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Elasticity of demand and offer in the airline industry

Elasticity is define as the "quality sth has being able to stretch and go back to its original decoration". (Oxford advanced learners dictionary 6th edition)

In Physics elasticity is defined as "the house of a substance that enables it to improve its length, volume, or condition in direct reaction to a force effecting such a big change and to recover its original form upon removing the force. " (dictionaryreference. com).

Suppose that your employer allows you to work extra hours more after your contracted hours for extra pay by the end of the month, the amount of extra money you will earn at the end of the month will depend on how much more extra hours it is possible to work. Then how responsive you are to the offer is seen as elasticity.

Therefore I am going to define elasticity as the way of measuring amount of responsiveness of any variable to extra stimulus.

From my example above elasticity can be calculated as

Em = percentage of extra money you earn/percentage of extra hours worked.

The concept of elasticity may be used to measure the rate or the exact amount of any change. In economics elasticity is utilized to measure the magnitude of responsiveness of an variable to an alteration in its determinants (sloman) such as (demand and offer) of goods and services.

For the goal of this essay am going to be examining the idea of elasticity of demand and offer in the airline industry.

Types of Elasticity

Price or own price Elasticity of demand

Income elasticity of demand

Cross elasticity

Price or own price elasticity of demand

It is the measure of the amount of sensitivity or responsiveness of quantity demanded is to an alteration in price of a product (Edgar. K. browing). Our assumption often is that all demand curves have negative slopes which means the lower the price the higher the quantity demanded but sometimes the degree of responsiveness change from product to product. For instance a reduction in the price of cigarettes might have only bring about a little increase in quantity demanded whereas a supermarket reduction in the price tag on washing up liquid will produce a large upsurge in quantity demanded The law of demand and even Common sense tells us that when prices change, the quantities purchased will change too. However, by how much? Businesses need to have more precise information than this - they have to have a specific measure of how the quantity demanded will change consequently of a price change.

Price elasticity is calculated as the percentage (or proportional or rate) of change in quantity demanded divided by the percentage (or proportional or rate) of change in its price.

Symbolically:

PD=%‹Q/%  p

Here denotes elasticity and  

Graphically

Elasticity measure in percentage because it allows a comparison of changes in qualitatively various things that are measured in two different units(sloman). It is the only sensible way of deciding how big a change in cost or quantity, so their calls a unit free measurement.

Generally when the prices of good escalates the quantity demanded decreases, thus either of the quantity will be negative which after division will finish up in a negative result, for this reason fact we always ignore the sign and simply focus on the absolute value, ignoring the sign to tell us how elastic demand is.

The larger the elasticity of demand, a lot more responsive the quantity demanded is of elasticity.

Degrees of elasticity

Perfectly elastic

Highly elastic

Relatively elastic

Relatively inelastic

Highly inelastic

Perfectly inelastic

Elastic Demand

Elastic demand occurs when quantity demanded changes by bigger percentage than price. (Sloman) Here customer has lot of other alternative. The worthiness is always greater than 1, the change in quantity has a larger effect on total consumer spending than in price. For example if there is a decrease in the price tag on a bottle of washing up liquid say from 1. 00 to 50p people will buy more probably to store up, in doing this they will finish up spending more on the merchandise than they'll do on a normal day.

An Inelastic Demand

Elasticity in airline industry

The airline industry is deeply influenced by the elasticity of demand, externalities, wage inequality, and monetary, fiscal, and federal policies. The elasticity of demand is situated purely on current market conditions, thcustomer's September 11th tragedy had a poor affect on the complete travel industry. It impacted the fiscal and monetary policies, supply and demand, and it created staffing problems nationwide. The pace of wage inequality is enhancing due to legislation that has generated a pay upsurge in participating cities over the USA. The airline industry is viewed has being unstable because it is dependant on current market conditions, and the market is obviously changing. purpose for travel, and available substitutes. Externalities continue to influence the elasticity of demand. The

Elasticity of Demand

The airline industry can be an extremely unstable industry because it is highly dependant after market conditions. Events such as inflation, terrorist attacks, and the price of oil have greatly influenced the demand for flight tickets throughout the years. Competition constantly affects the price of airline tickets because it provides customer other available choices. Substitutes that are existence is traveling by train, car, or avoiding travel whenever possible. Customers have resorted to all or any named substitutes during turbulent times in our economy. The elasticity of demand is greatly affected by the customer's purpose for travel. Airline customers typically fly for business or pleasure. While using wave of technology, a sizable percentage of business travel has been eliminated to conserve spending.

