I’m embarrassed. I messed up last week’s post which appeared at Angry Bear and on the Presimetrics blog. Essentially, I copied in some of the data on tax rates incorrect for three years in the late 1970s; the IRS source I was using was not in spreadsheet form. The error was spotted by JzB. To be honest, I kinda shouldn’t have written that post… it was evident even from the way I started it:
I’ve been kind of swamped, low on sleep, and doing a few book related things in my few waking hours that don’t work or parenting (buy a copy of Presimetrics!!!!), so posting has been light. But I thought I’d do a quick and dirty post today about a hot topic
OK. I’ve gotten a bit more sleep (not enough, mind you, but some), and while the baby is in my care right now, he’s quiet for the time being, so let’s get this done. First off, the topic… I want to look a the relationship between corporate tax rates and unemployment. Links to the data are posted below, but all the information I use, plus the analysis itself, sits in this google spreadsheet.
So, let’s begin.
Figure 1 below shows the data used in this post; the top corporate marginal tax rate (obtained from the IRS) is on one axis and the unemployment rate for individuals sixteen years and over (from the Bureau of Labor Statistics) is on the other axis.
Where we go from here was best explained in the last post:
Now, consider the correlation between the top corporate marginal tax rate and the unemployment rate. If it is true that lower taxes = lower unemployment, the correlation between the two series should be positive. A positive correlation means the series should move more or less in the same direction; as tax rates rise, unemployment rises, and as tax rates fall, unemployment falls.
If the correlation is, in fact, negative, that means that lower unemployment tends to happen when tax rates are higher. Correlation may not be causation, but it would be very hard to argue that cutting taxes on corporations leads to lower unemployment if we do not see a positive correlation between the two series.
Now, obviously, it may take time for tax rates to do whatever magic they might have. So Figure 2 looks at the correlation between the top corporate marginal tax rate and the unemployment rate in the same year, the unemployment rate the next year, etc., all the way through ten years out. Its really hard to see how the effect of tax rates should last beyond a couple of years, but I figured I’d be thorough and put up the figures. I’ll take a pass at interpreting them, but feel free to reach your own conclusions.
Additionally, because whatever effect tax rates might have on unemployment might change over time, each correlation is computed several times: once for the entire 1948 – 2009 sample, a second time for 1960 – 2009, a third time for the period since 1970, a fourth for the period from 1980 and a fifth time for the 1990 – 2009 period. (I didn’t look at just post-2000 because the top corporate rate has been frozen during that period.)
So what do we make of this? As I stated last week, I don’t think the outyears are all that relevant; I don’t think unemployment rates are affected by corporate tax rates ten years earlier, but I included them so no-one accuses me of cherry picking. If you think they matter, explain how in comments. Here’s what I’m seeing in the graph:
1. For the longer samples (1948 – 2009 and 1960 – 2009), the correlation between tax rates and unemployment rates is very close to zero. 2. For the three shorter time samples, the correlations are very big (positive for 1970 – 2009 and 1980 – 2009, negative for 1990 – 2009).
That seems to indicate that corporate tax rates did not use to have an effect on unemployment, but in recent years, they may be never used to have an influence on jobs. That influence may not be in the direction that tax cut proponents keep telling us about, though, as evidenced by the 1990 – 2009 series; anyone who has read Presimetrics won’t be at all surprised, as we point out the same thing in a completely different way. I think its because over time, our economy has become more loop-hole oriented than doing things oriented.
The Intelligence Report column in Parade Magazine, the newspaper that appears in Sunday newspapers across the country, wrote a short quiz based on Presimetrics. It will appear in this Sunday’s issue.
Michael Kanell and I will log in to the quiz’s site a few times on Sunday and (if all goes well) once more Monday to answer questions anyone might have about the book.
Meanwhile, I just got a copy of the book, and it looks very nice. I believe they’ll be shipping copies of the book to bookstores within days. Additionally, the publisher is working on a number of other media appearances.
