000 spent on attorneys, accountants and economists in the course of a \$100,000 project will gets you less tax evasion/avoidance than the same amount (or even a proportionately larger amount) spent in the course of a \$100,000,0000 project.
8. There is a government that collects taxes. (Note – In a nod to the libertarian folks, we don’t even have to assume anything about what the government does with the taxes. Whether the government burns the money it collects in a bonfire, or uses it to fund road building and control epidemics more efficiently than the private sector can won’t change the basic conclusions of the model.)

I trust there aren’t any assumptions on this list that seem particularly heroic or which contradict the real world in any important way. Additionally, I don’t think there’s anything here that a conservative or libertarian would object to either. So I figure we’re good to go.

Let’s focus on one particular economic actor (or entity or firm or player), and let’s put some numbers down for simplicity of keeping track of going on. Say this one actor has \$100 million (whether debt or equity is irrelevant to the model) which it can invest – and it can invest all, part, or none of that \$100 million. To keep things really simple, say this actor must decide how to allocate its funds between a single \$100 million investment and five \$20 million investments, each of which has an expected return of X% a year before taxes.

Essentially, this player has four forces acting upon its decision making process.

1. Risk aversion. That makes the actor lean away from the one big project and toward some number of the smaller projects, both to avoid having all its eggs in one basket, and because by avoiding the one big project it doesn’t have to invest the full \$100 million. Instead of investing in five small projects, for instance, it can invest in four at a cost of \$80 million, and keep \$20 million cash.
2. Economies of scale. That makes the actor lean toward the one big project over the five smaller projects.
3. The marginal tax rate. If its too high, that actor will simply sit on its hands. If not, it will invest some amount of its \$100 million.
4. Economies of scale in tax avoidance/evasion. That tends to lead toward the one big project over the five smaller projects, since the net benefits of tax avoidance from one big project exceed the net benefits of tax avoidance from several small projects.

Now, forces 1 and 2 push in opposite directions. Force 3 is orthogonal to 1 and 2, and force 4 is parallel to force 2. All of which means it is easy for a player who chooses to invest rather than sit on his hands, and who otherwise is evenly balanced between one large and multiple small projects (or even tilting slightly toward multiple small projects) by forces 1 and 2 to be pushed toward the one big project by force 4. Let me restate – under some circumstances, marginal tax rates are low enough not to preclude investment altogether, but are high enough that due to scale economies, the gains of tax avoidance/evasion from large projects so exceed the gains to tax avoidance/evasion from small projects to make a single large project more desirable than a group of small projects, even though the latter would have been more desirable in the absence of taxes. Furthermore, there is some positive probability that shrinking marginal tax rates reduces force 4 enough to keep this story from being true.

This follows in a straightforward way from the assumptions, and looks a lot like real world situations. I assume its not objectionable even if you’re fortunate not to have ever worked for a Big 4 accounting firm. But, it has important implications. See, by taking the single big project rather than the multiple small projects, our player increases economic growth several ways. These include:

1. Because of project lumpiness, by going the big project route, it has to invest the full \$100 million. Had it gone the small project route, there is a positive probability that risk aversion would have led it to invest \$80 million (or \$60 million) instead, meaning \$20 million (or \$40 million) would not have been put to work in the economy.
2. It spends less on tax avoidance/evasion services with the single large project than with multiple small projects. Since these services produce a private gain but don’t actually generate output, that reduces the drag on the economy.
3. As noted previously, small players are reluctant to take on big players – sure, it happens, but in general, small players prefer to go up against other small players than against big players. (Think Walmart and the centipede game.) But small players are priced out of the big projects. So if small players find bigger guys entering their potential space, they are more likely to sit on their hands (or focus on what amounts to the smaller, more wasteful projects among options available to them, potentially forcing out the even smaller guys, etc.).

But that is one single player. In a big enough economy, there can be many, many companies and/or individuals of many different sizes in just such a situation. With 310 million people and who knows how many companies in the economy, probabilities add up. (I note that the second benefit of biasing companies toward their largest available projects goes away when you consider the whole economy. After all, while company X saves on accountants/attorneys and economists by picking the larger projects, by leaving the smaller projects to smaller players, those players will be hiring accountants/attorneys and economists as well.)

