Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In a two-sided matching context we show how we can predict stable matchings by considering only one side's preferences and the mutually acceptable pairs of agents. Our methodology consists of identifying impossible matches, i.e., pairs of agents that can never be matched together in a stable matching of any problem consistent with the partial data. We analyze data from the French academic job market for mathematicians and show that the match of about 45 percent of positions (and about 60 percent of candidates) does not depend on the preferences of the hired candidates, unobserved and submitted at the final stage of the market.