Consistent with findings in Presimetrics, the book I wrote with Michael Kanell which will be released in August, I’ve had some posts whose results contradict standard economic theory. In some cases, readers have insisted that the results must be some sort of anomaly. Perhaps the biggest offender is a graph which appeared in this post. The graph shows growth rates in real GDP per capita by Presidency, where each President is color coded by whether he increased the tax burden (in this case defined as federal government revenues as a share of GDP) or decreased it:
The graph shows that Presidents who cut the tax burden produced slower growth, on average, than Presidents who increased the tax burden. For many people, this doesn’t make sense at all. (An explanation for why these results show up is provided here. Now, I’ve already some of the objections that have been raised to this, mostly in private or in comments at the Angry Bear blog. In fact, the graph above by itself answers one of the objections – I was told many times that FDR only produced faster growth because the economy accelerated during World War 2. Thus, the graph only shows FDR’s results through 1938, which avoids the war, the build-up to the war, and even Lend Lease.
Another criticism is that somehow the fact that I’m assuming that Presidents have an effect on the economy is the problem, and that a better way to do this is to look at the business cycle. But I’ve had posts looking at the business cycle, and increasing tax burdens doesn’t look all that much better as an economic strategy across the business cycle either. For instance, consider the following graph, one of several fairly damning graphs I posted here and here:
Figure 2 If cutting the tax burdens is the right prescription during a recession, it isn’t evident from the above graph.
I’ve also been accused of cherry-picking, though I’ve gone as far back as the data allows, and on most posts on this subject, I’ve actually chucked the WW2 years. In addition, I’ve noted that throwing out outliers doesn’t change results materially. But what if the last eight decades have all been an anomaly? After all, shouldn’t we consider the “Roaring ‘20s,” a period which every textbook tells you was a period of rapid growth. After all, such intellectuals as Glenn Beck, Thomas Woods, and Amity Shlaes are quick to assure that the prosperity of the 1920s was due to tax cuts.
Unfortunately, the NIPA tables don’t go that far back, so we don’t have actual data on real GDP per capita (or the tax burden). But I’ve done my best to examine that claim; the following graph, which first appeared here, shows the top marginal tax rates and the periods (shown in gray) when the economy was in recession.
While the 1920s was, indeed, a period when marginal tax rates were reduced, it was a period of recession followed by recession followed by recession followed by the Great Depression. The longest consecutive months spent outside of recession during the 1920s was 27 months! Two years and a quarter. In fact, the economy was in recession (if not outright Depression) a full 44% of the time during the 1920s. If these were roaring years, those roars were extremely short-lived.
Which moves us to the next issue – I’ve been criticized for focusing on the tax burden rather than the marginal tax rate, the exception being in periods where the tax burden is not available. Frankly, of all the criticisms, that one seems especially weak. For those with enough income, the marginal tax rate is as much a fiction as the MSRP on a car; Warren Buffett’s offer of $1 million dollars to anyone on the Forbes 400 list who could prove they pay a higher share of their income in taxes than their secretary remains untaken.
Moving on to the next criticism… I’ve been told my interpretation of the above graph is wrong – it is not so much that higher tax burdens are correlated with faster economic growth, but rather that administrations that produced rapid economic growth tended to feel they could raise tax burdens, and that administrations suffering from poor growth kept tax burdens low to try to remedy a bad situation.
I dealt with that issue in recent posts by grouping the periods from 1929 to the present into eight year administrations, where possible. Those administrations were made up of the eight year terms of Presidents who served two terms (or in FDR’s case, the first two terms only). Added to those were eight year periods in which a Vice Presidents took over for a President who died or otherwise left office. For each of those eight year terms, I created the following graph . It shows the change in the tax burden in the first two years of each administration along one axis, and economic growth on the other axis:
Clearly, economic growth in years 3 through 8 cannot explain changes in the tax burden in earlier years unless one assumes time travel, clairvoyance, or one heck of a coincidence.
And speaking of coincidence, we arrive at another common complaint: there simply are not enough observations to reach a conclusion of any sort. Left unstated, of course, is that though the tax cutting Presidents had the lousiest economies, and that (as per graph 4) tax cutting led the economic growth, somehow a supposed lack observations validates the idea that lower taxes does produce faster economic growth.
