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It Was Completely Legal

On January 22, 2012, in Uncategorized, by admin

Cross-posted at the Angry Bear blog.

Almost two decades ago, an acquaintance asked me to lunch in Los Angeles. Said acquaintance mentioned he had connections with a few nightclubs – he stressed that they were nightclubs – in Macao and Hong Kong, and that “Brazilian girls” – women from about 18 to 26 or so – were becoming a hot commodity in both places. Given that I grew up in Brazil, my acquaintance thought he could have me fly down to Brazil and do some recruiting for them.

We never got down to brass tacks because, though it would have been very lucrative, it wasn’t something I could do. Yes, I’m pretty sure it would have been very easy to locate women either “in the trade” or (what they were really after) semi-professionals willing to move to Macao or Hong Kong for promises that they’d make a lot more money. And my bet is that some of these women I would have located would have made a lot of money. And I’m sure that my end of the operation, recruiting, would have been entirely legal by the laws of the US, Brazil, Hong Kong and Macao. Certainly the way the business was described to me, my acquaintance and his connections had no difficulties with the law either. Things were set up in such a way that what they were doing was completely legal.

But there was a problem for me. I didn’t know all that much about the industry with which my acquaintance had turned out to be associated, but I did know it can be a very dangerous one for the type of person they wanted me to recruit. I could only imagine that the potential dangers would be even greater in a foreign country where the person had no ties and little or no status.

I had no illusions that my refusal to participate would make any difference at all. I don’t recall if I ever saw that acquaintance again, but I would be surprised if he didn’t find someone else who took care of recruiting for him. The industry in Hong Kong and Macao, no doubt, continued apace.

I should also note that whoever took that job would have made a lot of money, more than I did in any remotely comparable amount of time. The only thing my walking away accomplished was that whatever happened going forward, I had nothing to do with it. To this day, I have no regrets that I turned my acquaintance down.

I mention all this because the “it was completely legal” defense has been cropping up a lot lately in things I read. I think its use may be about to increase a lot more in the near future. And if I might contribute one thing to the discussion, please remember this: that an activity is completely legal isn’t an excuse for participating in it.

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Cross-posted at the Angry Bear blog.

Via Paul Krugman, I learned of this paper by Peter Diamond and Emmanuel Saez. Diamond, of course, is a Nobel Laureate. I will be shocked if Saez isn’t one too in ten or fifteen years.

Long story made very short, Diamond and Saez jump through a lot of hoops and find that the optimal top marginal income tax rate (all in, that is, including federal, state and local), which they define as maximizing social welfare, is about 73%.
Now, long time readers may recall I’ve been doing this sort of analysis for years, though of course I’ve been looking at tax rates that maximize real GDP growth. Simply put, you cannot maximize long run social welfare if you aren’t maximizing economic growth.

My approach is much simpler than that followed by Diamond and Saez. I like to think its much more intuitive and easier to explain. I note that US data shows a simple quadratic relationship between real GDP growth from one year to the next and tax rates:

growth in real GDP, t to t+1 = f(top marginal tax rate, top marginal tax rate squared, other variables)
One recent post on the topic is here. (Unlike the Laffer curve, the coefficients come out statistically significant and with the right signs.)

I mention all this to note that no matter what I throw into the equation, I find that the top marginal tax rate that maximizes economic growth is somewhere around 65%. Of course, I’ve focused only on federal tax rates… add in state and local it comes pretty close to what Diamond and Saez have found.

As I noted above, my approach is somewhat simpler, and easier to follow than that of Diamond and Saez. Part of the reason is that they come at it from a point of view of elasticities. But with all due respect to my betters (Diamond and Saez, and Krugman as well considering the explanation in his post) I think this is the wrong way to consider the problem. It requires all sorts of assumptions and generalizations about people’s behavior, some of which are both false and create resistance from folks on the right.

For example, there is a notion that raising tax rates will reduce people’s willingness to work… which is only true above certain thresholds. (That threshold, of course, varies per individual.) As anyone who has ever had a business will tell you (when they’re not busy demanding tax reductions), you don’t pay taxes on income from the business if you turn around and reinvest that income. (An accountant would talk to you about decreasing your tax liability by increasing expenses which amounts to the same thing.) You only pay taxes on that income you take that income out, presumably for consumption purposes.

So to simplify, consider an example…. is a successful businessperson more likely to take money out of the business if his/her tax rate is 70% or if its 25%? In general, a person is more likely to take that money at 25%, as there’s less of a penalty. At 70% tax rates, there is more of an incentive to reinvest in the business, creating more growth in the business in subsequent years, and more economic growth thereafter. 70% tax rates are more likely to generate faster economic growth than 25% tax rates precisely because people are self-interested and the higher tax rates induce people to continue investing in things they do well.

(Of course, tax rates can get too high. At 95%, people will reinvest almost every dime… even if they have exhausted every good investment opportunity they have. Thus, to avoid taxes they’ll be making lousy investments which in turn slow economic growth.)