Note that relaxing a few assumptions makes it even easier to understand why US macro data shows a positive correlation between marginal tax rates and real economic growth. For instance, it isn’t difficult to imagine that the government actually does something useful (i.e., growth generating) with the some of the tax money it collects. Additionally, smaller firms are often more innovative than larger firms, even within the same space (one has to compete somehow). Our little story is one where under many circumstances, smaller firms are more likely to enter the market when tax rates rise than when tax rates fall.

Thus, this little story, while requiring only a few realistic assumptions, does something that as far as I know is unique in the field of economics: it explains why US macro data shows a positive correlation between the top marginal tax rates and economic growth for all but the most cherry picked data sets, and it does it by sticking to micro foundations. I’m sure it could be improved, but but I think its a good start. Your thoughts?

## Economic hitman

by Mike Kimel

Cross posted at the Presimetrics blog.

I guess when you’re a very not famous (co-)author like yours truly, people start contacting you with information about their books. I got an e-mail today from another currently very not famous author plugging his book, and I found it to be an interesting concept.

The book is called The Economics of Ego Surplus by Paul McDonnold, and it is a “novel of economic terrorism.” This website allows you to read the first 54 pages of the novel. I personally read about ten or so, and decided to order the rest of the book. (Note – I don’t know Paul McDonnold, never heard of him before he sent me an e-mail, and am getting nothing out of this. He did want to send me, maven that I am, a free copy of the book but I am sending him a check.) FWIW, its not so polished that it doesn’t come across as a first novel, but on the other hand, it smoothly blends in some economics/finance with a Dan Brown-style conspiracy. Put another way – it reads like the bestseller I picked up at the airport a couple of weeks ago before getting on a plane with the added benefit of dealing with a field I find interesting. Put yet another way, it reads like books by Paul Erdman, who I used to read for fun back in college and grad school. For those of us who like our economics/finance, and enjoy the occasional (for me, time is a major constraint these days, and thus we’re mostly talking when I travel) thriller its nice to see another example of the two combined.

Can you think of other examples from this genre?

Tags: ,

## Hauser’s Law is Extremely Misleading

by Mike Kimel

Cross posted at the Presimetrics blog.

A friend sent me a link to this Wall Street Journal opinion piece by W. Kurt Hauser. Who is he, you ask? Here’s what it says at the bottom of the article:

Mr. Hauser is chairman emeritus of the Hoover Institution at Stanford University and chairman of Wentworth, Hauser & Violich, a San Francisco investment management firm. He is the author of “Taxation and Economic Performance” (Hoover Press, 1996).

Before I go on, let me note that in this piece, Hauser masterfully demonstrates the Hoover Institution approach to data. The piece contains enough, er, material that I could write several posts on it. Maybe I will, but for now I want to focus on his key point. Here are the opening paragraphs of the essay modestly entitled “There’s No Escaping Hauser’s Law”:

Even amoebas learn by trial and error, but some economists and politicians do not. The Obama administration’s budget projections claim that raising taxes on the top 2% of taxpayers, those individuals earning more than \$200,000 and couples earning \$250,000 or more, will increase revenues to the U.S. Treasury. The empirical evidence suggests otherwise. None of the personal income tax or capital gains tax increases enacted in the post-World War II period has raised the projected tax revenues.

Over the past six decades, tax revenues as a percentage of GDP have averaged just under 19% regardless of the top marginal personal income tax rate. The top marginal rate has been as high as 92% (1952-53) and as low as 28% (1988-90). This observation was first reported in an op-ed I wrote for this newspaper in March 1993. A wit later dubbed this “Hauser’s Law.”

Over this period there have been more than 30 major changes in the tax code including personal income tax rates, corporate tax rates, capital gains taxes, dividend taxes, investment tax credits, depreciation schedules, Social Security taxes, and the number of tax brackets among others. Yet during this period, federal government tax collections as a share of GDP have moved within a narrow band of just under 19% of GDP.

