Relevant and even prescient commentary on news, politics and the economy.

The Pound’s Not Sinking, The Yen’s Not Keeping Up…

Spent the past few minutes reading Alea. jck notes that, over the past five years, the Pound has grown in importance at the expense of the yen and that the Euro has done the same against the dollar.

If this goes against your memory, you’re not part of the IMF.

Even better is when jck gets his funny on. For those screaming about PIIGS, he presents the evidence:

Note: Banks and government debt rollovers amount to €210 bln for 2011, €15 bln lower than in 2010, you would never guess that reading the funny (pink) papers.

Somewhere, an FT editor is reading that and cheering that they’re on holiday until the 4th.

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The Myth-ing Logic of Phony IOUs in the Soc Sec Trust Funds

by Bruce Webb

There has been a recent mini-surge of op-eds claiming that the ‘assets’ of the Trust Funds, all $2.6 trillion, are simply mythical. The most recent Scrooge to argue this line may have been Thomas McClanahan of the KC Star in this piece from Christmas: More mythbusting on Social Security ‘money’” in which he informs us how mistaken we are:

Many people believe the trust fund contains securities that have real economic value. So, a few days ago, I wrote an editorial titled “The myth of the trust fund.”


To turn trust fund bonds into real money, the government must do what it would have to do if the trust fund did not exist: borrow, cut spending somewhere else, or raise taxes. The trust fund bonds may be assets from the point of view of Social Security, but they’re a liability for the government as a whole, and for us as taxpayers

Well the argument is at base nonsense on historical, political, legal and economic grounds, but it also suffers a logical hole big enough to run a Prison Bus through. Because if it is true in 2010 it was equally true in 1993 when the Trust Funds first got back to actuarial balance (per McClanahan another myth presumedly) and more to the point in 1983 when Reagan agreed to the tax increase via the Greenspan Commission. Which would logically make Dutch and the Maestro Greenspan pre-meditated thieves and liars.

And more to the point you would have to add almost everyone in the political, media and academic establishment who didn’t raise this seeming clear objection back in 1993, there being no logical point of separation whereby real assets suddenly turned into mythical ones. True some people have been arguing one version or another of phony IOU for years, but somehow that never got translated into proposals to cut FICA on wage earners, instead they happily collected the money while apparently per Mr. McClanahan never intending to pay it back, the Treasuries never being real to start with.

Sorry this argument cannot be made in good faith, not unless this guy had a Road to Damascus/Could Have Had a V-8 moment in the very recent past. A polite term for this is special pleading, though Conspiracy to Commit a few million Counts of Larceny fits pretty well.

Do the assets in the Trust Fund have economic value? Well since 2006 the DI Trust Fund has been taking first interest and starting last year principle in cash. And the ‘checks’ are clearing, what more proof do you need that Special Treasuries have exchange value beyond the fact they are exchanged for value every workday of the year? Now there are arguments that would plausibly suggest that the strain of this redemption will create a problem at some future date, but those arguments should come with numbers and dates and percentages of GDP attached and not just be advanced via ‘Shazam! Its a Myth! Oh Foolish One!’ No it is a proposal by liars to promote theft. (Which probably blows my opportunity for a party invitation Chez McClanahan).

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Economics: a visual approach

Lee Arnold has a series of visual presentations at a high school level that might be useful to introduce different topics to friends and family that do not follow econoblogs. Here is the introduction to the series, which Lee tells me is for dissemination. (The visuals are copyrighted to help prevent voice overs and other pirating.)

Lee Arnold

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Top Marginal Income Tax Rates & Real Economic Growth, a Bar Chart

by Mike Kimel

Top Marginal Income Tax Rates & Real Economic Growth, a Bar Chart
Cross posted at the Presimetrics blog.

The chart below shows tax rates on one axis and the growth rates in real GDP that accompanied those tax rates on the other:

I broke the tax ranges into 5 percentage point increments centered around intuitive numbers (30%, 35%, etc.). Growth rates are the median observed for each range. For ranges which did not occur in the real world, growth rates are left blank.

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, the graph uses data starting in 1929.

Consider this post a quick follow up to my previous post on optimal tax rates that appeared at the Presimetrics blog and Angry Bear blogs. There will be a lot more follow-ups, but it occurs to me that a look a the data might be useful before going on.

