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α: calibrated so average coauthorship-adjusted count equals average raw count
We provide a guide to estimating matching functions in a spatial context. Several interactions in space take place in a decentralized fashion, such as passengers searching for taxis, ships meeting cargo, exporters meeting importers etc. A convenient modeling device to capture these meetings is the matching function, which has been used extensively in labor market settings. However in the spatial context, data availability is often limited to only one side of the market; for instance it is usually hard to find data on the number of passengers searching for a taxi. We discuss an approach to estimating matching functions that allows the researcher to recover the unobserved side of the market with relatively few assumptions. In addition, our approach obtains the matching function non-parametrically, allowing for significantly more flexibility than is commonly assumed. This additional flexibility can be key when deriving welfare and policy implications.