Jacob William Faber is an Assistant Professor of Public Service at the NYU Robert F. Wagner Graduate School of Public Service.
Jacob William Faber is an Assistant Professor at New York University's Robert F. Wagner School of Public Service. His research and teaching focuses on spatial inequality. He leverages observational and experimental methods to study the mechanisms responsible for sorting individuals across space and how the distribution of people by race and class interacts with political, social, and ecological systems to create and sustain economic disparities. While there is a rich literature exploring the geography of opportunity, there remain many unsettled questions about the causes of segregation and its effects on the residents of urban ghettos, wealthy suburbs, and the diverse set of places in between.
Dr. Faber earned his PhD in Sociology from New York University and worked as a Postdoctoral Research Associate in the Department of Sociology at Princeton University. He also graduated from the Massachusetts Institute of Technology with Master’s degrees in Telecommunications Policy and Urban Studies and Planning and a Bachelor’s degree in Management Science. Between stints at graduate school, Dr. Faber worked as a Senior Researcher for the Center for Social Inclusion, a racial justice policy advocacy organization.
Students in this course will explore the spatial aspects of inequality, including racial segregation, concentrated poverty, and government structure. Course materials will investigate the consequences of these inequalities for individuals, communities, and American society as a whole, as well as how these seemingly-intractable problems were created by and continue because of public policy decisions. This course will be an interactive experience, requiring preparation before coming to class and active exchange during class.
Multiple regression is the core statistical technique used by policy and finance analysts in their work. In this course, you will learn how to use and interpret this critical statistical technique. Specifically you will learn how to evaluate whether regression coefficients are biased, whether standard errors (and thus t statistics) are valid, and whether regressions used in policy and finance studies support causal arguments.
In addition, using a number of different datasets, you will compute the statistics discussed in class using a statistical computer package, and you will see how the results reflect the concepts discussed in class. If you choose, you can do a larger data and regression project in a team.
During the past decade, housing markets across the United States experienced dramatic upheaval. Housing prices rose rapidly throughout much of the country from 2000 until the start of 2007 and then fell sharply during the next two years. Many households lost substantial amounts of their equity during this downturn; in aggregate, U.S. homeowners lost $7 trillion in equity from 2006 to 2009. Aggregate home equity holdings had fallen back to 2000 levels by early 2009. While this intense volatility has been well documented, there remain unanswered questions about the variation in experiences across racial groups, particularly among those who purchased their homes before the boom and kept them through the collapse of the market. Did this housing market upheaval widen the already large racial and ethnic gaps in housing wealth? Using the American Housing Survey, we analyze differences in the changes in home equity experienced by homeowners of different races and ethnicities between 2003 and 2009. We focus on homeowners who remained in their homes over this period and find that blacks and Hispanics gained less home equity than whites and were more likely to end the period underwater. Black-white gaps were driven in part by racial disparities in income and education and differences in types of homes purchased. Latino-white disparities were most dramatic during the market’s bust.
Although previously theorized, virtually no rigorous empirical evidence has demonstrated an impact of neighborhood stigma on individual outcomes. To test for the effects of neighborhood stigma on economic transactions, an experimental audit of an online classified market was conducted in 2013–2014. In this market, advertisements were placed for used iPhones in which the neighborhood of the seller was randomly manipulated. Advertisements identifying the seller as a resident of a disadvantaged neighborhood received significantly fewer responses than advertisements identifying the seller as a resident of an advantaged neighborhood. The results provide strong evidence for an effect of neighborhood stigma on economic transactions, suggesting that individuals carry the stigma of their neighborhood with them as they take part in economic exchanges.
“Superstorm Sandy” brought unprecedented storm surge to New York City neighborhoods and like previous severe weather events exacerbated underlying inequalities in part because socially marginalized populations were concentrated in environmentally exposed areas. This study makes three primary contributions to the literature on vulnerability. First, results show how the intersection of social factors (i.e., race, poverty, and age) relates to exposure to flooding. Second, disruption to the city’s transit infrastructure, which was most detrimental for Asians and Latinos, extended the consequences of the storm well beyond flooded areas. And third, data from New York City’s 311 system show there was variation in distress across neighborhoods of different racial makeup and that flooded neighborhoods remained distressed months after the storm. Together, these findings show that economic and racial factors overlap with flood risk to create communities with both social and environmental vulnerabilities.
The literature on neighborhood effects frequently is evaluated or interpreted in relation to the question, “Do neighborhoods matter?” We argue that this question has had a disproportionate influence on the field and does not align with the complexity of theoretical models of neighborhood effects or empirical findings that have arisen from the literature. In this article, we focus on empirical work that considers how different dimensions of individuals' residential contexts become salient in their lives, how contexts influence individuals' lives over different timeframes, how individuals are affected by social processes operating at different scales, and how residential contexts influence the lives of individuals in heterogeneous ways. In other words, we review research that examines where, when, why, and for whom do residential contexts matter. Using the large literature on neighborhoods and educational and cognitive outcomes as an example, the research we review suggests that any attempt to reduce the literature to a single answer about whether neighborhoods matter is misguided. We call for a more flexible study of context effects in which theory, measurement, and methods are more closely aligned with the specific mechanisms and social processes under study.
Subprime mortgage lending in the early 2000s was a leading cause of the Great Recession. From 2003 to 2006, subprime loans jumped from 7.6% of the mortgage market to 20.1%, with black and Latino borrowers receiving a disproportionate share. This article leveraged the Home Mortgage Disclosure Act data and multinomial regression to model home-purchase mortgage lending in 2006, the peak of the housing boom. The findings expose a complicated story of race and income. Consistent with previous research, blacks and Latinos were more likely and Asians less likely to receive subprime loans than whites were. Income was positively associated with receipt of subprime loans for minorities, whereas the opposite was true for whites. When expensive (jumbo) loans were excluded from the sample, regressions found an even stronger, positive association between income and subprime likelihood for minorities, supporting the theory that wealthier minorities were targeted for subprime loans when they could have qualified for prime loans. This finding also provides another example of an aspect of American life in which minorities are unable to leverage higher class position in the same way as whites are. Contrary to previous research, model estimates did not find that borrowers paid a penalty (in increased likelihood of subprime outcome) for buying homes in minority communities.