Pollution and Persistent Neighborhood Sorting



There exist no adequate data on historical pollution and data on residential sorting

are equally scarce. Our empirical analysis combines detailed pollution information

from the time of the Industrial Revolution with unique panel data at the neighborhood

level spanning nearly 200 years. Three methodological innovations help us

generate these data. First, we develop an algorithm to geo-locate industrial chimneys

from historical Ordnance Survey (OS) maps of the 70 largest metropolitan areas in

England over the period 1880-1900. 1

Second, we use the world leading modelling

system for atmospheric emissions (ADMS 5) 2 that incorporates within-city information

on terrain, wind directions, chimney dimensions, exit velocity and coal burning

temperature to predict pollution dispersion from each individual chimney. Third,

we develop a novel algorithm to overcome a shortcoming of old censuses: as data are

nested in large spatial units, such as ancient parishes in England, they are of limited

use for within-city analysis of neighborhoods. Our algorithm geo-locates entries of

the 1881 English census and matches them to low-level administrative units (for our

purpose, the 2001 Lower Super Output Areas (LSOA)). This gives us a great deal

more detail–across the 70 metropolitan areas, we observe 4,500 LSOAs versus 500

parishes, and cities like Bristol, Liverpool or Manchester are covered by about 90

LSOAs instead of 10 parishes. 3

There is a strong correlation between air pollution and the share of low-skilled

workers in 1881. A pollution differential equivalent to the one between the 10%

and 90% most polluted neighborhoods of Manchester would be associated with a

gradient of 18 percentage points in the share of low-skilled workers.

While the

spatial distribution of pollution results from the interaction of industry locations,

wind patterns and city-specific topography, we show that this correlation is robust

to the addition of a large set of controls including distance to the major public

amenities in the city (to capture location amenities), distance to waterways, elevation

(to capture the mere impact of topography), latitude, longitude, and fixed effects

at the parish or Medium Super Output Areas (to capture the variation implied by

wind patterns).

The ideal experiment to identify the causal impact of pollution on neighborhood

sorting would be to randomly locate a chimney, and compare upwind and downwind

neighborhoods at the same distance from the chimney. To get closer to this thought

experiment, we first include proxies capturing the proximity to factories. However,

conditioning on proximity to factories does not take into consideration that chimneys

1 We also consider domestic chimneys but their contribution to overall pollution is small.

2 Atmospheric Dispersion Modelling System (ADMS) models have been developed to make use

of the most up-to-date understanding of the behavior of the lower levels of the atmosphere.

3 The median LSOA in our sample covers an area of 0.3 square kilometers.


More magazines by this user
Similar magazines