the group of countries that can do better in terms of number of tourists. Moreover, STP can also do better in terms of the unobservable component. The unobserved component contains all other tourism determinants that have not been considered explicitly in the model, in particular, location, culture and other fixed determinants of the countries in the sample. The fact that STP ranks low in this is associated with one of those. Probably, the main candidate is related to location and connectivity (which is not captured by LN_SEATS, an aggregate measure of arriving flights). The analysis for expenditure per tourist, however, reveals that STP has a better performance than what would be otherwise expected with the hedonic model. This confirms previous empirical analysis indicating that STP is one of the destinations where expenditure per tourist has increased the most in absolute and relative terms.

# **Vb. Flights and seats arrivals**

Since STP is an island, the change in tourist arrivals is accompanied by a marked increase in flight arrivals, but a decrease in seats per flight. Table 2 presents, in detail, all flights that have STP as a destination: as of 2016, Angola, Ghana, and Portugal were the countries of departure from which most tourists came. Moreover, Angola and Portugal are the ones for which the number of connections has been more stable over the past years, a result that indicates the importance of the colonial past in the present configuration. Figure 10 and Figure 11 present the number of flight arrivals both in absolute and in relative terms (with respect to 2010). STP shows the steepest increment over the last years when total flights are 2.5 times the number in 2010. However, when looking at seats (Figure 12 and Figure 13), STP had a similar increase as the five countries in the comparison group, at the same level as Maldives and the Seychelles. As a result, it seems that STP had an increment mostly based on flights with fewer seats. The ration of seats per flight declined from 109 in 2010 to 67 in 2016. Figure 14, indeed, shows that STP is a unique case for this abrupt decline.

The connectivity pattern is the result of its colonial past and cultural affinity with Portugal. Another barrier, as in the case of STP, the language spoken is Portuguese, which is not widely known outside Portuguese-speaking countries, and which does not represent a large tourist-issuing market. An obvious implication of this is that STP workers in the tourism industry should have appropriate knowledge of foreign languages.

The lack of connectivity can be clearly observed when analyzing flight connections as a full network graph. Take all countries with flight data to be a collection of nodes or vertices, where origin and destination of flights define an edge or link. Consider, thus, an unweighted and undirected network analysis of the flight data for 2016, with special emphasis on STP and related tourism destinations. The unweighted network structure is preferred because the main interest is on the connectivity potential of STP and not in the number of flights, which are related to the node size (i.e., tourist capacity), but on its potential for connectivity (i.e., attracting tourists). Consider also an undirected network as most destinations have the same amount of incoming and outgoing flights. Of interest for a given node is a measure of centrality which determines how important each node is for the full network structure and cohesion.

14