Now, those who complain about the lack of observations generally insist that a) I don’t know the first thing about statistics and b) you need 30 or more observations to reach a conclusion. Now that critique dates back at least three years, having been made by an anonymous blogger for the Economist who I understand is now known as Megan McArdle, and my answer (to point b.) back then is as good of an answer as any:
Which is what degrees of freedom are for… Maybe there’s something wrong with the textbooks on my shelf, but the t distribution tables in the back of those textbooks have as few as 1 (one) degree of freedom. When the degrees of freedom are low, the t-statistics has to be really high in order in to reject H0. Or something like that – what do I know?
Allow me to explain. If there are a large enough number of observations to work with, it is possible to find a statistically significant difference between two things (events, peoples, outcomes, whatever) that at first glance or from a distance look very similar to the unaided eye. However, if there are a very small number of observations, then differences have to be larger and more obvious for them to be statistically significant. Consider three medications to extend the lives of patients with a specific type of cancer, where two have obtained FDA certification and the third was cooked up by the creepy guy who lives two houses down and uses cat puke as an active ingredient. It might take hundreds of observations to tell which of the first two medications is more effective, but it shouldn’t take very many to tell how well the third one compares.
And while in the past I sometimes decided to answer this question by running a t-test or some non-parametric test, it always seems to lead to questions about the assumptions by people who clearly never ran a hypothesis test in their lives but have one or another political point to defend to the death. So let me try something else – an argument by analogy. Consider the graph below.
If such a graph came from a study comparing outcomes of medications, where patients were assigned a medication but otherwise told to go about their daily business, there’d be no argument that, at least as a first approximation, there’s no reason whatsoever to assume that Medication 2 was more efficacious than Medication 1. And if the disease in question was a particularly rare one, and the graph above represented the testing performed on every known sufferer since 1929, most of us would be appalled if a doctor decided to treat the next sufferer with Medication 2. At the very least, we would assume that the burden of proof going forward should lie not with the proponents of Medication 1 but rather with the supporters of Medication 2, whether we understood the mechanisms by which either of these pharmaceuticals worked (or purported to work) or not.
And yet, this graph is identical to Figure 1, except that I changed the title and some labeling.
But there is another problem with the small sample objection. See, there are many ways to test whether there has been a negative correlation between tax burdens and economic growth, and looking at the national economy is only one of those ways. I’ve also had a number of posts at the Angry Bear blog looking at how states have fared over the years, and there are 50 of those. In fact, that was the topic of the first post I ever wrote four years ago next month. In that post, a comparison of the states, using data from 1990 to 2005 yielded the following conclusion:
Thus, the data doesn’t seem to support the idea that lower taxes are associated with faster growth rates. In fact, the opposite is true, especially for the fastest growing states. One way to interpret this is to conclude that taxes are actually below their optimal rates, and therefore, at the margin, the government is actually more efficient than individuals at converting its spending into growth. Society needs a certain amount of public goods (infrastructure, public health, confronting the Canadian menace, etc.) for businesses to thrive, and perhaps we currently have too little provision of public goods rather than too much.
And the other posts I’ve had using similar state level data have all led to the same findings.
Another problem frequently brought up is that growth is too complicated to be explained by a single variable. We agree, and in the book, we actually provide a model that uses several variables. But be that as it may, it isn’t reasonable to state that while cutting tax burdens produces faster economic growth, that effect gets swamped by opposing forces when you look at the data systematically, whether you’re looking at the performance of Presidents, at the growth during business cycles, or the performance of states. Clearly, if reducing the amount that people pay in taxes is so beneficial to the economy, somewhere that effect would show up. It shouldn’t be overwhelmed by other variables pushing in the opposite direction every time one tried to test it systematically and consistently.
Moving on, we have another little gem – that the performance of the two regimes (tax cutters and tax hikers) is not independent. The argument is this: tax hikers do well because they follow tax cutters who laid the foundation for growth. Tax cutters do poorly because they have the misfortune of following tax hikers who set up the economy for a fall.
This one is particularly easy to hit out of the park. First, note that there is only one tax hiker that is followed by another tax hiker: LBJ followed JFK. And LBJ produced the second fastest growth in our sample, which is to say that simply following a tax hiker is no guarantee of poor performance.