Still, its gratifying to see others who are more, er, credentialed doing similar work. If I might end on a digression, though, I can think of a number of examples of work being done on blogs by people who are essentially hobbyists which is somewhat ahead of the academic literature. However, to a large extent, if something wasn’t published in the academic literature, for all practical purposes it didn’t happen. Which is a shame, because most of us who aren’t academics don’t have time or the resources required for such publication (such as access to econlit). That inevitably slows economic development three ways:

1. the lack of recognition discourages hobbyists who have the potential and otherwise would have the willingness to improve on the existing literature
2. should such hobbyists persist and do the research, that research will not be widely disseminated even if it is an improvement over the academic literature
3. it maintains an insular attitude among those who are not hobbyists

Thanks to Steve Roth of Asymptosis and Jazzbumpa of Retirement Blues for notifying me of Krugman’s post.

And since I always offer… if anyone wants any spreadsheets showing the quadratic relationship between tax rates and economic growth or anything else I’ve done, drop me a line. I’m at my first name (mike) then a period then my last name (kimel – with one m only!!!) at gmail.com.


Cross-posted at the Angry Bear blog.

In this post, I will show that during the New Deal era, changes in the real economic growth rate can be explained almost entirely by the earlier changes in federal government’s non-defense spending. There are going to be a lot of words at first – but if you’re the impatient type, feel free to jump ahead to the graphs. There are three of them.

The story I’m going to tell is a very Keynesian story. In broad strokes, when the Great Depression began in 1929, aggregate demand dropped a lot. People stopped buying things leading companies to reduce production and stop hiring, which in turn reduced how much people could buy and so on and so forth in a vicious cycle. Keynes’ approach, and one that FDR bought into, was that somebody had to step in and start buying stuff, and if nobody else would do it, the government would.

So an increase in this federal government spending would lead to an increase in economic growth. Even a relatively small boost in government spending, in theory, could have a big consequences through the multiplier effect – the government hires some construction companies to build a road, those companies in turn purchase material from third parties and hire people, and in the end, if the government spent X, that could lead to an effect on the economy exceeding X.

This increased spending by the Federal government typically came in the form of roads and dams, the CCC and the WPA and the Tennessee Valley Authority, in the Bureau of Economic Analysis’ National Income and Product Accounts tables it falls under the category of nondefense federal spending.

Now, in a time and place like the US in the early 1930s, it could take a while for such nondefense spending by the federal government to work its way through the economy. Commerce moved more slowly back in the day. It was more difficult to spend money at the time than it is now, particularly if you were employed on building a road or a dam out in the boondocks. You might be able to spend some of your earnings at a company store, but presumably the bulk of what you made wouldn’t get spent until you get somewhere close to civilization again.

So let’s make a simple assumption – let’s say that according to this Keynesian theory we’re looking at, growth in any given year a function of nondefense spending in that year and the year before. Le’t's keep it very simple and say the effect of nondefense spending in the current year is exactly twice the effect of nondefense spending in the previous year. Thus, restated,

(1) change in economic growth, t =
f[(2/3)*change in nondefense spending t,
(1/3)*change in nondefense spending t-1]

For the change in economic growth, we can simply use Growth Rate of Real GDP at time t less Growth Rate of Real GDP at time t-1. The growth rate of real GDP is provided by the BEA in an easy to use spreadsheet here.

Now, it would seem to make sense that nondefense spending could simply be adjusted for inflation as well. But it isn’t that simple. Our little Keynesian story assumes a multiplier, but we’re not going to estimate that multiplier or this is going to get too complicated very quickly, particularly given the large swing from deflation to inflation that occurred in the period. What we can say is that from the point of view of companies that have gotten a federal contract, or the point of view of people hired to work on that contract who saved what they didn’t spend in their workboots, or storekeepers serving those people, they would have spent more of their discretionary income if they felt richer and would have spent less if they felt poorer.

And an extra 100 million in nondefense spending (i.e., contracts coming down the pike) will seem like more money if its a larger percentage of the most recently observed GDP than if its a smaller percentage of the most recently observed GDP. Put another way, context for nondefense spending in a period of rapid swings in deflation and inflation can be provided by comparing it to last year’s GDP.

So let’s rewrite equation (1) as follows:

(2) Growth in Real GDP t – Growth in Real GDP t-1
f[(2/3)*change in {nondefense spending t / GDP t-1},
(1/3)*change in {nondefense spending t-1 / GDP t-2}]

Put another way… this simple story assumes that changes in the Growth Rate in Real GDP (i.e., the degree to which the growth rate accelerated or deccelerated) can be explained by the rate at which nondefense spending as a perceived share of the economy accelerated or deccelerated. Thus, when the government increased nondefense spending (as a percent of how big the people viewed the economy) quickly, that translated a rapid increase in real GDP growth rates. Conversely, when the government slowed down or shrunk nondefense spending, real GDP growth rates slowed down or even went negative.