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Robert Reich and old people ‘clogging up the pipes’

Video of Robert Reich is here discussing unemployment and (updated) uses the lump of labor fallacy description:

>>and when we’re at a time when there’s so many people in their 50s who are unemployed and may not be able to get back into the job — the job market, I mean, it’s unlikely to happen, but wouldn’t it be a good idea to actually lower the eligibility for social security retirement?

>>It might be, Sam. In fact, a lot of people right now are saying that the eligibility age for social security retirement given the depth of our continuing jobs recession — and this jobs recession does continue — maybe should be lowered so that you create openings for younger people coming into the job market who right now don’t have a chance because there are so many older people clogging up the pipes, as it were.

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Facts or Fallacies? Part II: In which Paul Krugman takes his lumps and eats them too while Jamie Galbraith runs afoul of the notorious lump-of-labor f

by Tom Walker
(Sandwichman at Ecological Headstand)

Facts or Fallacies? Part II: In which Paul Krugman takes his lumps and eats them too while Jamie Galbraith runs afoul of the notorious lump-of-labor fallacy-fallacy

In comments in response to Part I at Angry Bear, it was suggested that Paul Krugman has also made the lump of labor fallacy claim and that perhaps I should be talking about his arguments (or those of Paul Samuelson) instead of those of conservative think-tankers. Both of those liberal Keynesian economists have indeed advanced the fallacy claim. I would love to discuss the matter with Professor Krugman and sent him an invitation.

Meanwhile, indulge me while I rehearse my debating points on some archival material. I’d also like to bring in a few big names on my side: James K. Galbraith, Dean Baker and… Paul Krugman! Just to even things out, the pre-recession Krugman gets Bruce Bartlett and Larry Summers on his tag team.

(Update: Crooked timber picks up the lump theme.

Here’s what Krugman wrote in his 2003 column, titled Lumps of Labor .

Economists call it the lump of labor fallacy. It’s the idea that there is a fixed amount of work to be done in the world, so any increase in the amount each worker can produce reduces the number of available jobs. (A famous example: those dire warnings in the 1950’s that automation would lead to mass unemployment.) As the derisive name suggests, it’s an idea economists view with contempt, yet the fallacy makes a comeback whenever the economy is sluggish.

Sure enough, the lump-of-labor fallacy has resurfaced in the United States — but with a twist. Traditionally, it is a fallacy of the economically naïve left — for example, four years ago France’s Socialist government tried to create more jobs by reducing the length of the workweek. But in America today you’re more likely to hear lump-of-labor arguments from the right, as an excuse for the Bush administration’s policy failures.

Do I think “any increase in the amount each worker can produce reduces the number of available jobs”? Certainly not. But clearly large gains in productivity require some kind of adjustment. The adjustment process can be slow or fast, smooth or rough, complete or incomplete. Reducing the length of the workweek can be an important part of the adjustment process. So what motivates the “derision” and “contempt” of economists?

Personally, I prefer a piece Krugman wrote six years earlier, The Accidental Theorist in which he chided William Greider for being a “dull boy” and employed a playful hot dogs and buns economy to show why Greider’s alarmism in One World, Ready or Not: The Manic Logic of Global Capitalism was unwarranted. In that column, Krugman didn’t call it a “lump of labor fallacy” but the intent was clearly the same. Thirteen years later, the following comment appeared in reply to Krugman’s column:

Wow. Reading this after recently rereading the chapter entitled “The Alchemists” on the follies of big finance, written by Greider 13 years ago confirmed what I was recently becoming aware of: It turns out that Greider was articulating with shocking prescience what would happen 10 years later while economists like Krugman were mocking him for not consulting them. Well, Greider was right!

To be fair, when the facts change, Krugman changes his mind, as he did in November, 2009 when he endorsed Dean Baker’s proposal for a work-sharing subsidy:

Just to be clear, I believe that a large enough conventional stimulus would do the trick. But since that doesn’t seem to be in the cards, we need to talk about cheaper alternatives that address the job problem directly. Should we introduce an employment tax credit, like the one proposed by the Economic Policy Institute? Should we introduce the German-style job-sharing subsidy proposed by the Center for Economic Policy Research? Both are worthy of consideration.

So much for the non-accidental hot dogs and buns theory… But what about the specific question of older workers and the lump of labor? Couldn’t it be that some lumps are lumpier than others?

