Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application

Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naive estimates of both individual and group effects. This paper proposes a non-parametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in the canonical vertical sorting model of Epple and Platt (1998), that there is a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a non- parametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. The second part of the proposed strategy uses aggregation to develop suitable instruments for both exogenous and endogenous group attributes. Instrumenting for each individual's observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals eliminates the portion of the variation in neighborhood attributes due to sorting on unobserved individual attributes. The neighborhood effects application is based on confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks.

Community Trails in Community Context: GIS Analysis of Associations between Trail and Neighborhood Characteristics

This research examines the site and situation characteristics of community trails as landscapes promoting physical activity. Trail segment and neighborhood characteristics for six trails in urban, suburban, and exurban towns in northeastern Massachusetts were assessed from primary Global Positioning System (GPS) data and from secondary Census and land use data integrated in a geographic information system (GIS). Correlations between neighborhood street and housing density, land use mix, and sociodemographic characteristics and trail segment characteristics and amenities measure the degree to which trail segment attributes are associated with the surrounding neighborhood characteristics.

Understanding Racial Segregation: What is known about the Effect of Housing Discrimination

A central purpose of this chapter is to assess whether the available empirical evidence supports the view that current levels of housing discrimination are a significant contributor to residential segregation in U.S. cities and metropolitan areas. Through the course of this chapter, the reader will find that the empirical patterns of racial segregation in the U.S. are often inconsistent the available evidence on housing discrimination. Admittedly, strong evidence exists that both housing discrimination exists today and that housing discrimination throughout much of the Twentieth Century was central to creating the high levels of segregation that we observe in U.S. metropolitan areas today, but the appropriate policy responses may differ dramatically depending upon how these two phenomena are currently interrelated.

Racial Differences in Homeownership: The Effect of Residential Location

The rate of homeownership among African-American households is considerably lower than white households in American urban areas. This paper examines whether racial differneces in residential location outcomes are among the factors that contribute to the large racial differences in homeownership rates in major US metropolitan areas. Based on the 1985 metropolitan sample of the American Housing Survey for Philadelphia, the paper does not find any evidence that existing racial differences in residential location in Philadelphia decrease the homeownership rate among African Americans. Rather, the empirical evidence suggests that African-American residential location outcomes are associated with lower than expected racial differences in homeownership. Therefore, after controlling for neighborhood, racial differences in homeownership are larger than originally believed, and the ability of racial differences in endowments to explain hoeownership differences is more limited.

The Discontinuous Nature of Kriging Interpolation for Digital Terrain Modeling

Kriging is a widely employed method for interpolating and estimating elevations from digital elevation data. Its place of prominence is due to its elegant theoretical foundation and its convenient practical implementation. From an interpolation point of view, kriging is equivalent to a thin-plate spline and is one species among the many in the genus of weighted inverse distance methods, albeit with attractive properties. However, from a statistical point of view, kriging is a best linear unbiased estimator and, consequently, has a place of distinction among all spatial estimators because any other linear estimator that performs as well as kriging (in the least squares sense) must be equivalent to kriging, assuming that the parameters of the semivariogram are known. Therefore, kriging is often held to be the gold standard of digital terrain model elevation estimation. However, I prove that, when used with local support, kriging creates discontinuous digital terrain models, which is to say, surfaces with “rips” and “tears” throughout them. This result is general; it is true for ordinary kriging, kriging with a trend, and other forms. A U.S. Geological Survey (USGS) digital elevation model was analyzed to characterize the distribution of the discontinuities. I show that the magnitude of the discontinuity does not depend on surface gradient but is strongly dependent on the size of the kriging neighborhood.

Now You See it, Now You Don't: Why Do Real Estate Agents Withhold Available Houses from Black Customers?

Potential home buyers may initiate contact with a real estate agent by asking to see a particular advertised house. This paper asks whether an agent's response to such a request depends on the race of the potential buyer or on whether the house is located in an integrated neighborhood. We build on previous research about the causes of discrimination in housing by using data from fair housing audits, a matched-pair technique for comparing the treatment of equllay qualified black and white home buyers. However, we shift the focus from differences in the treatment of paired buyers to agent decisions concerning an individual housing unit using a sample of all houses seen during he 1989 Housing Discrimination study. We estimate a random effect, multinomial logit model to explain a real estate agent's joint decisions concerning whether to show each unit to a black auditor and to a white auditor. We find evidence that agents withhold houses in suburban, integrated neighborhoods from all customers (redlining), that agents' decisions to show houses in integrated neighborhoods are not the same for black and white customers (steering), and that the houses agents show are more likely to deviate from the initial request when the customeris black than when the customer is white. These deviations are consistent with the possibility that agents act upon the belief that some types of transactions are relatively unlikely for black customers (statistical discrimination).