Elasticity

In the airline industry, price elasticity of demand is sectioned off into two segments of consumers and is considered to be both elastic and inelastic. A good example of how elastic demand relates to the airline industry is with regards to travel for pleasure. Pleasure travellers will be damaged by the amount of travel they actually predicated on the demand increase or decrease, influenced by prices that lower with high demand or prices that rise with low demand; directly related to competition in the forex market (Gerardi & Shapiro, 2007). Inversely, the business traveller would apply to an inelastic demand because of this market. This has shown by demand increases or decreases, as well as the price distribution attributed, which includes little influence on the buying power of the business person (Gerardi & Shapiro, 2007). Furthermore, Voorhees and Coppett (1981) explain that elastic demands exist for the pleasure traveler due to demand increase rising while prices lower and vise versa. The business enterprise traveler activities an inelastic demand due to the quantity of service demanded and quantity hasn't decreased as prices have risen. In other words, this travel is seen as a necessary business tool, not afflicted by price changes in the demand curve.

As we have seen, the airline industry is extremely price elastic. Small shifts in prices have dramatic effects on the consumer base. Externalities, such as noise ordinances, can cause unwanted effects, driving cost

upward and threatening loss popular due to a cost sensitive customer base. Since deregulation, competition throughout the market have kept prices on the market low and have caused airlines to force cuts in areas such as wages; adding to a growing concern of wage inequality.

Refrences:

Gerardi, K. , & Shapiro, A. (2007, April). THE CONSEQUENCES of Competition on Price Dispersion in the Airline Industry: A Panel Analysis. Working Paper Series (Federal Reserve Bank of Boston), 7(7), 1-46. Retrieved April 30, 2008, from Business Source Complete database.

Mankiw, N. G. (2004). Principles of economics (3rd ed. ). Chicago, IL: Thomson South-Western.

Morrison, S. , Watson, T. , & Winston, C. (1998). Fundamental Flaws of Social Regulation: The Case of Airplane Noise. Retrieved May 8, 2008, from http://www. brookings. edu/~/media/Files/rc/papers/1998/09_airplane_winston/09_airplane_winston. pdf

Voorhees, R. , & Coppett, J. (1981, Summer). New Competition for the Airlines. Transportation Journal, 20(4), 78-85. Retrieved April 30, 2008, from Academic Search Premier database.

The airline industry is an exclusive good. Mankiw (2004), states that private goods are excludable and rival goods. One must look out of the anti-trust laws and regulations that tempt some to call the industry a natural monopoly; airlines still reserve the right to administer price and destination. The airline industry implies that it can be an excludable good by having the capacity to place prices on fares and having the ability to refuse service to anybody for whatever the reason why. The airline industry also implies that it is a rival good because when someone purchases fare for a seat, it diminishes the power for another person to get a seat on the plane. As the airline industry is a private good, in a competitive market place, prices, supply, and demand are extremely sensitive to new policies or tax incidences put on them.

Associated content. com viewd 18/11/10

Wordpress. comThis phenomenal increase in the demand for domestic air travel is not surprising. Airfare can be an expensive commodity that few people can afford or are prepared to pay for it. Also, a typical consumer may not have the ability to avail such commodity regularly. It takes time for the consumer to demand for this again.

In economics, this scenario is being explained by its ELASTICITY. The concept of elasticity has been referred as the responsiveness of the quantity demanded of a good or service to a change in its price, income, or cross price. This post provides an improved understanding on this matter, specifically the price elasticity.

Analysis

Below consists of indicators that determines the elasticity of an good/service. Domestic air travel has been employed as a sample commodity.

Substitutes. (The greater substitutes it includes, the bigger the elasticity. ) Airlines have numerous substitutes such as land or sea transportation.

Percentage of Income. (The higher the percentage that the product's price is of the consumer's income, the bigger the elasticity. ) Airfares are very costly relative to household income.

Necessity. (Basic goods have lower elasticity. ) Airline tickets are luxury goods.

Duration. (The longer a price change holds, the higher the elasticity. ) Airline fare does not change for a long period.