Note that GDP and nondefense spending figures are “midyear” figures. Note also that at the time, the fiscal year ran from July to June… so the amount of nondefense spending that showed up in any given calendar year would have been almost completely determined through the budget process a year earlier.

As an example… nondefense spending figures for 1935 were made up of nondefense spending through the first half of the year, which in turn were determined by the budget which had been drawn up in the first half of 1934. In other words, equation (2) explains changes in real GDP growth rates based on spending determined one and two years earlier. If there is any causality, it isn’t that growth rates in real GDP are moving the budget.

Since there stories are cheap, the question of relevance is this: how well does equation (2) fit the data? Well, I’ll start with a couple graphs. And then I’ll ramp things up a notch.

Figure 1 below shows the right hand side of equation (2) on the left axis, and the left hand side of equation (2) on the right axis. (Sorry for reversing axes, but since the right hand side of the equation (2) leads it made sense to put it on the primary axis.)

Figure 1
Figure 1.

(Click here for a larger version.)

Notice that the changes in nondefense spending growth and the changes in the rate of real GDP growth correlate very strongly, despite the fact that the former is essentially determined a year and two years in advance of the latter.

Here’s the same information with a scatterplot:

Figure 2
Figure 2.

(Click here for a larger version.)

So far, it would seem that either the government’s changes to nondefense spending growth were a big determinant of real economic growth, or there’s one heck of a coincidence, particularly since I didn’t exactly “fit” the nondefense function.

But as I noted earlier in this post, after the first two graphs, I would step things up a notch. That means I’m going to show that the fit is even tighter than it looks based on the two graphs above. And I’m going to do so with a comment and a third graph.

Here’s the comment: 1933 figures do not provide information about how the New Deal programs worked. After all, the figures are midyear – so the real GDP growth would be growth from midyear 1932 to midyear 1933. But FDR didn’t become President until March of 1933.

So… here’s Figure 2 redrawn, to include only data from 1934 to 1938.

Figure 3
Figure 3.

(Click here for a larger version.)

While I’m a firm believer in the importance of monetary policy, for a number of reasons I don’t believe it made much of a difference in the New Deal era. As Figure 3 shows, changes in nondefense spending – hiring people to build roads, dams, and the like, explain subsequent changes in real GDP growth rates exceptionally well from 1934 to 1938. This simple model explains more than 90% of the change in real GDP growth rates over that period.

Of course, after 1938, the relationship breaks down… but by then the economy was on the mend (despite the big downturn in 1938). More importantly (I believe – haven’t checked this yet!), defense spending began to become increasingly important. People who might have been employed building roads in 1935 might have found employment refurbishing ships going to the Great Britain in 1939.

As always, if you want my spreadsheet, drop me a line. I’m at my first name (mike) period my last name (kimel – not only one “m”) at gmail.com.

An additional comment that did not appear at Angry Bear. This post partly grew out of a small debate I had with Scott Sumner of The Money Illusion. I had a few posts at Angry Bear. This one was my last one commenting on Sumner. But I felt it would be an idea to lay out not “what is wrong with the monetary position” but rather “this is why Keynesian policy was right in the New Deal period.” Hence, the current post.

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Cross-posted at the Angry Bear blog.

I was searching for some information and I stumbled on a post Scutt Sumner wrote last year about Robert Skidelsky’s biography of John Mayndard Keynes. I haven’t read Skidelsky’s book, nor do I know Skidelsky, and its been awful long time since I read Keynes, but this seems an odd complaint:

I’m afraid that his analysis is both misleading and inaccurate. The US gradually depreciated the dollar between April 1933 and February 1934. During that period unemployment was nearly 25% and T-bill yields were close to zero. Keynes argued that monetary stimulus would not be effective under those circumstances, and Skidelsky seems to accept his interpretation (which was published in the NYT during December 1933.)
[Note that Keynes certainly did believe in the "pushing on a string" theory--I frequently get commenters insisting that Keynes didn't believe in liquidity traps.]

Unfortunately, Keynes and Skidelsky are wrong. The US Wholesale Price Index rose by more than 20% between March 1933 and March 1934. In the Keynesian model that’s not supposed to happen. The broader “Cost of Living” rose about 10%. Industrial production rose more than 45%.

Sumner goes on to impugn Skidelsky:

The “disappointing” results that Skidelsky mentions come from cherry-picking a few misleading data points.

All that seems very odd to me. If I were making an argument that conventional monetary policy doesn’t work in a liquidity trap, but that the traditional Keynesian prescription does, I’d start that argument with something very much like the sentences Sumner wrote right after stating “Unfortunately, Keynes and Skidelsky are wrong.”

(Note – I can imagine a “monetary” prescription that I think would help tremendously in a liquidity trap, but it doesn’t look at all like what was done in the 1930s, or what was done since 2007, or from what I can tell, what Sumner suggests. That can be a post for another time.)

