Gentrification and Changes in the Spatial Structure of Labor Demand

Gentrification and Changes in the Spatial Structure
of Labor Demand
Nathaniel Baum-Snow, Brown University
Daniel Hartley, Federal Reserve Bank of Chicago
September 30, 2015
[Preliminary and incomplete. Please do not cite without permission from the authors]
The views expressed are those of the authors and do not necessarily represent the views of the
Federal Reserve Bank of Chicago or of the Board of Governors of the Federal Reserve System
or its staff.
1
1
Introduction
In the decades following WWII, the central regions of most U.S. metropolitan areas were in decline.
Between 1960 and 2000, the aggregate central city population share in the 100 largest metropolitan
areas fell from 49 percent to 24 percent while employment share declined from 0.61 to 0.34 (BaumSnow, 2014). Sometime after 1980, however, the populations of many large cities began to stabilize
and central areas of large cities began to rebound. Since 2000, the downtown areas of most central
cities have experienced remarkable demographic change. Central neighborhoods in most cities have
experienced rapid income growth and new residential housing construction. Areas near central
business districts in many cities have experienced faster than average population growth since 1980
(Lee & Lin, 2014). This paper systematically characterizes the roles of various aspects of changes
in demographic supply and neighborhood demand conditions to explain the changing fortunes of
central neighborhoods in cities. Using the structure of a standard model of neighborhood choice
which we will estimate with highly disaggregated decennial census tabulations, we will assess the
welfare consequences of gentrification for various demographic groups, with a particular focus on
assessing such consequences for incumbents in gentrifying neighborhoods.
Table 1 reports statistics describing neighborhood change within 5 km of central business districts of 118 large U.S. metropolitan areas since 1970. Column 1 shows that the fraction of the
population in these central areas declined in each decade after 1970, though the rate of decline
has slowed and central area population levels have declined only slightly in each decade since
1980. Despite these continued population declines, evidence in the remaining columns indicate that
downward demand shifts for central neighborhoods reversed sometime between 1980 and 2000. Each
column shows the fraction of central area population living within a census tract within the top
tercile of the CBSA distribution of the variable indicated in the header, with terciles recalculated in
each year. That all fractions are below one-third indicates that these central areas have remained
more disadvantaged than average CBSA neighborhoods during our entire study period. However,
results in Columns 3-4 indicate that central neighborhoods experienced rapid relative growth in
their white and college educated populations after 2000. Results in Column 5 indicate that income declines from the 1970s reversed during the 1980s, with very strong relative income growth
in central neighborhoods since 2000. The higher levels in Column 4 than Column 3 indicate that
central neighborhoods have always been relatively attractive to more educated minorities. Column
6 shows results for an equally weighted index of socioeconomic status (SES), constructed using
standardized versions of the variables used to construct Columns 2-4.1 With low housing supply
elasticity, greater housing and land demand for central neighborhoods by higher SES individuals
1 While
greater fraction white does not directly indicate higher socioeconomic status, it has been found to indirect
indicate this. Altonji, Duraszelski and Segal (2000) review evidence that blacks have lower wealth than whites
conditional on income. Neal & Johnson (1996) find that blacks have lower levels of pre-labor market preparation
conditional on education.
2
can manifest itself mostly as population turnover rather than overall population growth.
Evidence in Table 1 represents a benchmark against which we compare counterfactual neighborhood compositions that remove changes in various components of residential demand for central
neighborhoods since 1980. These results indicate that central neighborhood population and SES
status would have declined even more rapidly had demand by the college educated, whites and
higher income households not increased after 1980. Increases in the fraction of the population
college educated explain most of the shifts in the racial composition of central area populations
and a bit less than half of the change in education levels of these areas, with the rest explained by
changing neighborhood choices by the college educated. Our joint examination of race and income
yields strong evidence that income growth in these central neighborhoods has primarily been driven
by increases in demand by higher income households to livere there. Future revisions of this paper
will additionally investigate the roles of shifts in family compositions and the age structure of the
population, along with shifts in demand amongst narrowly defined demographic groups within these
broad classifications.
The remarkable post-2000 demographic change in central neighborhoods comes in the context
of convergence in racial composition and income across neighborhoods in most CBSAs since 1980.
However, consistent with evidence in Chetty et al. (2014) using individual level data, there exists
considerable variation across CBSAs in the prevalence of such convergence. In particular, we provide evidence that more rapidly growing metropolitan areas experienced more rapid neighborhood
convergence in the 1980s and 2000-2010 period. Such neighborhood convergence, and downtown
gentrification in particular, may actually harm poor residents on average, as they are more likely
to be renters and thus be faced with financing the capital gains of their landlords as land values
increase. Busso, Gregory & Kline (2013) discuss such a possibility in the similar case of neighborhood improvements that come from government investment. Below we develop a theory that will
be used in future versions of this paper to evaluate such welfare gains.
A better understanding of the drivers of neighborhood demographic change may also provide
clues about some reasons for the growth in income inequality nationwide since 1980. Gould, Lavy
& Paserman (2011) and Damm & Dustmann (forthcoming) provide independent evidence of the
effects of neighborhoods on long-run outcomes. To the extent that neighborhood quality influences
outcomes, it is important to better isolate the mechanisms that have driven changes in neighborhood inequality. In particular, it will be important to understand the extent to which gentrifying
neighborhoods retain incumbent residents (who can benefit from positive spillovers) or price them
out.
A complete characterization of changes in demand for neighborhoods requires accounting for
housing supply conditions in addition to neighborhood demand conditions. As residential demand
for a neighborhood increases, housing supply conditions regulate the price increases which determine
changes in population quantities. Therefore, isolating the magnitudes of neighborhood demand
3
shifts requires a model that explicitly incorporates price adjustments. Future versions of the paper
will adapt standard models of neighborhood choice developed in Bayer, Ferreira & McMillan (2007),
Galiani, Murphy & Pantano (2014) and Bayer et al. (2015) to aggregate data to recover structural
estimates of demand parameters for each neighborhood by various demographic groups. Critical
to this exercise is the consideration of general equilibrium effects in which demand shifts partially
determine home price changes, which end up redistributing some population to closely substitutible
neighborhoods. For our purposes, this is an important extension to the more partial equilibrium
treatments of neighborhood choice in the existing literature.
As is laid out in Couture & Handbury (2015), outward demand shifts for central neighborhoods
may be driven by some combination of increases in their consumer amenities and nearby labor
demand. A common narrative describes how post-WWII declines in central city amenities more
valued by higher income residents including safety, public school quality (Baum-Snow & Lutz,
2011) and public capital may have ceased or reversed in many cities by 1990. In addition, changing
racial attitudes and a slow reversal of the Great Migration, which initially promoted white flight
(Boustan, 2010), may have be important. On the labor demand side, industries with more educated
workers have remained relatively centralized and have experienced greater than average national
growth rates. Services, transportation, communications and public utilities, public administration
and finance, insurance and real estate are the 1-digit industries that are more centralized than
average. Besides public administration, these were the fastest growing 1-digit industries nationally
1960-2000. These same industries also enjoy the highest productivity benefits of density (BaumSnow, 2014). These facts are in line with Kain’s (1992) classic analysis indicating that the shifting
composition of central city labor demand toward skilled workers had already begun in the 1970s,
thereby exacerbating the potential mismatch between the locations of unskilled jobs and unskilled
workers. Our results indicate that cities with more robust overall demand growth experienced
greater neighborhood convergence and more prevalent recent downtown gentrification. While overall
CBSA demand growth has driven some downtown revitalization, cities with downtown employment
specialized in growing industries have experienced particularly strong residential demand growth.