Breadth of Definition. (The broader this is, the low the elasticity. ) Domestic airline travel has more specific definition than ordinary air transportation.

1. Introduction

The purpose of this study is to report on all or almost all of the economics and business literature dealing with empirically estimated demand functions for flights and to collect a variety of fare elasticity measures for air travel and offer some judgment as to which elasticity values would become more representative of the true values found in several markets in Canada.

While existing studies can include the leisure - business class split, other important market distinctions are often omitted, likely therefore of data availability and quality. [3] One of the principal value added features of this research and what distinguishes it from other surveys, is that we develop a meta-analysis that not only provides measures of dispersion but also recognizes the quality of demand estimates based on lots of selected study characteristics. In particular, we create a means of scoring features of the studies such as focus on amount of haul; business versus leisure; international versus domestic; the inclusion of income and inter-modal effects; age the study; data type (time-series versus cross section) and the statistical quality of estimates (adjusted R-squared values). By scoring the studies in this manner, policy makers are given with a sharper focus to aid in judging the relevance of varied estimated elasticity values. [4]

2. Elasticity in the Context of FLIGHTS Demand.

Elasticity values in economic analysis provide a "units free" measure of the sensitivity of 1 variable to some other, given some pre-specified functional relationship. The mostly utilized elasticity concept is that of "own-price" elasticity of demand. In economics, consumer choice theory starts with axioms of preferences over goods that translate into utility values. These utility functions define choices that generate demand functions from which price elasticity values can be derived.

"Own-price" elasticity of demand concept - airtrav_2e. gif - (1, 979 bytes)

Therefore elasticities are summary measures of people's preferences reflecting sensitivity to relative prices and changes in a resource-constrained environment. The ordinary or Marshallian demand function comes from consumers who are postulated to maximize utility at the mercy of a budget constraint. Being a good's price changes, the consumer's real income (which may be used to take all goods in the decision set) changes. Furthermore the goods price in accordance with other goods changes. The changes in consumption brought about by these effects carrying out a price change are called income and substitution effects respectively. Thus, elasticity values derived from the ordinary demand function include both income and substitution effects. [5]

Own-price elasticity of demand measures the percentage change in the number demanded of the good (or service) resulting from a given percentage change in the good's own-price, holding all other independent variables (income, prices of related goods etc. ) fixed. The ratio of percentage changes thus allows for comparisons between your price sensitivity of demand for products that could be measured in several units (natural gas and electricity for example). 'Arc' price elasticity of demand calculates the ratio of percentage change in quantity demanded to percentage change in cost using two observations on price and quantity demanded. Formally this can be expressed as:

Equation(1)

where:

Equationrepresent the observed change in quantity demanded and price

Equationrepresent the average price and quantity demanded. The elasticity is unitless and can be interpreted as an index of demand sensitivity; it is measuring the degree to which a variable appealing changes (passenger traffic in our case) as some policy or strategic variable changes (total fare including any added fees or taxes in our case).

In the limit (when Equationare very small) we obtain the 'point' own-price elasticity of demand expressed as:

Equation(2)

where:

Q(P, S) is the demand function

P = a vector of most relevant prices

p = the good's own-price.

q = equals the quantity demanded of the good

S = a vector of most relevant shift variables other than prices (real income, demographic characteristics etc. )

We expect own-price demand elasticity values to be negative, given the inverse relationship between price and quantity demanded implied by the 'law' of demand, with absolute values significantly less than unity indicating 'inelastic' demand: a less than proportionate reaction to price changes (relative price insensitivity). Similarly, absolute values exceeding unity indicate elastic or more sensitive demand: a more than proportionate demand respond to price changes (relative price sensitivity).

The ratio of change in quantity demanded to change in price [equation (1)] highlights that elasticity measures involve linear approximations of the slope of the demand function. However, since elasticity is measuring proportionate change, elasticity values changes along almost all demand functions, including linear demand curves. [6] Estimation of elasticity values is therefore most useful for predicting demand responses in the vicinity of the observed price changes. To be a related issue, analysts need to identify that in markets where price discrimination can be done aggregate data won't enable accurate predictions of demand responses in the relevant market segments. In flights, flights by the carrier are essentially joint products consisting of differentiated service bundles that are identified by fare classes. However the yield management systems utilized by full-service carriers (FSCs) also produce a complex form of inter-temporal price discrimination, in which some fares (typically economy class) decline and some increase (typically full-fare business class) as the departure date draws closer. This implies that ideally, empirical studies of air travel demand should separate business and leisure travellers or at least have the ability to include some home elevators booking times in order to account for this price discrimination, which price data should be calibrated for inter-temporal price discrimination: for example, the utilization of full-fare economy class ticket prices as data will overestimate the absolute value of the purchase price elasticity coefficient. In the set of differentiated service bundles that comprise each (joint product) flight, the relative prices are important in explaining the relative ease of substitution between service classes. Given the type of inter-temporal price discrimination for flights, the relative price could also change significantly in the time period prior to a departure time.