OK. So, Hauser’s point is clear – no matter what happens to taxes, the government only manages to collect about 19% of GDP. Presumably then, from a perspective of paying down debt, there’s no benefit to raising taxes and plenty of benefit to cutting taxes. (Later he goes on to argue that lower taxes = faster growth, which I’ve dispensed with in the past – latest example here. Still, if given time, I might come back and examine Hauser’s special way of reaching his conclusion. But that’s for another day.)

Now, they say a picture is worth a thousand words, so let me put up a graph. And for grins, let me embed a small table in that graph. The graph shows total federal receipts divided by GDP. However, it is color coded. In years when there is a cut in the top individual marginal tax rate, or when the most recent change in the top marginal tax rate was a tax cut rather than a tax hike, the area under the curve is colored gray. When there is a tax hike, or the most recent change was a tax hike, the same area is colored red. Here’s what it looks like:

Figure 1.

So there it is. There’s Hauser’s law. Notice the size of his narrow band – its width is over 5% of GDP! Now take a gander at the little table. In tax hike periods, the smallest amount collected was 18.3% of GDP. By contrast, the median collection in tax cut periods is 18.2%; in other words, in over half of the tax cut years, collections were less than the smallest amount ever brought in during the tax hike periods. Furthermore, both the median and average for the two series are a full percent of GDP apart. Hauser is essentially sweeping humongous differences under the table.

Think Hauser doesn’t know this? I don’t. He’s been staring at the data, and using it to make arguments for a very long time. He also writes extremely precisely. At no point does he make a false statement, but I for one reached all sorts of mis-impressions just from his opening paragraphs. Like I said, its a masterful example of the Hoover craft.

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## A Proposed Bet for Professors Bryan Caplan and David R. Henderson

by Mike Kimel

A Proposed Bet for Professors Bryan Caplan and David R. Henderson (and Anyone Else Who Believes Lower Taxes Generate Faster Economic Growth)
Cross posted at the Presimetrics blog.

Professors Caplan and Henderson,

Both of you have had recent posts that indicate you have some enthusiasm for betting on economic outcomes. (Your co-blogger at Econlog, Arnold Kling seems less enthusiastic about bets, and thus I have not addressed him by name here.) I have a few criticisms of your approach to betting. The first is that, frankly, y’all are betting on some rather peripheral issues. Why not cut to the chase? Why not propose a bet on something vital to your way of thinking but with which many people disagree? For example, as libertarians you believe that lower marginal tax rates on the “producer class” result in faster economic growth in a well-functioning, more or less market based economy, and that this outcome can be observed in the US economy. (Forgive the wordiness, but I want to be precise so you don’t think I’m trying to trap you up in a technicality or some oddball example.) I believe you are generally wrong, at least about the US economy. Many people share your beliefs, and many people share mine, so this would be an ideal topic on which to bet if your goal is to prove a point.

Another criticism I have of the bets I’ve seen you propose is that your bets tend to be based on a small number of events, typically one or a handful of observations occurring over ten years or less. But that is too short a period to leave out the effect of random fluctuations, acts of God, or long running conditions. For example, though I haven’t verified the data myself, I understand that it has been pointed out that had the Julian Simon bet (a favorite of Professor Henderson’s) occurred a few years later results would have been different. A truly fair bet would look at more data. In fact, an ideal bet would look at many different overlapping long time periods. Results over ten year windows, twenty windows, thirty year windows, etc., would all combined to ensure that the results aren’t just an artifact of the data.

Another safeguard which helps get at a “true outcome” rather than some random fluctuation is to consider whether the effect you are looking at can have lags of different lengths. For example, it may be that the marginal tax rate in 2010 might affect growth rates from 2009 to 2010, or from 2010 to 2011, or from 2010 to some later year. After all, as any libertarian would say, if you pay less in taxes this year, it means more money in your pocket this year. Since you spend more efficiently than the government, that creates more growth this year, and that additional growth has positive effects next year too. Of course, at some point, the future effects of today’s tax rates dissipate. Not having a precise theory, it probably pays to consider several of these “effect periods” to (perhaps) coin a term.

