I am confused about a result in my OLS regression.
I am regressing health on both crime level and ubanization and a couple of commonly encountered covariates in the literature such as, for example, age, gender, education, cultural background, income etc.
Health is a variable at an individual level. Crime level is a variable at a regional (German state) level aggregated. Urbanization is a variable at an individual level. I have around 50,000 observations.
I first find that crime is insignificant in my OLS regression. However, if I take urbanization out of the specification, crime becomes suddenly significant.
Therefore I believe that urbanization is an apparent confounder that is correlated with crime. However, I have difficulties to interpret this concisely, or in other words, figure out the causality.
I can hardly imagine that crime 'predicts' urbanization, which in turn 'predicts' health. I would have expected the opposite to happen, perhaps that urbanization at the individual level becomes insignificant when adding crime to the specification.
Is there a rationale for crime to become insignificant when urbanization was added, or in other words, why did crime become insignificant and not urbanization? With my limited understanding I can't make sense of it. What is the rule for which variable to become insignificant when adding a control variable that confounds the relationship?