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Technically, endogeneity occurs when a predictor variable (x) in a regression model is correlated with the error term (e) in the model. This can occur under a variety of conditions, but two cases are especially common in inequality research: (1) when important variables are omitted from the model (called “omitted variable bias”) and (2) when the outcome variable is a predictor of x and not simply a response to x (called “simultaneity bias”). At least part of the latter problem is often called “selection.”

Hello, With reference to the above, can I ignore simultaneity bias if my key independent variable occurs before the dependent variable? As such, if my independent variable is a requirement that remains fixed, and I want to study the impact of such requirement on the performance or outcome. Also, there are no empirical studies that determines the level of requirement. With the case I am working on, the requirement varies at a group level. Some groups have higher requirement and some lower and I want to test the level of requirement on their performance. There is no disclosure as to how this requirement is determined for different groups. From private sources I know that the groups can negotiate by demonstrating why the requirement must be lowered or can willingly accept a higher requirement.

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You should ask yourself: "who decided to impose such requirements? what was the ratio behind them? is the reason related to variables that I'm not including in my model (among the $X$s) and to the outcome variable?". If the requirements happen to be motivated by previous outcomes, then you have omitted variable bias. If the requirements are decided exogenously/independently of your outcome measure, this would not be a potential channel of bias (there could be others though, as usual).

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