The partial derivative in (2) indicates that elasticity measures price sensitivity independent of all other variables in the demand function. But when estimating demand systems as time passes, you can expect that some important shift variables will never be constant. It's important that these shift variables be explicitly recognized and incorporated in to the analysis, as they will affect the value of elasticity estimates. This will also be true with some cross-sectional studies or panels. [7] Specifically changes in real income and the prices of substitutes or complements will affect demand. In air travel demand estimations, income and prices of other relevant goods should be contained in the estimation equation. Alternative transportation modes (road and rail) are essential variables for short-haul flights, while income effects should be measured for both short and long-haul. The lack of an income coefficient in empirical demand studies will lead to own-price elasticity estimates that may be biased. Without income coefficient, observed price and quantity pairs won't distinguish between movements along the demand curve and shifts of the demand curve. [8]

The slope of an demand function, which affects the own-price elasticity of demand, is normally likely to decrease (become shallower) with:

The volume of available substitutes;

The amount of competition on the market or industry;

The ease with which consumers can search and compare prices;

The homogeneity of the product;

The duration of that time period period analyzed. [9]

Given the implied relationships above, any empirical demand study should carefully define market boundaries to add all relevant substitutes and complements and exclude products that could be related through income or other more general variables.

In flights, ideally market segment boundaries should be defined by first separating leisure and business passengers and second long-haul and short-haul flights. Associated with that we expect different behaviour in each one of these markets. Within each of these categories, distinctions should then be produced between the following:

Connecting and origin-destination (O-D) travel;

Hub and non-hub airports;[10]

Routes with dominant airlines and routes with low-cost carrier competition.

In addition, for the UNITED STATES context, long-haul flights should be further divided into international and domestic travel (within continental North America). These market segment boundaries are illustrated in figure 2. 1 below, which also highlights the relative need for intermodal competition for short-haul travel.

While distinctions in price and income sensitivity of demand between business and leisure or long and short-haul travel are more intuitive, other distinctions are perhaps less obvious. If available, data that distinguishes between routes, airlines and airports would provide important estimates of how price sensitivity relates to the number of competing flights and the willingness to pay of passengers utilizing a hub-and-spoke network, in accordance with those traveling point-to-point, additionally associated with low cost carriers. For the extent that existing studies assume that all passenger observation represents O-D travel, they'll not be capturing fare premiums usually associated with hub-and-spoke networks and full service carriers, nor will they necessarily capture the complete itinerary of travellers utilizing a number of point-to-point flights with an inexpensive carrier. For example, a passenger who travels from Moncton to Vancouver with Air Canada, and utilizes the hub at Pearson International airport, is being given lots of services that includes baggage checked through to the ultimate destination and frequent flyer points as well as a choice in flights and added flight and ground amenities. The fare for Moncton-Vancouver carries a premium for these services. Now consider a passenger that is travelling with WestJet from Moncton to Hamilton, and then with JetsGo from Toronto Pearson Airport to Vancouver. In cases like this there are no frequent flyer points to be attained and baggage needs to be collected and re-checked after a road transfer between Hamilton and Pearson International. Although the origin and destination is the same for these passengers, the itineraries are significantly different. Oftentimes data used for demand estimates would not able to account for these differences.

Route-specific data can also capture competition that could exist between airports and the services they give as well as airlines. This can be especially true for certain short-haul routes where intermodal competition (road and rail) can play an important role in shaping flights demand.

Figure 2. 1

Market segments in air travel demand.

Intermodal competition (road and rail) - airtrav_7e. gif - (12, 673 bytes)

3. Measurement Issues

Oum et al. (1992) provide a valuable set of pitfalls that occur when demand models are estimated and therefore affect the interpretation of the elasticity estimates from these empirical studies.

