Price Elasticity in Air Travel

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30/05/17 Economics Reference this

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Elasticity is define as the “quality sth has being able to stretch and return to its original size and shape”. (Oxford advanced learners dictionary 6th edition). In Physics elasticity is defined as “the property of a substance that enables it to change its length, volume, or shape in direct response to a force effecting such a change and to recover its original form upon the removal of the force.” (

Suppose that your employer allows you to work extra hours more after your contracted hours for extra pay at 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 you are able to work. Then how responsive you are to this offer can be seen as elasticity.

Therefore I will define elasticity as the measure of degree 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 can be used to measure the rate or the exact amount of any change. In economics elasticity is used to measure the magnitude of responsiveness of a variable to a change in its determinants (sloman) such as (demand and supply) of goods and services.

For the purpose of this essay am going to be examining the concept of elasticity of demand and supply 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 degree of sensitivity or responsiveness of quantity demanded is to a change 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 vary from product to product. For example 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 of washing up liquid will produce a big increase 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 need to have a clear measure of how the quantity demanded will change as a result 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.



Here Є denotes elasticity and ∆


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

Generally when the prices of good increases the quantity demanded decreases, thus either of the number will be negative which after division will end up in a negative result, due to this fact we always ignore the sign and just concentrate on the absolute value, ignoring the sign to tell us how elastic demand is.

The larger the elasticity of demand, the 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 value is always higher than 1, the change in quantity has a bigger effect on total consumer spending than in price. For example if there is a reduction in the price of 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 end up spending more on the product than they will do on a normal day.

An Inelastic Demand

Elasticity in airline industry

The airline industry is deeply impacted by the elasticity of demand, externalities, wage inequality, and monetary, fiscal, and federal policies. The elasticity of demand is based purely on current market conditions, thcustomer’s September 11th tragedy had a negative affect on the entire travel industry. It impacted the fiscal and monetary policies, supply and demand, and it created staffing problems nationwide. The rate of wage inequality is improving due to legislation that has created a pay increase in participating cities across the United States. The airline industry is viewed has being unstable because it is based on current market conditions, and the market is always changing. purpose for travel, and available substitutes. Externalities continue to influence the elasticity of demand. The

Elasticity of Demand

The airline industry is an extremely unstable industry because it is highly dependant upon current market conditions. Events such as inflation, terrorist attacks, and the price of oil have greatly influenced the demand for airline tickets throughout the years. Competition consistently affects the price of airline tickets because it gives the customer other options. Substitutes that are existence is traveling by train, car, or avoiding travel whenever possible. Customers have resorted to all 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. With the wave of technology, a large percentage of business travel has been eliminated to conserve spending.


In the airline industry, price elasticity of demand is separated into two segments of consumers and is considered to be both elastic and inelastic. A good example of how elastic demand is related to the airline industry is in relation to travel for pleasure. Pleasure travellers will be affected by the amount of travel they do based on the demand increase or decrease, affected by prices that lower with high demand or prices that rise with low demand; directly attributed to competition in this market (Gerardi & Shapiro, 2007). Inversely, the business traveller would apply to an inelastic demand for this market. This has shown by demand increases or decreases, as well as the price distribution attributed, which has little effect 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 traveler experiences an inelastic demand due to the quantity of service demanded and quantity has not decreased as prices have risen. In other words, this travel is seen as a necessary business tool, not affected 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 negative effects, driving cost

upward and threatening loss in demand due to a price sensitive customer base. Since deregulation, competition in the economy have kept prices in the industry low and have caused airlines to force cuts in areas such as wages; contributing to a growing concern of wage inequality.


Gerardi, K., & Shapiro, A. (2007, April). The Effects 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

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 a private good. Mankiw (2004), states that private goods are excludable and rival goods. One needs to see through 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 shows that it is an excludable good by having the power to place prices on fares and having the ability to refuse service to any person for whatever the reason. The airline industry also shows that it is a rival good because when someone purchases fare for a seat, it diminishes the ability for another person to get a seat on the plane. Because the airline industry is a private good, in a competitive market place, prices, supply, and demand are very sensitive to new policies or tax incidences placed on them.