Concurrent with increases in the importance of skill intensive industries more likely to locate
in urban areas, the composition of the working age population has changed markedly since 1970.
Women’s labor force participation has increased, families are having fewer children, and the age of
women at childbirth is increasing. All of these demographic changes have pushed up households’
value of commuting time relative to their demand for living space and may have represented quantitatively important forces for residential centralization given the greater centralization of jobs than
residences. Moreover, the rising age at first marriage means that a greater share of the population
is less constrained about living closer to work. Future versions of this paper will investigate the
potential importance of all of these channels for driving recent downtown revitalization.
This paper proceeds as follows. Section 2 presents our gentrification measures, the data, and
4
some additional descriptive evidence on the changing fortunes of downtown areas and trends in
neighborhood inequality. Section 3 evaluates potential explanations for changes in neighborhood
inequality and examines the roles of central neighborhoods. Section 4 lays out a methodology for
constructing counterfactual neighborhood compositions and explores the results of various counterfactual exercises. Section 5 discusses a full neighborhood choice model that will be estimated in
future versions of the paper, allowing us to more carefuly quantify changes in demand for neighborhoods by different demographic groups and the consequences of gentrification for incumbent
residents. Finally, Section 6 concludes.
2
Characterizing Neighborhood Change
We aim to construct intuitive measures of neighborhood change that capture demand shifts for
living in each neighborhood going back at least to 1980 and can be constructed using data available
at the census tract level. Depending on the local elasticity of housing supply, outward demand
shifts must manifest themselves as some combination of increases in housing prices, population and
the socioeconomic status of residents. Evidence in Table 1 shows that most central neighborhoods,
whose changes we are most interested in understanding, did not experience population growth but
did experience marked improvements in the socioeconomic status of their residents. We thus view
changes in the fraction white, fraction of adults with a college degree and mean household income
as useful indicators of changes in residential demand for a neighborhood.2 While far from an ideal
measure, fraction white proves particularly useful as available data allows us to construct tract racial
compositions under many different counterfactual scenarios, as is explored in detail below in Section
4. While measures constructed from tract home values and rents represent a valuable additional
source of information about neighborhood change, they are not as straightforward to construct
consistently or interpret. We thus primarily delay our discussion of such measures to Section 4,
after having developed a conceptual framework that shows how to extract useful information from
them.
We construct a summary measure of neighborhood change using the three individual demographic measures discussed above. This summary measure for tract i is the average number of
standard deviations tract i is away from its mean in each year for each of these components. We
call this equally weighted tract z-score the socioeconomic status (SES) index. For tract i in CBSA
j in year t and variables indexed by k the SES index is calculated as
SESijt =
k
− y kjt
1 X yijt
,
k
3
σjt
k
2 Future versions of the paper will consider median individual income or earnings rather than mean household
income.
5
k
where y kjt and σjt
are calculated with tract population weights. While we will also experiment
with using the first principal component of these same three underlying variables, we prefer the
equally weighted z-score approach as it doesn’t mechanically assign more weight to a variable only
because it has more variation and we think that all three measures indexed by k are roughly equally
important indicators of neighborhood status.
We also use our three demographic measures and the SES index to generate summary measures
of changes in neighborhood inequality for each CBSA since 1980. The process for doing so resembles
that in Chetty et al.’s (2014) summary measures of intergenerational income mobility, but as applied
to census tracts over time instead of parent-child pairs. In particular, we calculate correlations
between CBSA demeaned outcomes between year t and t-10 or 1980, applying tract population
weights in the base year. Correlations of 1 indicate no change in neighborhood inequality on
average while correlations of less than 1 indicate neighborhood convergence. Chetty et al. (2014)
and Lee & Lin (2014) use percentile ranks in each year rather than demeaned outcomes as a basis for
describing intergenerational mobility and neighborhood popoulation change respectively. However,
our analysis benefits from distinguishing neighborhoods that experienced small changes from those
with large changes in their outcomes (relative to CBSA means), even if they had the same changes
in rank.3
Figure 1 depicts four measures of neighborhood change in the Chicago CBSA between 1980 and
2010, allowing for visualization of trends in neighborhood inequality. We calculate demeaned share
white (Panel A), college graduate share (Panel B) log household income (Panel C) in each tract in
1980 and 2010, weighting by tract population. These demand indicators are graphed against each
other in a scatterplot, with 45 degree and regression lines indicated. Both of these lines pass through
(0,0) in each panel by construction. Green dots represent tracts within 5 km of the CBD whereas
orange dots represent other Chicago CBSA tracts. Panel D shows results using our overall SES
index of neighborhood quality. Regression slopes of less than 1, seen for log mean tract household
income, tract share white and the composite SES index, indicate that Chicago neighborhoods have
experienced convergence in these dimensions. The slopes of these regression lines are our 1980-2010
neighborhood change measures for Chicago. Points above a regression line that are far to the left
of a 1980 mean represent gentrifying census tracts.
Figure 1 reveals much heterogeneity in neighborhood change in Chicago 1980-2010, with our
three SES status measures clearly capturing distinct things. Chicago tracts experienced divergence
on average in fraction college but convergence in the other measures. Results for share white in
Panel A are closest to evidence that has been documented in the literature to date. The masses of
points at the bottom left and top right of Panel A represent large concentrations of stable minority
and white census tracts respectively. The relatively large number of tracts along the right edge
3 We
experimented with additional summary measures of CBSA neighborhood change that are allowed to depend
on initial tract conditions.
6
of the graph at almost 100 percent white in 1980 and ending up less than 70 percent white may
have experienced tipping (Card, Mas & Rothstein, 2008). But a handful of tracts went in the
other direction between 1980 and 2010, seen in the upper left area of the graph. These largely
minority tracts in 1980 that gained white share much faster than the typical Chicago tract are
almost exclusively within 5 km of the CBD. Indeed, all but 3 of the tracts within 5 km of the CBD
that were less than 80 percent white in 1980 experienced increases in white share between 1980 and
2010, even though share white decreased on average. Such downtown area gentrification is clearly
visible for the other measures as well in Figure 1, with green dots clustered in the upper left area
of each panel.
Figure 2 contains analogous graphs depicting changes in Chicago tract SES indexes over each
decade of our study period. It shows that Chicago experienced a small amount of neighborhood
convergence in each decade 1970-2010. Green dots clustered on both sides of the regression line to
the left of 0 in Panels A and B but only above the line in Panels C and D indicate the gentrification
that began during the 1990s in Chicago. In Section 3, we document statistically that such patterns
of neighborhood change near CBDs applies not just to Chicago, but is pervasive across medium
and large metropolitan areas, and that poor tracts near CBDs turned around on average only after
1990.
2.1
Data Construction
We primarily use 1970-2010 decennial census data and the 2008-12 American Community Survey
(ACS) data tabulated to the tract level for this analysis. Central to our investigation is the need for
joint distributions of population by race, education, income, age and family structure across census
tracts. To recover as many of these joint distributions in the most disaggregated form possible, we
make use of both summary tape file (STF) 3 and 4 census tabulations. We also use information
about family structure and age by race from STF1 data from the 2010 census. (Because the 2010
census did not collect information about income or education, we must rely in the 5 year ACS
data for these tract distributions.) The STF4 data is necessary to incorporate trends in income
distribution and family structure by race into the analysis. All census tracts are normalized to year
2000 geographies using census bureau reported allocation factors. We include regions of all year
2008 definition metropolitan areas (CBSAs) that were tracted in 1970 and had a population of at
least 250,000, leading to a sample of 118 CBSAs.4 Much of the analysis weights each tract such that
each CBSA is weighted equally. Future versions of the paper will supplement this information with
census tabulated commuting patterns from 2000 and panel data from Equifax describing changes in
the composition and creditworthiness of households in census tracts. The Data Appendix provides
more detail about data construction.
