That sentiment, coined by James Carville for Bill Clinton’s successful
1992 presidential campaign, is just as true today as it was then and likely has
always been. Go figure, people care
about the economy because it affects our everyday lives. From filling our gas tanks to putting food on
our table to receiving a paycheck from our jobs, nearly every aspect of our
daily lives are affected in one way or another by the state of the economy.
Having said that, the effect the economy has is nebulous and somewhat
rarely are we actually able to see the smoking gun. The perfect example of said “smoking gun” are
oil and gas prices. Given the reserves
that we have within our borders as well as some of the governments we have to
deal with to obtain crude oil as well as its crucial role in our economy, the
price of crude oil is very commonly reported and often politicized.
A more difficult effect to wrap your mind around is the salary you earn
at your job. If you earned $50,000 last
year and $51,000 this year, it’s very simple to conclude that your salary went
up 2% and that despite issues elsewhere in the economy, you are doing pretty
well. Well, let’s say that we are in the
midst of mild inflation and the Consumer Price Index (CPI) rises from 220 to
230. That would mean that, adjusting for
inflation, your income fell from
$50,000 to $48,782.61, a drop of 2.4%.
Often times, when looking at the economy, it’s easy to get sucked in by
the allure of numbers that have no context.
If our government has a $1.4 trillion dollar deficit and a presidential
candidate says that he will reduce the deficit by $1 trillion, the obvious
assumption is that if elected, that candidate will reduce the deficit from $1.4
trillion to $400 billion (which, by the way, would likely qualify as a miracle). However, what if that reduction is spread out
over 10 years? Now, all else being
equal, that candidate has pledged to put us on a track where the deficit will
be $1.3 trillion for the next ten years instead of $1.4 trillion. While saving a trillion dollars (especially
when a government frequently runs deficits) is a very good thing, the context
is critical, especially when listening to politicians talk about the economy.
Which brings us to the crux of this particular project; which party is
better for the economy? Is there a
relationship between which party controls the three major parts of our
government (for this discussion, the House of Representatives, the Senate, and
the Presidency) and how well the economy does over that time period?
HYPOTHESIS
Since this is my first quasi-research project, I’ll make a hypothesis;
there will be a relationship, it won’t be what you expect, and I don’t believe
it will end up being all that significant (although I do predict it will be
statistically significant) because of the litany of asterisks that will be
required.
ECONOMIC THEORY
In principle, the economy of the United States is a free market
capitalist model. Two main things
promote efficiency in free markets; number of firms/buyers and their ability to
get to the market. Anything that reduces
the number of buyers or sellers in a market (below a certain point) and
anything that hinder the two of them meeting in the marketplace has the
potential to reduce the efficiency of the market.
Historically speaking, the single largest hindrance to American
economic efficiency is… the American government. Just about the only thing that the government
does to increase economic efficiency is levying taxes (or some other
instrument) to account for negative externalities, or negative side effects of
a process that are not accounted for in the price of a good. The perfect example of a negative externality
is pollution; it’s a byproduct of the production of a good but because the good
is distributed to people not affected by the pollution that does not come into
the price of the good. The government
can step in and correct this imbalance.
However, many other things the government does (such as other taxes)
lower economic efficiency and this makes far more sense when we consider that
that is not their goal. You could easily
argue that given our current distribution of government funds and
redistribution of wealth, our nation is more concerned with social equality
than economic efficiency. Whether that
is right or wrong is a discussion for another time.
This is where party identification comes into it; Republicans typically
favor smaller governments that spend less and interfere in the markets
less. All of these (in a vacuum) promote
economic efficiency. Therefore, logic
would dictate that during congressional sessions where Republicans have more
power than Democrats, the economy should perform better.
THE DATA
The next question that comes up is how do we measure the performance of
the economy? Without that, we can’t even
begin to determine whether or not a party’s position in congress has anything
to do with a successful economy or not.
There are two main measures that have been used (right or wrong) as
measures of the health of our economy that have data available for several
decades; GDP and the Dow Jones Industrial Average.
GDP is one of the solid go-to’s when it comes to economic
analysis. Logic dictates that when it
comes to an economy, the more you’re producing and the more you’re consuming,
the healthier the economy is.
Intuitively, this makes sense. On
a micro scale, if you have less money, you buy less and if you have more money,
you buy more. The more financially
healthy you are, the more you consume (for the average consumer). The same logic applies here, only we’re
applying it to a country instead of to your wallet.
A lot is going on in this chart so allow me to explain it. The blue line is GDP (in billions of dollars). The red line is GDP that has been adjusted
for inflation using the Consumer Price Index (CPI). The green line is one that I will be using in
my regression that comes up later; real GDP per capita. One of the issues when using GDP to gauge the
production of an economy over time is that population is a very important
factor. If a country has 100 people and
200 units of output (GDP) and then ten years later has 500 people and 300 units
of output, can we say that the economy has done well? The GDP per capita has gone from 2.0 to 0.6,
a huge reduction in the standard of living for that country (unless there has
been serious deflation issues with their currency… either way, it doesn’t look
good).
