# Presidents, Congress, and Economic Growth – The Return of Presimetrics

by Mike Kimel

Presidents, Congress, and Economic Growth – The Return of Presimetrics

If you’re a regular reader, you may know that a couple of years ago, Michael Kanell of the Atlanta Journal-Constitution and I had a book published called Presimetrics. Nobody much read the book, but we had a lot of fun writing it. In it we looked at how Presidents performed on a wide variety of issues – everything from curbing abortions to the murder rate to the national debt. We tried to be as objective as possible, always using data from whichever source collected it, and treating each variable the same way.

Thus, for instance, when looking at the murder rate – we collected data from the FBI. Then we measured the change in the murder rate from right before a President took office to right before a President left office. We found that on a number of issues, particularly economic ones, Presidents who did well followed similar policies to other Presidents who did well. Conversely, Presidents who did poorly on a given issue tended to either show it not much attention or follow the same approach to the issue as other Presidents who did poorly. That is to say, policies matter.

In this post I want to focus on economic growth, but I want to do something a bit different than what we did in the book, and a smidge more complicated than the graphical approach in Presimetrics.

In the book, we looked at the annualized change in real GDP per capita from before a President took office to right before he left office. This time, I want to look at the following variable: Annualized Growth rate over the next four years less Annualized Growth rate for the previous four years.

Thus, for, say, 1981, Reagan’s first year in office, that variable would have this formula:

annualized growth rate from 1981 to 1985 less annualized growth rate from 1977 to 1981

Thus, it would show the difference in the growth rates between Reagan’s first four year term, and the four year term of his predecessor, Jimmy Carter.

I went with four year periods because just about every President in the sample served at least one four year term (or most of at least one four year term), and four years is enough time to see if policies are working or not, on average.

The BEA’s data on real GDP goes back to 1929, so the variable could be created for the years from 1933 to 2007.

I ran a regression attempting to explain this change in the growth rate using the following dummy variables:

1. A Republican becomes President. This variable took values of 1 in 1953 (the year Ike took office), 1969 (the year Nixon took office), 1975 (Ford actually took office in August of 1974, but I rounded up for Presidents who took over in the second half of the year), 1981 (Reagan), 1989 (Bush 1) and 2001 (Bush 2). The variable took values of zero all other years.

2. A Democrat becomes President. This variable took values of 1 in 1933 (FDR’s first year), 1945 (Truman took office in April 1945), 1961 (JFK), 1964 (LBJ took office in November of 1963), 1977 (Carter) and 1993 (Clinton).

3. A Republican President begins his second term. This variable took values of 1 in 1957 (Ike), 1973 (Nixon), 1985 (Reagan), and 2005 (Bush 2).

4. A Democrat begins his second (or plus) term. This variable took values of 1 in 1937 (FDR), 1941 (FDR), 1949 (Truman), 1965 (LBJ), and 1997 (Clinton).

5. A sitting Republican Veep ascends to the Presidency. This one takes values of 1 in 1975 and 1989.

6. A sitting Democrat Veep ascends to the Presidency, which occurred in 1945 and 1964.

7. Republicans take over both houses of Congress from Democrats. That happened in 1947, 1953, and 1995.

8. Democrats take over both houses of Congress from Republicans, which we saw in 1933, 1949, 1955 and 2007.

Note that variables 7 and 8 only count instances where one party took control of both houses of congress from the other party and not from a mixed Congress. Some instances of mixed Congresses looked a bit like one party control so I decided to simply those out.

Obviously, the fit of this simple model won’t be that great as all the variables are dummy variables, and that’s the sort of thing that really doesn’t help your cause when it comes to patterns in the residuals, but hey, the goal here is not to explain the change in growth rates (I’ve done that before, and perhaps will follow up this post) but to look at which of these variables.

If, for example, a given party’s Presidents tend to enact policies that produce growth, we would expect to see the coefficient associated with that party’s presidents be positive and statistically significant.

Results of the regression are shown below.

Figure 1

I could comment, but from past experience, I know that if I do, I’ll be excoriated by people insisting I’m a partisan. So I highlighted the significant and almost significant variables, and I’m calling it a day. Please leave your thoughts about what this means and why in the comments section.

Well, I guess there is one comment I do have to make as I wipe a touch of egg off my face… the coefficient (and quasi-significance) of the Democrat Veep ascendance variable kinda throws into question at least part of what I wrote here.

And now, my usual closing statement: if anyone wants my spreadsheet, just drop me a line. I’m at: my first name (mike), my last name (kimel – one m only) at gmail.