This dissertation consists of three chapters addressing several matching market questions applied to school choice, vaccine allocation and job search problems. In the first chapter, I study the role of information uncertainty in a many-to-one matching problem in a centralized college admissions system. I find that the mistakes of excluding almost surely out-of-reach programs from the rank-order list (ROL) do not change the allocation outcome significantly but using the observed ROLs as the true preferences would lead to biased estimates of student preference and misleading counterfactual results. I build a model where students are allowed to make certain mistakes in their ROLs when they face uncertainties in their priority scores. I argue that stability of the admission outcome is satisfied asymptotically. I estimate student preferences based on stability using a Gibbs sampler. I analyze two counterfactual policies in which students from rural high schools receive benefits in a form of additional priorities and seats. The policies would not hurt the welfare of the urban students significantly more than they would benefit the welfare of rural students.

The second chapter (co-authored with Júlia Koltai, Ágnes Szabó-Morvai, Gergely Röst, M'arton Karsai, Péter Biró, Balázs Lengyel) studies the vaccine acceptance and the assessment of five vaccines during the end of the third wave Covid-19 pandemic in Hungary based on a nationally representative survey. Individuals could reject the assigned vaccine to wait for a more preferred alternative that enables us to quantify revealed preferences across vaccine types. We find that hesitancy is heterogenous across vaccine types and is mostly driven by individuals’ trusted source of information. We argue that the greater selection of available vaccines and individuals’ free choice of vaccines create desirable conditions to increase the vaccination rate in societies.

In the third chapter I study one-to-one matching markets in a labor setting, where applicants and firms’ pay wasteful costs to search and screen, respectively. Unlike in Arnosti et al. (2021) firms might receive a biased signal about the applicant’s compatibility. I ask the research question: How much additional cost would the applicants be willing to pay to eliminate firms’ biased screening?