If my regression model $$ y_i = \alpha + \beta x_i + \epsilon_i $$
suffers from OVB the error contains one variable which we assume correlated with $$ \epsilon_i = \gamma w_i + u_i $$
my estimate of $\beta$ will be biased by a component which depends on $\gamma Cov(x_i,w_i)$. I would colloquially say that OLS can't distinguish between variation that's due solely to $x_i$ from variation that's shared with the omitted variable. Can it then be said the parameter in the model is not uniquely identified in the original model? Are identification and endogeneity the same thing here? And if yes, is that always the case?