4 100%
of the 2000 definition tract must have been tracted in 1970 to be in our sample.
7
Figure 3a shows a map of the 118 CBSAs in sample. Figure 3a shades the CBSAs by the share
of central area residents living in a top tercile census tract in 1980, for which tracts are ordered
by the SES index. Notice that the preponderance of metro areas are shaded dark green, indicating
that their central areas are lagging far behind the rest of the CBSA. The two notable exceptions
are New York and Santa Barbara which had central areas that were much more prosperous than
their CBSA remainders. Figure 3b shows the same map but for 2010. Here we see many CBSAs
that moved across the 0.33 threshold, meaning their central areas have a higher SES index than
the average neighborhood for the CBSA. These include many large educated cities we think of as
having experienced rapid recent gentrification, but with some additional cities as well. Figure 4
provides a more detailed picture of the geography of our central areas by only showing the area of
the Midwest near Chicago. In Figure 4 the tracts in our CBSA sample that are greater than 5km
from the CBD are shaded blue while those within 5km of the CBD are shaded red.
2.2
Patterns of Neighborhood Change
Table 2 shows the fraction of the population within 5 km of the CBD in a typical CBSA living
in tracts moving more than 20 percentile points or 1/2 a standard deviation of the CBSA tract
distributions of our four baseline measures of neighborhood socioeconomic status. These numbers
are calculated weighting by tract share of CBSA population in the base year. Commensurate with
evidence in Table 1, each of our four measures indicate that central area tracts were on balance in
decline during the 1970s. Results in Panel D show that central neighborhoods’ declines reversed
sometime in the 1990s, when 3.1 percent of the central area population moved up at least 20
percentile points, relative to 2.5 percent in rapidly declining central tracts. Similarly, 5.3 percent of
this population lived in tracts moving up at least 1/2 a standard deviation relative to 3.4 percent
living in tracts moving down this much. This increase in the SES index of central tracts during
the 1990s was mostly driven by income gains. As in Table 1, evidence in Table 2 shows that the
resurgance of central areas really took off between 2000 and 2010. During this period, 7.0 percent of
central population lived in tracts moving up 20 percentile points relative to only 1.9 percent living
in tracts moving down in the typical CBSA.
Figure 5 summarizes neighborhood changes in household incomes across all 118 CBSAs in our
sample over each decade in our sample period. It reports the 10th, 50th and 90th percentiles of
decadal changes in each 5 percentile bin of base year household income distributions. Slopes of
regression lines indicate the average amount of neighborhood household income convergence across
CBSAs over the indicated decade. Figure 6 presents analogous plots using the SES index. Evidence
in Figures 3 and 4 indicates that neighborhoods in the typical CBSA experienced convergence in
both household income and the SES index in each decade since 1970 except household income for
the 2000-2010 period. However, there is a lot of variation across neighborhoods, with the largest
8
variation experienced by neighborhoods in the tails of the base year distributions. That is, the most
unstable neighborhoods are the ones that are either very distressed or very well off. Examination of
individual dots to the left of each panel provides information about how much poor neighborhoods
have rebounded in each decade.
3
Drivers of Neighborhood Change
We now characterize variation in neighborhood convergence across CBSAs and asses the extent
to which these differences are explained by housing and residential demand conditions. Using our
CBSA indices of changes in neighborhood inequality, we investigate which types of cities experienced
greater neighborhood convergence in each decade since 1970.
Table 3 presents information about the distribution of our indices of neighborhood convergence across CBSAs. Each column for each period reports results from a CBSA level regression
of the neighborhood change index using the measure at top on a constant, various demeaned base
year CBSA characteristics listed in the notes and the change in CBSA log employment over the
decade expressed in standard deviation units for some decades. Coefficients on control variables
are not statistically significant in all but a few cases. Normalizations of control variables and
∆ ln(Employment) to be mean 0 allows for interpretation of the coefficient on the constant to
be the average index of neighborhood convergence across CBSAs in the indicated decade. We
instrument for ∆ ln(Employment) with a Bartik (1991) type industry shift-share measure. This
instrument is constructed by interacting the 1-digit industrial composition of employment in each
CBSA in the relevant base year with national employment growth rates in each industry to generate
a predicted change in employment for each CBSA.5 The idea is to isolate demand shocks for living in
CBSA that are driven by national trends in industry composition rather than factors that could be
correlated with unobservables driving neighborhood change. Decades in which ∆ ln(Employment)
is not included in regressions exhibit insufficient first stage power.6
Table 3 features a number of interesting results. Neighborhood racial composition has been
converging more and more rapidly over time. The 1970s experienced stable neighborhood racial
composition, despite the fact that this decade experienced rapid ”white flight” from cities to suburbs. The average white share neighborhood change index was 0.98 in the 1980s and 0.87 in the
1990s. This evidence is consistent with that in Cutler, Glaeser & Vigdor (1999) and Glaeser & Vigdor (2012) who document that racial segregation peaked in 1970 and has declined in every decade
since. Negative coefficients on ∆ ln(Employment) indicate that more rapidly growing CBSAs ex5
P That is, we construct the Bartik instrument for CBSA j that applies to the period t − 1 to t as: Bartikjt =
k Sjkt−1 ln(empkt /empkt−1 ), where Sjkt−1 is the fraction of employment in CBSA j that is in industry k at time
t − 1 and empkt is national employment in industry k at time t.
6 We also experimented with using an alternative Bartik style instrument constructed using national trends in wages
by industry. This instrument yields similar results. The Data Appendix further explains instrument construction.
9
perienced more rapid convergence in neighborhood racial compositions.
In contrast to racial compositions, most CBSAs experienced neighborhood divergence in fraction college graduate on average. However, this divergence did abate over the course of our sample
period. Neighborhood convergence in fraction college was also somewhat stronger in more rapidly
growing CBSAs. In contrast to the other two measures, neighborhood income convergence was
strongest in the 1970s and has been declining since. In the 1970s, the index of household income
convergence was 0.88 on average across CBSAs, rising to 1.01 in the 2000-2010 period. But as with
the other measures, more rapidly growing CBSAs experienced more rapid neighborhood convergence. Indeed, in each decade the effect of overall growth on neighborhood convergence is strongest
when such convergence is measured in terms of household income. Taken together, the SES index
results in the final column of Table 3 are roughly an average of the results for components in the
other three columns.
Evidence in Table 4 documents that demand growth in areas near CBDs only became established during the 1990s and is concentrated in the poorest neighborhoods. Moreover, it shows that
downtown areas disproportionately benefitted when CBSAs experienced stronger economic growth
overall, and particularly so if downtown industries were doing well.
Each column in each panel of Table 4 presents results from a separate tract level regression of
the change in the SES index on 5 km CBD distance ring indicators cbddisdij , log distances to natural
amenities ln(amendism ) indexed by m and indicators for distances from top quartile SES index
tracts in 1970 topdisdij , with each tract weighted by its CBSA population share. We control for
distance to natural amenities to account for the possibility that CBDs are more likely to be located
near such anchors of high income neighborhoods (Lee & Lin, 2013). We control for distance to top
quartile tracts in order to exclude the possibility that tracts near CBDs gentrified simply because
of expansions of nearby high income neighborhoods (Guerrieri, Hartley & Hurst, 2013). Because
we want to distinguish reasons for which poor tracts gentrify from reasons for which richer tracts
change, we run these regressions separately for each 1970 defined CBSA tercile of the SES index.