Now, look what happens when you consider only Presidents that followed their tax burden cutting peers:
Notice that following a tax cutting President doesn’t mean one will turn in a poor performance… unless one is also a tax cutting President. In fact, tax cutting Presidents that followed other tax cutting Presidents did worse than tax cutting Presidents who followed tax hikers. Imagine that. It’s almost as if the longer tax burdens are cut, the worse the outcome.
And yes, I included Hoover in the above graph though we don’t have the data to know with certainty that his predecessor, Calvin Coolidge cut the burden since Coolidge is renown as a small government guy. But leave out Hoover, and leave out Obama’s first year, and you still aren’t left with anything other than: In fact, tax cutting Presidents that followed other tax cutting Presidents did worse than tax cutting Presidents who followed tax hikers.
Which leads to the sorriest objection I’ve heard, namely that the American public, the constantly gulled American public, has the ability to reason out the outcome of economic policies on the macroeconomy to near-perfection, at least in 4 year increments. And the way it manifests itself here is this: when the economy is about to sour, we elect tax cutters, who, in turn, manage to limit the scale of the impending disaster.
This, ahem, theory (gurgle, choke) is the efficient market hypothesis on LSD. But it has the advantage of being able to explain pretty much anything. The problem is that it does so by breaking everything down to utter nonsense. For instance, it would indicate that the recent housing bubble and economic meltdown, rather than being a surprise, was actually anticipated on some unconscious level by the American public, and selected for as being much better than the alternative. Ditto the Great Depression. So what was this worse thing that was avoided? Locusts? Famine and pestilence? Billions of furious yetis descending on us from their Himalayan stronghold?
And yet, despite the fact that this story makes a virtue out of nonsense, it still isn’t internally consistent. For instance, if the American public understood that only a series of tax cuts were going to save us from something worse than the Great Recession, then wouldn’t GW have managed to have won the popular vote in 2000 and achieved a landslide in 2004. Conversely, does the fact GW received fewer votes than Al Gore indicate that perhaps the American public did not perceive that really big threat a few years in the future? It’s easy to knock down a story that is built on nonsense.
All of which brings us back to the point of this post. Michael Kanell and I have noted that lower tax burdens are not correlated with more rapid economic growth. In fact, from 1929 to the present (and in the book, we focus on the period from 1952 to the present) administrations that have cut the tax burden have performed worse than administrations that raised the tax burden.
And I think we, together and separately, have answered every reasonable objection that has come up, and even quite a few unreasonable objections to boot. And we’ve done so in a consistent and open manner. In our book and in my posts, we’ve been open and clear about our methods and data sources, and we’ve made an effort to treat the data as consistently and systematically as we were able. At some point, the burden of proof should no longer lie with us, but rather on those who cling to a story that simply is not consistent with the data we have observed in the U.S. over the past few decades. Frankly, I think we’re well past that point.
Taxes and Private Sector Investment – Evidence from the Real World Last week I had a post (which appeared both here and at Angry Bear). The post included the following graph:
The graph looks at every eight year period since 1929 (the first year for which National Accounts data is available from the Bureau of Economic Analysis) that can be thought of as a complete “administration.” It notes that there is a very strong negative correlation between the tax burden in the first two years of an administration and the economic growth that follows in the remaining six years of the administration. In plain English – the more the tax burden was reduced during the first two years of an administration, the slower the economic growth in the following six years. Conversely, the more the tax burden was raised during the first two years of each administration, the faster economic growth was during the following six years.
At this point I note… this is not my opinion, it is what the data shows. And there is no cherry picking – I went back as far as there was data and included every eight year stretch for which a single President occupied the Oval Office or in which a VP took over from a President in the middle of a term. And these real world results contradict just about everything that standard economic theory (Classical, Austrian, you name it) tells you.
So I tried providing an explanation:
Michael Kanell and I advanced several theories in Presimetrics but the one I think makes the most sense is that changes in the tax burden are a sign of the degree to which an administration enforces laws and regulations.
The logic is simple – (1) collectively, Americans cheat on their taxes and (2) whether the tax burden, the percentage of GDP that the government collects in taxes, rises or falls seems to have nothing whatsoever to do with whether marginal income tax rates rise or fall. Thus, one way for tax burdens to go up is increased enforcement, and one way for tax burdens to fall is decreased enforcement.
