This summary provides an overview of the key points from the document: The document examines the development and growth of Port Columbus International Airport (CMH) over time. It analyzes how infrastructure expansions and renovations from 1979 onwards helped increase airport capacity and passenger traffic. A conceptual model is proposed to examine the relationship between airport infrastructure, population changes, and passenger numbers. An empirical model is then outlined to test this relationship, with variables such as expansion, population, seasonal trends, and a pre/post 9/11 indicator. The goal is to understand how different factors influenced passenger growth at CMH over the decades.

1. Port Columbus International Airport (CMH)
by
Isaac Griffin
University of Oregon
isaacg@uoregon.edu
9/29/2015
Abstract
Port Columbus International Airport is a C Class airport located 6 miles east of downtown
Columbus Ohio. This paper attempts to examine and explain the development and growth of
Port Columbus Airport. Here we will focus on renovation, expansion, population, and the
effects of the terrorist attacks of 9/11. We will analyze how well this model works at
explaining airport growth.
Paper prepared
in
Partial fulfillment of the requirements of Economics 421
Keywords: Airport Size, Airport Growth, Issues, Risk & Security Levels, Weather, 9/11, Ohio,
Columbus, Ohio
__________
Acknowledgements
This paper was completed in fulfillment of a grade for Economics 421 at the University
of Oregon. I would like to thank Wes and Nate for the opportunity to write this essay.

2. Introduction
This term project attempts to analyze the development and growth of the Port
Columbus Airport over time, and to develop an estimate model that accurately displays the
findings. According to a 2005 study, Port Columbus Airport receives over 50% of its passengers
from a 60 mile radius [1]. Port Columbus is also the largest passenger airport in the state of
Ohio. Citizens of Ohio could potentially be interested in the growth of the airport as trending
passengers could give them an indicator as to how the state population is traveling, growing,
etc. I hypothesize that they will also be concerned with how much demand the airport can
supply. Renovations in infrastructure will lead to higher amounts of potential air traffic, thus
supplying more potential passengers flights in a more effective and efficient manner.
Something similar can be said about any investors interested in investing funds or capital into
the Port Columbus Airport. These could be large corporations such as multi-national airlines,
or perhaps even smaller companies looking to implement their restaurant or gift shop into the
airport. While I think the Ohio citizen example is important to mention, I feel the latter will
be overall more important to this study. With these results I am expecting and I am hoping
that I will be able to adequately explain growth trends and development over time. I also
would like to really focus on how the airport was affected by the terrorist acts of 9/11, and
how (if any) these events changed trends in passenger growth and development. I am
expecting that the events of 9/11 will cause a slight decrease in passenger growth over time
initially, however, I see it bouncing back (and perhaps greater) to its pre-9/11 numbers. This
is due to it being the largest passenger airport in the state of Ohio, and the fact that America
West Airlines went into financial collapse in 2003 following the 9/11 terrorist attacks. There
have also been numerous expansions and renovations to the airport over the last 50 or so
years, thus i expect the passenger rate to increase and be correlated with airport size,
improvements, expansions, and renovations. Several of these infrastructure changes have also
came post 9/11 which i find quite interesting, and see it as something that is backing up my
initial claim.
“In 2001, Executive Jet Aviation (now known as NetJets, opened up a 200,000-square-foot
(19,000 m2) operational headquarters at Port Columbus International Airport [2].”
“In November 2006, Skybus Airlines began leasing 100,000 square feet (9,300 m2) of office
and hangar facilities at the Columbus International AirCenter adjacent to Port Columbus
[3].”
Background
Port Columbus Airport is located six miles east of downtown Columbus Ohio. The
airport code ‘CMH’ stands for Columbus Municipal Hangar which is an older name for Port
Columbus Airport [4]. It is the largest passenger airport in the state of Ohio, and also handles
over 0,411,920 units of freight and 8,537,279 units of mail (this is a 2006 figure) [5]. From
1979 to 1981 a 70 million dollar renovation to the airport increased the amount of incoming
and outgoing flights by 250 a day. This renovation added Concourse B to CMH, and this
happened because at the time CMH was a major hub for both America West Airlines (until
2003) and Sky bus (until 2008). In 1989 the construction of concourse A began, this second
renovation cost 15.5 million dollars. Concourse C was completed in 1996 and was expanded
upon in 2002. From 1998 to 2000 many expansions and renovations were completed including
a 25 million dollar terminal renovation. In 1999 new hangars were implemented for Netjets,
and there was also a 92 million dollar parking garage and underground terminal entrance

