Racial Sorting and Neighborhood Quality

In cities throughout the United States, blacks tend to live in significantly poorer and lower-amenity neighborhoods than whites. An obvious first-order explanation for this is that an individual%u2019%u2019s race is strongly correlated with socioeconomic status (SES), and poorer households can only afford lower quality neighborhoods. This paper conjectures that another explanation may be as important. The limited supply of high-SES black neighborhoods in most U.S. metropolitan areas means that neighborhood race and neighborhood quality are explicitly bundled together. In the presence of any form of segregating preferences, this bundling raises the implicit price of neighborhood amenities for blacks relative to whites, prompting our conjecture -- that racial differences in the consumption of neighborhood amenities are significantly exacerbated by sorting on the basis of race, given the small numbers of blacks and especially high-SES blacks in many cities. To provide evidence on this conjecture, we estimate an equilibrium sorting model with detailed restricted Census microdata and use it to carry out informative counterfactual simulations. Results from these indicate that racial sorting explains a substantial portion of the gap between whites and blacks in the consumption of a wide range of neighborhood amenities -- in fact, as much as underlying socioeconomic differences across race. We also show that the adverse effects of racial sorting for blacks are fundamentally related to the small proportion of blacks in the U.S. metropolitan population. These results emphasize the significant role of racial sorting in the inter-generational persistence of racial differences in education, income, and wealth.

Separate When Equal? Racial Inequality and Residential Segregation

This paper hypothesizes that segregation in US cities increases as racial inequality narrows due to the emergence of middle-class black neighborhoods. Employing a novel research design based on life-cycle variations in the relationship between segregation and inequality, we test this hypothesis using the 1990 and 2000 Censuses. Indeed, increased black educational attainment in a city leads to a significant rise in the number of middle-class black communities and segregation for older adults both in the cross-section and over time, consistent with our hypothesis. These findings imply a negative feedback loop that inhibits reductions in racial inequality and segregation over time.

An Equilibrium Model of Sorting in an Urban Housing Market

This paper introduces an equilibrium framework for analyzing residential sorting, designed to take advantage of newly available restricted-access Census microdata. The framework adds an equilibrium concept to the discrete choice framework developed by McFadden (1973, 1978), permitting a more flexible characterization of preferences than has been possible in previously estimated sorting models. Using data on nearly a quarter of a million households residing in the San Francisco Bay Area in 1990, our estimates provide a precise characterization of preferences for many housing and neighborhood attributes, showing how demand for these attributes varies with a household's income, race, education, and family structure. We use the equilibrium model in combination with these estimates to explore the effects of an increase in income inequality, the findings indicating that much of the increased spending power of the rich is absorbed by higher housing prices.

What Drives Racial Segregation? New Evidence Using Census Microdata

This paper sheds new light on the forces that drive residential segregation on the basis of race, assessing the extent to which across-race differences in other household characteristics can explain a significant portion of observed racial segregation. The central contribution of the analysis is to provide a transparent new measurement framework for understanding segregation patterns. This framework allows researchers to characterize patterns of segregation, to decompose them in meaningful ways, and to carry out partial equilibrium counterfactuals that illuminate the contributions of a variety of non-race characteristics in driving segregation. We illustrate our approach using restricted micro-Census data from the San Francisco Bay Area that provide a rich joint distribution of household and neighborhood characteristics not previously available to the research community. In contrast to findings in the prior literature, our analysis indicates that individual household characteristics can explain a considerable fraction of segregation by race, explaining almost 95% of segregation for Hispanic, over 50% for Asian, and 30% for White and Black households.Residential Segregation, Racial Segregation, Sorting, Housing Markets

Residential Segregation in General Equilibrium

This paper studies the causes and consequences of racial segregation using a new general equilibrium model that treats neighborhood compositions as endogenous. The model is estimated using unusually detailed restricted Census microdata covering the entire San Francisco Bay Area, and in combination with a rich array of econometric estimates, serves as a powerful tool for carrying out counterfactual simulations that shed light on the causes and consequences of segregation. In terms of causes, and contrasting with prior research, our GE simulations indicate that equalizing income and education across race would be unlikely to result in significant reductions in racial segregation, as minority households would sort into newly formed minority neighborhoods. Indeed, among Asian and Hispanic households, segregation increases. In terms of consequences, this paper provides the first evidence that sorting on the basis of race gives rise to significant reductions in the consumption of local public goods by minority households and upper-income minority households in particular. These consumption effects are likely to have important intergenerational implications.Segregation, General Equilibrium, Endogenous Sorting, Urban Housing Market, Locational Equilibrium, Counterfactual Simulation, Discrete Choice

An Equilibrium Model of Sorting in an Urban Housing Market: The Causes and Consequences of Residential Segregation

This paper presents a new equilibrium framework for analyzing economic and policy questions related to the sorting of households within a large metropolitan area. We estimate the model using restricted-access Census data that precisely characterize residential and employment locations for households the San Francisco Bay Area, yielding accurate measures of preferences for a wide variety of housing and neighborhood attributes across different types of household. We use these estimates to explore the causes and consequences of racial segregation in general equilibrium. Our results indicate that, given the preference structure of households in the Bay Area, the elimination of racial differences in income and wealth would significantly increase the residential segregation of each major racial group, as the equalization of income leads, for example, to the formation of new wealthy, segregated Black and Hispanic neighborhoods. We also provide evidence that sorting on the basis of race itself (whether driven by preferences or discrimination) leads to large reductions in the consumption of housing, public safety, and school quality by Black and Hispanic households.Segregation, Sorting, Housing Markets, Locational Equilibrium, Residential Choice, Discrete Choice

A Unified Framework for Measuring Preferences for Schools and Neighborhoods

This paper develops a comprehensive framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous model of residential choice to address the endogeneity of school and neighborhood attributes. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than one percent more in house prices -- substantially lower than previous estimates -- when the average performance of the local school increases by five percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood race and housing prices is due entirely to the fact that blacks live in unobservably lower quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors: in particular, we find that households prefer to self-segregate on the basis of both race and education.

Conversation Station

We built a mobile application that improves speed and personalization in conversations for people struggling with verbal communication. Many people diagnosed with autism, Down syndrome, and other disorders face daily challenges involving communication due to speech impediments. Existing solutions allow users to communicate via speech cards or typing on a keyboard. However, these solutions make tradeoffs between personalization and speed, compromising what it takes to have fluid, natural) and rewarding conversations. Our solution speeds up personalized communication by applying Machine Learning principles, Artificial Intelligence, and Natural Language Processing. Our project will predict how a user will respond based on natural language processing results of verbal input and some base-layer artificial intelligence rules) allowing the user to communicate quickly with their own voice