We weight by the tract’s fraction of CBSA population. The estimation equation is as follows:
4
P
∆Sij = ρj +
αd cbddisdij + α1b cbddis1ij Bartikj + α1s cbddis1ij Spatbartikj
d=1
+
4
P
d=1
βd topdisdij +
P
m
δm ln(amendism
ij ) + εij
To understand heterogeneity in neighborhood change for those within the inner CBD distance ring
of 0-5 km, we interact this indicator with the same Bartik variable used in Table 3 and an analogous
version built only using the industrial composition of employment within 5km of the CBD as of
year 2000 explained below, both standardized into separate z-scores. Because we do not observe the
change in employment within 5 km of CBDs, we cannot use it as a regressor directly, for which we
10
would instrument with the spatial Bartik instrument. For this reason and to maintain consistency
across the two Bartik demand shifters, we estimate the reduced form described by (??).7
The goal of the spatial Bartik variable Spatbartikj is to isolate labor demand shocks that hit the
CBD area harder than other areas of a CBSA. The overall fraction of employment near the CBD at
P
F emp nationwide is given by F emp = k Skemp fkemp , where Skemp is the share of overall employment
in industry k and fkemp is the fraction of industry k employment near the CBD. We think of
fkemp as being driven by fundamental attributes of the production process like the importance of
agglomeration spillovers to TFP. Therefore, we predict the change in the fraction of employment
near the CBD to be
Spatbartikjt =
X
emp
emp
Sjkt−1
fk2000
ln(empkt /empkt−1 ).
k
Results in Table 4 demonstrate the reversal of fortunes experienced by many low SES tracts
after 1990. Panel A shows that on average bottom tercile tracts near the CBD were declining during
the 1970s and 1980s, with a clear reversal of this decline during the 1990s and in the 2000-2010
period which was unique to low SES tracts. Results in Panel B show that middle and high tercile
tracts near CBDs were consistently on the decline or stable respectively after 1990.
As we explore further throughout the remainder of the paper, this downtown gentrification could
have been driven changes in consumer amenity conditions or labor demand conditions. Interactions
with the two Bartik variables explore the potential importance of shifts in labor demand. Evidence
in Table 4 indicates that bottom tercile neighborhoods near CBDs in CBSAs with positive employment growth overall did better in the 1990s but worse in the 1970s. That is, CBSA economic growth
actually hurt central areas of cities during the 1970s. Overall CBSA growth had no statistically
significant effect for other types of central neighborhoods in any time period studied.
In contrast, central areas of CBSAs with an industry composition that was more likely to be
near CBDs did better in lots of cases, and never worse. Amongst all types of census tracts, central
tracts did better in the 2000-2010 period in CBSAs with CBD oriented industry compositions, with
middle tercile tracts in such CBSAs also improving more during the 1980s. This is evidence that
the types of production activities taking place in central areas of CBSAs have been driving at least
some of the residential resurgance in these areas.
Table 5 presents regressions analogous to those in Table 4, except that an index of tract housing
value growth rates is used as the dependent variable. In particular, the dependent variable in
Table 5 is calculated as the residuals from a regression of log mean tract housing value on various
characteristics of owner occupied housing and CBSA fixed effects. Because positive demand shifts
for neighborhoods will be reflected as some combination of increases in quantities of residents,
7 We are forced to maintain year 2000 industrial composition for the ”Spatial Bartik” instrument because of
limits to data availability in other years. We use the census journey to work tabulations to calculate this industrial
composition.
11
potential income of residents and housing prices, we view evidence of house value growth and/or
increase in SES index for neighborhoods as signs of outward demand shifts. Indeed, CBSAs with
high housing supply elasticities (Saiz, 2010) may have had some neighborhoods with large outward
demand shifts but experienced only small relative changes in housing costs in such neighborhoods.
However, because they have the smallest availability of developable land, central areas of cities are
likely to have such supply elasticities that are amongst the lowest in any given CBSA.
Evidence in Table 5 shows increases in home values in bottom tercile tracts near CBDs but
declines in home values in middle and upper tercile tracts near CBDs. It is well known that housing
prices are negatively serially correlated at decadal time scales (Baum-Snow & Marion, 2009), so the
results for the bottom and top terciles are not surprising. Coefficients on the Bartik interaction are
not significant in any instance. However, coefficients on the spatial Bartik interaction are positive
and significant for the bottom tercile in 2000-2010, the middle tercile in 1980-1990 and the top
tercile for 1980-1990 and 1990-2000. That is, home prices did increase in some decades in central
areas of CBSAs with CBD oriented employment mixes. Overall, evidence in Table 5 is broadly
consistent with the evidence in Table 4, that poor central neighorhoods have seen a resurgance, and
especially those in CBSAs with CBD oriented employment mixes.
4
Counterfactual Neighborhood Compositions
To separate out the roles of CBSA level demographic change from changes in individual groups’
neighborhood demands, we carry out decompositions of the sources of neighborhood change along
the lines proposed by DiNardo, Fortin & Lemieux (1996). In particular, we calculate indices of
neighborhood change in two types of counterfactual environments. First, we hold the shares of
CBSA population in various demographic groups fixed over time, but allow neighborhood choices
of these demographic groups to shift as in equilibrium to evaluate the extent to which changes in the
overall demographics of CBSAs have driven neighborhood change. That is, these counterfactuals
adjust the composition of CBSA demographic shares to resemble those in 1980, but not allocations
of people in any particular observed demographic group across tracts. Second, we allow the CBSA
demographic shares to evolve as they have in the data since 1980, but allocate these quantities to
each census tract using the fractions that each demographic group made up of the tract in 1980.
This second exercise allows us to determine the extent to which changes in the demand for each
census tract by each observed demographic groups, rather than overall demographic shifts, have
driven neighborhood change for different locations.
12
4.1
Construction of Counterfactual Neighborhoods
We observe the joint population distribution fjt (i, r, e) of race, r, and education, e, and joint
household distribution fjt (i, r, y) of race and income, y, across census tracts i in CBSA j in year
t. Given the structure of available data, we are forced to evaluate counterfactual distributions in
only two demographic dimensions at a time.8 To clarify notation, we present how we calculate
counterfactual tract populations based on joint distributions of race (white, black and, other)
indexed by r and education (youth, less than high school, high school, some college, college or more)
re
indexed by e across tracts. Denote Njt
as the total number of people in group re in CBSA j at time
t and Njt as the total population of CBSA j at time t. The density function of CBSA demographics
P
is fjt (r, e) = i fjt (i, r, e). Crucially, we treat CBSA-level allocations fjt (r, e) and populations Njt
as exogenous. It is important to note that while aggregate population does not influence conclusions
drawn from these mechanical counterfactuals, it will matter once we incorporate housing supply.
4.1.1
Adjustments for Changes in Demographic Shares
For the first set of counterfactuals, we revert the share of the CBSA population in each r and e cell
back to 1980, according to the following equation:
fbjt (i, r, e) = fjt (i|r, e)fj8 (r, e).
(1)
This is akin to “quantity re-weighting” in the DiNardo, Fortin & Lemieux (1996) setup. fbjt (i, r, e)
measures the fraction of people of each race/education combination that would live in each tract if
the overall CBSA fraction of each race/education combination did not change after 1980.