In contrast to the delayed retirement age prescriptions of Jason Kuzicki, Francois Melese or Andrew Biggs (see Part I, Jamie Galbraith has been advocating a temporary suspension of the early retirement penalty as a way to open up more jobs for the young. Let Old Folks Retire Early and Make Way for the Young was Galbraith’s contribution to Dan Froomkin’s Huffington Post series on job creation ideas. One comment in response to Jamie’s suggestion declared Galbraith is a victim of the “lump of labor fallacy.

Unfortunately, the commenter, “bgladish”, did not elaborate on his declaration. However, in a Forbes column published last February, Bruce Bartlett explained why “early retirement, work sharing and tax credits won’t boost employment.” To his credit, Bartlett cited not only the ubiquitous lump-of-labor fallacy as the reason why not but also a pair of studies: one from the Social Security Administration and the other by economists at the International Monetary Fund.

The problem is, the IMF study (which I had critiqued two years earlier on EconoSpeak misrepresented the standard rationale in Europe for early retirement — which was not so much to open up jobs for the young as to divert workforce reduction away from the low-seniority, younger workers. The IMF study did helpfully point out, however, with regard to the alleged lump-of-labor fallacy, that “Those who make the fallacy claim fail to offer specific evidence of the supposed belief in a fixed amount of work.”

The main conclusion of the SSA study was, to put it bluntly, utterly irrelevant to the case Bartlett was trying to make. Larry DeWitt, the SSA historian, rejected the hypothesis that the retirement earnings test of social security was designed to open up jobs for the young on the grounds that “the aged had already been forced out of the workforce” so they were “not a major factor in blocking opportunities for the young…” Other points raised by DeWitt were the “ineffectiveness of the supposed incentives,” the gradualness of implementation of the program and, finally, the limited coverage of the original Social Security program. None of these points spoke to the issue of whether or not in today’s circumstances early retirement might be effective in boosting employment among the young.

In his column, Bartlett described work-sharing (which Krugman had endorsed a few months earlier) as “another bad idea making the rounds” and cited the lump of labor fallacy as his rationale for why work-sharing wouldn’t boost employment.

The problem is that the amount of income being produced would still be the same. While some unemployed workers would gain jobs and income, current full-time workers would become underemployed and see a reduction in their incomes.
True to form for lump-of-labor claims, Bartlett failed to cite specific evidence
of the supposed belief in a fixed amount of work.

So I made a point of asking Dean Baker whether or not his work-sharing proposal assumed a fixed amount of work.

Here is Dean Baker whether or not his work-sharing proposal assumed a fixed amount of work. Here is his reply:

Actually, I don’t assume a fixed amount of labor, but in the context of an economic downturn, we definitely are in a situation where there is deficiency of labor demand. In this context any reasonable person would ask whether it is better to have more workers employed at fewer hours per workers or fewer workers (more unemployed people) employed 40 hours a week.

In a more general context, I think we should definitely be trying to have the U.S. follow the rest of the world in promoting shorter workweeks, longer vacations, paid time off for family leave, sick days etc. There is nothing natural about the current workweek. In fact, one of the main reasons that we have not followed the rest of the world in moving toward shorter workweeks is because of the high overhead costs (most importantly health care) associated with hiring workers. Firms would often rather pay a worker time and a half for overtime hours, or even double-time, rather than incur these overhead costs by hiring another worker.

Since this is a rehearsal for a debate with Paul Krugman, I’ll leave the last word to the good Professor:

Should America be trying anything along these lines? In a recent interview in The Washington Post, Lawrence Summers, the Obama administration’s highest-ranking economist, was dismissive: “It may be desirable to have a given amount of work shared among more people. But that’s not as desirable as expanding the total amount of work.” True. But we are not, in fact, expanding the total amount of work — and Congress doesn’t seem willing to spend enough on stimulus to change that unfortunate fact. So shouldn’t we be considering other measures, if only as a stopgap?

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Facts or Fallacies? Part I: BLS Data v. the Zombie Lump-of-Labor Fallacy-Fallacy

Facts or Fallacies? Part I: BLS Data v. the Zombie Lump-of-Labor Fallacy-Fallacy

by Tom Walker (Sandwichman at Ecological Headstand)

In the third quarter of 2010 real GDP in the U.S. was 21 percent higher than it had been in the fourth quarter of 1999. Labor force participation grew during the same period by 9 percent, an increase of nearly 14 million people. However, between December 1999 and September 2010, total non-farm employment fell by just over 200,000.