1. Price and Service Attributes of Substitutes: Air travel demand can be damaged by changes in the costs and service quality of other modes. For short-haul routes (markets) the relative price and service attributes of auto and train would need to be contained in any model; particularly for short-haul markets. Failure to include the price and service attributes of substitutes will bias the elasticity. For example, if airfares increase and auto costs are also increasing, the airfare elasticity would be overestimated if auto costs were excluded.

2. Functional Forms: Most studies of flights demand use a linear or log-linear functional specification. Elasticity estimates can vary widely depending on the functional form. The decision of functional form should be selected based on statistical testing not simple interpretation.

3. Cross-Section vs. Time-series Information: Over time demand elasticities for non-durable goods and services are larger in absolute terms, than in the short run. This follows because in the long run there are many more substitution possibilities that can be used to avoid price increases or service quality decreases. In place there are usually more opportunities to avoid these changes with substitution possibilities. Data tends to be cross-sectional or time-series although more recently panels have grown to be available. A panel is a mixture of cross-section and time-series - information on several routes for a multi-year period is a panel. Cross-sectional information is normally regarded as indicating short run elasticities while time-series data is interpreted for as long run elasticities. In time-series data the information reflects changes in markets, growth in income, changes in competitive circumstances, for example. Policy changes should rely on long run elasticities since these are long haul impacts that are being modelled. Short run elasticities become important when considering the competitive position of organizations in a highly dynamic and competitive industry.

4. Market Aggregation/Segmentation: As the amount of aggregation increases the amount of variation in the elasticity estimates decreases. This occurs because aggregation averages out a few of the underlying variation relating to specific contexts. Since flights market segments varies significantly in character, competition and dominance of trip purpose, interpreting a decrease in variation through aggregation as a very important thing would be erroneous. Such estimates may have relatively low standard deviations but would be be relatively inaccurate when used to examine the result of changes in fares in a particular market.

5. Identification Problem: In most cases only demand functions are estimated in attempts to gauge the demand elasticity appealing. However, it is well known that the demand function is part of the simultaneous equations system consisting of both supply and demand functions. Therefore, a straightforward estimation of only the demand equation will produce biased and inconsistent estimates. The issue of identification can be illustrated by describing the procedure by which fares and travel, for example, are determined in the origin-destination market simultaneously. To model this technique in its entirety, we must develop a quantitative estimate of both demand and offer functions in something. If, before, the supply curve has been shifting due to changes in production and cost conditions for example, while the demand curve has remained fixed, the resultant intersection points will trace out the demand function. On the contrary, if the demand curve has shifted due to changes in personal income, while the supply curve has remained the same, the intersection points will trace out the supply curve. The most likely outcome, however, is movement of both curves yielding a pattern of fare, quantity intersection points from which it'll be difficult, without further information, to tell apart the demand curve from the supply curve or estimate the parameters of either. [11]

Earlier we identified resources of bias that can arise from issues with aggregation, data quality, implicit assumptions of strong separability amongst others. Almost all demand studies come with an implied assumption of strong separability for the reason that they only consider aviation markets in the analysis. Such studies in effect constrain all changes or responses in fares or service to be wholly contained in the aviation element of people's consumption bundle. The paper by Oum and Gillen (1986) is the main one exception where consideration of substitution with other areas of consumption was included in the modelling. It would be difficult to extract a conclusion from this one study concerning existence, degree and direction of bias in elasticity estimates when other parts of consumption are and aren't contained in the modelling. However, having said this, an inspection of the elasticity estimates out of this study shows they are not significantly unique of other time-series estimates.

3. 1 Data Issues

Elasticity estimates depend critically on the product quality and extent of the data available. Currently, the best data for demand estimation is the DB1A 10 percent ticket sample in america, but even this data has some problems. [12] The DB1A sample represents ten percent of all tickets sold with full itinerary identified by the coupons mounted on the ticket. However with electronic tickets, as increasingly more tickets are for sale online, there is a growing portion of overall travel that may well not be captured in the sample. This means that the proportion is not ten percent but something less. [13] Other important considerations will be the amount of travel on frequent flyer points, by crew and airline personnel.