Now, to me that’s self-evident. But I’m starting to realize not everyone sees it this way, so let’s run a simple test. If a regime tolerates corruption or encourages companies to game the system rather than to be productive, we should expect growth in the private sector to be minimal at best. All else being equal, we should expect faster growth in the private sector the less rot there is in the system. I assume this is not remotely controversial. And it implies that if tax collections are indeed an indicator of an administration’s intolerance for shenanigans, then growing tax burdens should be followed by rapidly increasing private sector activity and falling tax burdens should be followed by relatively slow growth in private sector activity.
Crazy, right? Lower taxes leading to less private sector activity! Insanity! It defies economic theory. And common sense. But how does it fit with what happened in the real world? Extremely well, actually.
The graph below shows the change in the tax burden in the first two years of each 8 year administration on the horizontal axis, and the annualized change in real private investment per capita in the remaining six years along the vertical axis.
Notice… administrations that cut the tax burden early saw mediocre increases in private investment later. On the other hand, administrations that started out by increasing the tax burden enjoyed big increases in private investment in the remainder of their term. This is yet another instance where real world results contradict just about everything that standard economic theory teaches, particularly the Chicago School, Austrian, and Libertarian variety. And sadly, that theory has so permeated our collective thought processes that it has come to be referred to as “common sense.” Just as it was common sense at one point that the earth was flat, and the center of the universe.
It’s worth pointing out, by the way, that the relationship between the tax burden and real private consumption is similar; administrations that raised the tax burden saw greater increases in real private consumption per capita than administrations that reduced the tax burden. The relationship, albeit a strong one, is slightly weaker than the relationship between tax burdens and investment. By contrast, the relationship between changes in the tax burden in years 1 and 2 and changes in real Federal Government spending per capita are much, much weaker.
So let me revisit once more the explanation that Michael Kanell and I put forward in Presimetrics and which is consistent with the data presented in both graphs above. Administrations that cut the tax burden tended to do so mostly by reducing enforcement of tax laws and regulations. But people who don’t believe in enforcing tax laws are also not particularly fond of most other forms of rules and regulations, preferring a laissez faire “pro-business” government in all walks of life. Sure, there may well be many private sector winners when the government allows a free-for-all. However, as the costs of exploiting loopholes, breaking laws and creating externalities falls relative to the costs of doing productive things, fewer truly useful productive activities take place, and that kills growth.
As always, the change in any series over the length of an administration is measured from the year before the administration took office (the “baseline” from which it starts) to its last year in office.
I intend to look at the relationships described in this post in a bit more detail going forward. However, expect the next post to cover another issue which seems to come up a lot – whether the results I’ve been posting are statistically valid or not.
Note also… if it’s not obvious, this post deals with the tax burden, the share of GDP going to the Federal government, and not marginal tax rates. Please do not insist on commenting on a topic unrelated to this post.
Presidents, the Tax Burden and Corruption – Explaining Economic Growth
One of the topics we cover in Presimetrics is the relationship between the tax burden (i.e., the share of income going to taxes) and economic growth. As shown also in a recentpost, Presidents who cut the tax burden tended to produce slower growth than Presidents who raised the tax burden.
In this post, I want to begin to address causality. As we stated in the book and as I’ve since said a few times, I don’t think higher tax burdens in and of themselves cause faster economic growth, but rather that increasing tax burdens are correlated with some other criteria that create conditions that help produce economic growth.
But let us start by addressing the issue of timing first. A number of people have indicated in comments or offline that perhaps the reason for the strong correlation between tax burdens and economic growth could be because when the economy is growing rapidly, Presidents feel comfortable boosting taxes.
I’ve pointed out a few a problems – theoretical and empirical – with this line of argument, but I think I can illustrate it best with a simple graph. Since 1929, the first year for which data is available from the Bureau of Economic Analysis’ National Income and Product Accounts (NIPA) tables, there have been five Presidents that have served an eight year or more term: FDR, Ike, Reagan, Clinton and GW. Additionally, there are several more “quasi-eight year terms.” These are instances in which a VP took over upon the death or resignation of the President and maintained a similar a policies similar to those of his predecessor (JFK/LBJ and Nixon/Ford), or in which a VP took over a mere few months into a new terms and thus could put his own stamp on just about the entire eight years (Truman).