Using the graphing tool from FRED, the Federal Reserve Economic Database maintained by the St. Louis Fed, we can show the one year percentage change in both PPI (producer price index) and CPI (consumer price index) from January 1932 to December 1935.

Figure 1.

Here’s what we see: after some massive deflation during the Great Depression, prices start to rise more or less when FDR took office. The annual percentage change in PPI peaked around 23% and change in February 1934, and the CPI peaked a few months later at about 5.6%.

Elsewhere, Sumner attributes that to:

We all know what happened next (well not exactly, but I’ll explain that in another post), so let’s jump ahead to 1933. FDR takes office in March, promising to boost wholesale prices back up to pre-Depression levels. He uses several tools, but the most effective was loosely based on Irving Fisher’s “compensated dollar plan.” Fisher’s plan was to raise the price of gold one percent each time the price level fell one percent. An obscure agricultural economist named George Warren was a big fan of Fisher’s idea, and sold it to FDR with all sorts of fancy charts.

And it worked.

Initially it worked better than any other macroeconomic policy in American history. But at first the policy’s success was mostly accidental, just a matter of talking the dollar down, not enacting Fisher’s specific plan. Nevertheless, prices immediately began rising sharply. Industrial production rose 57% between March and July, regaining over half the ground lost in the previous 3 1/2 years. Then in late July FDR decided to cartelize the economy and sharply raised wages (the NIRA) and industrial output immediately began falling. By late October FDR was desperate for another dose of inflation, and asked Warren to come up with a plan. They decided to have the US government buy gold at a price that would be continually increased in order to reflate the price level.

Sumner even helpfully tells us:

It was a very confusing plan, as they never bought enough gold to equate the government buying price with the free market price in London.

I agree that what Sumner describes is confusing. And yes, the times were desperate, and FDR was flailing around throwing all sorts of things against a wall to see what would work, but when I look at the graph above, and take into account the extremely rapid economic growth that took place during the New Deal era, I see a much simpler story.

1. Aggregate demand was very slack when FDR took office..
2. FDR showed up in Washington with a plan to start spending a lot of money and thus boost aggregate demand.
3. The immediate effect was to convince factories they’d be running down their inventories. That boosted producer prices. It had a much smaller effect on consumer prices because everyone knew the gubmint was going to buy a heck of a lot more producer goods than consumer goods. (The government did buy some consumer goods for the various programs, plus there was a spillover effect, but as the graph clearly shows, the action was on the producer side.)
4. After a bit of time, the public realized FDR wasn’t planning just a one-off, but rather a sustained program of purchases of industrial items. That led them to start using some of their idle capacity, which meant not just selling the fixed amount that was in inventory. The rate of price increases thus dropped.
5. GDP increased at the fastest rate in the United States peacetime history since data has been kept. There was a big hiccup, of course, in 1937 when the government cut back on spending for a while.

By contrast, here’s Sumner explaining his theory:

There is a great deal of evidence that I won’t get into here that suggests the suspension of the gold standard in March 1933, and gradual devaluation between April and February 1934, almost certainly explain most of the increase in goods prices, stock prices, and industrial production during that period. But why? Not because it boosted our trade balance, which actually worsened as the rapid recovery pulled in imports.
Both Gauti and I believe that only the rational expectations hypothesis can explain these events. He focuses on how the regime change led to higher inflation expectations, and thus reduced real interest rates. I prefer to think in terms of specific policy signals sent as rising gold prices changed the future expected gold price, and hence the future expected money supply. I don’t see any non-Ratex explanation that can account for the extraordinary rise in prices and output during March-July 1933. Nominal interest rates didn’t change much, and open market purchases in 1932 (under the constraint of the gold standard) had accomplished little or nothing.

So…. his story requires the devaluation of the currency to worsen the trade balance, and rational expectations to cause a one time explosion in industrial prices and a rather smaller recovery in consumer prices. Rational expectations, however, that came an abrupt halt, at roughly the same amount of time one would predict companies might decide that demand will be sustained enough to start producing more rather than just selling off inventory sitting in warehouses. And his story doesn’t explain why growth was so much faster during the New Deal era than any other period of peacetime since the US began keeping data, nor why there was the big hiccup in 1937.

Sumner is essentially trying to tell a story about an unusual set of events, but his story seems to assume that most extraordinary events of the era (and what sets that era apart) kind of just happened to occur for no particular reason so he misses the big picture and ends up focusing on details. With all due respect to Sumner, I prefer to think the US economy is not Forrest Gump.

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Cross-posted at the Angry Bear blog.

I often post about the relationship between the top marginal tax rate and economic growth, which I’ve noted can be described this way:

% change in real GDP from t to t+1 = a + b*Top Marginal Tax Rate at time t
+ c* Top Marginal Tax Rate squared at time t

(I’ve modestly described that relationship as the “Kimel curve.”)

I’ve done this many times, and have added all sorts of things to the curve, but in every case, the results are more or less the same: the top marginal tax rate that maximizes growth is somewhere in the neighborhood of about 64%, give or take about 5%.

