• Save
JetBlue Route Entry - Econometric Analysis
Upcoming SlideShare
Loading in...5

JetBlue Route Entry - Econometric Analysis



Econometric analysis of JetBlue's entrance into the NYC market and the impact to overall route fares.

Econometric analysis of JetBlue's entrance into the NYC market and the impact to overall route fares.



Total Views
Views on SlideShare
Embed Views



2 Embeds 2

http://www.linkedin.com 1
https://www.linkedin.com 1


Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

JetBlue Route Entry - Econometric Analysis JetBlue Route Entry - Econometric Analysis Document Transcript

  • Cheap Frills: The JetBlueEffect on Ticket Prices in New York City Zachariah Cheema Macalester College Economics 381 May 2005
  • Section I: Introduction Have we reached a new era of the airline industry where low-cost airlines willdominate the market, unless major carriers can match their prices? Despite a largeconsumer base and a highly inelastic demand for longer flights, the U.S. airline industryhas suffered extreme financial hardships over the last few years1. Recent economicrecession and the events of September 11th have made it harder for major airlines toproduce a profit. However, while these major carriers have struggled to survive, newcarriers such as Southwest have found a profitable niche in the industry2. Theintroduction of new low-cost airlines has given consumers better deals and more frequentflights. As previous research has shown, these low-cost airlines have had a significanteffect on the ticket prices of the routes they enter through bringing in more passengers,and forcing the major carriers to respond to this new competition. However, a new cropof airlines has emerged such as JetBlue, which not only offer a lower fare, but also offermany amenities not even found on the major carriers. When broken down individually byroutes with a new JetBlue presence, the effects of the low-cost airline should be evengreater than previous research has indicated. Consumers do not have to choose between acheap flight and a safe and reputable flight anymore, thus forcing ticket prices to fall forall carriers wishing to stay in the market. Section II of this paper will deal with the microeconomic theory behindcompetition in the airline industry. Section III will be a review of the empirical literature,including both direct models of airline ticket price and other related airline studies.Section IV will develop our conceptual model for analyzing ticket prices, the expectedsigns for the explanatory variables, and will cover the ideal data necessary for such an1 Chatfield-Taylor (2003). http://cmi.meetingsnet.com/ar/meetings_flight_risk_airline/2 Najda (2003, p.11). Lists operating costs and revenue for domestic carriers in 2001.
  • experiment. Section V will look at the actual data, including the specifics of our model,and how it’s formulated. Section VI will look at the results estimated, and compare thedifferent models run. Finally, Section VII will be the conclusion, which will offer futureavenues of study in the field.Section II: Microeconomic Theory In the long-run, the price of a good will become equal to the minimum averagecost, according to Eaton, Eaton, and Allen’s (2002) microeconomic theory3. Underperfect competition, other airlines would be forced to cut costs or be forced out of themarket. Eaton, Eaton, and Allen (2002) explain the five assumptions needed for perfectcompetition. First, they say that there must be no significant individual firm in the market,which is true in the New York City, since many different airline companies act as majorcarriers for different routes4. Another assumption is that perfect information must existfor airline prices, which has become true thanks to online services such as Expedia.comthat look for the cheapest deal. However, the airline industry is quite unique in certain ways, making it unable tocomply with all the assumptions of perfect competition. As Eaton, Eaton, and Allen(2002) also state, the high fixed-costs of the airline industry make it difficult for firms toleave the market, or even scale down in the short-term. United Airways may not be ableto cancel certain flights because the revenue helps contribute to fixed costs of the airplane.Also, whether or not consumers view airlines as homogeneous is not certain, althoughevery flight in theory performs its function, which is getting the passengers to where theywant to go. Consumers may value JetBlue amenities to the point where they will fly onnothing else unless necessary. Whatever the degree of homogeneity, JetBlue should be3 Eaton, Eaton, and Allen (2002, p.273-274, 287)4 Mayer and Sinai (2002, Table 2b)
