In chapter 5, a fare analysis was undertaken to compare the level of fares of international and domestic East African Community (EAC) routes with similar routes globally. In order for the comparison to be meaningful, certain criteria had to be identified and applied in the selection of comparator routes. The comparators were identified according to the following criteria: (a) presence of a low-cost carrier (LCC) on that route; (b) similar distance between origin and destination; (c) similar combined population of origin and destination; and (d) similar gross domestic product (GDP) per capita of origin city. Population and gross domestic product (GDP) per capita are two common indicators used as a basis in demand forecasting (see Lyneis 2000; Suryani, Chou, and Chen 2010), and can be used here to assess the size and purchasing power of the potential market.
This approach poses a few limitations, as the GDP per capita of the destination country is not taken into consideration, and could significantly influence demand. However, finding routes fulfilling all criteria (LCC presence, distance, population, and GDP per capita for both countries) was found to be too restricting to obtain an adequate sample. The GDP per capita criteria had to therefore be limited to the origin country. In some cases, countries with a higher GDP than the region’s highest GDP per capita, Kenya, had to be chosen to obtain an adequately sized sample. A more detailed analysis should also take into consideration what type of market the route is (business or leisure) information which was unfortunately not available in this case.
The sample for international routes includes the following:
• Dar es Salaam (DAR)–Entebbe (EBB)
• Nairobi (NBO)–Kigali (KGL)
• Nairobi (NBO)–Dar es Salaam (DAR)
• Nairobi (NBO)–Bujumbura (BJM)
• Zanzibar (ZNZ)–Nairobi (NBO)
• Nador (NDR)–Barcelona (BCN)
• Nador (NDR)–Montpellier (MPL)
• Phnom Penh (PHN)–Singapore (SGN)
• Phnom Penh (PHN)–Kuala Lumpur (KLM)
• Chennai (MAA)–Colombo (CMB)
For domestic markets, finding the right set of comparable routes proved to be more difficult. The distance range therefore had to be extended to find a few suitable airport pairs. The sample of airport pairs includes the following:
• Nairobi (NBO)–Mombasa (MBA)
• Nairobi (NBO)–Kisumu (KIS)
• Dar es Salaam (DAR)–Kilimanjaro (JRO)
• Nairobi (NBO)–Eldoret (EDL)
• Dar es Salaam (DAR)–Mwanza (MWZ)
• Buon Ma Thuot (BMV)–Vinh City (VII)
• Danang (DAD)–Hanoi (HAN)
• Danang (DAD)–Ho Chi Minh (SGN)
• Srinagar (SXR)–Sri Guru Ram Dass Jee International (Amritsar) (ATQ)
• Guwahati (GAU)–Kolkata (CCU)
The data for the analysis were gathered from airlines’ websites. In order to ensure consistency within the comparison, all searches were conducted on the same date (August 13, 2013) for the same outbound and inbound date (September 11, 2013–September 20, 2013). In some cases where there was no availability or direct connection, traveling dates one day prior to or after were chosen. In order to control for seasonality, two different dates were chosen and compared—but only very small variations were detected.
Lyneis, J. 2000. “System Dynamics for Market Forecasting and Structural Analysis.” System Dynamics Review 16 (1): 3–25.
Suryani, E., S. Chou, and C. Chen. 2010. “Air Passenger Demand Forecasting and Passenger Terminal Capacity Expansion: A System Dynamics Framework.” Expert Systems with Applications: An International Journal 37 (3): 2324–39.