"background": "Industrial machinery fleets in developing economies present significant operational safety challenges. Existing risk management frameworks are often adapted from high-income contexts and lack rigorous, context-specific evaluation regarding their effectiveness in reducing incident rates. ", "purpose and objectives": "This working paper aims to methodologically evaluate the efficacy of a structured, technology-assisted risk reduction protocol for heavy machinery fleets. The primary objective is to quantify the causal impact of the intervention on safety incident frequency using a quasi-experimental design. ", "methodology": "A difference-in-differences design was employed, comparing incident rates before and after the intervention across treatment and control groups of machinery units. The core statistical model is Y{it = \0 + \1 + \2 + \ (\) +, where Yit is the incident count. Inference is based on robust standard errors clustered at the fleet level. ", "findings": "Preliminary analysis indicates a substantive reduction in reported safety incidents attributable to the intervention. The estimated average treatment effect shows a reduction of approximately 32% in incident frequency within the treatment group. The coefficient \ was statistically significant at the 5% level. ", "conclusion": The quasi-experimental design provides credible evidence that the implemented risk reduction methodology effectively lowers incident rates in the studied industrial setting. This underscores the value of tailored, evaluated engineering safety protocols. ", "recommendations": "Practitioners should adopt evaluated, data-driven risk frameworks over generic checklists. Further research should validate this methodology across different industrial sectors and machinery types to establish generalisable principles. ", "key words": "industrial safety, quasi-experimental design, difference-in-differences, machinery risk, impact evaluation, engineering management", "contribution statement": "This paper provides a novel application of causal inference methods for evaluating engineering safety interventions in an industrial African
Sarr et al. (Thu,) studied this question.