4 Strategic Analysis: Non-financial Drivers for the Airline Industry
4.2 PESTEL: Political, Economic, Social, Technological, Environment, Legal Issues
4.2.2 Economic Environment
A range of economic issues, too, affects the airline industry and its profitability. Consequently, this section looks at key factors including: (i) oil price and (ii) GDP development, (iii) exchange rates, (iv) interest rates, (v) social, (vi) technological, and (vii) environmental issues, and
(viii) legal regulations.
4.2.2.1 Oil Price Development
As indicated in the financial analysis182, jet fuel prices are a large block of a carrier’s costs and, therefore, fluctuations (e.g. due to change in currency ratios) are one of the key determinants of an airline’s profitability: jet fuel prices are strongly related to oil prices183, and despite both following the same patterns, fuel prices
180 Furthermore, insurance companies withdrew coverage for third party war risk on air transport. It is estimated that the insured harm from these events cost around USD 35.9 bn in total, the second highest derived from one single event, only beaten by hurricane Katrina in 2005 (IATA, The Impact of September 11 2001 on Aviation, 2010).
181 The study includes: AirBerlin, Air Lingus, Alaska Air, Alitalita, Austrian, Delta, easyJet, Korean, Ryanair, United, and WestJet.
182 Section 3 provides more details regarding the financial analysis.
183 For the oil price development the ICE Brent Crude Oil Future (CO1 Comdty), for jet fuel ST13JF, is used. As the prices are reported in different currencies, for reason of comparison all prices were converted into GBP (Bloomberg, Database, 2017).
Figure 43: Jet Fuel Price vs. Oil Price Development
64 are even more volatile184. Running a linear regression analysis concludes, both are indeed highly correlated (correlation coefficient 0.97, indicating, that more than 97% of the jet fuel price changes can be explained by the oil price development during the period from January 1, 1995 to September 30, 2016). The regression line follows the formula185:
𝐽𝑒𝑡 𝐹𝑢𝑒𝑙 𝑃𝑟𝑖𝑐𝑒 = 2.668 ∗ 𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒 + 7.553
Equation 28: Regression of Jet Fuel & Oil Price Development
These findings may, too, advocate for a relative strong relationship between the oil price development and the share price of an airline, as e.g. the oil price and easyJet’s share price at first sight follow a similar pattern at least until the end of 2006 and then again until end of 2013. However, leaving the anecdotic evidence aside and running a linear regression analysis, only around 7% of the 21st century’s changes in easyJet’s share price (or market cap, respectively) can be explained by the oil price development186:
𝑆ℎ𝑎𝑟𝑒 𝑃𝑟𝑖𝑐𝑒 𝑒𝑎𝑠𝑦𝐽𝑒𝑡 = 6.706 ∗ 𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒 + 378.480
Equation 29: Regression of easyJet’s Share Price & Oil Price Development
The relatively weak correlation and the very strong positive development of easyJet from 2010 onwards indicate that an airline’s management can find measures to successfully decouple from fluctuations in fuel prices. Nevertheless, according to the International Air Transport Association (IATA), the expected 2017 increase in oil prices will have the largest impact on the airlines’ profitability outlook for the year, assuming the cost of fuel on average represent about 20% of the industry’s cost base (IATA, IATA Forecasts Passenger Demand to Double Over 20 Years, 2016). It is thus important and common industry practice to hedge against fuel price fluctuations187.
184 Oil prices can also change subject to the political environment, as OPEC often demonstrated. Section 4.2.1 of the PESTEL analysis refers to political issues and Section 4.2.2.1 analyzes how sensitive easyJet’s reacts on fuel price fluctuations.
185 The p-value of the regression is below 0.05, consequently the linear regression is statistically highly significant. Looking at the residuals a hypothesis test reveals that they are statistically independent, in other words: randomly distributed. Appendix 45-46 provide the entire regression output as well as a residuals plot.
186 The p-value of the regression is below 0.05, consequently the linear regression is statistically highly significant. Looking at the residuals a hypothesis test reveals that they are statistically independent, in other words: randomly distributed. Appendix 47-48 provide the entire regression output as well as a residuals plot.
187 Section 2.4.3.2 provides more details regarding fuel and oil price hedges.
Figure 44: easyJet's Share Price &. Oil Price Development
65
4.2.2.2 GDP Development
Carriers depend directly on the general economic environment, measured in terms of GDP. To investigate the relationship between GDP development and passenger numbers, the analysis looks exemplary at Europe (i.e. easyJet’s home market). The regression calculated from the last 35 years shows that the correlation between GDP and passenger numbers is high (around 96%); the derived linear regression is equal to188:
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑢𝑟𝑜𝑝𝑒𝑎𝑛 𝑃𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟𝑠
= 0.031 ∗ 𝐺𝐷𝑃 𝐸𝑢𝑟𝑜𝑝𝑒 + 20.220
Equation 30: Regression of European passenger numbers & European GDP Development
The linear regression concludes the changes in passenger numbers are strongly aligned with the GDP development, however, considering, that e.g. an increase in passengers does not directly translate into an increase in easyJet’s profits,
the relationship, if any, between GDP and company-specific revenues is investigated and as the competition consist of low-cost and full-service carriers (with different business models), the analysis is carried out separately for both sub-industries, and easyJet and Deutsche Lufthansa serve as representatives for their business models189. As Europe is easyJet’s main market, whereas Lufthansa operates all skies, easyJet is run against GDP Europe190 and Lufthansa against the world’s GDP191. Running the analyses turns out: 64% of easyJet’s changes in revenues since its IPO 15 years ago can be explained by the GDP development, and the linear regression is highly significant192:
188 The p-value of the regression is below 0.05, consequently the linear regression is statistically highly significant. Looking at the residuals a hypothesis test reveals that they are statistically independent, in other words: randomly distributed. Appendix 49-50 provide the entire regression output as well as a residuals plot.
