This DBs' behavior has strong welfare implications: higher prices lead to lower entry, which in turn causes consumer harm. In other words, we show that competing DBs can cause significant harm, without the need of colluding, even in the absence of strong data synergies.

If modularity is high enough to ensure superadditivity, DBs coordinate their prices and fully extract firms' WTP, as in a nice paper by Gu,
@leonardomadio.bsky.social and @marcoreggiani.bsky.social
(Data Broker co-opetition). Coordination emerges even when data synergies are very low!

The intuition is that data brokers sell data to multiple competing firms. Then, choosing not to buy data puts firms at a disadvantage against its rivals, increasing its WTP. Anticipating this, DBs charge higher prices!

Instead, datasets are superadditive if the 𝐯𝐚𝐥𝐮𝐞 of the combined dataset is higher than the sum of the individual ones. We find that supermodularity is 𝐧𝐨𝐭 necessary for superadditivity!

Formally, we have two data brokers (DBs) that sell datasets of different accuracy (i.e., probability of operating first-degree price discrimination). Datasets are supermodular if the 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 of the combined dataset is higher than the sum of the individual ones.

shorturl.at/4nuab The answer to this conundrum is: technological additionality and economic additionality differ when you take firm competition into account! We refer to the first one as "modularity", and to the second one as "additionality".

Yeah that makes sense, so training data will be even more essential now

Hi, IO assistant professor focused on data markets theoretical models here!