I study the behavior of municipal authorities towards their neighbors. The theory is that some properties of municipalities (like a level of indebtedness) make them more 'aggressive'. I have a database of the statements each municipality made per year, and the number of those categorized as negative (like threat to file a lawsuit). The dataset looks something like that: The problem is that there are huge municipalities (with several millions of inhabitants, like Municipality A in example), and tiny municipalities like B above, with different capacities to produce statements. In addition one important property is that overwhelming majority of statements are neutral or positive (like sign a cooperation agreement), so negative statements usually account for a small fraction, no more than 5% of all statements.
What is the correct way of analyzing such type of data to account for variability across different items?
I could just look at the relative share of 'negative' statements in all statements made, but that would not work for small municipalities because they have a huge variation. Perhaps the correct way of doing it is using count data models such as Poisson regression, but I wonder if these models take into account the different 'sizes' of the items. What I mean is that the change in 10 items for the large municipality generally producing hundreds of statements per year should be treated differently from the similar situation for a small municipality which usually produces something slightly above zero.