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

• 批准号: 2041304
• 负责人: Max Besbris
• 金额: $ 5.72万
• 依托单位: University of Wisconsin-Madison
• 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: 2020-07-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041304&HistoricalAwards=false
• 关键词: Collaborative; Research; Geography; Information; Testing

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，种族、族裔和社会阶层之间存在着相当大的城市居住隔离，这对社区的形成和不平等以及学校隔离都有影响。我们知道，美国大多数城市购房者现在使用互联网作为他们寻找新住所的主要来源。尽管有这种发展，我们对租房者如何选择他们的住房知之甚少，因为有关可用住房的信息越来越多地转移到网上。 虽然这种技术转型在很大程度上没有得到检验，但一些先前的研究表明，出租房屋的广告并不都是一样的;相反，它们根据广告所处房屋所在社区的人口统计数据而系统地有所不同。该项目分析了网上发布的出租房屋广告，以调查这些差异是否对人们在寻找房屋时有影响。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，这对居住不平等和种族/民族隔离的未来有影响。研究结果将促进对住宅选择过程的理解，并帮助政策制定者扩大和平等地获得寻求住房的信息，对改善城市地区的社会和经济福祉产生影响。考虑到在线租赁广告在促进社区组成方面的作用，值得注意的是，我们对个人如何解释这些信息知之甚少。 使用自然语言处理来分析发布在Craigslist上的美国50个最大城市的数百万个出租房屋广告，之前的工作已经确定了不同类型的信息在不同种族/民族和贫困率的社区中的分布模式。本提案将使用这些信息来测试这些现实世界中的差异的因果关系的方式，在个人的住房和邻里偏好的五个大城市地区：洛杉矶，旧金山湾区，纽约市，芝加哥，和休斯顿的单位做广告。 该项目将在每个地区实施三项调查实验，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对社区的看法与其他类型的信息（包括社区人口统计数据）对个人对住房单位兴趣的影响。选择这五个大的领域允许该项目对黑人，拉丁美洲人和亚洲少数民族受访者进行过度抽样。每个调查实验将首先构建为代表城市地区（n= 1，000/地区），然后从每个地区的特定少数民族人口中收集额外的受访者：旧金山湾区额外100-300名亚裔受访者的过样本;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（包括讲西班牙语的人）。通过使用具有代表性的调查，特定的城市地区与少数民族居民的过度抽样，该项目将分析-通过组合的手段测试和多元回归模型的差异-如何反应房屋广告不同的民族，种族群体。 该项目将有助于社会学理论关于邻里和单位的选择过程，住宅排序，地方声誉的形成，以及未来的租户如何形成他们的居住环境的印象。该项目还将通过展示大数据源和计算技术的实用性，帮助改变调查实验在社会科学领域的开发和实施方式。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。