  • favored by all since it’s cheaper and more comfortable. Therefore, while perfectcompetition does not exist in the airline industry, logically, every consumer would wantto switch over to JetBlue if they could, and thus other carriers would have to respond.Section III: Review of the Empirical Literature Part I: This part includes related airline studies that don’t deal specifically withlow-cost entry, but help us to understand competition among the different carriers. Siegmund (1990) has the earliest research on airline concentration, in which helooked at the merger between Trans World Airlines (TWA) and Ozark Airline in St.Louis, and the effect between 1985 and 1988. In this case, the merger created monopoliststanding, where TWA/Ozark went from having 39% of non-stop routes to having 79% ofthem5. Thus, since potential airlines will have to play serious catch-up to match TWA,these major carriers can have more control over the market. In research a decade later,Bamberger, Carlton, and Neumann (2001) looked at whether similar partnershipsbetween domestic airlines, through reducing direct competition, would raise ticket prices.The authors use the change in average fare pre- and post alliance as the dependentvariable, and dummy for the cities that are part of these alliances. Using comparative data5 Siegmund (1990, p.659)
  • between the third quarter of 1994 and that of 1996 for the Continental-America Westpartnership, the authors found that average fares fell for both airlines (using an alliancedummy-yielded a coefficient of -.084) through shared facilities and better coordination.Since Continental and America West compete to sell the same seat on co-partneredflights, it creates an incentive to reduce the cost of the ticket. Therefore, we see that theairline industry is responsive to competition, whether self-created or otherwise. Before the emergence of low-cost airlines as serious competition, Borenstein andRose (1991) looked at price discrimination within the same flight, using data from thesecond quarter of 1986. The data suggested that dispersion in prices was greater withinflights than between them, indicating that isolating certain types of flyers remains ofgreater importance than matching the prices of other airlines. Using price dispersion as adependent variable and concentration as one of the explanatory variable, the paper founda significant relationship between price dispersion and concentration at the airport,signifying that more choices allow consumers to have more flexibility with what they pay.Later work by Windle and Dresner (1993) used the number of competitors as a variablein regards to the average ticket fare in the market. The coefficient of competitioncompared to ticket price is significant at -.104. Although the data were taken in the thirdquarter of 1987 before the low-cost resolution, so to speak, it shows that allowing morecompetitors for airline routes will create a market for the number of customers that itneeds to subsist.Part II: This part will deal with the three specific papers that analyzed the effect of tickerprices on low-cost carriers: Dresner and Windle (1995), Alderighi (2004), and Najda(2003).
  • Dresner and Windle (1995) took data between 1991 and 1994 to look at theimpact of these low-cost carriers, specifically the emergence of Southwest Airlines, onaverage ticket fares across the U.S.. The data were from a 10% ticket sample from theU.S. Department of Transportation’s Database 1a. Market concentration, distance, andpassengers made up the explanatory variables, with dummies for vacation routes, intra-Hawaii routes, quarterly data, and specific carriers. The report obtains a statisticallysignificant (at 99%) coefficient of -69.71 for Southwest. Looking at the Figure I, you cansee the effect of the entry of Southwest on ticket prices, even before Southwest actuallyentered the market. This raises issues regarding whether the actual effect of these newairlines actually happens before they even appear.
  • Figure I 6 Recent work by Alderighi, Cento, Nijkamp, and Rietveld (2004) explored thesame phenomenon, but instead in Europe. The data for this study, which took placebetween April 2001 and July 2003, came from KLM airlines and already publishedreports. In this study, ticket price is a function of distance, GDP, and concentration, withvarying low-cost dummies. LC, the dummy variable indicating a low-cost presence, wasnegative and strongly significant, like in the United States model. However, the dummyfor the average traveler (-66.07) was much greater than for the business traveler (-45.85).Thus, since businessmen may have to make flights at specific times for meetings, they arenot as flexible to switch flights to save money. Najda (2003)’s model is the most similar to what we are trying to duplicate. Theauthor’s data includes the emergence of JetBlue, but did not limit his data simply to thatlow-cost airline. Using first a 10% random sample of all U.S. flights from the secondquarter of 2002 from the U.S Department of Transportation’s Databank 1b, Najda (2003)then applied dummy variables to take into account price level and length of routes. The6 Dresner and Windle (1995, p. 18)
