Accounting for unobserved heterogeneity, endogeneity, and temporal instability in occupational accident severity...

This study provides a simulation-based diagnostic assessment of three interrelated challenges in accident severity modeling: unobserved heterogeneity, endogeneity, and temporal instability. While these issues have been widely examined in isolation, their combined influence on statistical inference and model reliability remains insufficiently understood. To address this gap, a transparent Monte Carlo simulation framework is developed to generate synthetic accident data with controlled structural properties, enabling systematic evaluation of model performance under known conditions. The analysis employs a mixed ordered logit specification, augmented with a control function approach for endogeneity correction and a rolling time window strategy to capture temporal variation. Results demonstrate that neglecting these factors-individually and jointly-can lead to substantial bias in parameter estimates, misidentification of statistically significant variables, and unstable inference across time periods. In particular, endogeneity is shown to induce systematic distortion in behavioral effect estimates, unobserved heterogeneity alters both magnitude and significance patterns, and temporal aggregation masks dynamic structural changes. Rather than offering direct policy prescriptions, the findings are interpreted as methodological insights that highlight the risks of model misspecification in accident severity analysis. The study contributes by providing a reproducible framework for evaluating inferential robustness and by clarifying the limitations of commonly used modeling approaches, thereby informing more rigorous empirical applications using real-world safety data.