One difficulty when considering the counterfactual, fbjt (i, r, e), is that it does not separately
reveal the extent to which changes were due to shifts in the racial or education composition of
the population. To better understand which element matters more, we also carry out such shares
adjustments for race only conditional on education. For example, the counterfactual distribution
allowing education shares to evolve but holding CBSA race shares constant is
r
fbjt
(i, r, e) = fjt (i|r, e)fj8 (r|e)fjt (e).
(2)
r
Comparing statistics about neighborhood changes generated using fbjt
(i, r, e) and fbjt (i, r, e) sheds
light on the extent to which changes in the shares of the population in each education group
influenced neighborhood composition while holding overall racial shares constant.
8 Future
versions of the paper will additionally make use of information about family and age structure of tract
populations by race.
13
4.1.2
Adjustments for Changes in Neighborhood Choices
The other type of counterfactual distributions we calculate tells us the extent to which changes in
neighborhood choices by particular demographic groups have driven gentrification. The most general version of this counterfactual is to reallocate year t total population in each demographic group
to 1980 locations (using the fraction of that each demographic group contributed to the population
of the census tract in 1980) but maintaining the contemporaneous CBSA-level demographic shares.
fejt (i, r, e) = fjt (r, e)fj8 (i|r, e).
(3)
Simultaneous implementation of demographic shares and neighborhood choices adjustments
described in (1) and (3) yields fj8 (i, r, e), the actual density function from 1980. Therefore, we
can think of these counterfactual experiments as together fully describing changes in neighborhood
compositions between 1980 and year t.
4.1.3
Implementation for Race-Income
In order to calculate counterfactual distributions of race and income, data limitations force us to
focus on households and families rather than individuals. We observe the number of families by
race in various income bins in 1980 and households by race in income bins in later census years.
To make income categorizations consistent over time, we calculate quintiles of the national family
income distribution in 1980 and the national household distributions in later decades using the 5%
census micro data public use sample. Using this information, we calculate the fraction of families
or households by race in each quintile of the national distribution in each year. That is, fjt (i, r, y)
is the fraction of households in CBSA j and year t who are of race r, in national income quintile
y and living in tract i for t > 1980. For t = 1980, this is the fraction of families instead. We
have assembled the necessary data, and we are currently switching to using households for the 1980
income measures instead of families.
In addition, future versions of the paper will also analyze roles of changes in the joint distributions of race-family structure and race-age in understanding central area neighborhood change.
4.2
Counterfactual Results
Table 6 shows how the share of CBSA population living within 5km of CBDs would have changed
under the various counterfactual scenarios laid out in the prior sub-section. These numbers are
calculated analogously to those in Table 1 Panel B Column 2, except using counterfactual data
sets. Panel A uses data with tract level education-race joint distributions whereas Panel B uses
data with tract household income-race joint distributions to construct counterfactual data sets.
Column 1 in Panel A repeats data on the evolution of the fraction of CBSA populations and
14
households living within 5 km of CBDs from Table 1 Panel B Column 2. These numbers are a
benchmark against which we compare counterfactuals using population data. Column 1 in Panel B
shows an analogous benchmark calculated using data on families in 1980 and households in other
years. Because the way in which income data is tabulated, we are forced to use households or
families rather than individuals, and thus, this is the appropriate benchmark. The reason that the
first entry in Panel B Column 1 is positive is that non-family households are more likely to be
located near CBDs and these units are excluded from the family counts. Future versions of the
paper will resolve this problem by using household income throughout.
Overall, adjusting CBSA shares to resemble those in 1980 has a sufficiently small effect on the
results so as not to be quantitatively important for understanding changes in shares of people and
households living within 5 km of CBDs. Adjusting CBSA population shares for race conditional on
education (Column 2 in Panel A) yields slightly more rapid declines in central areas as these areas
are less white than average. That is, the increasing fraction of minority populations has resulted
in a small boost for populations in central neighborhoods of CBSAs. However, maintaining 1980
education-race CBSA shares yields almost no difference between central area counterfactual and
actual population growth. The rise in shares of CBSA populations that are college graduates has
counteracted the rise in minority share to cancel out. This means that changing neighborhood
choices must have driven the patterns seen in Panel A Column 1. Indeed, results in Column 4
confirm that holding neighborhood choices constant for 1980 and allowing shares to change over
time generates slight counterfactual growth in central area populations. That is, the decline in
the white population and increase in the college educated population approximately offset to keep
central neighborhoods stable in this counterfactual world in which neighborhood choices don’t
change.
When examining counterfactual race-income population distributions (Table 6 Panel B), similar
results hold, though it is clear that the rise in the fractions of both high and low income households
has helped support downtown populations. Absent the shifts in the income distribution, the fraction
of families/households living in downtowns would have declined by 3.8 percentage points rather than
the 2.4 percentage points they actually declined between 1980 and 2010 (last row, Panel B Column
3). Counterfactual allocations holding neighborhood choices constant in Column 4 reveal an even
starker pattern. Here we see a counterfactual increase of 0.036 relative to an actual decline of
0.024. Evidence in Table 8 discussed below indicates that this reflects reductions in the propensity
of nonwhites and the poor to live in downtown areas. Had these groups remained in downtown
areas, their populations would have grown robustly - because they are a growing fraction of the
overall population.
Table 7 examines downtown neighborhood racial compositions in the same counterfactual environments as in Table 6. Benchmark statistics in the data are listed in Column 1 with analogous
statistics under various counterfactual scenarios in the remaining columns. All statistics for counter-
15
factual populations are reported using tract tercile designations that have been recalculated using
the counterfactual data. Results in Column 2 examine how racial compositions of central area
tracts would look if shares are adjusted for race only conditional on education (Panel A) or income
(Panel B). Results indicate that absent changes in the racial composition in the population, central
areas would have experienced more rapid increases in their white populations, except during the
1980s. This reflects the fact that conditional on income, whites chose to live in these neighborhoods
at higher rates over time. Comparison of results in Columns 3 and 4 in Panel A show that the
full increase in fraction white can be accounted for by the fact that college graduates increased
as a fraction of the population, and college graduates are more likely to be white. In particular,
holding the white population at 1980 shares conditional on education generates a counterfactual 1.6
percentage point increase in the share of near-CBD residents living in the whitest third of neighborhoods within the CBSA 1980-2010, whereas holding both education and racial shares constant
generates a counterfactual 1980-2010 drop of 0.2 percentage points in the mweasure. This means
that increases in the college educated population outweighed declines in the white population to
account for its full 1980-2010 increase. It is important to note that all of this phenomenon is driven
by these demographic trends in the 1980s and 1990s. Results in Column 4 show that about 50% of
the 1980-2010 growth in the white population in central areas can be explained by whites choosing
to live in these neighborhoods at higher rates than in 1980.
Panel B examines the roles of race and household income. Here we see a much larger role
for changes in the composition of the population across income groups than we saw for changes
in racial or education composition. Evidence in Column 2 reveals a small role for shifts in the
neighborhood choices of whites conditional on income group, with 1980-2010 counterfactual declines
of 1.5 percentage points relative to a benchmark of 1.9 percentage points. However, evidence
in Column 3 is striking. It shows that reductions in the fraction of the population in income
groups disproportionately located in central areas of CBSAs has worked strongly against growth
of fraction white in these areas in each decade 1980-2010. Moreover, results in Column 4 shows
that virtually the entire change in fraction white in central neighborhoods can be explained by the
greater propensity for higher income households to choose to live in these neighborhoods. Because
the results showing changes in choices by race conditional on income have not been an important
driver of neighborhood change, it appears that income rather than race is what really matters here.