  • author used price as the dependent variable, with the length of flight, route distance, loadfactor, size of the plane, frequency of flights, origin & destination market share, routeshare, low-cost market share, and cost of comparison airlines as the explanatory variables.Najda (2003) also used dummy variables to isolate tourist and non-tourist routes, hub andnon-hub starting points, and low-cost and non low-cost presence. The data find statisticalsignificance that major airlines who share the same route with low-cost carriersexperience a fall in the price of the ticket. However, the effect is much greater for thelonger, more expensive flights (-.039) than for the shorter, less expensive flights (-.024).These data make sense, as people are more willing to look for a deal on an expensiveflight than a relatively inexpensive one.Section IV: The Conceptual Model and Ideal Data Ticket Price (Average for Route) = f(Distance, Passengers, Market Share(largecarrier), Frequency (large carrier), Cost per Mile (large carrier), Frequency (lowcarrier), Market Share (low carrier), and Ticket Price (low carrier)) The conceptual model will be based around Najda’s (2003) model7, and willinclude new variables to build off of his work. Distance, Passengers, Market Share (largeand low carriers), Frequency (large), Ticket Price (low carrier), and Cost per Mile (large)all build off of Najda’s (2003) model. However, we are excluding load factor, the size ofthe plane, and origin & destination market share. Route-specific data for these variablesare difficult to obtain, and will not have a significant effect on the model. The effect ofthe load factor can easily be explained by the number of passengers on the route. Whileairlines can use different sized planes for the same route, yet again, this becomes afunction of how many passengers are on the route, which is already in the equation.Origin and Destination Market Share works with models that have multiple origin and7 Najda (2003 p. 42-51)
  • destination airports, which is not pertinent to this New York City-specific model.However, I included the frequency of the low-cost carrier as part of our model, sincelogically, ticket price would be affected by the number of flights the low-cost carrier has. The expected signs of these explanatory variables will follow the same formulaas for Najda (2003). We expect a positive correlation between ticket price and cost permile, since firms with high costs need to charge more to keep afloat. We expect a positivecorrelation between percent market share of large carriers and ticket price, since airlinesoperating under monopoly conditions can charge more. We expect a positive correlationbetween frequency of flights and price, since more convenient flights for consumers areof higher value, and thus higher price. We expect a negative correlation between ticketprice and number of passengers on the route, as more full flights raises total revenue, thusallowing firms to lower ticket prices to keep rates competitive in a densely populated area.We expect a positive correlation between ticket price and length of flight, since longerflights use more fuel/manpower, and thus are more expensive. We would also expect allthe low-cost explanatory variables to follow a logical relation with the ticket price. Aslow-cost carriers charge less, the average ticket price falls (positive relationship). Aslow-cost carriers obtain a greater market share and have more frequent flights, theaverage price drops because people have more opportunities to switch over to the low-cost carrier (negative correlation). After developing the perfect model, we will dummy for data pre-JetBlue entryand post-JetBlue entry, and then look at the coefficient to see exactly how large the effectof the low-cost carrier is. We will also dummy for September 11th, tourist routes, and
  • quarterly data, to see exactly how much dispersion exists within the model in terms of thelow-cost effect. Ideally, the dependent variable, ticket price, would be for all other carriers in themarket excluding the low-cost carrier. That way, we can look solely at what effect low-cost entry had on the competition, and not just on the route as a whole. Ideally, we hopethat all JetBlue flights offer the lowest fare available for the route, as another low-costpresence makes isolating the effect more difficult. Also, while the other explanatoryvariables are easy to measure, the actual cost per mile of the flight becomes more difficult,since costs for these flights vary tremendously due to geography and day of the week.Ideally, it would be an average of all cost-per-mile flights, and not just one flight drawnat random. Finally, we wish that the entry of a JetBlue flight matches up exactly with thequarterly data. Therefore, we can completely isolate and the pre- and post- effect of entrywith the data used.Section V: Actual Model Part I: Specifics of the Model Since the goal is to isolate JetBlue’s effect on a more micro level, the focusremains on flights out of New York City, where JetBlue is located (at JFK InternationalAirport, to be exact). The airline market in New York City greatly differs from that ofother major cities. Both LaGuardia and JFK airports in New York City have low airportconcentrations, signifying that no one carrier has a significant number of connectionsthere vis-a-vi other major airports8. Thus, NYC airline consumers have more choices.Secondly, the New York City area has five airports: JFK, LaGuardia, White Plains,Newark, and Long Island. While the extent to which each airport is substitutable remainsuncertain (Newark’s airport is located off the New Jersey Turnpike, which is an8 Mayer and Sinai (2002, Table 2b)