Here is what Bill McBride at Calculated Risk (“Older Workers and the Lump of Labor Fallacy”) thinks is supposed to happen:

The number of jobs in the economy is not fixed, and people staying in the work force just means the economy will be larger.

True enough, the economy did get larger ñ by 21 percent. But the number of jobs wasn’t just “fixed”; it actually fell by a fraction of a percent, even though labor force participation also grew. According to McBride, the result would be a classic lump of labor fallacy if it was a fear or assumption that people held about the effects of immigration or seniors staying on the job past retirement age.

This is a common error people make with immigration – that immigrants displace other workers, when in fact immigration increases the size of the economy. I suspect we will see more and more of this age related “lump of labor” fallacy.

So, you see, people make a common error when their ideas about how the economy works agree with the facts. (edited by Rdan) Let’s explore this further.

(Rdan here…I want to clear up a point of intent…Bill is not being included in the Mises or AEI camp of philosophy. Krugman and Samuelson talk of the lump of labor fallacy in a similar manner to Calculated Risk. Krugman also chastises the use of )

McBride suspects we will see more of this age-related fallacy. Indeed, we are already seeing more of this kind of fallacy rhetoric just in the last few months from the minions of right-wing Thinktankia urging that the Social Security retirement age be raised. (Pay attention, Bruce Webb and Coberly!)

Here’s Jason Kuznicki from the Cato Institute (“In which the French are explained, but they remain mistaken”) sneering at young people in France who were protesting against raising the retirement age:

It’s our old friend, the lump of labor fallacy: Force the oldsters into retirement, and it’s like a jobs program for everyone else. There is only so much labor to go around ó not like jobs are ever created, you know – so we’d better be sure we get our fair share of it. Or so the theory goes. The protest signs, insofar as they communicate anything worth repeating, have often read Place aux jeunes! Make room for the young! – or similar.

Not that this approach to economics makes any sense, either theoretically or practically. Putting someone out of work faster means he’s not producing anymore, which makes the economy worse off on the whole. And ‘his’ job won’t necessarily stick around, because retirement is often the least painful time at which to eliminate a position entirely. Today’s workers aren’t likely to be trained for the same types of work as their parents and grandparents, and they shouldn’t necessarily want to be. The lump of labor fallacy imagines a world frozen in time, not one of dynamism and growth.

Or how about Francois Melese at the Ludwig von Mises Institute (“French Students Should Celebrate Pension Reform”) chiding those youngsters, professors, and politicians for not better explaining the facts and fallacies of lump-of-labor life to them:

At first glance, students’ fears also seem warranted. If an older worker is forced to work an extra couple of years, it seems obvious this would delay a young person’s entry into the labor force. If we assume there are a fixed number of jobs, the net effect would be to crush employment opportunities for young people. With youth unemployment already absurdly high – over 20 percent – it’s no wonder students spilled out onto the streets to protest.

However, the fact that students are in the streets demonstrating against this particular pension reform suggests professors and politicians deserve an F ó they have failed to explain what economists call the lump-of-labor fallacy. Jobs are not fixed and do not depend exclusively on the supply of labor.

O.K., now I’m confused. Bill McBride and Jason Kuznicki just told me that more workers means more growth and hence more jobs but now Francois Melese explains that jobs do not depend on the supply of labor. Which is it? Maybe Andrew Biggs at the American Enterprise Institute (“The Case for Raising Social Security’s Early Retirement Age”) can straighten things out:

Increasing the number of older workers is the first step toward increasing retirement security, but there must also be demand for this large workforce. Some argue that no jobs exist for older workers. But this claim commits what economists call the “lump-of-labor fallacy,” which holds that there are a limited number of jobs for which workers must compete. Economists almost universally reject this view, and the fact that employment continues to rise despite immigration and rising worker productivity (which presumably would reduce the need for extra workers) speaks against it.

Phew! Now I think I get it! There must also be demand for the larger workforce. And the fact that employment continues to rise in spite of immigration and rising productivity proves that there is not a limited number of jobs…

Oh, wait… That brings me back to the BLS figures I started with. The number of jobs is not increasing. In fact it fell by 200,000 jobs over the last eleven years. How can “the fact that employment continues to rise” speak for or against anything when, in fact, employment doesn’t continue to rise? It’s going to take another blog post (or two) to unravel this unholy mess!

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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

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.

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