Associated viewd 18/11/10

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

In economics, this scenario is being explained by its ELASTICITY. The concept of elasticity is being 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 will provide a better understanding on this matter, specifically the price elasticity.


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

Substitutes. (The more substitutes it has, the higher 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 higher the elasticity.) Airfares are too expensive 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 time.

Breadth of Definition. (The broader the definition, the lower 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 most of the economics and business literature dealing with empirically estimated demand functions for air travel and to collect a range of fare elasticity measures for air travel and provide some judgment as to which elasticity values would be more representative of the true values to be found in different markets in Canada.

While existing studies may include the leisure – business class split, other important market distinctions are often omitted, likely as a result 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 a number of selected study characteristics. In particular, we develop a means of scoring features of the studies such as focus on length of haul; business versus leisure; international versus domestic; the inclusion of income and inter-modal effects; the age of 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 way, policy makers are provided with a sharper focus to aid in judging the relevance of various estimated elasticity values.[4]

2. Elasticity in the Context of Air Travel Demand.

Elasticity values in economic analysis provide a “units free” measure of the sensitivity of one variable to another, given some pre-specified functional relationship. The most commonly 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 price levels and changes in a resource-constrained environment. The ordinary or Marshallian demand function is derived from consumers who are postulated to maximize utility subject to a budget constraint. As a good’s price changes, the consumer’s real income (which can be used to consume all goods in the choice set) changes. In addition the goods price relative to other goods changes. The changes in consumption brought about by these effects following 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 quantity demanded of a 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 the price sensitivity of demand for products that might be measured in different 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 price using two observations on price and quantity demanded. Formally this can be expressed as:



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 of interest will change (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:



Q(P,S) is the demand function

P = a vector of all relevant prices

p = the good’s own-price.

q = equals the quantity demanded of the good

S = a vector of all 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 less than unity indicating ‘inelastic’ demand: a less than proportionate response to price changes (relative price insensitivity). Similarly, absolute values exceeding unity indicate elastic or more sensitive demand: a more than proportionate demand response 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 a demand function. However, since elasticity is measuring proportionate change, elasticity values will change 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. As a related issue, analysts need to recognize that in markets where price discrimination is possible aggregate data will not allow for accurate predictions of demand responses in the relevant market segments. In air travel, flights by a carrier are essentially joint products consisting of differentiated service bundles that are identified by fare classes. However the yield management systems employed by full-service carriers (FSCs) also create 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 be able to include some information on booking times in order to account for this price discrimination, and that price data should be calibrated for inter-temporal price discrimination: for example, the use of full-fare economy class ticket prices as data will overestimate the absolute value of the price elasticity coefficient. Within 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 nature 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 the other variables in the demand function. However when estimating demand systems over time, one can expect that some important shift variables will not be constant. It is important that these shift variables be explicitly recognized and incorporated into the analysis, as they will affect the value of elasticity estimates. This will also be true with some cross-sectional studies or panels.[7] In particular 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 included in the estimation equation. Alternative transportation modes (road and rail) are important variables for short-haul flights, while income effects should be measured for both short and long-haul. The absence of an income coefficient in empirical demand studies will result in own-price elasticity estimates that can be biased. With no income coefficient, observed price and quantity pairs will not distinguish between movements along the demand curve and shifts of the demand curve.[8]

The slope of a demand function, which affects the own-price elasticity of demand, is generally expected to decrease (become shallower) with:

The number of available substitutes;

The degree of competition in the market or industry;

The ease with which consumers can search and compare prices;

The homogeneity of the product;

The duration of the time period analyzed.[9]

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

In air travel, ideally market segment boundaries should be defined by first separating leisure and business passengers and second long-haul and short-haul flights. The reason is that we expect different behaviour in each of these markets. Within each of these categories, distinctions should then be made 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 North American 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 importance of 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 is related to the number of competing flights and the willingness to pay of passengers utilizing a hub-and-spoke network, relative to those traveling point-to-point, more commonly associated with low cost carriers. To the extent that existing studies assume that each passenger observation represents O-D travel, they will 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 a low cost carrier. For example, a passenger who travels from Moncton to Vancouver with Air Canada, and utilizes the hub at Pearson International airport, is being provided with a number of services that includes baggage checked through to the final destination and frequent flyer points as well as a choice in flights and added flight and ground amenities. The fare for Moncton-Vancouver includes 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 this case there are no frequent flyer points to be attained and baggage has 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. In many cases data used for demand estimates would not able to account for these differences.

