DOI: 10.5281/zenodo.17970128
title: "It's a Small World After All": A Critical Analysis of the 2024 Disney DAS Policy Shift.

description: Dataset and Code Repository for "It's a Small World After All": A Critical Analysis of the 2024 Disney DAS Policy Shift

This repository contains survey data, Monte Carlo simulation code, visualizations, and analysis results supporting the manuscript submitted to Tourism Management.

Contents

Survey Data & Analysis



DAS_DATA.xlsx - Anonymized survey responses (n=269) from prior DAS users post-July 2024 policy change

DAS_Analysis_Results.xlsx - Statistical analysis results including confidence intervals, effect sizes, and significance tests

SURVEY.ipynb - Jupyter notebook containing complete survey analysis code (Python)


Monte Carlo Simulation



MONTECARLO.ipynb - Complete Monte Carlo simulation code (10,000 iterations, 6 scenarios, Python)

mc_summary_table.csv - Main results: viability probabilities, mean/median net benefits, VaR(5%), costs

mc_sensitivity_analysis.csv - Spearman rank correlations (ρ) between input parameters and net benefit outcomes


Visualizations

Survey Results (Figures 1-5):



fig1_approval_and_impact.png - Approval rates and attendance impact breakdown

fig2_anxiety_distribution.png - Anxiety level distribution (90.30% severe anxiety)

fig3_key_metrics.png - Key metrics with 95% confidence intervals

fig4_confidence_intervals.png - Statistical confidence intervals for primary outcomes

fig5_approval_pathways.png - Application pathways and denial rates


Monte Carlo Results:



mc_net_benefit_distributions.png - Net benefit distributions across 6 scenarios (overlaid histograms)

mc_cost_distributions.png - Cost structure comparison (box plots by scenario)

mc_probability_positive.png - Viability probability (P(Net>0)) horizontal bar chart

mc_sensitivity_tornado.png - Tornado diagram showing Spearman correlations (ρ) ranked by magnitude


Key Findings

Survey Evidence (n=269):



68.29% conditional denial rate among applicants

90.30% severe anxiety [95% CI: 86.16%-93.29%], NNH=1.24

85.13% attendance reduction [95% CI: 80.67%-89.22%]

$2.13M estimated annual revenue loss per 500-family cohort


Monte Carlo Results (10,000 iterations):



Labor-intensive models fail: Baseline Professional 0.6% viable, -$4.38M mean loss

Technology-mediated crisis support succeeds: 93.2% viable, +$1.48M mean benefit

Cost differential: $6.46M → $646K (90% labor cost reduction)


Sensitivity Analysis (Spearman Rank Correlations):



Family spending: ρ=0.736 (dominates 54% of variance)

Technology effectiveness: ρ=0.090 (explains <1% of variance)

Fraud rate: ρ=-0.104 (economically irrelevant)

Finding: Cost structure drives viability; operational parameters (fraud prevention, tech quality) barely matter


Methodology

Survey:



Cross-sectional design, August-November 2024

Recruitment: IAAPA 2024 conference, disability advocacy networks

7-question instrument measuring denial rates, anxiety, behavioral intentions

Statistical tests: Binomial tests, Cohen's h effect sizes, Number Needed to Harm


Monte Carlo:



10,000 iterations with common random numbers (fair scenario comparison)

6 scenarios: Baseline Professional, Peer Volunteer, Technology-First, Premium Paid, Capacity-Limited, IBCCES-Verified

Demand: Triangular distribution (66, 115, 165 families/day)

Fraud rates: Beta distributions (standard 5%, IBCCES 2.5%)

Tail risk: 15-21% annual probability (from 90% anxiety survey result)

Convergence verified: 0.7% change n=7,500→10,000


Sensitivity Analysis:



Spearman rank correlations between 9 input parameters and net benefit

Robustness testing under worst-case scenarios (tech failure, high fraud, recession)


Reproducibility

All code is provided in Jupyter notebooks with:



Complete parameter specifications

Random seed (42) for reproducibility

Inline documentation and comments

Library versions specified


Required Python libraries:



NumPy 1.24+

SciPy 1.10+

Pandas 2.0+

Matplotlib 3.7+

Seaborn 0.12+


License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to:



Share — copy and redistribute the material

Adapt — remix, transform, and build upon the material


Under the following terms:



Attribution — You must give appropriate credit and indicate if changes were made


Acknowledgments

Survey respondents who shared experiences during a difficult policy transition. IAAPA 2024 conference organizers for facilitating data collection. Disability advocacy organizations for recruitment assistance.

Data Availability Statement

Survey data are anonymized to protect participant confidentiality. Raw individual responses are not included; only aggregate statistics and anonymized response patterns are provided. Monte Carlo simulation code is fully reproducible with provided random seed.