The third problem I have with your bets is that, frankly, it takes too long to find out who won. Professor Henderson indicates in one post that he’ll probably be settling up with the estate of fellow bettor. If bets are intended as a way to help move the field, not to say the bettors beliefs, forward, results have to come in more quickly to make an appreciable difference. Now, at first glance, this last complaint kind of clashes with my previous criticism that ten years of data is just not enough. But if you think about it, there’s an easy way to square the circle: the obvious solution is to bet on outcomes that occurred on the past.

Now, before you say that’s silly, hear me out. You wouldn’t (dare I say couldn’t?) be a libertarian if you didn’t believe that historically, economic growth in the US was faster when the marginal income tax rates on what you would term the productive class were lower than when they were higher. And in the unlikely event you’ve read anything I’ve written, whether on the Presimetrics or Angry Bear blogs, or the book I cowrote with Michael Kanell, you would be aware that I am pretty certain the US economy is not characterized by such a relationship between marginal tax rates and economic growth. Simply put, what each of us knows about the past contradicts what the other knows. At least one of us has to be wrong.

Given how bet-happy you all are, and your core beliefs, I would have expected you to propose this one (not necessarily to me, who you no doubt don’t know from Adam) a long time ago. To be precise, here’s the bet I would have expected you to issue:

For the vast majority (say, at least 70%) of overlapping windows, the correlation between top marginal individual income tax rate and the growth in real GDP will be negative. Windows of data to be considered are ten years long, twenty years long, etc., through sixty or seventy years long. Growth rates to be compared with marginal tax rates at time period t include t – 1 to t, t to t+1, t to t+2, t to t+3, t to t+4, and t to t+5.

Now, I could see variations of that bet. For instance, Professor Henderson has indicated in a recent working paper that he doesn’t believe National Accounts data for the WW2 years are accurate, so perhaps he would structure the bet to only use data from 1946 on, rather than the 1929 on which is possible with the official BEA data. Alternatively, perhaps t to t+5 might seem a little much to consider, or perhaps one would prefer to include t to t+x where x is something larger than 5. Nevertheless, this is very close to the bet I would have expected to be proposed from people with strong libertarian beliefs who like to engage in wagers on economic outcomes.

One other thing – notice that I indicate the correlations should be negative well over half the time. I have yet to hear a libertarian hedge when he/she tells me about the benefits of lower taxes. Getting a touch above 50% just doesn’t fit that with that sort of certainty, and is more akin to random fluctuation, is it not? (But don’t worry, where we’re going, the distinction between 50.00001% and something more appropriate to your level of certainty won’t matter.)

What is left is to consider – what should have been the size of the bet we should have expected to see. Now, Professor Caplan has recently noted:

But why are small sums enough to deter 95%+ of the people who disagree with me? I see two main reasons:

1. We aren’t just betting \$100. We’re betting \$100 plus reputation plus bragging rights. That’s why I prefer to bet the famous. The Simon-Ehrlich victory wouldn’t have been nearly as awesome if Simon bet a random Malthusian.

2. Many spouses, perhaps most, disapprove of betting. They think you’re irresponsible when you bet, and stupid when you lose. Imagine how badly they’d react if the stakes were \$25k! Even the victor might find himself stuck in the doghouse.

So… \$100 plus bragging rights is about right. Of course, I’m not famous, so I doubt the bet would have been issued to me. I would have taken the \$100 bet, though, if offered. More – well, probably not, despite my certainty, given item number 2. Nevertheless, I am surprised that neither of you offered this bet to someone.

But here’s the thing. You would have lost that bet. And we’re not talking by a smidge, we’re talking by a country mile… or seventeen and a half.

Here’s what I get:

by Mike KimelFigure 1.

(Note – you might have to click on the figure to see it in full. It seems to cut off on my browser. The same is true of the next figure.)

