Abstract Introduction Changes in arousal result fMRI signal fluctuations. Identifying sleep stage inside the MRI scanner can facilitate study of sleep-associated changes blood oxygen-level dependent signals. As simultaneous electroencephalography [EEG] is often not feasible, developing machine-learning-based algorithms for fMRI-based scoring needed. Methods Participants included 11 university students, who provided multiple 15-minute-long EEG-fMRI scans (TR=2.1 seconds). This used first two...

We explore the impact of public school assignment reforms by building a households' choice model that considers two important margins through which households may respond: (1) residential location and (2) opt-out to outside schooling options.

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the deferred-acceptance matching environment. The key insight that uncertainties faced by applicants, e.g., due tie-breaking lotteries, render some costly, allowing us reliably infer relevant preferences. Our extracts all information on robustly payoff-insignificant mistakes. apply it data from Staten Island, NYC....

We develop a signaling model of prestige seeking in competitive college applications. A prestigious program attracts high-ability applicants, making its admissions more selective, which turn further increases prestige, and so on. This amplifying effect results with negligible quality advantage enjoying significant equilibrium. Furthermore, applicants "sacrifice" their fits for programs pursuit the misallocation fits. Major choice data from Seoul National University provides evidence our...

We explore the impact of public school assignment reforms by building a households’ choice model with two key features—(1) endogenous residential location and (2) opt-out to outside schooling options. Households decide where live taking into account that locations determine access schools—admissions probabilities commuting distances schools. are heterogeneous both in observed unobserved characteristics. estimate using administrative data from New York City’s middle system. Variation boundary...

This paper explores the dynamic relationship between school choices made at different educational stages and how it affects racial segregation across schools. We use New York City public choice data to ask: "How does middle that a student attends affect her high application assignment?" take two approaches answer question. First, we exploit quasi-random assignments schools generated by tie-breaking feature of admissions system. find evidence students who attend high-achievement apply are...

This paper explores the dynamic relationship between school choices made at different educational stages and how it affects racial segregation across schools. We use New York City public choice data to ask: "How does middle that a student attends affect her high application assignment?" take two approaches answer question. First, we exploit quasi-random assignments schools generated by tie-breaking feature of admissions system. find causal evidence students who attend high-achievement apply...

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the deferred-acceptance matching environment. The key insight that uncertainties faced by applicants, e.g., due tie-breaking lotteries, render some costly, allowing us reliably infer relevant preferences. Our extracts all information on robustly payoff-insignificant mistakes. apply it data from Staten Island, NYC....