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Collaborative Research: The Geography of Information: Testing the Effects of Unequal Information in the Market for Rental Housing

合作研究：信息地理：测试租赁住房市场中不平等信息的影响

基本信息

批准号：
1947591
负责人：
Ariela Schachter
金额：
$ 29.07万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-15 至 2023-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947591&HistoricalAwards=false
关键词：
Collaborative Research Geography Information Testing

项目摘要

There is considerable urban residential segregation in the United States by race, ethnicity, and social class, with implications for community formation and inequality, as well as school segregation. We know that the majority of urban home-seekers in the United States now use the internet as their primary source to find new places to live. Despite this development, we know relatively little regarding how renters select their housing at a time when information about available housing is increasingly moving online. While this technological transformation has largely gone unexamined, some prior research shows that advertisements for rental housing are not all the same; rather, they differ systematically depending on the demographics of the neighborhood where the housing being advertised is located. This project analyzes rental housing advertisements posted online to investigate if these differences matter for people during their housing search. Understanding how individuals interpret the information they see in the online housing market is key to explaining why people move to certain places and not others, which has implications for the future of residential inequality and racial/ethnic segregation. The findings will advance understanding of residential selection processes and aid policy makers looking to expand and equalize access to information for home-seekers, with implications for improved social and economic well-being in urban areas. Given the role that online rental advertisement plays in promoting neighborhood composition, it is notable that we know so little regarding how individuals interpret this information. Using natural language processing to analyze millions of advertisements for rental housing in the 50 largest U.S. cities posted on Craigslist, prior work has identified patterns in the distribution of different types of information in neighborhoods that vary by race/ethnicity and poverty rate. This proposal will use this information to test the causal effects of these real-world differences in the ways units are advertised on individuals’ housing and neighborhood preferences in five large urban areas: Los Angeles, SF-Bay Area, New York City, Chicago, and Houston. The project will implement three survey experiments in each area to test how online advertisements shape housing decisions and residents’ perceptions of local neighborhoods. The project will compare the effects of information in housing ads and perceptions of neighborhoods to the effects of other kinds of information, including neighborhood demographic data, on individuals’ interest in housing units. Selection of these five large areas allows the project to oversample Black, Latino and Asian minority respondents. Each survey experiment will first be constructed to be representative of the urban area (n=1,000/area), but will then collect additional respondents from specific minority population(s) within each area: oversamples of 100-300 additional Asian respondents in the SF Bay Area; Black respondents in New York and Chicago; and Latino respondents (including Spanish-speakers) in Los Angeles, Chicago, and Houston. By using representative surveys of specific urban areas with oversamples of minority residents, the project will analyze—through a combination of difference of means tests and multiple regression models—how reactions to housing advertisements vary across ethno-racial groups. The project will contribute to sociological theory regarding neighborhood and unit selection processes, residential sorting, the formation of place reputations, and how prospective tenants form impressions of their residential contexts. The project also will help to transform how survey experiments are developed and implemented across the social sciences by demonstrating the utility of big data sources and computational techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国，城市居住存在着相当大的种族、族裔和社会阶层隔离，这对社区形成和不平等以及学校隔离产生了影响。我们知道，美国大多数城市购房者现在使用互联网作为寻找新住所的主要来源。尽管取得了这一进展，但当有关可用住房的信息越来越多地转移到网上时，我们对租房者如何选择住房知之甚少。虽然这种技术变革基本上没有经过检验，但之前的一些研究表明，出租房屋的广告并不完全相同；相反，它们根据广告中的住房所在社区的人口统计数据而系统地有所不同。该项目分析了在线发布的租赁住房广告，以调查这些差异对人们在寻找住房期间是否重要。了解个人如何解释他们在在线住房市场上看到的信息是解释为什么人们搬到某些地方而不是其他地方的关键，这对未来的居住不平等和种族/民族隔离具有影响。研究结果将增进对住房选择过程的理解，并帮助决策者扩大和平等地获取购房者的信息，从而改善城市地区的社会和经济福祉。鉴于在线租赁广告在促进社区构成方面发挥的作用，值得注意的是，我们对个人如何解释这些信息知之甚少。之前的工作使用自然语言处理分析了 Craigslist 上发布的美国 50 个最大城市的数百万条出租房屋广告，确定了社区中不同类型信息的分布模式，这些信息因种族/民族和贫困率而异。该提案将利用这些信息来测试现实世界中的单位广告方式差异对五个大城市地区（洛杉矶、旧金山湾区、纽约市、芝加哥和休斯顿）个人住房和社区偏好的因果影响。该项目将在每个地区实施三个调查实验，以测试在线广告如何影响住房决策和居民对当地社区的看法。该项目将比较住房广告中的信息和社区看法与其他类型信息（包括社区人口数据）对个人对住房单元兴趣的影响。选择这五个大区域使项目能够对黑人、拉丁裔和亚裔少数族裔受访者进行过度抽样。每个调查实验首先将被构建为代表城市地区（n=1,000/地区），但随后将从每个地区内的特定少数民族人口中收集其他受访者：对旧金山湾区 100-300 名额外的亚洲受访者进行过采样；纽约和芝加哥的黑人受访者；以及洛杉矶、芝加哥和休斯顿的拉丁裔受访者（包括西班牙语使用者）。通过对特定城市地区进行代表性调查，并对少数族裔居民进行过抽样，该项目将通过手段差异测试和多元回归模型的结合来分析不同民族群体对住房广告的反应有何不同。该项目将为关于社区和单元选择过程、住宅分类、地方声誉的形成以及潜在租户如何形成对其居住环境的印象的社会学理论做出贡献。该项目还将通过展示大数据源和计算技术的实用性，帮助改变社会科学领域调查实验的开发和实施方式。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。