The way to read this graph…. consider the cell with t to t+3 on the horizontal and 50 years on the vertical. That cell has 62.1% in it. That indicates that of the 29 fifty year windows in which you can measure the growth in real GDP from a given year to three years later, 18 of them (or 62.1% of them) show a positive correlation between the top marginal tax rate. That is to say, in 62.1% of those windows, growth is faster when top marginal tax rates are higher than when tax rates are lower.

Notice… most of the squares have numbers above 50% in them. That means, in most situations we considered, more often than not, the correlations between marginal tax rates and growth rates are positive, not negative. When the negative correlations do occur, they tend to occur over the very short term. Put another way – they have negative repercussions that hit later. (And yes, that is what the table indicates.) Over longer periods of time, the percentage of time positive correlations are observed approaches 100%. This cannot in any way be reconciled with libertarian theory.

FWIW, the table above represents a grand total of 1,652 observed correlations between the top marginal tax rate and growth rates of real GDP. 56.5% of those correlations are positive.

Note… I haven’t included it in the table, but for giggles I checked the t to t+10 results. For ten and twenty year windows, the percentages are below 50%. For thirty year windows and up, the percentages are above 50% and go above 70% at 40 year windows and hit 100% at the 70 year windows. Put another way… t to t+10 looks an awful lot like t to t+4.

Now, say you’re Professor Henderson and you want to discard the data through the end of WW2. In that case, you come out looking even worse:

Figure 2.

Now, 64.6% of all the correlations observed are positive.

Now, this post is starting to get awfully long, so let me wrap it up. I think you should offer this bet. In fact, my advice to any libertarian or conservative is to offer to make this bet. Sure, its easy for me to say, because the bet goes against you, but I promise if you offer the bet or something similar I will refrain from jumping in so I’m not making that suggestion for personal gain. The reason I think you should offer this bet is that, knowing you’d lose gives only a few options:

1. You can change your beliefs.
2, You can tapdance into the opposite result. To some extent, that’s where the economic profession is now. There are any number of studies by well known academics that show that cutting taxes lead to faster economic growth under some or most conditions, and they all require either weird special cases or assumptions that, frankly, could be used to show that a 400 year old sketch of a chicken is a nuclear submarine.
3. You can pretend none of this ever happened.
4. You can show there is a problem with what I have done or proposed.

Now, its possible I’ve made a mistake, but to repeat myself, if you’ve read anything I’ve written before, you’ll find that I’ve been on a “the data shows that lower taxes do not equal faster economic growth” kick for a long time. I’ve gotten here every which way, using data from all sorts of sources and at all levels of granularity. In this case, I’m guessing that if you included windows of 11, 12, 13, etc. years, you might push the percentage of positive correlations down. For all I know, with judicious fiddling, you might even get to a point where a slight majority of cases have a negative correlation. I don’t have an institute or a university paying me to make this sort of argument and I’m running out of spare time this afternoon. But even if you got that percentage down a bit – the libertarian position is not that lower taxes lead to faster economic growth somewhere around half the time, is it? And frankly, it would take a heck of a lot to get that number down for a Henderson post-WW2 look. And no matter what, you aren’t going to escape one more detail – over longer periods of time, the correlations are overwhelmingly positive. I’d hate be touting the benefits of lower taxes and having to explain that fact.

Moving on, the problem with option 2, the status quo, or option 3, is that its simple enough to show what I’ve shown. The results are there. As noted above, I’ve done this sort of thing so many times, so many ways, with so many different data sets, and at so many levels of granularity. Sooner or later someone that other folks do listen to will discover the same thing. Then what?

As to option 1, well, Upton Sinclair said it best a long time ago, “It is difficult to get a man to understand something when his job depends on not understanding it.” And frankly, its hard to see GMU or the Mercatus Institute or Hoover or even the blog where you write keeping you on if you start telling people that higher top marginal rates are correlated with faster economic growth. You have a lot to lose if you change your beliefs.

