I am running a basic multiple regression model with Unemployment proportion (logit transformation of it) at block group level (from ACS 5-year data) as the response variable. In each of the ACS 5-year releases of the past 7 years (2013 to 2019) Unemployment counts are estimated to be 0 in more than a few block groups (resulting in 0 unemployment proportions for those said block groups) and there are also a few block groups that result in relatively higher unemployment counts. Because of this, my model fit is quite ugly and I am trying to brainstorm ways to solve this issues. I would really appreciate some references if anyone is familiar with this kind of a situation. Thank you for your time.
In general you should use Poisson regression for count data with small counts rather than logistic regression. What is the scientific question that you are trying to answer ? What are your covariates in your regression ? You might also consider loglinear models which are related to "raking" and the iterative proportional fitting algorithm.