A context-specific causal model for estimating the effect of extended length of overnight stay on traveller's total expenditure

TL;DR

This paper develops a context-specific causal model to estimate how extending overnight stays impacts traveler expenditure, considering different trip purposes and using Bayesian methods on survey data.

Contribution

It introduces a hierarchical Bayesian approach that accounts for trip purpose contexts to estimate causal effects of stay length on expenditure, including sensitivity analysis.

Findings

Extended stays increase expenditure, with effects varying by trip purpose.

Bayesian model provides posterior distributions of causal effects.

Sensitivity analysis assesses robustness against omitted variables.

Abstract

Tourism significantly affects the economies of many countries. Understanding the causal relationship between the length of overnight stay and traveller's expenditure is crucial for stakeholders to characterize spending profiles and to design marketing strategies. Causal mechanisms differ between personal and work-related travel because the decision-making processes have different drivers and constraints. We apply context-specific independence relations to model causal mechanisms in contexts specified by trip purpose and identify the causal effect of the length of stay on expenditure. Using the international visitor survey data on foreign travellers to Finland, we fit a hierarchical Bayesian model to estimate the posterior distribution of the counterfactual expenditure due to extending the length of stay by one night. We also perform a Bayesian sensitivity analysis of the estimated causal effect with respect to omitted variable bias.