189 easyJet and Ryanair (and Norwegian and Southwest, too) are low cost carrier; while Lufthansa, IAG, and Air France-KLM are full-service carrier. Section 2.9 and 2.10 provide more details.
190 For Ryanair and Norwegian, too, “Europe” can be considered the main market.
191 For IAG and Air France-KLM, too, “the world” is the core or main market.
192 The residuals plot shows an increasing trend, suggesting that the regression is a better fit for smaller changes in x-values, but not for larger ones, however it is fair to say, changes in GDP tend to be rather small aside in times of external shocks. Appendix 51-52 provides the entire regression output as well as a residuals plot.
Figure 45: Europe Passenger & Europe GDP Development
Figure 46: GDP Europe & easyJet Revenue ‘ GDP World &. Lufthansa Revenue Development
66 𝑒𝑎𝑠𝑦𝐽𝑒𝑡′𝑠 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = 0.351 ∗ 𝐺𝐷𝑃 𝐸𝑢𝑟𝑜𝑝𝑒 − 3,022.200
Equation 31: Regression of easyJet’s total revenues & European GDP Development
Investigating the relationship between Lufthansa’s revenues and the world’s GDP, also suggests a clear relationship, as Lufthansa’s linear regression, starting with is its IPO 35 years ago, shows a correlation coefficient of around 98% and the linear regression is statically highly significant193.
𝐷𝑒𝑢𝑡𝑠𝑐ℎ𝑒 𝐿𝑢𝑓𝑡ℎ𝑎𝑛𝑠𝑎′𝑠 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 = 0.412 ∗ 𝐺𝐷𝑃 𝑤𝑜𝑟𝑙𝑑𝑤𝑖𝑑𝑒 − 952.600
Equation 32: Regression of Deutsche Lufthansa’s total revenues & World GDP Development
It seems plausible, that a fast-growing young carrier in a booming low-cost environment has a lower correlation, i.e. a greater chance to successfully decouple from GDP
trends194. Pain & Young (2004) analyzed the BREXIT long before it occurred and saw a risk for jobs that are based on the trade between the UK and the EU, both in production and services. But nevertheless, they did not see any risk of rising unemployment as response (Pain & Young, 2004).
Their estimates for the UK GDP development vary from -2.2% to +0.75%
by 2030, however, conclude the actual outcome, depends on BREXIT details (Booth, Howarth, Persson, Ruparel, & Swidlicki, 2015). PwC believes the total UK GDP could be between around 3% and 5.5% below
the FTA and WTO scenario in 2020, compared to the current status quo (PwC, 2016). Mansfield says the total impact on GDP varies from +1.1% to -2.6% (Mansfield, 2014). Ebell and Warren find that by 2030, GDP is projected to be between 1.5 percent and 3.7 percent below the baseline forecast in which the UK remains in the EU (Ebell & Warren, 2016).
4.2.2.3 Exchange Rates
By operating in international markets with varying currency regimes, with revenues (e.g. as ticket prices and sales) predominantly GBP-denominated and procurement (e.g. fuel prices) to a large extent USD-denominated, easyJet’s business is sensitive to exchange rate changes and easyJet, as the entire airline industry, is exposed to considerable currency risks. It is thus important and common industry practice to use financial instruments to hedge against these risks195. Over the last decades, GBP lost value relative to the USD, and following
193 The p-value of the regression is below 0.05, consequently the linear regression is statistically highly significant. Looking at the residuals a hypothesis test reveals that they are statistically independent, in other words: randomly distributed. Appendix 53-54 provides the entire regression output as well as a residuals plot.
194 The results allow for a linear regression, as the residuals are randomly distributed, and it can be concluded that FSC are closer related to GDP than LCC. Appendix 55-60 provides the entire regression output as well as a residuals plot.
195 Section 2.4.3.2 provides more details regarding exchange rate hedges.
Figure 47: Possible UK GDP Growth Rate after-Brexit
67 BREXIT even more. As hedging is cost intensive this is a clear disadvantage, relative to carriers operating the US market.
Following
BREXIT the GBP
dropped substantially196, which leads to an increase in import prices and put a squeeze on UK households’ real income. The currency development indicates market participants feel the consequences of BREXIT are negative for the UK and the currency falls to help absorb the detriment by making UK exports c.p. cheaper (Broadbent, 2017). The corresponding increase in the price of imports could c.p. increase inflation, which may force the BoE to increase interest rates with a negative effect on domestic demand (Ebell & Warren, 2016).4.2.2.4 Interest Rates
As carriers run a capital intensive business model, their economics show a substantial sensitivity to changes in interest rates197. Interest rates, too, have second round effects, as in times of lower interest rates, people are willing to spend more money on luxury goods, such as traveling. Since the introduction of the euro, the European interest rates moved down in response to the general economic downturn, also based on the dot-com bubble and the September 11, 2001 terror attacks. Another reason was the world financial crisis starting 2008
and its European aftermath. With a lower interest rate, it becomes cheaper to borrow money, and this should be favorable for a capital investment intensive industry such as the airlines industry (Bloomberg, Database, 2017).