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

3. Measurement Issues

Oum et al. (1992) provide a valuable list 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 affected by changes in the prices 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 included 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 air travel demand use a linear or log-linear functional specification. Elasticity estimates can vary widely depending on the functional form. The choice of functional form should be selected on the basis of statistical testing not ease of interpretation.

3. Cross-Section vs. Time-series Information: In the long run 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 effect there are more opportunities to avoid these changes with substitution possibilities. Data tends to be cross-sectional or time-series although more recently panels have become available. A panel is a combination of cross-section and time-series – information on several routes for a multi-year period is a panel. Cross-sectional information is generally regarded as indicating short run elasticities while time-series data is interpreted 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 run impacts that are being modelled. Short run elasticities become important when considering the competitive position of firms in a highly dynamic and competitive industry.

4. Market Aggregation/Segmentation: As the level of aggregation increases the amount of variation in the elasticity estimates decreases. This occurs because aggregation averages out some of the underlying variation relating to specific contexts. Since air travel market segments may differ significantly in character, competition and dominance of trip purpose, interpreting a reduction in variation through aggregation as a good thing would be erroneous. Such estimates might have relatively low standard deviations but would be also be relatively inaccurate when used to assess the effect of changes in fares in a specific market.

5. Identification Problem: In most cases only demand functions are estimated in attempts to measure the demand elasticity of interest. However, it is well known that the demand function is part of a 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 problem of identification can be illustrated by describing the process by which fares and travel, for example, are determined in the origin-destination market simultaneously. To model this process in its entirety, we must develop a quantitative estimate of both the demand and supply functions in a system. If, in the past, 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 will be difficult, without further information, to distinguish the demand curve from the supply curve or estimate the parameters of either.[11]

Earlier we identified sources of bias that can arise from problems with aggregation, data quality, implicit assumptions of strong separability among others. Almost all demand studies have an implied assumption of strong separability in 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 component of people’s consumption bundle. The paper by Oum and Gillen (1986) is the one exception where consideration of substitution with other parts of consumption was included in the modelling. It would be difficult to extract a conclusion from this one study as to existence, degree and direction of bias in elasticity estimates when other parts of consumption are and are not included in the modelling. However, having said this, an inspection of the elasticity estimates from this study shows they are not significantly different than other time-series estimates.

3.1 Data Issues

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

In Canada we have poor quality data because it is incomplete, even if it were accessible. Airports collect traffic statistics but these data make it very difficult to distinguish OD and segment data. Airlines report traffic data to Statistics Canada (or are supposed to) but these data do not include fare information or routing. Knowing the itinerary or routing is important because of differences in service quality and hubbing effects. Fare data is also more useful than yield information since it identifies the proportion of people travelling in different fare classes. Yet, in many cases yield information is used as a weighted average fare. There is also the problem 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 only to those airlines that are members of IATA.[14] The current public data available in Canada simply does not permit estimation of any demand models.

Besides demand side data it is also important to have 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 extensive survey to collect multimodal data,[17] was the High Speed Rail study sponsored jointly by the Federal, Ontario and Quebec governments. This study, which had three different demand modelling efforts, examined the potential for High Speed Rail demand, and subsequent investment, in the Windsor-Quebec corridor. The analysis included intermodal substitution between air, rail, bus and car. The study was undertaken in the early 1980s. However, it is not possible for public access to any 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 have stated, price elasticity measures the degree of responsiveness to a change in own or other prices (fares). However, care must be exercised in interpreting the elasticity since 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 is important to keep in mind that the empirical specification implies a certain consumer preference structure because of the duality between utility functions and demand functions. It is equally important to remember 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 lead to 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 in modal elasticities not the aggregate amo

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