The data are then aggregated on a yearly basis and; thus, they correspond to the total flights that occurred during the year between a unique pair of origin and destination, measured as the log of the yearly total number of seats available on the incoming flights (LN_SEATS).

Finally, data on Instagram posts, as a proxy of digital presence, are also collected for studying STP tourism performance. These data consist of the daily counts of posts containing #hashtags following #travel[COUNTRYNAME] pattern or similar, if applicable, from January 2016 to October 2018. These hashtags contain the most relevant posts to this task. Construction is based on Instagram API output following a query of said hashtags on a global scale using a unique data access.

Other country-level information (GDP, population, exports, prices, nominal exchange rates) is used as control variables and taken from the World Development Indicators database. The variables are used in logs and they are given by LN_GDP, LN_POP and LN_X. The real exchange rate indicators are constructed using the exchange rate and prices of the country and with respect to the euro area, the United States and the United Kingdom. These are given by the variables, RER_EURO, RER_US and RER_POUND, respectively.

Over the last two decades, STP experienced major changes regarding its tourism industry. In order to construct a comparison group, STP performance is studied in several dimensions with respect to other “similar” tourism destinations. First, this note uses islands that are at a similar distance with respect to major tourism origins (i.e., Europe, the United States). The list of countries used for this group is Cabo Verde (CPV), Madagascar (MDG), Maldives (MDV), Mauritius (MUS), and the Seychelles (SYC). The econometric analysis below expands this control group to include Caribbean and Pacific islands as well as some other countries with strong tourism sectors.

**IIIa. Baseline random-effects model**

This first model establishes correlation and association between the tourism the outcome variables and a set of tourism determinants. In order to study the determinants of tourism, consider first a baseline random-effects panel data model of the form: **Yit=** β **Xit-1 +** γ **Zi +** φ **Wt-1 +** δ **t +** μ **i + εit** , where **_i_** indexes tourism destinations and **_t_** time, **_Y_** is the dependent variable of interest (log of tourist arrivals, or log of expenditures per tourist), **X** is a set of control variables that varies with respect to both i and t, **Z** varies only across i and **W** only with respect to time. contains year effects, a country specific random-effect and **_ε_** the unexplained component of the model. For **X** , the note δδ considers the destination specific information: log of GDP (LN_GDP), log of population (LN_POP), μμ log of total number of seats in incoming flights (LN_SEATS), real exchange rate (with respect to euro, RER_EURO); for **Z** , the notes considers the size of the country (LN_SIZE), and for **W** , it considers the log of GDP of the euro area (LN_GDPEURO). The variables are described in the previous section. This model does not intend to establish causal relationships among the control variables and the dependent variables, but rather to establish correlation and association. The model, thus, produces a hedonic regression, in which a set of characteristics are used as determinants of a given outcome 9