Corruption and population health outcomes: an analysis of data from 133 countries using structural equation modeling

Objectives: The current study aims to develop a theoretical framework for understanding the antecedents of corruption and the effects of corruption on various health indicators. Methods: Using structural equation models, we analyzed a multinational dataset of 133 countries that included three main groups of variables—antecedents of corruption, corruption measures, and health indicators. Results: Controlling for various factors, our results suggest that corruption rises as GDP per capita falls and as the regime becomes more autocratic. Higher corruption is associated with lower levels of health expenditure as a percentage of GDP per capita, and with poorer health outcomes. Countries with higher GDP per capita and better education for women have better health outcomes regardless of health expenditures and regime type. Conclusions: Our results suggest that there is no direct relationship between health expenditures and health outcomes after controlling for the other factors in the model. Our study enhances our understanding of the conceptual and theoretical links between corruption and health outcomes in a population, including factors that may mediate how corruption can affect health outcomes.