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.

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

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.

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.

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:

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

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.

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.

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/