Abstract
Electricity theft remains a persistent challenge, particularly in developing economies where
infrastructure limitations and socioeconomic disparities contribute to illegal connections.
This study analyzes the determinants influencing electricity theft in Kinshasa, the Democratic
Republic of Congo, using a logistic regression model applied to 385 observations,
which includes random bootstrapping sampling for enhanced stability and power analysis
validation to confirm the adequacy of the sample size. The model achieved an AUC
of 0.86, demonstrating strong discriminatory power, while the Hosmer–Lemeshow test
(p = 0.471) confirmed its robust fit. Our findings indicate that electricity supply quality,
financial stress, tampering awareness, and billing transparency are key predictors
of theft likelihood. Households experiencing unreliable service and economic hardship
showed higher theft probability, while those receiving regular invoices and alternative
legal energy solutions exhibited lower risk. Lasso regression was implemented to refine
predictor selection, ensuring model efficiency. Based on these insights, a multifaceted policy
approach—including grid modernization, prepaid billing systems, awareness campaigns,
and regulatory enforcement—is recommended to mitigate electricity theft and promote
sustainable energy access in urban environments.