Endogeneity, a pervasive issue in econometric modeling, arises when an explanatory variable is correlated with the error term in a regression model. This correlation violates a fundamental assumption of ordinary least squares (OLS) regression, leading to biased and inconsistent parameter estimates. Consequently, inferences drawn from such models are rendered unreliable, jeopardizing the validity of policy recommendations and theoretical conclusions. Endogeneity can stem from several sources, including omitted variable bias, simultaneity, and measurement error. Omitted variable bias occurs when a relevant variable, correlated with both the included explanatory variable and the dependent variable, is excluded from the model. Simultaneity arises when the dependent variable influences the explanatory variable, creating a feedback loop. Measurement error, present when explanatory variables are imperfectly measured, also induces correlation between the explanatory variable and the error term. Addressing endogeneity requires employing instrumental variables (IV) techniques or, in some cases, utilizing panel data methods with fixed effects. IV estimation involves finding an instrument, a variable correlated with the endogenous explanatory variable but uncorrelated with the error term, allowing for consistent estimation. Ignoring endogeneity can lead to significantly flawed analysis, undermining the integrity of empirical economic research.