  • unpopular highway to travel), it still gives these consumers more available options.Thirdly, New York City is not geographically isolated from other major cities, soconsumers face a trade-off between taking a short flight and driving to such places asRochester, NY, Buffalo, Burlington, VT, and Washington D.C. Finally, New York Cityacts as a layover for passengers taking international flights, so consumers may take anairline such as Delta to New York City as a stopover in order to arrive at Paris. Overall,the unique nature of the New York City market makes it much easier for a low-costcarrier such as JetBlue to enter and find a niche. What exactly separates JetBlue from the other major carriers should be addressed9.In addition to cheaper airfare, JetBlue’s planes contain leather seats, televisions withDirectTV, satellite radio, completely ticket-less travel, and environmentally-friendlyAirbus A320 aircrafts. In addition to these amenities, JetBlue was also the first airline toinstall security cameras on flights, as well as bullet-proof cockpits. Thus, these lowerairfares and specialty services do not come at the expense of a less safe flight. Obviouslythis low-cost, high-frills strategy is working, as JetBlue has been the recipient ofnumerous awards, including the Best U.S. airline in 2004, according to the AirlineQuality Ranking Survey10. How is JetBlue able to have all these features, while stillkeeping price down? It has mainly to do with having labor costs 30-40% lower thanstandard carriers, as a result of needing fewer employees, and having a non-unionizedworkforce11 With JetBlue receiving some apparently well-deserved attention, consumershave even more incentive to try JetBlue and see what the fuss is all about.9 All information comes from JetBlue Fact Sheet: http://www.jetblue.com/learnmore/factsheet.html10 JetBlue: Awards and Accolades: http://www.jetblue.com/learnmore/awards.html11 Najda (2003, p.10-11)
  • Part II: Actual Data Data for the average price of a route (including JetBlue), market share of the largeand small carriers, the route distance, the number of passengers, and the ticket price ofthe low-cost carrier were found through the Consumer Air Fare Report, which is reportedby the Office of Aviation Analysis12. Data, recorded by quarter, were obtained betweenthe second quarter of 1997 and the third quarter of 2004 (the most recent information).Data were taken for twenty different routes that JetBlue operates, which are specified inTable III13. The frequency data were obtained through the Bureau of TransportationStatistics’ On-Time Performance Database, which records the on-time performance of12 Domestic Airlines Fares Consumer Report: http://ostpxweb.dot.gov/aviation/X-50%20Role_files/consumerairfarereport.htm13 JetBlue Timeline. http://www.jetblue.com/learnmore/timeline.html
  • every flight from an airline that has at least 1% of total routes in the United States14.Since this database records information from every flight each day, it provides a goodestimate of how many flights per quarter go from Point A to Point B. Despite the available data, the flaws in the model are unavoidable. Figuring outthe cost per mile was unfeasible for such a paper, as the actual values would need to beestimated and derived individually, which would take hours upon hours that weren’tpossible for this study. Secondly, the frequency data are not perfect, as the values wereestimated by looking at the third day of the last month of each quarter, and seeing howmany flights each day the major carrier had. Data could not be found for some of theroutes, and the flights with Continental as the major carrier (Ft. Myers, New Orleans,Tampa, and West Palm Beach) used Newark Airport as the origin. Since the Bureau ofTransportation Statistics only records data for carriers with at least 1% of total U.S.operations, frequency for JetBlue wasn’t available until the beginning of 2003. Therefore,including frequency in the regression will result in a significant decrease in the samplesize. Thirdly, in some cases, JetBlue ended up becoming the major carrier for the route,which makes it harder to see the effect JetBlue has on its competitor’s prices. Thisoccurred more often on shorter routes and in cities located near other major airports(Oakland, Ontario). Whether or not flights from New York to Oakland had an effect onticket price from New York to San Francisco was not addressed, as this study needed tofocus on single routes for time and simplicity purposes. However, looking at ticket pricesof neighboring airports, or the number of nearby airports as a variable, would be a goodway to build on the current model.14 Airline On-Time Statistics: Bureau of Transportation Statistics:http://www.bts.gov/programs/airline_information/airline_ontime_statistics/DetailedStatistics/
