Insularity's Dual Role in Tourism: Bridging Competitive Advantage and Structural Fragility in Mature Island Destinations
Description
This dataset supports an empirical study analysing the determinants of competitive vulnerability in island tourism destinations, with the Canary Islands as a case study (2010–2024). The central research hypothesis states that the coexistence of high tourism performance and underlying structural fragilities can be explained through a set of factors aligned with the Crouch and Ritchie (2003) destination competitiveness model. Specifically, we hypothesise that: (i) higher tourism intensity and economic tertiarisation increase competitive vulnerability; (ii) stronger air connectivity reduces structural–economic vulnerability; (iii) systemic shocks (e.g., COVID-19) generate persistent increases in vulnerability; and (iv) higher median income is associated with lower internal fragility. The dataset consists of a balanced panel covering five islands (Tenerife, Gran Canaria, Lanzarote, Fuerteventura, and La Palma), integrating annual indicators on tourism demand, accommodation supply, air transport capacity, socioeconomic conditions, and labour market characteristics. The data were obtained entirely from official secondary sources, including the Instituto Nacional de Estadística (INE) and the Instituto Canario de Estadística (ISTAC). All variables were harmonised, indexed to a common base year (2010), and processed to ensure comparability across islands and years. These components were normalised (0–100 scale) and weighted to construct the Competitive Vulnerability Index (CVI/IVC). The dataset therefore enables replication of the index, comparative analysis across islands, and examination of long-term trends.
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Steps to reproduce
The dataset was produced using annual statistical information from the Instituto Nacional de Estadística and the Instituto Canario de Estadística for the period 2010–2024. All indicators were downloaded from these official sources and harmonised to ensure consistent units, definitions and temporal coverage across the five islands analysed. Once compiled, the data were cleaned, missing values were resolved using standard imputation procedures, and all variables were transformed into index values using a common base year to enable comparability. After the initial preparation, several derived indicators were created through a Python workflow included in this deposit. These derived variables capture market concentration, tourism intensity, demand volatility, socioeconomic fragility and structural characteristics of the tourism model. All indicators were then normalised to a common scale and combined into a composite measure of competitive vulnerability following the weighting scheme described in the article. The entire process, from raw data to the final dataset, can be reproduced by running the accompanying Python script, which performs the cleaning, transformation, normalisation and construction of the composite index in a fully transparent and replicable manner. Any researcher with access to the original INE and ISTAC datasets can regenerate the results by following the same sequence of steps.