"background": "Occupational safety in industrial settings remains a critical challenge in developing economies, with machinery-related incidents contributing significantly to workplace morbidity. Existing risk assessment methodologies often lack robust counterfactual frameworks to evaluate the efficacy of fleet-wide safety interventions. ", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental econometric model to quantitatively assess the impact of a structured machinery inspection and maintenance programme on reducing incident rates within industrial fleets. ", "methodology": "A difference-in-differences model was employed, analysing panel data from treatment and control groups of industrial machinery units. The core specification is Y{it = \0 + \1 + \2 + \ (\) +, where Yit is the incident rate. Inference is based on cluster-robust standard errors at the firm level. ", "findings": "The intervention yielded a statistically significant reduction in reported incident rates. The estimated average treatment effect was a 34% decrease (95% CI: 22% to 46%) in machinery-related incidents for the treatment group relative to the control cohort following programme implementation. ", "conclusion": "The methodological approach provides a rigorous, evidence-based tool for evaluating engineering safety programmes. The results demonstrate that systematic fleet management interventions can substantially mitigate occupational risks in industrial contexts. ", "recommendations": "Industrial regulators and firms should adopt structured, data-driven fleet evaluation protocols. Future research should integrate real-time sensor data into the modelling framework to enhance predictive capabilities. ", "key words": "difference-in-differences, occupational safety, machinery fleet management, quasi-experimental design, risk assessment", "contribution statement": "This paper presents a novel application of a difference-in-differences model to isolate the causal effect of an engineering safety intervention on industrial machinery incident rates, providing a replicable methodology for the
Kato et al. (Sun,) studied this question.
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