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α: calibrated so average coauthorship-adjusted count equals average raw count
We study the effectiveness of nonexperimental strategies in adjusting for comparison group differences when using data from several programs, each implemented at a different location, to compare their effect if implemented at alternative locations. First, we adjust for individual characteristics differences simultaneously across all groups using unconfoundedness-based and conditional difference-in-difference methods for multiple treatments. Second, we adjust for differences in local economic conditions and stress their role after program participation. Our results show that it is critical to have sufficient overlap across locations in both dimensions and illustrate the difficulty of adjusting for local economic conditions that differ greatly across locations. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.