Exploring the Heterogeneous Effects of Ecological and Structural Factors on Intensifying Neighborhood Economic Polarization

ABSTRACT

Scholars have recently reported the rise of neighborhoods at the extremes of the income distribution—both affluent and poor neighborhoods—and the loss of middle- or mixed-income neighborhoods. As the majority of studies on neighborhood change have focused on the cyclical process of neighborhood change, especially for poor or disadvantaged neighborhoods, this study contributes to the literature by exploring the mechanisms of affluent and poor neighborhoods’ persistence in their economic status over time. First, this research descriptively shows that affluent and poor neighborhoods within the 100 largest U.S. metropolitan statistical areas (MSAs) as of 2010 were likely to retain their economic status during the 2000s, whereas other, relatively middle-income neighborhoods presented more diverse economic transitions. Second, by employing multilevel regression models, this research finds that several ecological and structural factors heterogeneously affect affluent and poor neighborhoods. The results suggest that affluent neighborhoods tend to respond more effectively to the decline process generated by ecological, economic, and structural forces than poor neighborhoods do. This study contributes to the urban neighborhood change scholarship by integrating different theoretical perspectives from the social science literature to understand how neighborhoods at the extremes of the income distribution are likely to persist in their economic status.

KEYWORDS:

• neighborhood change
• neighborhood inequality
• multilevel logistic regression

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes

1. Following previous studies (e.g., Ellen & O’Regan, 2008; Jun, 2013), census tracts with more than 50% of their populations institutionalized are not considered in the study. Moreover, I exclude tracts with populations of fewer than 500 residents to avoid the possibility of biased results stemming from the small amount of data (Galster & Mincy, 1993; Jun, 2013; Wei & Knox, 2014).

2. High-end jobs include occupations from industries of finance, insurance, real estate, rental and leasing, professional, scientific, management, administrative, and waste management services.

3. One-year lag variables are used to consider possible mutual causal relationships. Although this approach does not solve the endogeneity problem, Galster and Mincy (1993) assumed that 1-year lag variables at the MSA level can be used for predicting the next year’s neighborhood economic change. The data on jobs are obtained from 2000 SF3 Sample Data and 2005–2009 ACS 5-year estimates at the county level distributed by the U.S. Census Bureau. I then aggregate county-level data to the corresponding MSAs.

4. I used Stata’s xtmelogit algorithm to create a multilevel logistic model.

5. I also conducted an additional analysis focusing on neighborhoods whose percentile of relative income is above 85% and 75% (affluent neighborhoods) and below 25% and 15% (poor neighborhoods) to see whether the results vary by the classification of neighborhoods. The results show very similar results to the quintile approach. To be specific, affluent neighborhoods entered the redevelopment stage earlier than poor neighborhoods, and the coefficient of the poverty rate was associated with experiencing economic prosperity in affluent neighborhoods. Moreover, the results suggest that structural forces intensify neighborhood economic polarization. For example, the growth of high-end jobs was positively associated with the odds of experiencing economic prosperity in affluent neighborhoods, whereas it was negatively associated with the odds of experiencing economic prosperity in poor neighborhoods. The growth of poverty rates at the MSA level was negatively associated with the odds of experiencing economic prosperity in poor neighborhoods, whereas the coefficient was not statistically significant in affluent neighborhoods.

6. To provide a general picture of the effects of ecological and structural factors on neighborhood change for the entire neighborhood distribution, I additionally conduct a multilevel logistic regression analysis for neighborhoods in the second, third, and fourth quintiles of the income distribution, which is provided in Table A2. The model for the fourth quintile (60–80%) presents very similar results compared with those for affluent neighborhoods, whereas the models for the third and second quintiles (40–60% and 20–40%, respectively) show similar results to those for the model of poor neighborhoods. For the dependent variable, if fourth-quintile neighborhoods in 2000 became affluent neighborhoods in 2010, it was coded as 1, and 0 otherwise. If third-quintile neighborhoods in 2000 became either affluent or fourth-quintile neighborhoods, this was coded as 1, and 0 otherwise. If second-quintile neighborhoods in 2000 became affluent, fourth-, or third-quintile neighborhoods, this was coded as 1, and 0 otherwise.

Additional information

Notes on contributors

Jongho Won

Jongho Won received his PhD in Urban and Environmental Planning and Policy from the University of California, Irvine. His research focuses on the political and structural barriers that hinder disadvantaged groups from accessing high-opportunity living environments. His current research includes a spatial analysis of income inequality in metropolitan regions, the neighborhood quality of publicly assisted housing developments, and the use of public transit by low-income tenants in subsidized housing.