3. added to CMH. In 2004 a. 195 feet new control was built, and this marked the beginning of
CMH starting the construction of several control tower enhancements that are forecasted to
be complete in 2025. 2012 marked a renovation to Concourse A which should be completed in
2016. In 2013 a 140 million dollar runway improvement was taken up which added to increase
in the volume of air traffic. In 2014 Port Columbus reached 6,355,974 passengers in the year
2014 [6]. Port Columbus was established in 1929 and proceed to grow until 1958. In 1964 jet
airline flights began to depart from the airport. Port Columbus was previously a hub for
America West Airlines until 2003, due to financial collapse following the 9/11 terrorist
attacks. Port Columbus was also a hub to Skybus Airlines from 2007-2008 when it ceased
operations. Numerous airport renovations have gone underway since 1979. Large
infrastructure changes have also greatly increased the capacity, size, and growth of the
airport since its’ early days.
Conceptual Model
I will mainly be discussing which variables I will be including, and why I am including
them. The variables that I plan on including in this model are an infrastructure expansion
variable, a 9/11 variable that will be a dummy variable showing pre and post 9/11, a
population variable, my 3 quarter variables, and an incident and accident variable (i do not
suspect this will affect much but it will make things interesting and may reflect very small
and slight variances within the model). I plan to closely examine renovation and
infrastructure of the CMH airport in order to gauge its growth and find the relation between
infrastructure growth and how this growth seeks to accommodate and supply to passenger
demand. I hypothesize that infrastructure and growth of CMH will highly correlate with the
demand of passengers, and this increase in infrastructure will allow for higher rates of
supplying flights to passengers. Some key aspects that could be driving these trends in
passengers could be renovations and increases in the capacity that the airport can handle.
This could potentially coincide with population growth, as the increases in demand in flights
could affect the amount of renovations that CMH undergoes in order to supply the demand of
passengers. For example, the 1981 completion of Concourse B increased flight traffic by 250
flights per day. There could be potential and very slight variances in the observed data such
as incidents and accidents, however, I believe that these incidents will affect the data so
slightly that it will not make much of a difference. Passengers with a high enough demand for
a flight will take it regardless of incident or accident and I feel that this driver will not
necessarily affect demand in such a way that it will affect anything in a manner that is
detrimental. I do feel that these are important aspects to mention however, because there
are many factors that can affect the data and the passenger trends, so mentioning every
possible data and model variance is important. This is also important to mention as not every
single variable can be mentioned within the paper, so bringing issues like these to light could
potentially add to the study of CMH in the future. The quarter variables will simply explain
seasonal trends. This is my overall conceptual model which may change or vary slightly, but
not much.

4. Empirical Model
In this portion of the essay I will develop the empirical framework, and specify the
econometric model that will propel this essay. From novice economic theory we can assume
that passengers will be explained by a function that can be dumbed down to simple supply
and demand:
Q* = Q*(Xd , Xs)
Here our Q is endogenous and without error. While our model may display some sort of
biasedness we can account for that by using instrumental variables and various tests such as
2SLDS
ln(Q) = B1 + B2*Expansion +B3*9/11DUMMY + B4*Population + + B5*QuarterDUMMY + ui
Hopefully, all of our classic OLS BLUE Gauss Markov theory assumptions will uphold. This
means that our econometric model will be:
❏ Linear in parameters and correctly specified
❏ Our X variables will have variance.
❏ Homoskedastic,
❏ E(Xu) = XE(u) = 0 [i.e. non stochastic and uncorrelated with our error term].
We want assume that our econometric model will have homoscedasticity meaning that our
variance within the disturbance term is constant throughout all of our observations. We want
a precise variation in this sense. If it is not constant then we will have to accommodate for
that via testing. We also want unbiasedness so that we do not get consistently wrong
estimates. This part of the assumptions of the model for CMH is pretty unlikely which is why
will we most likely need to regress with instrumental variables, and do a 2SLS test.
In addition, our assumptions about our disturbance error term (u) are as follows:
❏ Zero mean
❏ Homoscedastic
❏ Independent and normally distributed (ie our covariance is 0)
ASIDE: [Since our disturbance is normal, that Y will be a linear function of u, and our Y is a
normally distributed non-random variable.]
I theorize that there should be fairly high auto-correlation with Port Columbus Airport which I
believe will need to be accounted for and will surely cause issues with the empirical
framework as a whole. The explanatory variables should explain most of the supply and
demand relations of this airport.

