I'm working on a public procurement dataset where I have information on all the participants that presented offers in 358 tenders. I'm analysing relationships between all the companies of the dataset (1242). I'm running a logistic network regression that allow to predict a relationship knowing another. In my case, for example, I'm interested in understanding the extent to which companies that frequently submit the same offer in a tender are also part of the same cartel (I have information on 8 different colluding cartels active in these tenders).
For each dyad (pair of companies) I'm calculating:
a) the number of times they participated together, and b) the number of times they submitted exactly the same offer.
I am unsure about the b) measure. When I calculate it, I obviously get missing values everytime two companies did not participate together in a tender and so did not have the "opportunity" to submit the same offer. This variable seems to create problems in regression because of the many missing values (85% of the dyads-observations are missing). Consider that the missing are not random and, as I said, I'm perfectly aware that they are missing "by default", because companies that did not participate together, did not bid on the same contract and by consequence could not bid the same offer! I thought that I could fill missing values with "0", thus without requiring this variable to depend to much co-bidding in the same tender. Do you think this approach makes sense or is it a way to force the data too much?