Domestic tourism represents a large share of the total tourism volume in Spain, but it is still an under‐researched topic. This study focuses on the determinants of domestic flows in Spain at provincial level. The prior assumption is that domestic tourism demand may be affected by specific local conditions that previous studies, mostly based on more aggregate data, would hardly capture. A gravity model and various spatial econometric models are estimated assuming alternative spatial weighting matrices. Results suggest that income and relative prices affect tourism demand in Spanish provinces as well as weather, natural amenities, infrastructures, and recreational activities.
The optimum time students should spend on homework has been widely researched although the results are far from unanimous. The main objective of this research is to analyze how homework assignment strategies in schools affect students' academic performance and the differences in students' time spent on homework. Participants were a representative sample of Spanish adolescents (N = 26,543) with a mean age of 14.4 (±0.75), 49.7% girls. A test battery was used to measure academic performance in four subjects: Spanish, Mathematics, Science, and Citizenship. A questionnaire allowed the measurement of the indicators used for the description of homework and control variables. Two three-level hierarchical-linear models (student, school, autonomous community) were produced for each subject being evaluated. The relationship between academic results and homework time is negative at the individual level but positive at school level. An increase in the amount of homework a school assigns is associated with an increase in the differences in student time spent on homework. An optimum amount of homework is proposed which schools should assign to maximize gains in achievement for students overall.
This paper investigates the possibility of improving the predictive ability of a tourism demand model with meteorological explanatory variables. The authors use as a case study the monthly British tourism demand for the Balearic Islands (Spain). For this purpose, a transfer function model and causal artificial neural network are fitted. The results are compared with those obtained by non-causal methods: an ARIMA model and an autoregressive neural network. The results indicate that incorporating meteorological variables can increase predictive power, although the most accurate prediction is obtained using a non-causal model -specifically, an autoregressive neural network.
The main goal of this study is to estimate the price and income elasticity of demand for tourism to Spain. This estimation is done separately for the major international source markets for Spain: Germany, the UK, Italy and the Netherlands. For this purpose, the authors use the autoregressive distributed lag (ARDL) approach to cointegration and the bootstrap method to construct empirical confidence intervals for each estimate. The results reveal that the tourism demand in all the countries studied has a similar income elasticity, which is approximately unitary. However, there is an important difference with regard to price elasticity: tourism demand from the UK is statistically price inelastic, but demand is elastic for the remaining countries. This finding is relevant because, first, it underlines the importance of studying the source markets separately instead of analysing an aggregate international tourism demand, and, second, it supports the need to implement different tourism policies and strategies with respect to the pricing decisions for each source market.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.