Its usually my practice to use all data available when I’m estimating that relationship. That means going back to 1929, the first year for which the BEA keeps data on real GDP. In part that’s because I get accused of cherrypicking if I don’t use all the data available. But lately I’ve started getting accused of somehow trying to bias results by using all the data available.

So I’m going to run a version of the model similar to the one I ran last week, but I’m going to start with data from 1981, which is when Reaganomics set in.

The specific model I’m going to estimate is this

% change in real GDP from t to t+1 = a + b*Top Marginal Tax Rate at time t
+ c* Top Marginal Tax Rate squared at time t
+ d* % of pop 35 – 44 at time t
+ e* % of pop 45 – 54 at time t
+ f* President is a Republican (Y/N) at time t

Here are the results:

Figure 1
Figure 1

If the numbers are too small click here.

A few things to note: the percentage of the population 35 to 44 is not significant, though it was significant when data going back to 1929 was used. Conversely, the percentage of the population 45 to 54 was not significant with data going back to 1929, but is significant here. What that tells me is that for much of the period from 1929 to the present, the percentage of the population 35 to 44 was one of the more important demographics for driving growth. However, more recently, the population 45 to 54 has become more important reflecting the reduced importance of manual labor.

The Republican dummy wasn’t significant when using data going back to 1929… and it isn’t significant in the more recent period either.

But what really matters, in my opinion at least, is this: the coefficients on the top marginal tax rate and the top marginal tax rate squared are both significant. The former is positive and the latter is negative. That means the Kimel curve applies to the period since 1981 to the present as well.

The growth maximizing tax rate obtained when using data for the period from 1981 on is 44%, about 20 percentage points below the top marginal rate obtained when using data going back to 1929. So, why the difference? I don’t know, but I have some ideas:

1. The period from 1981 on includes one observation where the tax rate was above 50% (i.e., 1981, when the economy was in a recession), several at 50%, and the rest below. The model simply hasn’t “observed” higher tax rates and the growth rates associated with those higher tax rates.
2. Prior to Reagan, the public was more accepting of the idea that tax rates should be higher. Reagan changed the zeitgeist. The government became the problem, not the solution, and taxes, well taxes came to be viewed as theft.

My guess is that its a combination of both factors. But even if you lean primarily toward the second reason, and assume the optimal tax rate has shifted down over time… its still well above where tax rates are now.

In fact, tax rates have been below 40% since 1987. The annualized growth rate in real GDP during the 23 years from 1987 to 2010 was 2.6%. The last time real GDP grew that slowly during a 23 year period was… well, who knows – it never happened before then in the time since the BEA has been keeping data. If you decide to, well, cherry pick and leave out the Great Recession… the annualized growth rates in real GDP from 1987 to 2007 was a hair over 3% a year. Previous to that, the last time annualized growth rates were that low for the same number of years occurred at the end of World War 2. Put another way… the period from 1987 on may have been a success in terms of keeping tax rates low, but it has been a failure in terms of economic growth, as the only time the economy has done worse was when it switched out of a command economy and went immediately into a recession. (I can’t help but notice that the only time when the economy did worse happened to come during what David R. Henderson refers to as the Post War Economic Miracle.) Better policy would get us faster economic growth and that would be good for everyone, even those paying the highest marginal rates.

Housekeeping… GDP data came from the BEA and tax rates came from the IRS.

A few other comments… the correlation between residuals at time t and time t+1 in the model estimated is about 16.5%. One could do other checks, but it seems very, very unlikely that autocorrelation is a problem here. Of course, we’re also missing some important variables… the fit could be a lot higher. I’ll start incorporating some of the suggestions I received from readers after the last post.

As always, if you want my spreadsheet, drop me a line. I’m at my first name (mike) period my last name (kimel – note only one m) at gmail.com.

Cross-posted at the Angry Bear blog.

I’ve been writing about the relationship between tax rates and growth since I started blogging in 2006. A lot of those posts have focused on the quadratic relationship between tax rates and growth. That is, it turns out that if you take US data going back to when the BEA started keeping track, 1929, you can easily build a model of the following form:

% change in real GDP from t to t+1 = a + b*Top Marginal Tax Rate at time t
+ c* Top Marginal Tax Rate squared at time t

I have modestly referred to that as the Kimel curve. Now, it turns out that for most variations on that theme I’ve come up with, b is positive, c is negative, and both are significant at the 5% or 10% level. That allows you to find a top marginal tax rate that maximizes growth… which turns out to be somewhere between 60% and 70% depending on how the model is specified.

In this post I want to address a few criticisms by running two additional regressions with more or less the form. Parts of this may get a bit wonky but I’m going to keep it so that even if you’ve never done any statistical analysis, hopefully you’ll be able to follow the outcomes.