Higher income people of all races have been choosing to live in downtowns in greater numbers.
Results in Table 8 examine how education and income compositions of central area tracts would
look under the same set of counterfactual scenarios. Results in Panel A Column 2 indicate that
about 60 percent of the increase in central area population living in the most educated areas
of CBSAs is due to the fact that the fraction of college graduates in the population increased
(counterfactual 1980-2010 growth of 0.020 versus 0.048). Results in Panel A Column 3 indicate
that changes in neighborhood choices amongst those in race-education cells accounts for a slightly
16
greater fraction of the increase in the propensity for the college educated to live in downtown areas
(counterfactual growth of only 0.016). Taken together, this evidence indicates roughly equally sized
roles for demographic change and changes in neighborhood choices of college graduates and whites
for generating more gentrified central neighborhoods.
Results in Table 8 Panel B indicate that changes in the income distribution have worked against
the income growth in downtown areas. Holding the race-income distribution as it was in 1980,
the fraction of central area populations living in top income tercile tracts would have risen by
9.7 percentage points rather than 6.6 percentage points between 1980 and 2010. Instead, we see in
Column 3 that changing neighborhood choices are driving the icnome growth. Holding these choices
at their 1980 states, we see 0 counterfactual 1980-2010 income change in central neighborhoods.
5
A Model for Interpretation
Here we develop a neighborhood choice model in the spirit of Berry, Levinsohn and Pakes (1995)
that will facilitate recovery of estimates of elements of changes in household demand for individual
neighborhoods by household type in future drafts of the paper. The model demonstrates how to
use information about the fraction of CBSA residents in various demographic categories in each
tract in each year as an indication of demand by each type for these tracts. That is, the model
shows how to make use of conditional choice probabilities (CCPs) as in Bayer, McMillan, Murphy
& Timmins (2015). One common challenge faced in demand estimation is in how to handle the
endogeneity of prices, in this case housing prices. We have the benefit of multiple years of data,
which allows us to recover sufficient information about the supply side of the housing market to
effectively endogenize house prices in the model. That is, the model will explicitly incorporate
the existence of upward sloping housing supply (Saiz, 2010) in each neighborhood, which pushes
back against population growth in neighborhoods with the most rapidly rising residential demand,
redistributing these households elsewhere in the CBSA.
The indirect utility of household s of type h residing in census tract i is modeled as follows:
v shi = β0h Zi + β1h pi + β2h dhi + δXsh + ξi + εshi = vhi + εshi
In this expression, Zi is observed neighobrhood characteristics, pi is the price of one unit of housing
services in tract i, Xsh is household characteristics, dshi is commuting distance, ξj is unobserved
neighborhood characteristics and εshi is an i.i.d. random utility shock distributed extreme value
Type I. We think of this being a long-run equilibrium where moving costs are negligible. This setup
delivers the following population shares of household type h in each census tract i.
exp(vhi )
πhi = P
0
i0 exp(vhi )
17
Inverting this expression, we solve for vhi up to a scale from conditional choice probabilities:
!
vhi = ln πhi − ln
X
exp(vhi0 )
i0
This suggests the possibility of recovering estimates of β0h Zi + β2h dhi + ξi given that we observe
πhi and house prices with some structure on the data generating process for house prices. One way
of restating the question of why gentrification has occurred is to ask whether we can understand
changes in πij because of changes in Zi , dhi and ξi holding parameters constant, or whether we
need to shift parameters over time to justify patterns in the data. For ease of notation, we have
suppressed CBSA and time subscripts.
To model housing supply, we only impose that the supply function is constant over time in each
tract. That is, Hi = Ai pθi i . To close the model, we impose the equilibrium condition that supply
P
equals demand in the housing market: Hi =
Nh πhi . Because Nh is exogenous, it is possible to
h
simultaneously recover information about supply elasticities θi (assuming they are fixed over time)
and the combination β0h Zi + β2h dhi + ξi , which could change over time. As in Bayer, McMillan,
Murphy & Timmins (2014), we will then be able to recover estimates of β0h and β2h in a second
step of estimation.
5.1
Using the Model to Construct Counterfactuals
With only information on conditional distributions like f (i|r, e) and total CBSA populations by
group Njre , we have all the information we need to recover estimates of β0h Zi + β2h dhi + ξi for each
tract/household type combination.
5.2
The Welfare Consequences of Gentrification
Estimates from the model will be used to evaluate the welfare consequences of gentrification for
households in different demographic groups. The estimated model will allow us to assess the extent
to which welfare changes would have been different had demand for central neighborhoods by higher
income households, college educated individuals and whites not increased since 1980.
6
Conclusions
To be completed.
18
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20
Table 1: Trends in Downtown Population and Demographic Composition
Population Within 5 km of a CBD
Total
CBSA Share
Tercile Criterion
(1)
(2)
Share of Pop Within 5km of CBD that Lives in a Top CBSA Tercile Tract
Fraction White
Frac College Ed Mean HH Income
SES Index
(3)
(4)
(5)
(6)
Panel A: Levels
1970
1980
1990
2000
2010
19,382,696
17,332,137
16,973,575
16,967,954
16,846,052
0.237
0.190
0.167
0.149
0.136
0.138
0.140
0.125
0.125
0.151
0.251
0.268
0.271
0.270
0.315
0.133
0.093
0.110
0.117
0.153
0.179
0.154
0.165
0.168
0.208
Panel B: Decadal Changes
1970-1980
-2,050,559
-0.047
0.002
0.016
-0.040
-0.025
1980-1990
-358,562
-0.023
-0.015
0.003
0.017
0.012
1990-2000
-5,621
-0.018
0.000
-0.001
0.007
0.003
2000-2010
-121,902
-0.013
0.026
0.045
0.036
0.040
1980-2000
-486,085
-0.053
0.011
0.048
0.060
0.054
Note: Numbers are calculated using data at the census tract level in each year. For Columns 2-6, each tract is weighted by tract
share of CBSA population, such that each CBSA is equally weighted. Similar patterns exist for those living within the largest city in
each CBSA.
Table 2: Share of Population within 5km of CBD
in Tract Changing by at Least
20 Percentile Points
up
down
1/2 Standard Deviation
up
down
Panel A: Fraction White
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
8.3%
5.1%
6.9%
9.3%
9.2%
11.9%
8.3%
5.7%
5.4%
5.5%
11.8%
6.8%
9.4%
13.5%
25.3%
13.8%
9.8%
9.3%
9.0%
19.8%
Panel B: Fraction College Educated
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
6.9%
4.8%
4.1%
9.3%
10.1%
8.7%
5.0%
4.8%
3.9%
3.8%
9.8%
6.4%
5.2%
12.5%
19.7%
7.8%
6.7%
6.3%
5.3%
14.4%
Panel C: Average Income
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
1.7%
5.2%
5.3%
8.3%
8.5%
9.2%
2.1%
2.4%
3.4%
3.5%
12.9%
9.4%
13.8%
15.5%
24.1%
14.8%
12.6%
6.2%
11.8%
20.3%
Panel D: SES Index
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
2.8%
2.9%
3.1%
6.8%
7.0%
7.2%
3.2%
2.5%
2.0%
1.9%
5.1%
3.5%
5.3%
9.1%
20.0%
8.6%
3.9%
3.6%
3.4%
13.2%
Notes: Numbers are calculated analogously to those in Table 1. Each tract is
weighted by its share of CBSA population.
