I am having a hard time deciding which modeling approach I should take. I have a survey data from a random sample from New York City. I want to explore the effect of NYC's number of crime incidence (city level) on people's perception of safety (individual level). I have data from 2 time points (year 2014, 2018). Here I wonder if I should either cluster standard errors or take multilevel modeling approach to deal with the clustered nature of the dataset (individuals clustered in NYC) when I have only one group at the group level (I have only NYC data).
Or maybe I should just do a typical pooled-OLS?