In Canada we've poor quality data since it is incomplete, even if it were accessible. Airports accumulate traffic statistics but these data make it very hard to tell apart OD and segment data. Airlines report traffic data to Statistics Canada (or are likely to) but these data do not include fare information or routing. Knowing the itinerary or routing is important because of distinctions in service quality and hubbing effects. Fare data is also more useful than yield information since it identifies the proportion of individuals travelling in different fare classes. Yet, oftentimes yield information can be used as a weighted average fare. Addititionally there is the condition that carriers of different size may have different reporting requirements. Some researchers and consultants have been cobbling together data sets for analysis by using the PBX clearing house information. These data are limited and apply and then those airlines that are members of IATA. [14] The existing public data available in Canada simply will not permit estimation of any demand models.

Besides demand side data additionally it is important to acquire supply side information. Elasticity estimates should emerge from a simultaneous equations framework. This data is more accessible through organizations like the OAG[15], which provide information on capacity, airline and aircraft type for each flight in each market. [16] These data measure changes in capacity, flight frequency and timing of flights.

One study, which undertook an considerable survey to collect multimodal data, [17] was the BROADBAND Rail study sponsored jointly by the Federal, Ontario and Quebec governments. This study, which had three different demand modelling efforts, examined the potential for BROADBAND Rail demand, and subsequent investment, in the Windsor-Quebec corridor. The analysis included intermodal substitution between air, rail, bus and car. The analysis was undertaken in the early 1980s. However, it is not possible for public usage of the technical documents that would allow an assessment of the study. Attempts in the past to obtain access to the data have proven fruitless.

3. 2 Distinguishing Elasticity Measures

As we've stated, price elasticity measures the degree of responsiveness to a big change in own or other prices (fares). However, care must be exercised in interpreting the elasticity given that they differ according to how they have been estimated. Many empirical studies of air travel demand estimate a log-linear model. In evaluating such studies, it's important to keep in mind that the empirical specification implies a certain consumer preference structure due to duality between utility functions and demand functions. It really is equally important to keep in mind that empirically estimated demand functions should contain some measures of quality and service differences or quality changes over time. Failure to include metrics for frequent flyer programs, flight frequency, destination choice or service levels in estimating an air demand function can result in downward bias in the price elasticity estimates.

Price elasticities can be estimated for aggregate travel demand as well as modal demand. Figure 3. 1 illustrates the differences between aggregate and modal elasticities. [18] Our interest is modal elasticities not the aggregate amount of travel but it is important eventually that any policy analysis take account of the impact of any policy change on aggregate travel as well as modal redistribution. The impact of your change in price on aggregate demand would be measured by the -fis in Figure 3. 1 whereas the Fiis would measure the impact on air travel demand. The Fiis are a composite or combo of the fis and the Miis.

The Canadian aviation industry has undergone significant change in the last many years. In 2000 Air Canada completed its takeover of Canadian Airlines, which left it with more than 80 percent market share. Market dominance leads to different fare and service quality levels. Due to higher fares, for example, we have to find higher absolute values of elasticities of demand simply because with higher fares we have moved further the demand curve. In 1996 Westjet entered the marketplace and has continued to grow each year. Canada 3000 exited the marketplace in 2001, as did Canjet and Royal (as part of Canada 3000). Roots airline has come and gone but Canjet has reemerged in eastern Canada and JetsGo is offering some degree of service on longer haul domestic flights as well as in the Montreal-Toronto market.

The entry of low cost carriers brings about lower fares for a subset of traffic and competitors will provide a supply of seats to match these fares. Lower average fares should lead to lessen demand elasticity estimates, while increases in the number of competitors on the market will lead to higher demand elasticity estimates.

Figure 3. 1

Figure 3. 1 -Ddifferences between aggregate and modal elasticities - airtrav_8e. gif - (6, 091 bytes)

One should not confuse low cost carriers with a seeming lack of exploiting monopoly power. High prices or fares are not synonymous with monopoly and low fares with competition. Airlines like Westjet where they will be the sole airline serving the market may still become a monopolist but charge low(er) fares. Profit maximizing monopolists price where marginal cost equals marginal revenue, if marginal cost is low, one should expect to see lower fares but still marginal cost and revenue are equalized. Monopolists are generally viewed as being high price because they're high cost and the high costs are attributable to some degree from too little competitive discipline on the market. Full service carriers operating with hub-and-spoke systems have a higher cost business design while low priced carriers have a low cost business model.

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