Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose an identification strategy for diversion based on win/loss data. First, we show that win/loss data from the merging firms and market shares in two periods for all firms are sufficient to identify the diversion ratios between the merging partners. Second, we show that win/loss data from the merging firms alone are sufficient for partial identification, and we construct a lower bound that provides a good approximation to the diversion ratio when switching costs are high. We demonstrate the performance of our method with numerical simulations and with an application to the Anthem/Cigna merger.