  • Matching up the entry of JetBlue with quarterly data can become difficult,especially when JetBlue enters in the middle of the time interval. Dresner and Windle(1995) noted how other airlines adjust their ticket prices in anticipation of new entry, sothe appropriate cutoff date becomes hard to determine. For our model, post-entry data isdefined as such if the low-cost carrier enters at any day in that specific quarter, whichremains the safest way to divide up the data. In routes with late JetBlue entry such asSacramento, the entire effect may not be visible yet, since JetBlue may end up developinga larger market share of this route in the coming years. Finally, I must note the potentialfor human error when creating the data set. While tremendous care was taken in makingsure all the necessary data were included, mistakes can happen when dealing with thismany routes and periods.Section VI: Model Results Before proceeding with the dummy variables for tourist routes, September 11th,quarterly data, and pre- and post- entry of JetBlue, the best base model to use needs to be
  • determined. Table IV looks at the different potential regressions tested for correlationwith theory and fit of the model. Regression 1 has a much smaller adjusted R-squaredthan the other regressions, thus it must be missing statistically significant variables.Regressions 3, 4, and 5 add new low-cost variables to the equation. The actual signs inthese regressions match up with predicted theory, and the ticket price (low carrier) andmarket share (low carrier) are statistically significant at the 99% level. However, thesample size diminishes greatly in each case, and since regression programs omit periodsthat don’t have all the explanatory variables, data in these equations are only availableafter the low-cost entry. Regression 6 has a high R-squared and a significant newexplanatory variable (low cost market share), but has two major flaws. First is that thisregression only picks up post-entry data. Secondly, as we mentioned in the Actual Modelsection, the market share for these low-carriers was only recorded when JetBlue becamethe lowest carrier. While what frequency data was available was random, the low-costmarket share data excluded flights where JetBlue did not become the cheapest carrier inthe market. Thus, including the low-cost market share would be overestimating the effectof ticket prices by JetBlue. Thus, since it would be theoretically impractical to dummythese regressions for pre- and post-entry data, we must eliminate these four choices.Therefore, Regression Two becomes the optimal model for estimating ticket price. Ticket Price (average of route) = f(Distance, Passengers, Market Share (large), Frequency (large))15 The coefficient signs of passengers (negative), distance (positive), market share(positive), and frequency (positive) match up with the established theory demonstrated byNajda (2003), and all are statistically significant. The coefficient value of distance is .11,15 After running Variance Inflation Factor Tests for Regression 2 between the explanatory variable, nosignificant value turned up that would indicate multicollinearity
  • meaning that the average ticket price increases at 11 cents per new mile. This relativelysmall number reaffirms the idea of the airline industry as heavily centered on fixed costs,where most of the expenses occur before the airline even leaves the ground. Thecoefficient value for passengers is a relatively small (-.005), meaning that each individualpassengers lowers the ticket price by half a penny. While the effect of passengers issignificant, this tells us that changes in a small number of passengers will not notablyaffect ticket price, since flights usually have extra room available. Only when there is astrong change (like a new major family theme park opening up in Fort Lauderdale) willwe see a change in ticket prices due to more passengers. The market share’s coefficientvalue is equal to 1.65, which signifies that a 1% rise in market concentration will causeticket price to rise by $1.65. We’d expect a strong correlation, since when either thelargest carrier adds more flights or the competitors drop out, it becomes more convenientfor the consumer to use the major airline. Thus, the major carrier can exploit this bycharging more per ticket. Finally, the frequency coefficient is equal to 5.84, signifyingthat adding a new flight increases the average cost of the flight by $5.84. Having yourflight be at the exact time you want it to holds more value, and thus is more expensive.Since our model is in corroboration with theory, and the coefficient values can belogically explained, the next step is to see dispersion in the data through dummying outairline-specific issues.