The Continuing Practice and Impact of Discrimination

This chapter provides a detailed discussion of the evidence on housing and mortgage lending discrimination, as well as the potential impacts of such discrimination on minority outcomes like homeownership and neighborhood environment. The paper begins by discussing conceptual issues surrounding empirical analyses of discrimination including explanations for why discrimination takes place, defining different forms of discrimination, and the appropriate interpretation of observed racial and ethnic differences in treatment or outcomes. Next, the paper reviews evidence on housing market discrimination starting with evidence of segregation and price differences in the housing market and followed by direct evidence of discrimination by real estate agents in paired testing studies. Finally, mortgage market discrimination and barriers in access to mortgage credit are discussed. This discussion begins with an assessment of the role credit barriers play in explaining racial and ethnic differences in homeownership and follows with discussions of analyses of underwriting and the price of credit based on administrative and private sector data sources including analyses of the subprime market. The paper concludes that housing discrimination has declined especially in the market for owner-occupied housing and does not appear to play a large role in limiting the neighborhood choices of minority households or the concentration of minorities into central cities. On the other hand, the patterns of racial centralization and lower home ownership rates of African-Americans appear to be related to each other, and lower minority homeownership rates are in part attributable to barriers in the market for mortgage credit. The paper presents considerable evidence of racial and ethnic differences in mortgage underwriting, as well as additional evidence suggesting these differences may be attributable to differential provision of coaching, assistance, and support by loan officers. At this point, innovation in loan products, the shift towards risk based pricing, and growth of the subprime market have not mitigated the role credit barriers play in explaining racial and ethnic differences in homeownership. Further, the growth of the subprime lending industry appears to have segmented the mortgage market in terms of geography leading to increased costs of relying on local/neighborhood sources of mortgage credit and affecting the integrity of many low-income minority neighborhoods through increased foreclosure rates.

Fast Algorithms Using Minimal Data Structures for Common Topological Relationships in Large, Irregularly-spaced Topographic Data Sets

Digital terrain models (DTM) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregu- larly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly-space data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.

Redlining, the Community Reinvestment Act, and Private Mortgage Insurance

This paper examines whether neighborhood racial or income composition influences a lender's treatment of mortgage applications. Recent studies have found little evidence of differential treatment based on either the racial or income composition of the neighborhood, once the specification accounts for neighborhood risk factors. This paper suggests that lenders may favor applicants from CRA-protected neighborhoods if they obtain Private Mortgage Insurance (PMI) and that this behavior may mask lender redlining of low income and minority neighborhoods. For loan applicants who are not covered by PMI, this paper finds strong evidence that applications for units in low-income neighborhoods are less likely to be approved, and some evidence that applications for units in minority neighborhoods are less likey to be approved, regardless of the race of the applicant. This pattern is not visible in earlier studies because lenders appear to treat applications from these neighborhoods more favorably when the applicant obtains PMI.

Which School Attributes Matter? The Influence of School District Performance and Demographic Composition on Property Values

Increasing levels of segregation in American schools raises the question: do home buyers pay for test scores or demographic composition? This paper uses Connecticut panel data spanning eleven years from 1994 to 2004 to ascertain the relationship between property values and explanatory variables that include school district performance and demographic attributes, such as racial and ethnic composition of the student body. Town and census tract fixed effects are included to control for neighborhood unobservables. The effect of changes in school district attributes is also examined over a decade long time frame in order to focus on the effect of long run changes, which are more likely to be capitalized into prices. The study finds strong evidence that increases in percent Hispanic has a negative effect on housing prices in Connecticut, but mixed evidence concerning the impact of test scores on property values. Evidence is also found to suggest that student test scores have increased in importance for explaining housing prices in recent years while the importance of percent Hispanic has declined. Finally, the study finds that estimates of property tax capitalization increase substantially when the analysis focuses on long run changes.

Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes

We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence of social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.