So if you can’t take any of these options, you really need a different approach. And what’s better than going on the offensive? Offer up the bet. Sound confident- a true believer would insist that correlations between lower taxes and faster growth should be there 90% of the time, right? Heck, issue odds. Do that and people might assume you know the results favor your position. People are lazy, and they don’t check. That’s why so many people believe so many things that simply don’t hold up when confronted by data.

Sincerely,

Mike Kimel

PS. The Excel file containing the data and analysis that went into this post is published as a webpage here. I’m not quite sure why but the ten year results seem to have acquired an error upon uploading into google. Everything else seems OK, but should anyone want the original Excel file, drop me a line at mike period and my last name, all at gmail dot com.

## E Pluribus Unum and Our Finest Hour

by Mike Kimel

E Pluribus Unum and Our Finest Hour
Cross posted at the Presimetrics blog.

But if we fail, then the whole world, including the United States, including all that we have known and cared for, will sink into the abyss of a new dark age made more sinister, and perhaps more protracted, by the lights of perverted science. Let us therefore brace ourselves to our duties, and so bear ourselves, that if the British Empire and its Commonwealth last for a thousand years, men will still say, This was their finest hour.

Winston Churchill, Speech to the House of Commons, June 18, 1940

Assuming, as many do, that the British Empire ended some time around the handover of Hong Kong, it did not last a thousand years. (Britain and its Commonwealth, of course, are still going strong.) Nevertheless, I suspect many would say that Churchill got his way, and that the Battle of Britain and the remainder of World War 2 was, in fact, the finest hour of the British Empire.

What about the American Empire? If we define that institution as existing from some time around the Spanish American War (1898) to the point where it was overextended and became unable to impose its will on friend or foe alike (i.e., some time around 2005), what was its finest hour? What were its most impressive achievements, those that will be written up in history books a thousand years from today?

I am not a historian, but I have a few guesses, in no particular order: (below the fold)

1. Serving as the arsenal of democracy in WW2
2. Putting people on the moon and bringing them safely back to earth.
3. The development of mass media and long distance communications.
4. Almost eradication of polio (yes, a worldwide effort, but just about every significant piece of the project was done in the US)
5. The Green Revolution (a little less US-dependent than the polio effort, but US entities played the biggest role)
6. The Manhattan Project and the development of nuclear energy
7. The early development of genetic engineering (I suspect US dominance in this field will be ending very soon)
8. Construction of the Panama Canal
9. Airplanes
10. An automobile in every driveway
11. The electric grid

I’m sure I’m forgetting something important, and there are, no doubt, things we regard as small that will be viewed as important one day. Still, I would be surprised if what is eventually viewed as the greatest achievement of the American Empire is not on that list. However, not all of the items listed will survive the test of time. Some will be forgotten, some may prove more or less irrelevant over the long haul, some will come to be viewed as a facade and some will be decried by our descendants. Still, its probably not a bad list, and I think its good enough for the purpose of this post, which is to note: the role of the private sector tends to play a relatively small role when it comes to the big achievements. Furthermore, the piece of the private sector that contributes the most to the big advancements, the ones that will be remembered, is the not-for-profit piece of it.

With the possible exception of 3, 9 & 10, the for-profit private sector played the role of sidekick or supporting actor. The main role, the driving force, the entity that either provided the original vision and/or drove that vision through to completion was the government, with much of the remainder provided by academia (heavily funded by the state whether public or private) or NGOs. But even where the private sector led the charge, the government’s role was huge. Henry Ford may have revolutionized the production of cars, but without the government producing roads (not to mention the freeway system), their development would be limited to where they could be used for local transportation. Most of the big achievements, and, I am comfortable making this statement, the finest hour of America, whatever that is judged to be a thousand years from now, are driven by government policy, government actions and government grants.

