The Racial Wealth Gap, Access to Credit, and Gentrification
Paper Session
Sunday, Jan. 7, 2024 8:00 AM - 10:00 AM (CST)
- Chair: Trevon Logan, Ohio State University
Who Benefits from Gentrification? Evidence from Three Decades of Amenity Data
Abstract
Gentrification in the early 21st century is categorized by young, college-educated households choosing to live closer to the city center. Theory suggests that increased demand for housing in the city and inner suburbs will elevate property values for incumbent Black and Latino families, however, persistent gaps in Black and Latino homeownership rates relative to Whites will mute house price appreciation as a mechanism for reducing inequality. This paper uses property transactions from 1990-2020 to study the timing of a house sale relative to local peaks in neighborhood home values. Our hypothesis is that households with tighter liquidity constraints will sell early in the gentrification process, and we estimate the aggregate forgone wealth that occurs from Black and Latino families selling their homes too soon.Racial Protests and Credit Access
Abstract
Do racial protests help or hurt access to credit for small businesses? This paper examines the effect of local racial demonstrations, such as Black Lives Matter (BLM) protests, and the subsequent racial justice movement following the death of George Floyd on racial disparities in the Paycheck Protection Program (PPP) loan disbursements. Using difference-in-differences, we find that local racial protests improve credit access for black business owners. We find that social media and public attention after the death of George Floyd amplified the broader BLM mission statement of racial equity, resulting in a positive moderating effect on loan amounts distributed to black owners relative to other racial-ethnic groups. Our findings show that racial implicit and explicit bias diminishes after George Floyd’s death with stronger effects in finance occupations.Analytic Approaches to Measuring the Black-White Wealth Gap
Abstract
Studies indicate that while wealth inequality is large and growing, racial wealth inequality is even larger and persistent. Yet, the scope of the discussion on racial wealth inequality is hindered, in part, due to data limitations and measurement issues. In this study we have two specific aims. First, we examine different analytic approaches to measuring the black/white wealth gap. Our analyses allow us to distinguish between estimation techniques that account for non-normal distributions, skewness, and a high concentration of negative and zero values such as quantile regression, inverse hyperbolic sine and logarithmic transformations, in addition to OLS models. We pay particular attention to how differences in wealth data from the Survey of Consumer Finances, and Panel Study of Income Dynamics, contribute to variations in the measurement of wealth inequality. Based on the outcomes from our first aim, our second aim addresses the best approach for modeling racial wealth inequality in the U.S. This section draws from a theoretical lens that emphasizes historical and policy mechanisms that have not only promoted group-based wealth accumulation for some but also, wealth loss and theft for others. We conclude with a discussion on assessing bias in methods and recommendations for best practices when analyzing wealth and measuring wealth differentials in a racialized context.Barriers to Building Wealth among High Income Black Workers
Abstract
The racial wealth gap is large and growing, yet wealth is a primary means to insure against economic shocks. We assess the financial and non-financial holdings among high income Black workers and discuss current barriers to growing wealth, while acknowledging the deep-seated root of this inequality. Using publicly available data from the Survey of Consumer Finances and the American Community Survey, combined with a restricted version of the National Financial Capabilities Study, we determine sociodemographic and geographic variation in Black wealth.Discussant(s)
Raffi García
,
Rensselaer Polytechnic Institute
Marcus Casey
,
University of Illinois-Chicago
Jamein Cunningham
,
Cornell University
Samuel Myers
,
University of Minnesota
Gregory Price
,
University of New Orleans
JEL Classifications
- J1 - Demographic Economics
- D3 - Distribution