A guide to estimating matching functions in spatial models

Giulia Brancaccio, Myrto Kalouptsidi and Theodore Papageorgiou

International Journal of Industrial Organization, 2020, vol. 70, issue C

Abstract: 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.

Keywords: Matching function; Spatial models; Transportation; Nonparametrics; Search frictions; Taxis; Shipping; Geography (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:indorg:v:70:y:2020:i:c:s0167718719300554

DOI: 10.1016/j.ijindorg.2019.102533

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