East-Side Story: Historical Pollution and Persistent Neighborhood Sorting

Abstract

Why are the east sides of formerly industrial cities often the more deprived? Using individual-level census data together with newly created historical pollution patterns derived from the locations of 5,000 industrial chimneys and an atmospheric model, we show that this results from the persistence of neighborhood sorting that first emerged during the Industrial Revolution when prevailing winds blew pollution eastwards. Past pollution explains up to 20% of the observed neighborhood segregation in 2011, even though coal pollution stopped in the 1970s. A quantitative model identifies the role of non-linearities and tipping-like dynamics underlying this persistence.

Widespread Race and Class Disparities in Surface Urban Heat Extremes Across the United States

“…Importantly and relatedly, longer‐run analyses over time, and linkages to more detailed labor market information are needed to better untangle present employment mechanisms that may drive these racial and ethnic disparities versus other policies (Zenou & Boccard, 2000). Within the UK, residential sorting can be traced back as far as 1817 and is sustained long after initial drivers declined (Heblich et al., 2021). In the US, in particular the legacy of redlining looms large—previous studies have shown that among 108 cities with historic redlining, 94% have higher LST in the formally redlined areas.…”

Widespread race and class disparities in surface urban heat extremes across the United States

“…Importantly and relatedly, longer-run analyses over time, and linkages to more detailed labor market information are needed to better untangle present employment mechanisms that may drive these racial and ethnic disparities versus other policies (Zenou & Boccard, 2000). Within the UK, residential sorting can be traced back as far as 1817 and is sustained long after initial drivers declined (Heblich et al, 2021). In the US, in particular the legacy of redlining looms large-previous studies have shown that among 108 cities with historic redlining, 94% have higher LST in the formally redlined areas.…”

Urban economics in a historical perspective: Recovering data with machine learning

“…Analysing the drivers of city structure leads to the question of its persistence over time, as often documented through the persistence of residential segregation related to environmental (dis)amenities. 2 Lee and Lin (2018) show how natural amenities shape neighbourhood composition within U.S. metropolitan areas; Villarreal (2014) identifies drainage conditions as a crucial factor in explaining urban development in New York; Heblich et al (2021b) show how coal-burning factories of Victorian England generated a persistent East-West gradient in neighbourhood composition. The persistence of city organisation can be overturned by large shocks to urban structure.…”

“…A recent, flourishing literature exploits historical data in order to understand the fundamental mechanisms underlying the spatial distribution of economic activity across countries, across regions or cities within countries, or across neighbourhoods within cities. Historical patterns of spatial development may indeed shed light on: the role of agricultural productivity on urban development (Matsuyama, 1992;Nunn and Qian, 2011); the long-term drivers of urban structure within and between cities (Ahlfeldt et al, 2015;Siodla, 2015;Redding and Sturm, 2016;Heblich et al, 2021b;Hornbeck and Keniston, 2017), and the specific role of transportation (Baum-Snow, 2007;Duranton and Turner, 2012;Brooks and Lutz, 2019;Heblich et al, 2021a); the effect of transportation on the spatial distribution of economic activity (Atack, 2013;Donaldson and Hornbeck, 2016;Donaldson, 2018;Campante and Yanagizawa-Drott, 2018;Pascali, 2017;Trew, 2020) and the role of distance and communication in sustaining economic exchanges (Bossuyt et al, 2001;Bakker et al, 2021;Juhász and Steinwender, 2018;Steinwender, 2018;Barjamovic et al, 2019); migration and how it shapes intergenerational mobility (Olivetti and Paserman, 2015;Abramitzky et al, 2012Abramitzky et al, , 2021bAger et al, 2019).…”