5. Data Source and Variables
The primary source of data used in this analysis is the Bureau of Transport Statistics 10
percent origin-destination survey [1]. From these data, passenger origins are developed for
CMH airport by aggregating all passengers that originate from this airport. These data were
supplemented by income and population statistics obtained from the Census Bureau’s “Small
Area Income and Poverty Estimates” which provides data on income and population at the
county level for 1993, 1995, and 1997-2009 [2] From the St. Louis Fed, a consumer price
index was obtained to deflate the monetary variables.
The dependent variable in the analysis is defined as Quantity demand. Following the
empirical representation of the theory and empirical model, origins are explained by
Expansion, a 9/11 dummy variable, population and a quarter dummy.
Expansion, a pre and post 9/11 dummy variable, population, quarter dummy variable.
Expansion will explain growth within the airport in terms of infrastructure and overall size of
the airport. I will model expansion by multiplying population within 20 miles and passenger
size. This should accurately reflect airport expansion. I expect this variable to model closely
with demand as the increases in infrastructure will likely be highly correlated with how many
flights are going in and out of CMH. I will regress this with each quarter to show it over time.
The 9/11 dummy variable will display how the terrorist acts of 9/11 affected CMH airport,
and how the demand was affected after 9/11 occurred. I expect a significant drop in demand
early on, but eventually this trend will end and demand will be relatively back to normal
levels after about 5 or 6 quarters. Population is fairly simple, population trends will likely
correlate strongly with flight demand trends. I expect this correlation to show how population
affects demand. I also expect it to be correlated with expansion which may cause
multicollinearity issues later on that will need to be addressed. The quarter dummy variable
will allow us to have a better sense of our time series data and be able to quantify distinct
time scales within years. I expect this variable will just allow the reviewing of trends and
data to be a bit simpler. The incidents variable will attempt to see if small airplane accidents
or incidents within the airport affected demand. I do not think this variable will add a whole
lot to the model, but I do think that it will help counter omitted variable bias which is why I
decided to keep it.
My theory is that airport size (expansion) and population will be the key variables in
explaining demand of flights out of CMH. Demand will be logged so it can be viewed as a
percentage change when the other variables are unit changes. I believe that percentage
changes in demand will be easier to examine and more effect in comparison to unit changes
in demand.
ln(Q) = B1 + B2*Expansion +B3*9/11DUMMY + B4*Population + + B5*QuarterDUMMY + ui
After running some regressions here is what we examine the following econometric results:

6. Econometric Results
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(Regression
1)
(Regression
2)
(Regression 3) (Regression 4) (Regression
5)
VARIABLES Quantity
Demanded
Quantity
Demanded
Quantity
Demanded
Quantity
Demanded
Quantity
Demanded
Expansion 9.18e-13*** 9.39e-13*** 1.08e-12***
(1.95e-14) (1.76e-14) (4.39e14)
t2 -0.000444* -0.000222 0.000755*** -1.46e-05***
(0.000236) (0.000197) (0.000189) (4.17e-06)
t3 5.28e-06 5.09e-06 -4.94e-05*** 3.42e-07***
(5.49e-06) (5.06e-06) (1.12e-05) (9.66e-08)
t4 -2.15e-08 -3.55e-08 7.72e-07*** -2.10e-09***
(3.86e-08) (3.60e-08) (1.86e-07) (6.78e-10)
post911 -0.0595 0.00234 -0.233***
(0.0885) (0.00155) (0.0575)
pop_20 -5.72e-07* -6.30e-07*** 2.10e-06***
(3.36e-07) (5.90e-09) (3.44e-07)
trend 0.0130*** 7.52e-05
(0.00280) (5.05e-05)
post911_trend 0.00200 -5.11e-05
(0.00250) (4.38e-05)
logEX 0.999***
(0.000344)
Constant 13.16*** 12.52*** 12.30*** -13.25*** 10.15***
(0.490) (0.0914) (0.0279) (0.0129) (0.515)
Observations 68 33 35 68 68
R-squared 0.994 0.993 0.993 1.000 0.405