Table 3: CBSA Demand Shifts and Neighborhood Inequality
Period
Fraction
White
Inequality Criterion
Fraction
Mean HH
College Ed
Income
SES
Index
1970-1980 Constant
1.001
(0.014)
1.114
(0.008)
0.883
(0.017)
0.869
(0.008)
1980-1990 ∆Ln(Employment),
standard devs.
Constant
-0.080
(0.032)
0.976
(0.011)
-0.036
(0.013)
1.109
(0.005)
-0.115
(0.055)
0.910
(0.025)
-0.038
(0.013)
0.963
(0.004)
1990-2000 Constant
0.934
(0.012)
1.056
(0.006)
0.896
(0.006)
0.946
(0.005)
2000-2010 ∆Ln(Employment),
standard devs.
Constant
-0.043
(0.023)
0.869
(0.011)
-0.009
(0.011)
1.002
(0.005)
-0.082
(0.025)
1.006
(0.009)
-0.032
(0.011)
0.963
(0.004)
1980-2000 ∆Ln(Employment),
standard devs.
Constant
-0.123
(0.053)
0.773
(0.021)
-0.085
(0.026)
1.184
(0.012)
-0.155
(0.064)
0.846
(0.026)
-0.091
(0.023)
0.849
(0.007)
Notes: Each column in each block reports coefficients from a separate regression of our CBSA
gentrification index on the indicated variables and share married, share of population that are
children, share college and share white in the base year. ∆ln(Employment) is expressed in
standard deviation units and is instrumented with a Bartik quantity instrument, as explained in
the text. Only periods with sufficiently strong first stages have reported employment
coefficients. Reported coefficients on the constant can be interpreted as the mean index across
CBSAs. Each regression has 118 observations and are weighted by initial year tract population
share of the CBSA. First stage F statistics are 19.99 (1980-1990), 22.19 (2000-2010) and 20.87
(1980-2010).
Table 4: Patterns in SES Index Gentrification of Tracts within 5 km of CBDs
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
0.053
(0.017)
0.043
(0.017)
0.004
(0.017)
12,245
0.097
0.063
(0.021)
0.007
(0.019)
0.034
(0.016)
12,236
0.062
0.081
(0.038)
0.100
(0.040)
0.042
(0.031)
11,979
0.136
-0.066
(0.017)
0.021
(0.020)
0.015
(0.027)
12,310
0.087
-0.022
(0.019)
0.006
(0.019)
0.046
(0.021)
12,304
0.068
-0.162
(0.046)
0.098
(0.046)
0.082
(0.041)
11,865
0.127
-0.033
(0.020)
-0.024
(0.017)
0.037
(0.023)
12,322
0.064
0.009
(0.020)
-0.025
(0.022)
0.048
(0.028)
12,316
0.048
-0.012
(0.044)
-0.002
(0.049)
0.088
(0.050)
11,990
0.089
Panel A: Bottom Tercile Neighborhoods
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
-0.081
(0.023)
-0.053
(0.022)
0.036
(0.021)
12,004
0.108
-0.047
(0.019)
0.003
(0.014)
0.008
(0.009)
11,992
0.073
Panel B: Middle Tercile
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
-0.167
(0.026)
-0.017
(0.018)
-0.015
(0.018)
11,882
0.106
-0.074
(0.022)
-0.001
(0.017)
0.036
(0.018)
11,879
0.065
Panel C: Top Tercile
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
-0.048
(0.041)
-0.058
(0.041)
0.068
(0.042)
12,016
0.120
0.011
(0.023)
0.003
(0.023)
0.019
(0.021)
12,009
0.070
Notes: Each column in each panel reports results from a separate regression of the tract SES gentrification index on
indicated variables and indicators for 5-10, 10-15 and 15-20 km from the CBD and 0-5, 5-10, 10-15 and 15-20 km from the
nearest highest quartile SES index tract as of 1970. The Bartik variables are standardized to be mean 0 and standard
deviation 1. Regressions are weighted by share of 1970 tract population in 1970 CBSA population.
Table 5: Patterns in Regression Adjusted Owner-Occupied Housing Cost of Tracts within 5 km of CBDs
1970-1980
1980-1990
1990-2000
2000-2010
1980-2010
-0.002
(0.015)
0.006
(0.013)
0.018
(0.011)
11,640
0.089
0.042
(0.023)
0.010
(0.017)
0.031
(0.017)
11,265
0.057
0.060
(0.029)
0.023
(0.028)
0.070
(0.024)
10,813
0.090
-0.045
(0.012)
0.014
(0.012)
0.018
(0.014)
12,177
0.106
-0.021
(0.014)
-0.014
(0.014)
0.019
(0.013)
12,084
0.038
-0.107
(0.024)
0.032
(0.023)
0.054
(0.022)
11,605
0.057
0.023
(0.035)
-0.002
(0.014)
0.030
(0.011)
12,080
0.126
-0.041
(0.015)
-0.003
(0.014)
0.002
(0.016)
12,040
0.094
-0.093
(0.031)
-0.003
(0.024)
0.107
(0.053)
11,596
0.092
Panel A: Bottom Tercile Neighborhoods
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
-0.133
(0.025)
-0.028
(0.018)
0.014
(0.015)
9,426
0.117
0.015
(0.019)
-0.011
(0.013)
0.020
(0.015)
11,217
0.092
Panel B: Middle Tercile
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
-0.049
(0.020)
-0.008
(0.022)
0.012
(0.016)
10,377
0.047
-0.038
(0.016)
0.004
(0.013)
0.026
(0.012)
11,719
0.038
Panel C: Top Tercile
1(< 5km to CBD)
Employment Bartik * 1(< 5km to CBD)
Spatial Employment Bartik * 1(< 5km to CBD)
N
R-Squared
0.037
(0.030)
0.014
(0.025)
0.000
(0.019)
10,373
0.071
-0.075
(0.034)
-0.014
(0.014)
0.076
(0.051)
11,687
0.078
Notes: Each column in each panel reports results from a separate regression of the tract owner occupied housing cost index
on indicated variables and indicators for 5-10, 10-15 and 15-20 km from the CBD and 0-5, 5-10, 10-15 and 15-20 km from the
nearest highest quartile SES index tract as of 1970. The Bartik variables are standardized to be mean 0 and standard
deviation 1. Regressions are weighted by share of 1970 tract population in 1970 CBSA population. The housing cost index is
formed from the residuals of a regression of log mean owner occupied home value on housing unit structure characteristics
(number of units in building, number of bedrooms in unit, age of building) of the tract and CBSA fixed effects.
Table 6: Counterfactual Changes in Fraction of Population Share within 5 km of CBDs
Data
(1)
1980 Shares
Race | X
All
(2)
(3)
1980 Choices
All
(4)
Panel A: X=Education
1980-1990
1990-2000
2000-2010
1980-2010
-0.023
-0.018
-0.013
-0.053
-0.024
-0.024
-0.013
-0.061
-0.020
-0.021
-0.011
-0.052
0.001
0.005
0.001
0.006
0.001
-0.014
-0.025
-0.038
0.029
-0.007
0.015
0.036
Panel B: X = Income
1980-1990
1990-2000
2000-2010
1980-2010
0.010
-0.020
-0.014
-0.024
0.025
-0.027
-0.017
-0.019
Notes: Each entry in Column (1) is constructed with actual data. Remaining entries are
calculated using counterfactual data. Entries in Columns (2) maintain the 1980 CBSA
fraction of the population in each cell of the race conditional on education or income
distribution. Entries in Column (3) maintain the 1980 CBSA fraction in the raceXeducation
or raceXincome joint distribution. Entries in Column (4) maintain the 1980 neighborhood
choices in each raceXeducation or raceXincome cell. The race conditional on income
distribution and raceXincome joint distribution use families in 1980 and households in
subsequent years as the underlying unit of observation, thereby requiring a different
baseline.