The early results, just by looking at this graph is that the United
States has done exceedingly well in the 20th and 21st
centuries. In 1929 the GDP was $104.6
billion and after adjusting for inflation and normalizing by the population,
that figure was $9.75. Over the course
of the past 85 years, those figures have jumped to $17.5 trillion and $51.50,
respectively.
The other measure I mentioned was the Dow Jones Industrial Average
(DJIA). This is very tricky because of
the way people have begun to use the stock market to get rich instead of
looking at it as a vehicle for long-term investments. Nevertheless, I believe it warrants inclusion
because of a self-fulfilling prophecy.
If the stock market goes up, people believe the economy is doing
better. This will cause them to go out
and consume more goods and services which will
make the economy better. Mass psychology
plays a huge role in the stock market but then again, when our consumers are
real people and not data points in a program, mass psychology has a role to
play in the economy at large as well.
Again, adjusting for inflation seemed like such a good idea that I did
it again here. My first instinct, when I
look at this chart, is from the 1920’s through about 1990, I feel like the
stock market was used as a long-term vessel for investment. There were some shocks (obviously, that time
range includes the Great Depression) but for the most part, the ups and downs
could likely be explained by the business cycle and the economic picture of the
time. That is simply not the case
anymore. With the proliferation of
computers and this whole internet thing, it has become easier and easier for
average people to put their money into the stock market and execute their own
transactions. This opening of the
markets to the masses has partially led to the huge swings that can be seen in
the 2000’s and 2010’s. Along with their
being more money in the market, the average know-how of a person executing
these trades has gone down. The experts
have been diluted by the average people looking to get rich on their own.
Thankfully, as can be seen from these graphs, data is available for
GDP, the DJIA, and the CPI from 1928 to 2014.
I have to be honest, when I think about the amount of data that is
available to anyone and everyone, I kinda geek out a little bit… but that’s
just me. Anyways…
CONGRESS
The next step in this analysis was straightforward. I’ve already identified my economic
indicators and gotten them into exceedingly usable forms. Next, I have to line up some more independent
variables; the makeup of Congress.
Thankfully, this is actually an even easier task given that we have
records of just about every Congress that has ever served. I readily found data going back to the
mid-1800’s and personally, I always like it when I find more data than I need.
From 1927 to 1929 (the 70th Congress), there were 96
senators and 435 representatives. This
was very close to our current makeup but to account for minor fluctuations in
the size of Congress over the past eight or nine decades, I decided to simply
take the percentage of total members that were either Democrats or
Republicans. For instance, in the 77th
Congress (1941-1943), there were 66 Democrats and 28 Republicans in the Senate
and 267 Democrats and 162 Republicans in the House. These figures became 68.8% Democrats in the
Senate and 61.4% in the House and then 29.2% and 37.2%, respectively, for the
Republicans. Those two years, there were
a total of 8 members (2 senators and 8 representatives) from other parties but
I don’t take them into account given their tiny minority.
From there, I simply created a number of dummy variables to describe
who controlled which aspects of the government, including whether or not a
single party controlled both houses of Congress, whether or not a single party
held all three (House, Senate, and White House), and still another to signify
whether or not Congress was divided.
Interestingly enough, out of the 44 Congressional sessions in the
sample, there have only been 6 instances of a divided Congress.
During Reagan’s first six years in office, he dealt with a Democratic
House and a Republican Senate. In George
W. Bush’s last two years in office, he had a Democratic House and a split
Senate. However, Republicans and Democrats
had 49 seats a piece so even with a Republican vice-president (who would be the
deciding vote in the event of a tie), I couldn’t give either party the edge in
that case. Lastly, in the past two
Congresses, President Obama has had a Republican House and a Democratic
Senate. In other words, in all six
instances of a divided Congress, the Senate and the White House shared a
party. There is your interesting tidbit
for today.
Lastly, I included dummy variables for several key features of economic
history that occurred in the past eighty years.
For instance, I included a dummy variable to indicate when Congress serves
in a time of open war. Rather than
getting too bogged down in what should be defined as a war, I accounted for the
major conflicts that happened during the time frame; World War II, the Korean
War, and the Vietnam War. I also
included a dummy variable to indicate the periods of time encompassed by the
Great Depression and the Great Recession.
Lastly, I included another variable for the 107th Congress,
which served from 2001 to 2003. This was
to account for both the dot com bust as well as the 9/11 attacks, both of which
had serious repercussions on the economy.
The purpose of these dummy variables is simple; they attempt to account
for something in the model that other variables do not account for. In other words, by including a variable that
indicates the nation was going through the Great Depression, I’m attempting to
determine if the poor economic performance was due to the makeup of Congress or
due to the Great Depression.
RESULTS
The largest problem with this analysis was simply a matter of sample
size. There were 43 Congresses in my
data set and while 86 years (technically 85 years; my data does not go through
the end of the current congressional session, perhaps because it has not yet
ended) might feel like plenty of time to draw statistically significant
conclusions, at this level of analysis, that is not the case.
Below are the output statistics from an OLS regression in Gretl.
