While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice-based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life-years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post-transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.
Manufacturers of durable goods can encourage consumers facing transaction costs to upgrade by accepting used units as trade-ins. These "buyback schemes" increase demand for new units, but increase the supply of used units if trade-ins are resold. I investigate the equilibrium effects of buyback schemes in the market for business jets. I find that buyback increases manufacturer revenue by 7.2 percent at fixed prices. However, in equilibrium this revenue gain is diminished by 43 percent due to substitution away from new jets among first time buyers. I show how the size of this cannibalization effect depends on preference heterogeneity.
We develop a model of consumer search with spatial learning in which sampling the payoff of one product causes consumers to update their beliefs about the payoffs of other products that are nearby in attribute space. Spatial learning gives rise to path dependence, as each new search decision depends on past experiences through the updating process. We present evidence of spatial learning in data that records online search for digital cameras. Consumers’ search paths tend to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. We estimate the structural parameters of the model and show that these patterns can be rationalized by our model, but not by a model without spatial learning. Eliminating spatial learning reduces consumer welfare by 12%: cross-product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for the power of search intermediaries. We use simulations to show that consumer-optimal product recommendations are that are most informative about other products.