In the first regression, I’m going to account for a few additional facts:
1. By going with every single observation the BEA produces, I’ve been accused of cherrypicking. So I’m going to throw in a dummy variable for Hoover.
2. I’ve been told the only reason growth was so quick during the New Deal was that there was a bounceback effect from the Great Depression… so I’m throwing in a dummy variable for FDR’s peacetime years (i.e., 33-41).
3. I’ve been told WW2 biases the results…. so there’s a third dummy for 1942-1944, the fast growing years in WW2.
4. I’ve also included two demographic variables: the percentage of Americans 35 to 44 and the percentage of Americans 45 – 54. The latter group tends to be the highest income group these days, but in an earlier era more focused on manual labor, those 35 to 44 might have been higher paid.
5. For grins, I threw in a dummy variable which is equal to 1 if the President is a Republican and 0 otherwise.

So… here’s what it looks like:

Figure 1
Figure 1.

So what does it all mean? Well, this set of variables explains about 43% of the observed variation in growth rates over the period for which we have data (see the adjusted R2). There’s obviously room to improve the model, variables I’m not accounting for, etc.

The percentage of Americans 35 to 44 has a positive coefficient and is almost significant at 10%. We’re almost at the point where we’d be comfortable saying as that percentage increases, growth increases. The percentage of Americans 45 – 54 has a negative coefficient, but isn’t close to being significant.

Not surprisingly, the Hoover dummy is associated with economic shrinkage, FDR’s peacetime period is associated with positive growth, and 1942 – 1944 is associated with even faster economic growth.

The Republican dummy is not significant – any difference in the growth rate observed between the two parties can be explained by other factors. Which other factors?

Well, the top marginal tax rate and the top marginal tax rate squared are both significant – the former is positive and the latter is negative, which means they trace out the desired upside-down-U shape.

Oh… and the top of the curve happens when tax rates are at 64%. That is, the fastest growth rates seem to occur when the top marginal tax rate is around 64%. Now, I’ve had post after post on this topic, and the top of the curve always seems to occur in more or less in the same place. It isn’t a coincidence folks.

I’ll post results of the second regression in my next post in the series. That regression will focus on the period since Reagan took office and thus will only include data from 1981 to the present. What does it say? Well, a hint: if you don’t like the results shown in this post, you won’t be happy about that one either. But remember, I’m just the messenger. The data is what the data is, and if it isn’t showing what you think it should, its up to you figure out what’s wrong with the analysis or with the data, to pontificate wisely and inaccurately, to ignore the evidence, or to change your mind.

If anyone has a line on a good inequality series with annual data that goes back to 1929, please let me know. I’d like to drop it into the model. I’d also love a good proxy for regulation. Don’t be afraid to offer other suggestions for data to drop into the mix are welcome too. I’m like a DJ, I take requests, but it helps if you can point to whatever data you want me to use.

As always, if you want my spreadsheets drop me a line at “mike” period “kimel” (note – one m only in my last name!!!!) at gmail.com.

Thanks to Bill McBride for pointing toward the demographic data and m. jed for suggesting its use.

The Laffer Curve and the Kimel Curve

On October 23, 2011, in Uncategorized, by admin

Cross-posted at the Angry Bear blog.

People always talk about the Laffer curve, but have you ever seen it estimated? Have you ever wondered why you don’t? If you’re a quant guy, you know the answer to that. Because if you’re a quant guy, at some point curiosity must have gotten the best of you. That means you pulled out some data and you plugged it into whatever piece of software happened to be handy. What happened next depends on what sort of a quant guy you are. If you’re the sort that let’s the numbers do the talking, you spotted the joke and probably left it at that. If you have a strong ideological leaning in a certain direction, on the other hand, you might have tried to “fix” it. You tried a few times, failed, and kind of just left it there as something to get back to some time, but no hurry because your ideology tells you what the answer should be.

Today, by coincidence, I got two e-mails asking me about the Laffer curve. And it occurred to me… maybe someone should let non-quant people into the joke. Because the only people really discussing it are those who are driven by ideology, whereas it should be afforded the Hauser’s law treatment.

So here’s how it works. Putting numbers to the Laffer curve pretty much comes down to estimating:

(1) tax collections / GDP = A + B*tax rate + C*tax rate squared + some other stuff if desired

A, B, and C are estimated statistically using a tool such as regression analysis.

If you plug in numbers, and find that B is positive and statistically significant and C is negative and statistically significant, then it turns out that you can trace out a quadratic relationship between tax collections / GDP and tax rates. i.e., tax collections / GDP is a function of tax rates that looks like an upside down U. If you increase tax rates when tax rates are “low,” growth will increase. On the other hand, to increase growth when tax rates are “high”, you have to decrease tax rates.

When you have such a shape, you look for the top of the upside down U and there’s your maximum.

So I started with the obvious:

(2) tax collections / GDP = A + B*tax rate + C*tax rate squared

In other words, the simplest version of (1) possible. I plugged in data. That would be current federal tax receipts, line 2 from NIPA table 3.2, divided by GDP, from the BEA, and the top marginal tax rate from IRS’ historical table 23. You can go all the back to 1929 – that’s when the GDP and current federal tax receipts begin.