Why is that? After all, the private sector, after all, makes up the biggest chunk of the economy. Size alone doesn’t isn’t enough to create achievement – the most significant achievements in the private sector usually aren’t those produced by the biggest companies. Similarly, its hard to construct a story that involves the government coming into a field and bigfoots over the early efforts of the private sector. Instead, the government is providing a role that the private sector simply isn’t, cannot, and will not. Why? I have a guess. I suspect it comes to the profit motive. Projects of this nature are risky and costly and hard to make money off of for a very long time, all of which are factors that discourage the private sectors. But the private sector has another problem with “finest hour” type accomplishments, which is evident when you think of Britain and Churchill’s speech. Britain may have been, to Hitler, little more than a “nation of shop-keepers”, but those shop-keepers were willing to fight for an idea, a cause they all had in common. However, its hard to imagine a company providing a vision that unites a nation. Occasionally, a company is able to inspire its employees to greatness – think Hewlett Packard before Carly Fiorina and the era of continuous layoffs. However, even then, the reach of its vision, its ability to bring others on-board, is generally limited to that company itself. This is due to human nature. The geniuses – the Einsteins and Borlaugs and Salks aren’t in it for the money, and the rest of us aren’t going to get the warm and fuzzies from increasing the profits of a company for whom we don’t work and in which we don’t own stock.

The only force that can unite the country, that can create a cause around which everyone will rally around, and then only certain circumstances, is the government. E pluribus unum. But that is why the American Empire has been petering out. We are less than two months shy of thirty years from the day when Reagan told us the government is the problem, and we have bought into that mantra hook line and sinker. And in the Tea Party era, it is hard to see how that can be turned around. The long, slow decline is becoming inevitable.

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## Extrapolated September 2010 Debt, by President

by Mike Kimel

Extrapolated September 2010 Debt, by President

Cross-posted at the Presimetrics blog.

This one is quick and dirty because I’m very low on time…. Anyhow, these days there’s a lot of talk about the debt, and some talk about how irresponsible Obama is, or maybe its GW. Who knows, there’s a lotta talk and very little hard facts, much less context. So, from working on Presimetrics, I had some debt data lying around and started with that.

The table below shows the total national debt and ending national debt for each President for December of the year before he took office and the growth rate over that time period. For grins I threw in the President’s extrapolated September 2010 debt. That was computed by taking the debt in December (in September 2010 dollars) of each President’s last year in office, and, assuming the rate at which debt had increased during his term would continue all the way to September 2010.

All data is in September 2010 dollars.

To interpret:

In December 1980, a month before Reagan took office, the debt was 2.354 trillion (Sept 2010 dollars). A month before he left office, in December 1988, that debt had increased to 4.866 trillion, which is an annualized growth rate of 9.50% a year. Starting with 4.866 trillion in December of 1988, and increasing at a rate of 9.50% a year would give you 35 trillion and change by September of 2010.

A few things to note… the two Presidents who added to the debt at the quickest rate were GW in first place and Reagan in second. They were followed by Ford, and then Obama, with GHW Bush not far behind. Now, I’ve been pretty critical of Obama for continuing GW’s policies (see Presimetrics, the book I wrote with Michael Kanell, and this) but all in all, as lousy as he’s been, he’s far, far from the worst perpetrator when it comes to fiscal irresponsibility. (And please, spare me the whole “the banks needed saving” when so did many businesses and households… which weren’t saved. I’d be less inclined to carp if the money was spent on keeping Main Street afloat rather than seeing so much flow to Wall Street.) I wonder how the Tea Partiers would react to that information, and whether they are are angrier at GW and Reagan than they are at Obama. Somehow I doubt it.

Note – the data, data sources, and analysis used in this post are available here.

## Post WW2 Private Investment v. New Deal Private Investment

by Mike Kimel

Post WW2 Private Investment v. New Deal Private Investment
Cross posted at the Presimetrics blog.

I had a post the other day (which appeared at the Presimetrics blog and Angry Bear, and which was followed up by my fellow Angry Bear, Spencer, here) looking at a paper by David R. Henderson about the supposed post-World War 2 economic boom.

I noted that his view fit into a libertarian/conservative story line, but required not only assuming the GDP (or GNP) data from WW2 is wrong, but also that the data at least through the early ’50s is wrong too, despite the fact that the data fits other known facts pretty well. By contrast, Henderson’s story conflicts with known facts in a number of places.

