🤖 AI Summary
This study investigates the robustness of econometric models in matching markets under transferable utility and separable joint surplus assumptions, with particular attention to the impacts of omitted variables and nonseparable structures on estimation accuracy. By integrating theoretical derivations, numerical simulations, and counterfactual analyses, the paper provides the first systematic assessment of the robustness boundaries of separable matching models when these assumptions are mildly violated. The findings indicate that such models remain reasonably robust under moderate departures from separability, yet their reliability critically depends on data quality and balance across groups. Innovatively combining theoretical and simulation-based approaches, this work establishes essential robustness criteria for the empirical application of matching models.
📝 Abstract
Since Choo and Siow (2006), a burgeoning literature has analyzed matching markets when utility is perfectly transferable and the joint surplus is separable. We take stock of recent methodological developments in this area. Combining theoretical arguments and simulations, we show that the separable approach is reasonably robust to omitted variables and/or non-separabilities. We conclude with a caveat on data requirements and imbalanced datasets.