The graph below shows the change in the tax burden in the first two years of each administration on one axis and the growth in real GDP per capita during the remaining six years of each administration on the other axis.
Figure 1 Notice… increases in the tax burden in the first two years of an administration tend to be followed by rapid growth during the remainder of that administration. Conversely, administrations that greatly decreased the tax burden during their first two years suffered from poor economic growth during their remaining six years. This relationship, at least, is very difficult to explain by insisting that administrations which enjoyed rapid growth simply were more able to raise tax burdens than administrations which grew more slowly. It is also impossible to explain by anything said by anything you hear out of the Austrians or the Chicago school.
(Incidentally – I have a simple explanation for why some administrations appear above the regression line and some below. I know that it applies to the administrations that begin in 1952 because I’ve written about it in the past. I’ll collect the data going a bit further back and some time in the future will write a post on that.)
So what is going on here? Michael Kanell and I advanced several theories in Presimetrics but the one I think makes the most sense is that changes in the tax burden are a sign of the degree to which an administration enforces laws and regulations. Consider this graph that appeared in a post last week:
Notice that among the Presidents to increase the tax burden are some who raised marginal tax rates (FDR, Clinton), others who decreased it (LBJ), and others still under whom marginal tax rates didn’t change. Similarly, tax burdens fell under some Presidents who cut marginal rates (Reagan, GW), Presidents who raised marginal rates (Bush Sr.), and others who left it unchanged. And for tax burdens to fall at a time of increasing marginal rates really requires more people avoiding taxes they legally owe. Similarly, for tax burdens to rise at a time of decreasing marginal rates one would need more people paying the taxes they legally owe. Thus, enforcement.
Furthermore, an administration willing to turn a blind eye toward one set of laws and regulations is probably more than willing to turn a blind toward other rules and regulations. It is not a coincidence that aren’t keen on tax collection also tend to view the government as the problem and not the solution.
Now, corruption (and let’s call it what it is) is a tough thing for which to test. But I think I there are signs that often appear among corrupt regimes, and one of them is the displacement of private sector by the government. Running an honest business when the government is dishonest is very difficult. The government will side with its favorites and everyone else will have a tough time. It becomes easier to make a buck by playing legal technicalities than by actually doing something useful. This is not to say that some countries do not succeed in having large government sectors without remaining honest and transparent – I suspect Denmark and Singapore are examples of that, though I’m not all that familiar with data for those countries. But we are not Denmark, and if a regime populated with flacks who insist they believe in small government takes over a growing piece of the economy despite taking steps to “encourage the private sector” it probably isn’t a good sign.
So with that… the next graph shows changes in the tax burden in years 1 and 2 on the one hand, and changes in the size of the federal government’s expenditures as a share of GDP during the remaining six years on the other hand.
Notice that administrations that started off by cutting the tax burden also went on to increase the government’s share of the economy. That relationship is stronger and more evident if one looks at changes in the tax burden in the first four years of an administration against changes in the size of the federal government during the remaining four years.
Clearly, in general, the more an administration cut the tax burden in its early years, the smaller the private sector’s share of the economy it its later years, contradicting all rhetoric of the tax cutters, not to mention all Chicago or Austrian “economic theory.” After all, those folks will tell you that lower taxes are going to jumpstart the private sector, right? Not what happened in the real world, is it?
Notice also that the relationship is stronger for the four post-War Presidents that served a full eight years than for the entire sample. A switch in administrations could lead to a break in our little “lower taxes as a sign of corruption shrinks the private sector” story. I note also something else… take a look at where FDR sits on the graph above. Does that fit with the accepted FDR narrative in this day and age?
Which leads me back to corruption. If cuts in the tax burden are a sign that the federal government is tolerating corruption, one would expect that administrations that start off by cutting that burden would end by seeing the private sector shrink relative to the public sector. And that is precisely what we have seen.
Do you have a better explanation?
Data sources and comments.