7. Our first regression here is our base regression. We have our quarterly variables, our
post 911 variables, our population within 20 miles variable, we also have a post 911 trend
variable, our expansion variable, as well as our constant. Here we can see that expansion is
9.18e-13 and is statistically significant at the 1% level. Our quarter two variable is also
statistically significant (at the 10%), as is our population and trend (at the 1%). What we can
gather from these tests is that these variables are generally a pretty good start to our model.
In addition, our R2 is very high and these likely describe the base model as one that describes
airport size and quantity demanded very well.
Our second regression here is a regression to display just expansion and our quarterly
variables effect on quantity demanded. Expansion is still statistically significant at the 1%
level meaning it likely does describe the model pretty well but none of our quarterly variables
are statistically significant, which could possibly signify something, is not right here. We have
also decreased our observations by only including expansion. We also get a decrease of .01 in
our R2, but this difference is small.
Our third regression here is to model pre-911 quantity demanded. All of our variables
are statistically significant at the 1% level. Our R2 is still at .993 as it was in the second
regression.
Our fourth regression here is to model a different expansion variable. The coefficients
on the expansion variable are very small and I do not necessarily think that I modeled it
correctly. It is still statically significant at the 1% level, however, it is still very small and I do
not think it is correct. The main issues I see with this is that we have perfect R2. This makes
me nervous as I likely included too many variables, but it also lets me know that taking the
log of our expansion variable was likely a good thing as it is statistically significant and it did
overall increase R2 and our models fit to quantity demanded. I am beginning to think this
model may not be exactly what we are looking for in terms of modeling quantity demanded
but I could be incorrect.
Our fifth regression here is just to see how post 911 and population affect quantity
demanded. Both are statistically significant at the 1% level. We do see what we projected as
post 911 has a negative relationship to quantity demanded. This is what we expected as
terrorist acts that take innocent civilian live de-incentivize traveling in these types of
circumstances. However our R2 reduces by approximately .50 points, which is not a good
thing. This is okay however, as this is not the base model, and it is just to gain a general idea
on how population and post911 effect the overall model, outside of quarterly data and
expansions.

8. Here is a scatter displaying a post 9/11 trend variable on passengers. This shows that
there is positive correlation between the two and that the terrorist attacks of 9/11 did in fact
effect quantity demanded. This shows that our 9/11 dummy variables is key in giving an
account of how terrible atrocities can affect the demand when it comes to travelling.
Conclusion
These results are very mixed. On one hand they do support my theories that 911 would have a
negative effect on quantity demanded by passengers. We can see that most of my variables
did explain the dependent variable relatively well. Most of the time the variables were
statistically significant with a positive effect on quantity demanded as well as there being a
constantly really high R2. However, the more regressions I ran, I did begin to see issues with
the way that I defined these variables (in particular expansion). I think the overall model did
explain and support my theories very well, but I do not know if I defined the variables well
enough to firmly state with 100% confidence. I believe that my model did work, and that the
model did accurately support my theories in defining quantity demanded, however, I am not
sure how if the way that I defined the variables is correct, thus making the model plausibly
corrupt. Overall, I conclude that this model did work, however, it may not be perfect.

9. Citations:
[1] http://www.port-columbus.com/about/CRAA-EIA-full%20brochure.pdf
[2] "Executive Jet, Inc. Inaugurates New Operations Center". Netjets Inc. June 14, 2000. Retrieved
August 16, 2007.
[3] "Skybus will establish headquarters at Columbus International AirCenter". Skybus Airlines Inc.
November 10, 2006. Retrieved August 16, 2007.
[4] Airport ABCs: An Explanation of Airport Identifier Codes". skygod.com. RetrievedJuly 22, 2007.
[5] "Port Columbus Sets New Passenger Record in 2007". Columbus Regional Airport Authority.
January 25, 2008. Retrieved February 11, 2008.
[6]http://www.dispatch.com/content/stories/business/2015/01/27/airport-passenger-traffic-rose-
in-2014.html
[1]http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=125&DB_Name=Airline
%20Origin%20and%20Destination%20Survey%20(DB1B)
[2] http://www.census.gov/did/www/saipe/