Table 7: Counterfactual Changes in Fraction of Central Area Population Living in a
Top Fraction White Tercile Tract
Data
(1)
1980 Shares
Race | X
All
(2)
(3)
All 1980 Choices
(4)
Panel A: X=Education
1980-1990
1990-2000
2000-2010
1980-2010
-0.015
0.000
0.026
0.011
-0.031
0.017
0.030
0.016
-0.040
0.004
0.033
-0.002
0.012
-0.005
-0.001
0.006
0.023
0.030
0.043
0.097
0.000
0.015
-0.023
0.000
Panel B: X=Income
1980-1990
1990-2000
2000-2010
1980-2010
-0.042
-0.001
0.024
-0.019
-0.046
0.005
0.026
-0.015
Notes: Columns are analogous to those in Table 6, except they describe the change in
the fraction of the population of central area tracts which live in a top tercile fraction
white neighborhood. The top block examines the role of education composition and
the bottom block examines the role of income composition.
Table 8: Counterfactual Changes in Central Area Population Living in a Top
Tercile Tract
Data
(1)
All 1980 Shares
(2)
All 1980 Choices
(3)
Panel A: Fraction of Central Area Population within Top Education Tercile
1980-1990
1990-2000
2000-2010
1980-2010
0.003
-0.001
0.045
0.048
-0.013
0.001
0.032
0.020
0.009
-0.001
0.009
0.016
Panel B: Fraction of Central Area Population within Top Income Tercile
1980-1990
1990-2000
2000-2010
1980-2010
0.018
0.010
0.038
0.066
0.023
0.030
0.043
0.097
0.000
0.015
-0.023
0.000
Notes: Columns are analogous to those in Table 6, except they describe the
change in the fraction of the population of central area tracts which live in a top
tercile neighborhood, as defined by education composition in Panel A and income
composition in Panel B.
−.8
−.6
−.4
−.2
0
Share White Relative to Mean in 1980
Best Linear Fit
Tracts within 5km of CBD
.2
Share with College Degree Relative to Mean in 2010
−.5
0
.5
1
Share White Relative to Mean in 2010
−.8
−.6
−.4
−.2
0
.2
Panel A
Panel B
−.2
45 degree line
Tracts more than 5km from CBD
0
.2
.4
.6
Share with College Degree Relative to Mean in 1980
Best Linear Fit
Tracts within 5km of CBD
45 degree line
Tracts more than 5km from CBD
−4
−3
Mean Z−Index in 2010
−2
0
2
4
Panel D
Income Relative to Mean in 2010
−2
−1
0
1
2
Panel C
.8
−3
−2
−1
0
Income Relative to Mean in 1980
Best Linear Fit
Tracts within 5km of CBD
1
45 degree line
Tracts more than 5km from CBD
−4
−2
0
Mean Z−Index in 1980
Best Linear Fit
Tracts within 5km of CBD
Figure 1: Chicago Tract Dynamics 1980 - 2010.
2
4
45 degree line
Tracts more than 5km from CBD
Mean Z−Index in 1990
−2
−1
0
1
2
−3
−3
Mean Z−Index in 1980
−2
−1
0
1
2
3
Panel B
3
Panel A
−3
−2
−1
0
1
Mean Z−Index in 1970
Best Linear Fit
Tracts within 5km of CBD
2
3
−3
45 degree line
Tracts more than 5km from CBD
−2
−1
0
1
Mean Z−Index in 1980
Best Linear Fit
Tracts within 5km of CBD
3
45 degree line
Tracts more than 5km from CBD
Mean Z−Index in 2010
−1
0
1
2
−2
−3
−3
−2
Mean Z−Index in 2000
−1
0
1
2
3
Panel D
3
Panel C
2
−3
−2
−1
0
1
Mean Z−Index in 1990
Best Linear Fit
Tracts within 5km of CBD
2
3
45 degree line
Tracts more than 5km from CBD
−3
−2
−1
0
1
Mean Z−Index in 2000
Best Linear Fit
Tracts within 5km of CBD
Figure 2: Chicago Tract Dynamics Z-Index. By decade.
2
3
45 degree line
Tracts more than 5km from CBD
Figure 3a: Sample CBSAs shaded by 1980 fraction of central area population living in a top tercile SES
index tract
Figure 3b: Sample CBSAs shaded by 2010 fraction of central area population living in a top tercile SES
index tract
!
Milwaukee
Madison
Grand Rapids
Lansing
!
!
!
Kenosha
!
Rockford
!
!
Chicago
Gary
!
!
Davenport
!
Fort Wayne
Peoria
!
Figure 4: Chicago area close-up of map showing sample. Black dots indicate CBD location.Tracts in sample are shown in blue
if farther than 5km from CBD and red if within 5km of CBD.
Income Relative to CBSA Mean in 1990
−1.5 −1
−.5
0
.5
1
1.5
Panel B
Income Relative to CBSA Mean in 1980
−1.5 −1
−.5
0
.5
1
1.5
Panel A
−1
−.5
0
.5
Income Relative to CBSA Mean in 1970
45 Degree Line
1
−1
Regression Line
−.5
0
.5
Income Relative to CBSA Mean in 1980
45 Degree Line
Slope = 0.82 (0.04)
1
Regression Line
Slope = 0.80 (0.04)
Income Relative to CBSA Mean in 2010
−1.5 −1
−.5
0
.5
1
1.5
Panel D
Income Relative to CBSA Mean in 2000
−1.5 −1
−.5
0
.5
1
1.5
Panel C
−1
−.5
0
.5
Income Relative to CBSA Mean in 1990
45 Degree Line
Slope = 0.90 (0.01)
1
−1
Regression Line
−.5
0
.5
Income Relative to CBSA Mean in 2000
45 Degree Line
Slope = 1.00 (0.01)
Figure 5: Tract Income Dynamics. CBSAs given Equal Weight
Regression Line
1
Panel B
Z−Index Relative to CBSA Mean in 1980
−1.5 −1 −.5 0 .5 1 1.5
Z−Index Relative to CBSA Mean in 1990
−1.5 −1 −.5 0 .5 1 1.5
Panel A
−2
−1
0
1
Z−Index Relative to CBSA Mean in 1970
45 Degree Line
2
−2
Regression Line
−1
0
1
Z−Index Relative to CBSA Mean in 1980
45 Degree Line
Slope = 0.86 (0.01)
2
Regression Line
Slope = 0.96 (0.00)
Panel D
Z−Index Relative to CBSA Mean in 2000
−1.5 −1 −.5 0 .5 1 1.5
Z−Index Relative to CBSA Mean in 2010
−1.5 −1 −.5 0 .5 1 1.5
Panel C
−2
−1
0
1
Z−Index Relative to CBSA Mean in 1990
45 Degree Line
Slope = 0.95 (0.00)
2
−2
Regression Line
−1
0
1
Z−Index Relative to CBSA Mean in 2000
45 Degree Line
Slope = 0.96 (0.00)
Figure 6: Tract Z-Index Dynamics. CBSAs given Equal Weight
Regression Line
2