Model 3: OLS, using observations 1-43
|
|
|
Dependent variable: GDP_Capita___Ch
|
|
|
Heteroskedasticity-robust standard errors, variant HC1
|
|
|
Omitted due to exact collinearity: Rep_Pres Div_Con_
|
|
|
|
|
|
|
|
|
Coefficient
|
Std. Error
|
t-ratio
|
p-value
|
Constant Term
|
90.7396
|
337.831
|
0.2686
|
0.7901
|
Democratic % (House)
|
0.839877
|
3.5372
|
0.2374
|
0.8139
|
Republican % (House)
|
0.478972
|
3.61369
|
0.1325
|
0.8954
|
Democratic % (Senate)
|
-1.48576
|
1.43847
|
-1.033
|
0.3099
|
Republican % (Senate)
|
-1.59213
|
1.61083
|
-0.9884
|
0.3309
|
Democratic President
|
-1.16063
|
8.48723
|
-0.1367
|
0.8921
|
Democratic Congress
|
-6.16873
|
4.80762
|
-1.283
|
0.2093
|
Democratic Government
|
2.71519
|
9.96121
|
0.2726
|
0.787
|
Republican Congress
|
4.77725
|
4.92264
|
0.9705
|
0.3396
|
Republican Government
|
-6.32027
|
9.30869
|
-0.679
|
0.5024
|
Depression or Recession
|
-15.1617
|
4.44234
|
-3.413
|
0.0019
|
War
|
7.27271
|
4.45552
|
1.632
|
0.1131
|
Other
|
0.774011
|
2.95253
|
0.2622
|
0.795
|
|
|
|
|
|
Mean dependent var
|
4.33168
|
S.D. dependent var
|
9.049915
|
|
Sum squared resid
|
1704.174
|
S.E. of regression
|
7.536962
|
|
R-squared
|
0.504578
|
Adjusted R-squared
|
0.306409
|
|
F(12, 30)
|
33.53435
|
P-value(F)
|
4.60E-14
|
|
Log-likelihood
|
-140.1265
|
Akaike criterion
|
306.253
|
|
Schwarz criterion
|
329.1486
|
Hannan-Quinn
|
314.6962
|
|
|
|
|
|
|
Excluding the constant, p-value was highest for variable 4
(House_Rep)
|
This model says volumes about the matter in question while
simultaneously saying relatively little.
The key figures to look at are the p-values in the right hand column and
this is a situation where lower is better.
In fact, to the majority of statisticians, a correlation isn’t
statistically significant unless the p-value is less than 0.1. By that standard, the only variable in the
regression that is statistically significant is the one indicating the presence
of a recession or depression.
From this model, it would appear that the makeup of Congress is not an
indicator of economic success or failure, according to the changes in GDP per
capita. A model substituting the
performance of the DJIA as the dependent variable yielded similarly poor
results.
So what are we to make of these results?
My take is that the economy is complicated and is affected by an
unbelievable variety of factors, just one of which is the makeup of Congress
and the laws they enact. To that end,
you can also notice in the table above that the adjusted r^2 value for this
regression is 0.306. That means that
just over 30% of the variability in the dependent variable (change in real GDP
per capita) was due to the variability in all of the other variables. In other words, after crunching these numbers
and finding this data, I was able to explain just 30% of what’s going on.
As a professor of mine would say, don’t get too caught up in that pesky
r^2 value; it’s not the be all and end all.
If we tried to create a model that entirely explained the changes in
real GDP per capita for the past 86 years, it would require a supercomputer to
execute… my poor little laptop would just not be up to the task. The more important thing to look at is
whether or not any of the independent variables were statistically significant;
that is where the real meaning of this regression analysis comes into
play. Unfortunately, none of the
variables that I wanted to be significant were; I am forced to reject my
hypothesis.
REFLECTIONS
Then again, I’m hardly surprised by this result. Our economy is enormous and even with the
government spending truly obscene amounts of money during the past 4-6 years,
government spending still only accounts for 20-25% of the size of our
economy. Even though they have enormous
power to enact laws that directly affect the economy, it appears (at least from
this analysis) that they are far more bystanders than they and the media would
have us believe.
After all, was it Hoover’s fault that the DJIA dropped 43.5% from 1929
to 1931 and then another 55.3% from 1931 to 1933? He was faced with one of the worst economic
crises the modern world has ever seen and he was doing exactly what classical economics
said that he should.
Was it the fault of George W. Bush in particular and Republicans in
general that the dot com bubble burst after he took office and then the 9/11
attacks rocked the country? Is it the
fault of either party that the banking industry started the largest economic
downturn since the Great Depression?
From the little reading I’ve done, that particular crisis can be
attributed to both parties over the course of fifteen or twenty years of
deregulation of the banking industry.
Just because a president was in office during good or bad economic
times does not mean that they caused those good or bad times. Presidents get far too much credit when the
economy is booming (Bill Clinton, for instance) and far too much blame when the
economy is stagnant (George W. Bush). Statistically
speaking, the same can be said for the occupants of Congress.
Essentially, at the end of the day, the picture on the right is flat
out wrong.
