Data availability

The validity of the propositions of a model is measured against the behaviour of observed data or experimental data (Mendenhall and Sincich, 2003). Hair et al (2010) argue that all variables used in a model have some degree of measurement error and a study should ensure validity and reliability when gathering such data so as to reduce these errors. Arguing that it is important to use those variables with higher reliability, they define validity as the degree to

7 _{Gini index measures the extent to which the distribution of income (or, in some cases, consumption} expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

which a measure accurately represents what it is supposed to. Reliability is defined as the degree to which the observed variable measures the true value and is error free. In this study, data had to be readily attainable, reliable and consistent for it to qualify for inclusion in the SADC econometric model.

6.2.3.1 Dependant variable

Statistics on the historical passenger numbers between those major city-pairs that had air service connections between them could not be ascertained easily because the figures are not publicly available. There is however alternative sources of intra-SADC air transport statistics which allow estimates of the number of passengers carried between cities to be made. As highlighted in section 4.5.2.1 the major sources of this type of data were OAG, MIDT, SADC countries, airlines, AASA and AFRAA.

It is important to note that a general weakness that is common to many of these data sources, is that the passenger measure is of passenger traffic carried between cities A and B, regardless of whether the demand for travel was from A to C via B, or indeed, from X to B via A (Derudder and Witlox, 2005). Thus the data collected does not reflect the true origin and destination for such traffic. Instead the data collected splits such flights into two separate origin and destination trips. For example, as there is no direct air service between Kinshasa and Lilongwe, demand must be satisfied by passengers travelling from the DRC to Malawi via Johannesburg (or other connecting points) and this is recorded on two city-pairs. Their flight will be captured on both the Kinshasa – Johannesburg and on the Johannesburg-Lilongwe routes. This results in reinforcing perceived passenger demand on services into airports where connecting flights are offered.

The OAG is a statistical database that contains information on schedules and route capacity of all scheduled commercial airlines of the world. This was used to obtain estimates of traffic volume between city pairs on the intra-SADC network. One weakness of the data is that it is primarily capacity focused. It tells the number of seats and flights each day provided by airlines operating between, say, cities A and B. To estimate the number of passengers carried requires the conversion of capacity (seats) to passengers by applying a load factor. This study used the actual average annual load factors for African airlines that are contained in IATA annual reports to deflate the seat capacity between intra-SADC routes.

6.2.3.1.2 MIDT database

The MIDT database contains the true origin–destination data on a transport network as it gathers information on passenger bookings from the Global Distribution Systems. Many of the intra-SADC airlines’ passenger reservations are managed through electronic platforms such as Galileo, Amadeus and Sabre. The only disadvantage with this database is the fact that those bookings made directly with an airline would not be included in the statistics. For the SADC region, with the exception of South Africa, the extent to which bookings are made directly with airlines is limited by technological and communication challenges. This made this data base most suitable for use in this research. Given the high cost of obtaining this data, figures for one year (2009) were sourced. These demand figures were used as the dependant variable in the calibration of the SADC model.

6.2.3.1.3 Member states’ air transport statistical databases

All member states of SADC maintain some air transport statistical databases as they use them for strategic planning purposes. Airport authorities, where statistics are critical for the purposes of passenger and airline user charges, maintain records per airline, per flight and per origin. Although this database reflects the actual passengers carried, it was difficult to get access to all airport authorities.

6.2.3.1.4 Airlines; AASA and AFRAA statistical databases

All airlines maintain data on their routes and have traffic forecasting units within their structures. Data from airlines is difficult to access as it is considered strategic and sensitive information. At the time this study was carried out, AASA and AFRAA did not have SADC airline industry statistics.

6.2.3.2 Explanatory variables

6.2.3.2.1 Airport passengers statistics

Data for passengers at airport terminals are obtainable from various sources; the ACI, ICAO and member states8. ACI is an international association of the world's airports and it publishes annual traffic data for its members. The database holds statistics on the total number of passengers (domestic and international) arriving and departing at an airport. One control the database has is that passengers arriving or departing on a flight bearing the same flight number are counted once.