The definition of the tax burden used in this post is Federal government current receipts from line 1 of NIPA Table 3.2divided by GDP from NIPA Table 1.1.5, line 1. Real economic growth was measured as the change in real GDP per capita, which was obtained from NIPA Table 7.1, line 10. The government’s share of the economy is measured the federal government’s current expenditures (line 23 of NIPA table 3.2) divided by GDP.
Note that in past posts I have tended to only consider the first eight years of the FDR administration to avoid even getting close to the war years. As noted in previous graphs, even leaving out the years after 1938, FDR oversaw the fastest economic growth or any President for whom there is reliable data available. However, in this post, I was trying to remain consistent by sticking to 8 year stretches of data. Note also that, as shown in the fourth graph, the federal government’s share of the economy actually shrunk from 1936 to 1940.
Growth rates are measured from the year before a President took office to his last year in office. Note also… if its not obvious, this post deals with the tax burden, the share of GDP going to the Federal government, and not marginal tax rates. Please do not insist on commenting on a topic unrelated to this post.
This post also appears at the Presimetrics Blog. It contains some information that has appeared in a few different Angry Bear posts, but I think I’m starting to manage to put it into a more coherent narrative. And as I’m able to do that, I’m able to move slowly to the next part of the story.
A couple of weeks ago I had a post on the Presimetrics blog, also on the Angry Bear blog looking at economic growth rates and political parties. The post shows that from 1929 (that’s as far back as GDP goes) to 2009, growth in real GDP per capita was faster when the president was a Democrat than when the President was a Republican. Furthermore, growth was faster for Democratic Presidents who faced a Democrat-majority Congress during their entire term than for those who did not face a Democrat-majority Congress for at least part of their administration. Similarly, Republican Presidents facing Democratic majority in Congress during their time in office tended to better than Republican Presidents facing Republican majorities for most or all of their term. It isn’t a message you’ll hear very often, but it is the only one that is compatible with the data, as you can easily check yourself.
In this post, I want to look at one of the major distinctions between Democrats and Republicans, and that is tax policy. Let’s start by looking at the Federal tax burden (total Federal government current receipts / GDP) by President. The data comes from the Bureau of Economic Analysis’ National Income and Product Accounts (NIPA) tables. Federal government current receipts were pulled from line 1 of NIPA Table 3.2, and GDP comes from NIPA Table 1.1.5, line 1. (Note – this is slightly different than the way we do it in Presimetrics but it is nice to change things up now and make sure that results don’t change.) The graph below ranks the Presidents by the annualized change in the tax burden. The change is measured from the year before a President took office (the “baseline” level) to his last year in office.
(As is my practice in these posts I tend not to include the years through after 1938 for FDR because otherwise someone is going to claim that whatever happened while FDR was in office was due entirely to World War 2.)
The graph shows that there is some correlation between the parties and changes in the tax burden. Every single Republican president for whom there is data reduced the tax burden. Conversely, every Democrat except Truman has raised the tax burden. Obama, at least during his first year, is on track to follow Truman and lower the tax burden.
The graph shows very clearly that Presidents who hiked the tax burden produced faster economic growth – by far – than the Presidents who cut the tax burden.
And should there be any tea-partiers reading this, yes, in his first year, Obama cut the tax burden. A lot. The so-called stimulus package involved a lot of tax cuts. But as I’ve already noted, to get out of a recession, government spending has historically been much more useful as a stimulus than tax cuts.
Here’s another way to look at things:
The graph below repeats Figure 3., but it includes a few labels if you want to know which point represents which President.
In any case, it’s pretty clear that if lower taxes provide any benefits to economic growth, those benefits are extremely well disguised. In fact, it appears that lower taxes are a prescription for slower, not faster economic growth. (Try reconciling the data with Republican, libertarian, or Austrian economic theory.)
Now… I do not believe that higher taxes, in and of themselves, are a cause of faster economic growth. In the book we suggest a few reasons why higher tax burdens might correlate with faster economic growth. But since the book went to press, I’ve had a bit of time to think about ways to test some of these ideas, and I’ve come up with a few new thoughts as well. I hope to try out a few of these ideas in blogs in future posts.