The problem is… the data isn’t quite amenable to shoehorning into the desired shape. The fit of the model sucks, B is negative, C is positive, and neither coefficient is significant at the ten percent level. But they aren’t so far off either.

All that together means that maybe, just maybe, a slightly better specified model might do the trick. The simplest solution… find another variable that has some explanatory power and throw it in. Well, I’m supposed to be on a hiatus from blogging, so I don’t want to spend a huge amount of time at this, but it occurs to me that year is probably such a variable. There’s a good chance that over time, tax collection has become a bit more efficient.

So I reran (2) as follows:

(3) tax collections / GDP = A + B*tax rate + C*tax rate squared + D*Year

Here’s what the results look like:

Figure 1
Figure 1
(If clicking on the figure doesn’t make it bigger, try here

B and C have the wrong sign. That means you don’t get an upside down U, you get a U. Here’s what it looks like when graphed:

Figure 2
Figure 2
(If clicking on the image doesn’t make it bigger, try here.)

The low point in tax collections happens to be about 32%. In other words… if the top marginal tax rate is below 32%, cutting it further will raise tax revenues. On the other hand, if the top marginal tax rate is above 32%, to boost revenues you have to raise tax rates.

Now this is quick and dirty, and it has boundary issues (i.e., 100% tax rate collects more than 99% tax rate – would it really? well, the model is extrapolating because its never observed tax rates of 99% or 100% in the wild). I should also throw in a few more variables to improve the fit. Worse, there’s autocorrelation. That means the error terms are correlated. The correlation between the residuals at time and the residuals at time t+1 is 75%. That in turn violates one of the assumptions of OLS regression analysis. Its fixable, but its also ignorable for our purposes since what it means is that the coefficient estimates are probably “correct” but merely less significant than they appear. Regardless, you won’t get anything that bears even a remote resemblance to what you hear from the crowd who perennially cites the Laffer curve so authoritatively.

Which brings up another piece of the joke. In the end, tax collections don’t matter. Its nobody’s goal to maximize tax collections. Taxes only matter because they pay for certain government services. They also take money out of our pockets. So there’s a tradeoff. But we’re made better off if the government services taxes pay for generate more value than they cost us. And at least to some extent, you can measure that by whether they generate more growth than they cost us.

Now, it turns out that the optimal tax rate for growth is easy to calculate. The data cooperates very nicely. There is a relationship, an easy to estimate curve which I’ve modestly called the “Kimel curve.” And the high point in the Kimel curve is somewhere around 65%. Now, the Laffer curve analysis shows us that getting to the level of taxation that produces the fastest economic growth rates would also increase our tax collections… not a bad thing at all in an era of rapidly rising national debts.

Which brings us to the biggest Laffer curve joke of them all: ain’t no way the folks who like to talk about the Laffer curve would support that.

As always, if you want my spreadsheet drop me a line. I’m at my first name (“mike”) period my last name (“kimel” – that’s with one m only!!!) at gmail.

One Graph That Explains About Amity Shlaes

On October 19, 2011, in Uncategorized, by admin

Cross-posted at the Angry Bear blog.

Thanks to Linda Beale, I headed over here:

The George W. Bush Institute announced today that Amity Shlaes has been named director of the 4% Growth Project, a key part of the Institute’s focus on economic growth. Miss Shlaes will open the project’s office in New York. The aim of the project is to illuminate ideas and reforms that can yield faster, higher quality growth in the United States, and to underscore the importance of growth in America’s future. Part of that work involves finding ways to make growth and economics generally accessible to more Americans, especially younger Americans. The program will conduct and sponsor research on all aspects of economic growth, host conferences, as well as partner with other institutions in such endeavors.

The following graph, I think, illustrates you need to know about Amity Shlaes:

Figure 1
Figure 1.

(If clicking on the image doesn’t enlarge it for you, try this link.)

OK. I lied. The graph is actually missing something. See, we only have official data going back to 1929. And the Great Depression began very, very early in Hoover’s term. And Hoover had been a cabinet secretary under Coolidge, and ran for office under a platform which essentially called for continuing Coolidge’s policies. And Shlaes’ forthcoming book is in praise of Calvin Coolidge. It should be noted that the economy was in recession during over 38% of the months in which Coolidge took office, which makes much of the Coolidge era a dry run (so to speak) for the monster that would come in 1929.

Put another way… Shlaes is part of a movement to praise policies responsible for a lousy economy culminating in the Great Depression (i.e., those of Coolidge and Hoover). Shlaes is also part of a movement to praise the policies responsible for a lousy economy culminating in the start of the Great Recession and the mess we’re in today. (Yes, the Great Recession started in 2007, and no, Obama hasn’t made any substantial changes on taxes or regulation from the way GW Bush ran the country.) Conversely, Shlaes is a well-known critic of the policies that produced the fastest period of peace time economic growth this country has seen.