However, there is one point – another libertarian/conservative myth which comes up in the paper that I’d like to focus on in this post. Henderson tells us:

Why did the economy do much better after the war than at the beginning? We can’t know for sure, but the most likely explanation is the change in administration from Roosevelt, who championed central government planning of the economy, to Truman, who was much less inclined to support government control.

See,

Before the United States entered into World War II, the New Dealers—the faction of Franklin Roosevelt’s administration that was most hostile to economic freedom—had significant power. During the war, they were largely displaced by more pragmatic people who were not hostile to free markets (thus the quote from Henry Stimson at the beginning of this section).

Moving on…

Roosevelt’s death cleared the way for President Harry Truman. Although he was a New Dealer, Truman had no love for “the long-haired boys” who were associated with the most anti-market parts of the New Deal—people such as Ben Cohen, William O. Douglas, trust-buster Thurman Arnold, price controller Leon Henderson, and Felix Frankfurter. In 1945 and 1946, Truman got rid of a number of New Dealers, including two of the most prominent ones: former vice president Henry Wallace and Harold Ickes.28

Higgs points out that the polling data bear out the perception of a regime change under Truman. As a result of the change, writes Higgs, “Investors were then much more willing to hazard their private property than they had been before the war, as both survey data and financial market data confirm.”29

And invest they did. As table 2 shows, gross private domestic investment in real 1964 dollars was \$44.4 billion in 1941. For all the war years it was half or less of that 1941 level. In 1946, it shot up to \$51.7 billion, grew slightly to \$51.8 billion in 1947, and then grew to \$60.6 billion in 1948.

So essentially, Henderson’s belief is that there was a boom after WW2 and that it was caused because greater economic freedom encouraged more private investment. We’ve already dealt with the supposed boom, but what about private investment? Was private investment really booming in the post-WW2 era relative to the pre-WW2 era? Simply put, no, as is evident from the following graph, constructed using data from NIPA table 1.1.6:

Figure 1

From the graph, its pretty obvious that the New Deal easily beat Henderson’s post-WW2 boom when it comes to encouraging private investment. The explanation for why is obvious to anyone who has not bought into libertarian or conservative beliefs about how the economy works.

## Discouraging Greg Mankiw From Working Would be Good for the Economy

by Mike Kimel

Discouraging Greg Mankiw From Working Would be Good for the Economy
Cross posted at the the Presimetrics blog.

A few days ago Greg Mankiw had an op ed piece in the NY Times talking about how even small increases in the marginal tax rate would keep him (and by extension, other talented folks like him) from working.

For a laugh, I pulled data on the top marginal tax rate from the IRS and real GDP per capita from the BEA’s NIPA tables. Data on the latter goes back to 1929, and the tax rate info goes back further.

It turns out that the correlation between the tax rate in any given year and the growth rate in real GDP per capita from that year to the next is small but positive. That is, higher top marginal tax rates don’t seem to reduce real economic growth. Look at the tax rate and the annualized growth rate in real GDP per capita for two years, or three, or four, or five, or six (which is as far as I went) and ditto – the higher the marginal tax rate, the faster the economic growth over the next X years. The correlation is positive, if small.

Now, you may be saying to yourself – sure, but the world is very different now than in 1929 or 1942 or 1968. What about in recent times? So let’s start in 1981, which is more or less when a) the ideas that Mankiw endorses took hold and b) Mankiw’s career began. Here’s what that looks like:

Figure 1.

Hmmm… a clear positive correlation between the top marginal tax rate and the growth in real GDP per capita over the next four years. Using three or five year lags decreases the correlation slightly but results are about the same. It would seem that discouraging Mr. Mankiw from working would be a very, very good thing for the economy. That shouldn’t come as a surprise to you if you’ve read my book and maybe I’ll write a bit more about this when I get a chance.

However, while it may seem like I’m being facetious about how discouraging Mankiw from working would be good for the economy, I really believe it. After all, Mankiw’s work consists in large part of advocating a position in his books, lectures, op eds, and as an advisor that, as the graph above shows, is consistent with slower economic growth, and he’s very good at what he does.

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