The ICAO provides online air transport industry statistical data which includes airport annual traffic for its member states. The database which holds the same statistics as those held by ACI does not have information for all the airports. Civil aviation authorities, airports in SADC member states also maintain data on passenger statistics at various airports. Negotiating access to data from various government agencies was difficult. The study made use of the ACI database as it met the requirements of the study. For those airports that are not members of ACI, data was obtained from the ICAO. This was the case for data on Maseru airport in Lesotho.

6.2.3.2.2 Data on GNI per capita

There are various sources for data on GNI for SADC countries. The sources are central statistical authorities in each member state, regional statistical agencies,

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the International Monetary Fund and the World Bank. This study considered the World Bank Development Indicators, an up to date database that is available online, as the appropriate source for data for the income variable.

6.2.3.2.3 Trade data

Data on trade is available from various sources. The major sources are the International Monetary Fund’s Direction of Trade Statistics (DOTS), World Integrated Trade Solution (WITS)9, The United Nations Commodity Trade Statistics Database (UN Comtrade), SADC trade database, member states10 and regional trade organisations. These databases except the UN Comtrade, whose focus is commodities, include both trade in merchandise and services. This research considered the International Monetary Fund (DOTS) as the source that best met the requirements for uniformity and consistency.

6.2.3.2.4 Tourism data

UNTWO and SADC member states are a good source of origin-destination data as they provide an indication of tourism flows by origin. This facilitates the computation of inter-country tourism flows. The major weakness however lies with the definition of tourism as in many cases it is overnight visitors only that are recorded. The omission of same day visitors, a travel profile typical of airlines’ business passengers, introduces a measurement error in the variable. To ensure consistency, data for this variable was sourced from the UNTWO database.

6.2.3.2.5 Data on shared borders and language

Data on shared borders and language and was obtained from member states and SADC websites.

6.2.3.2.6 Data on distance

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A database developed by the World Bank and the United Nations United Nations Conference on Trade and Development (UNCTAD).

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Data for distance was obtained from OAG. For those routes with no air service connections, data was calculated using the Great Circle Mapper (copyright © Karl L. Swartz).

6.2.3.2.7 Data on travel restrictions

Data for travel restrictions are available from the Regional Tourism Organization of Southern Africa (RETOSA) and government agencies responsible for immigration and tourism. Government agencies tend to have more accurate data as they are regularly updated. Travel restrictions data was therefore sourced from national governments’ websites.

6.2.3.2.8 Data on human development levels

There is only one source that holds data on this variable. This is the UNDP database which is available online. Data was therefore obtained from this source. The study considered this database to be a reliable source as it makes use of both the IMF and World Bank statistics.

6.2.3.2.9 Data on political stability

Data on political stability indices can be sourced from Economist intelligence, the World Bank’s World Governance Indicators, and various consultancy organisations. The World Bank database as it is available online and covers all countries that the study was looking at, was considered the most appropriate source.

6.2.3.2.10 Measurement of the explanatory variables

Gravity models assume the relationship between the variable to be predicted and the explanatory variables is multiplicative (the effects of each of the variables on traffic tend to multiply rather add up). The quantitative variables of the model, with the exception of trade and distance, are a product of the origin and destination interactions. Distance was squared to represent the impedance effect it has on intercity interaction. Trade was not considered multiplicative because the exports of one country are the imports of another country. To avoid double counting, the total trade was assumed to be imports plus exports. In

many cases there are variations in both exports and import figures because of timing differences between countries in recording of trade transactions. The higher figure in each case was used.

The distance used in this study is the great circle distance (as the crow flies). Kanafani (1983) argues that this variable has two conflicting effects. Increasing distance leads to lower social and commercial interactions. But longer distances on the other hand increase the competitiveness of air transport compared to other transport modes. Although it is possible that short haul trips (those below one thousand kilometres) are likely to face stiff competition from surface transport, the model did not adjust the distance variable because data on the nature of competition from surface transport was not available. Given the underdevelopment of surface transport, the study did not consider competition from surface transport to be important for inclusion in the model.