To me this feature of economics is kind of odd. For some reason, policies that have failed spectacularly over and over continue to have adherents. Policies that have worked spectacularly have critics. Debating the merits of a cavalry charge into the teeth of an armored column was barely excusable in August of 1939, but at least that debate was put to a rest by the German blitzkrieg. Its been generations since anyone argued that horsemen can go toe to toe with tanks.

Which leads me to a hypothetical. Say we lived in a parallel universe where Shlaes was a quisling, a real villain whose goal was to harm this country as much as she could by convincing the nation to commit economic suicide. How would the graph above and the two paragraphs that followed it look any different?

As always, if you want my spreadsheets, drop me a line at “mike” period “kimel” at gmail.

OK. Back on hiatus. Writing will continue to be very sparse for a while.

Temporary Hiatus

On October 11, 2011, in Uncategorized, by admin

Hi. I have to focus a bit more effort into consulting in the near future, so at least for a bit I won’t be posting at the blog.

As always, feel free to contact me at “mike” dot “kimel” and then gmail dot com.




Libertarians, Government and Choice

On September 30, 2011, in Uncategorized, by admin

Cross-posted at the Angry Bear blog.

Its been a very long time since I looked at the National Review. Apparently it is still there.

Jonah Goldberg (apparently also still there) had a post that begins like this:

And now let us recall the “Fable of the Shoes.”

In his 1973 Libertarian Manifesto, the late Murray Rothbard argued that the biggest obstacle in the road out of serfdom was “status quo bias.” In society, we’re accustomed to rapid change. “New products, new life styles, new ideas are often embraced eagerly.” Not so with government. When it comes to police or firefighting or sanitation, government must do those things because that’s what government has (allegedly) always done.

“So identified has the State become in the public mind with the provision of these services,” Rothbard laments, “that an attack on State financing appears to many people as an attack on the service itself.” The libertarian who wants to get the government out of a certain business is “treated in the same way as he would be if the government had, for various reasons, been supplying shoes as a tax-financed monopoly from time immemorial.”

If everyone had always gotten their shoes from the government, writes Rothbard, the proponent of shoe privatization would be greeted as a kind of lunatic. “How could you?” defenders of the status quo would squeal. “You are opposed to the public, and to poor people, wearing shoes! And who would supply shoes . . . if the government got out of the business? Tell us that! Be constructive! It’s easy to be negative and smart-alecky about government; but tell us who would supply shoes? Which people? How many shoe stores would be available in each city and town? . . . What material would they use? . . . Suppose a poor person didn’t have the money to buy a pair?”

All that is true. But what Rothbard apparently didn’t get, and no doubt Goldberg doesn’t either, is that it goes the other way too. If people always got their shoes from the private sector, it would never occur to anyone that the government might provide shoes. Now it might seem stupid for the government to be in the business of footwear distribution, and in general, outside of the military, my guess is that it is.

But sometimes a different approach is what works. Sometimes when the government is doing things, it is doing them inefficiently and the private sector can do better. But sometimes it goes the other way. Sometimes when the private sector is doing things, it is doing them inefficiently and the government can do better. And sometimes, sometimes its a good idea for things to be done worse, and in a way that only the government can.

I’ll give you an example. I’ve noted a few times that you can stroll into most car dealerships in Brazil today and buy a tri-flex car. That is, the same car can run on any mix of gasoline, ethanol and natural gas. (There are two fuel tanks – one for ethanol and/or gasoline and one for natural gas.) You can then drive that vehicle into any number of fueling stations and fill up with whatever fuel is going to get you the most miles (er, kilometers) for your dollar (er, real). The technology to run cars on a number of different fuels, which you won’t see in the US for a very long time, is marketed under such exotic brand names as GM, Ford, Toyota, Honda, Volkswagen and Fiat to name a few. (Look ‘em up if you haven’t heard of ‘em.)

I’ve posted on how it came to be that Brazilians have choices that Americans do not, namely to buy a tri-flex vehicle. The Brazilian government wanted to reduce the country’s dependence on gasoline, but it realized that nobody would buy a car that ran on a fuel other than gasoline if there was no place to buy that fuel, and hence no manufacturer would make such cars. The government also realized that Shell and Esso and Texaco (remember them?) weren’t going to start selling other types of fuel because there weren’t enough cars on the road that could use those fuels. But the Brazilian government owned an oil company that had a chain of gas stations. One fine day, that chain of gas stations started selling ethanol even though there was no market for it. It wasn’t profitable. It was insane. No private company would have done something that stupid. But the result, a few decades later, is that about 80% of cars sold in Brazil in 2010 were flex-fuel. Guess what percentage of cars sold in the US in 2010 were tri-flex?

Rothbard would never approve of what the Brazilian government did. Neither would Goldberg. Personally, I like having choices. I wish I could pick among three different fuels for my car and go with whichever is cheapest. I suspect that in a few decades, when that technology finally arrives in the US, Goldberg might like having those choices too.

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