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  1. Low-income families often live in low-upward-mobility neighborhoods. We study why by using a randomized trial with housing voucher recipients that provided information, financial support, and customized search assistance to move to high-opportunity neighborhoods. The treatment increased the fraction moving to high-upward-mobility areas from 15 to 53 percent. A second trial reveals this treatment effect is driven primarily by customized search assistance. Qualitative interviews show that the intervention relaxed bandwidth constraints and addressed family-specific needs. Our findings imply many low-income families do not have strong preferences to stay in low-opportunity areas and that barriers in housing search significantly increase residential segregation by income. 
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    Free, publicly-accessible full text available May 1, 2025
  2. Matvos, Gregor (Ed.)
    We show that search frictions in credit markets affect accepted interest rates and loan sizes and distort consumption. Using data on car loan applications and originations not intermediated by car dealers, we isolate quasi-exogenous variation in both the costs and benefits to searching for credit. After identifying lender-specific policies that price risk discontinuously, we study the differential response to offered interest rates by borrowers who face high and low search costs. High-search-cost borrowers are 10$\%$ more likely to accept loan offers with higher markups, consequently originating smaller loans and purchasing older and less expensive cars than lower-search-cost borrowers. 
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  3. Low-income families in the United States tend to live in neighborhoods that offer limited opportunities for upward income mobility. One potential explanation for this pattern is that families prefer such neighborhoods for other reasons, such as affordability or proximity to family and jobs. An alternative explanation is that they do not move to high-opportunity areas because of a lack of information or barriers that prevent them from making such moves. We test between these explanations using a twophase randomized controlled trial with housing voucher recipients in Seattle and King County. We first provided a bundle of resources to facilitate moves to high-upward-mobility neighborhoods: information about high-opportunity areas, short-term financial assistance, customized assistance during the housing search process, and connections to landlords. This bundled intervention increased the fraction of families who moved to high-upward-mobility areas from 15% in the control group to 53% in the treatment group. To understand the mechanisms underlying this effect, we ran a second phase with three arms: (1) information about high-opportunity areas and financial assistance only; (2) reduced support services in addition to information and financial assistance; and (3) full support services, as in the original bundled intervention. The full services had five times as large a treatment effect as the information and financial incentives treatment and three times as large an effect as the reduced support intervention, showing that high-intensity, customized support enables moves to opportunity. Interviews with randomly selected families reveal that the program succeeded by relaxing families’ bandwidth constraints and addressing their specific needs, from identifying suitable units to providing emotional support to brokering with landlords. Families induced to move to higher opportunity areas tend to stay in their new neighborhoods in subsequent years and report higher levels of neighborhood satisfaction after moving. Our findings imply that many low-income families do not have a strong preference to stay in low-opportunity areas and that barriers in the housing search process are a central driver of residential segregation by income. 
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  4. Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses. 
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