  • Using the above model, data is divided up into flights that occurred before JetBlueentered the route market and flights after the entry. We obtain a statistically significant(at the 99% level) value of -15.56 (located on Table V), meaning that JetBlue flights are$15.56 cheaper than the average fare. However, make note of the fact that the averagefare includes JetBlue flights as well, and we must remember that JetBlue also lowers theaverage fare when it enters. However, before concluding our model, other events or differences must be takeninto account that might affect ticket price. Theoretically, we’d expect popular touristroutes to be cheaper, since these routes have enough passengers to drag the average faredown. For our model, Las Vegas and all destinations to Florida (Orlando, Ft. Myers, Ft.Lauderdale, West Palm Beach, Tampa) and California (San Diego, Oakland, Long Beach,Ontario) were counted as tourist routes, since warm weather and being near beaches (or
  • gambling in the case of Las Vegas) make for popular places to relax. The averages priceof these routes remains $32 cheaper than non-tourist routes, which corresponds withtheory. September 11th had a major effect on both the financial state of the industry andwillingness to travel. Therefore, the data were dummied to isolate the third and fourthquarters of 2001, where consumer demand was logically at its lowest for flight travel.Regression 9 tells us that flights during these two quarters were $16 dollars cheaper, as aresult of a less willingness to fly. However, since some consumers may have purchasedtickets for these quarters before the September 11th attacks, and thus paid normal fare,this number underestimates the true effect of the period. Finally, different seasons shouldaffect ticket price, since free time and weather changes provide more incentive to travel.The cold winters in New York City make consumers eager to travel during the firstquarter of the year. This is evident by the dummy values for the other quarters inRegression 10, as ticket price are all cheaper than the base quarter (Quarter 1). Quarter 3has the lowest ticket fares, likely a result of having no major holidays in which to travel. The effects of all these dummies comes together in Regression 11, which usesnon-tourist, quarter 1, pre-entry, non-September 11th affected data, and compares thisagainst dispersions. The adjusted R-squared value increases to .9, and the F-valueremains greater than F-critical, thereby making this new unconstrained equation morevaluable that Regression 2. The most noticeable changes are from the tourist dummy(now $38 dollars cheaper as opposed to $32 before) and the JetBlue effect dummy (now$21 cheaper). This has to do with the propensity of consumers to fly to warm localesduring the first quarter. Since JetBlue offers lots of flight to Florida and California, theeffect that JetBlue brings to this market augments during this quarter. The September 11th
  • coefficient in this equation becomes statistically insignificant, since the effect on thirdand fourth quarter prices cannot be measured with a first quarter base. The quarterlydummies fall a bit in expense (Q2 changes $3.70, Q3 changes $1.91, Q4 changes $.14)when looking at pre-entry data as the base. This has to do with the fact that Quarter 1ticket prices were more expensive relatively before JetBlue came along (since JetBlue’scheap ticket routes cater to passengers traveling to warm locales), so thus the differencebetween quarters would be greater in the pre-entry era.Section VII: Conclusion Overall, while the effect of JetBlue remains strong and significant, it is not spreadout evenly across the data. Emergence in a tourist route, flights during a very snowyseason, and tragedies such as the events of September 11th change how willing consumersare to alter their flying habits for a cheaper ticket. However, overall, the effect of JetBlueon the average ticket price in the market remains strong and statistically significant. Sincedata are not available for the average fare of a ticket excluding JetBlue, the exact effecton its competitors remains unknown. However, the consumer on average saves $15 ontravel along these routes, and considering the how much the major airlines have cut coststo keep these customers, JetBlue’s effect on ticket prices can be accepted as stronglysignificant within the New York City model. In addition, unlike previous models thatlooked at low-cost, low-frills airlines, there exists every reason to believe that consumerswill continue to consume the JetBlue product. Passengers will become so used to theamenities offered through their services that their priority will eventually lie with JetBlue,even if the carrier is forced to raise ticket prices a bit in the future. As these new-era low-cost carriers continue to develop and spread across thenation, the possibilities for future research will expand. One key concern will be whether
  • or not JetBlue will be able to keep ticket prices down, while at the same time expand andoffer all these amenities. My notion is that JetBlue will have a lock on these passengers inthe future years, barring some labor cost crisis, but future data analysis will be needed forconfirmation. Upcoming models can also look at frequency data for JetBlue, which begancollection in 2003, and thus too late to measure how important this frequency is as part ofthe ticket price model. The emergence of low-cost, high-frills airlines in other parts of the nation willallow future tests on how well the New York City model holds up in markets withdifferent levels of air traffic and single-carrier dominance. Song Airlines (with manyflights out of Boston16), and Ted Airlines (with many flights out of Denver17) haverecently emerged, and are following the JetBlue model. Interestingly, these airlines areowned by Delta and United respectively, so it appears that these major carriers may betrying to compete with themselves. Hooters Air, a low-cost carrier with a rather obviousamenity, has emerged in the less-populated Myrtle Beach, South Carolina18, thusproviding a potential comparison test between the effect in a large and small base market.Finally, JetBlue has begun international flights to the Bahamas, Puerto Rico, and theDominican Republic19. Whether or not consumers will make significant switches to alow-cost carrier on an international flight remains a topic for the research in this ever-changing, exciting field20.16 Song Route Map: http://www.flysong.com/create_a_trip/where_we_fly/index.jsp17 Ted Airlines Route Map : http://www.flyted.com/traveltools/routemap.htm18 Hooters Air Route Map: http://www.hootersair.com/destinations/route_map/19 JetBlue Fact Sheet: http://www.jetblue.com/learnmore/factsheet.html20 I’d like to thank Professor Krueger of the Macalester College Economics Department, Course PreceptorMatija Vodopivek, and all the students of Econometrics 381 for helping me formulate my ideas and createan exciting model on what creativity and competition can do to an industry that has sufficiently lacked itfor so long.
  • Bibliography“Airline On-Time Statistics and Delay Causes.” Bureau of Transportation Statistics. http://www.transtats.bts.gov/OT_Delay/Ot_DelayCause1.asp“Airline On-Time Statistics: Detailed Statistics.” Bureau of Transportation Statistics. http://www.bts.gov/programs/airline_information/airline_ontime_statistics/Detail edStatistics/.Alderighi, Cento, Nijkamp, Rietveld, Piet. “The Entry of Low-Cost Airlines.” Tinbergen Institute. http://www.tinbergen.nl. July 2004.Bamberger, Carlton, and Neumann, Lynette. “An Empirical Investigation of the Competitive Effects of Domestic Airline Alliances.” National Bureau of Economic Research. http://www.nber.org/papers/w8197. March 2001.Berry, Carnall, and Spiller, Pablo. “Airline Hubs: Costs, Markups, and the Implications of Customer Heterogeneity.” National Bureau of Economic Research. http://www.nber.org/papers/w5561. May 1996.Borenstein, Severin and Nancy L. Rose. “Competition and Price Dispersion in the U.S. Airline Industry.” National Bureau of Economic Research. http://www.nber.org/papers/w3785. July 1991.Borenstein, Severin and Nancy L. Rose. “Do Airline Bankruptcies Reduce Air Service?” National Bureau of Economic Research. http://www.nber.org/papers/w9636. April 2003.Chatfield-Taylor, Cathy. “Flight Trouble: The Airline Industry in Trouble.” MeetingsNet. 1 June 2003. http://www.cmi.meetingsnet.com/ar/meetings_flight_risk/airline.“Domestic Airline Fares Consumer Report.” Office of Aviation Analysis. Q2 1997-Q3 2004. http://ostpxweb.dot.gov/aviation/X- 50%20Role_files/consumerairfarereport.htm.Dresner, Martin and Robert Windle. “Competition at “Duopoly” Airline Hubs in the U.S.” Transportation Journal. 32: 22-30. 1993.Dresner, Martin and Robert Windle. “The Short and Long Run Effects of Entry on U.S. Domestic Air Routes” Transportation Journal. 35: 14-25. 1995.Eaton, Curtis, Diane Eaton, and Douglas Allen. Microeconomics Fifth Edition. Toronto: Prentice Hall, 2002.Krueger, Gay and Marek Ciolko. “A Note on Initial Conditions and Liberalization during Transition.” Journal of Comparative Economics. 26: 718-734. 1998.
  • “JetBlue: Awards & Accolades.” http://www.jetblue.com/learnmore/awards.html.“JetBlue Fact Sheet.” http://www.jetblue.com/learnmore/factsheet/html.“JetBlue Timeline.” http://www.jetblue.com/learnmore/timeline.html.Mayer, Christopher and Todd Sinai. “Network Effects, Congestion Externalities, and Air Traffic Delays: or why all delays are not evil.” National Bureau of Economic Research. http://www.nber.org/papers/w8701. January 2002.Mead, Kenneth. “Air Carrier Flight Delays and Customer Service.” U.S. Department of Transportation. July 2000.Najda, Charles. “Low-Cost Carriers and Low Fares: Competition and Concentration in the U.S. Airline Industry.” econ.stanford.edu/academics/Honors_Theses/Theses_2003/Najda.pdf+%22Najda %22+%22Low+cost+carriers%22&hl=en&start=1. May 2003.Peña, Federico. “The Low-Cost Airline Service Revolution: A Report from the Federal Aviation Administration (U.S. Department of Transportation).” U.S. Department of Transportation. April 1996.Siegmund, Fred. “Competition and Performance in the Airline Industry.” Policy Studies Review. Vol. 9: 649-663. Summer 1990.Studenmund, A.H. Using Econometrics: A Practical Guide Fourth Edition. New York: Addison Wesley Longman, 2001.