gentrification and changes in the spatial structure of ... · women’s labor force participation...

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

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[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 Please do not cite without permission from the authors]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 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 (Baum-

Snow, 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 dis-

tricts 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 in-

come 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

1While greater fraction white does not directly indicate higher socioeconomic status, it has been found to indirectindicate this. Altonji, Duraszelski and Segal (2000) review evidence that blacks have lower wealth than whitesconditional on income. Neal & Johnson (1996) find that blacks have lower levels of pre-labor market preparationconditional on education.

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can manifest itself mostly as population turnover rather than overall population growth.

Evidence in Table 1 represents a benchmark against which we compare counterfactual neigh-

borhood 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 pro-

vide 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 neighbor-

hood 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 neighbor-

hood 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

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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 (Baum-

Snow, 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 quan-

titatively 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

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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 counter-

factual 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 neigh-

borhoods 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 demo-

graphic 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 =1

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∑k

ykijt − ykjtσkjt

,

2Future versions of the paper will consider median individual income or earnings rather than mean householdincome.

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where ykjt and σkjt 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

3We experimented with additional summary measures of CBSA neighborhood change that are allowed to dependon initial tract conditions.

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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.

4100% of the 2000 definition tract must have been tracted in 1970 to be in our sample.

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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

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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 conver-

gence 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 sub-

urbs. 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 & Vig-

dor (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 ex-

5That 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.6We 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.

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perienced more rapid convergence in neighborhood racial compositions.

In contrast to racial compositions, most CBSAs experienced neighborhood divergence in frac-

tion 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 conver-

gence. 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 estab-

lished 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:

∆Sij = ρj +4∑d=1

αdcbddisdij + αb1cbddis

1ijBartikj + αs1cbddis

1ijSpatbartikj

+4∑d=1

βdtopdisdij +

∑mδm ln(amendismij ) + 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

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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

F emp nationwide is given by F emp =∑k S

empk fempk , where Sempk is the share of overall employment

in industry k and fempk is the fraction of industry k employment near the CBD. We think of

fempk 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 =∑k

Sempjkt−1fempk2000 ln(empkt/empkt−1).

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 employ-

ment 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,

7We are forced to maintain year 2000 industrial composition for the ”Spatial Bartik” instrument because oflimits to data availability in other years. We use the census journey to work tabulations to calculate this industrialcomposition.

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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.

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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)

indexed by e across tracts. Denote Nrejt 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

is fjt(r, e) =∑

ifjt(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:

f̂jt(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. f̂jt(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, f̂jt(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

f̂rjt(i, r, e) = fjt(i|r, e)fj8(r|e)fjt(e). (2)

Comparing statistics about neighborhood changes generated using f̂rjt(i, r, e) and f̂jt(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.

8Future versions of the paper will additionally make use of information about family and age structure of tractpopulations 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 gen-

eral 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.

f̃jt(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 distribu-

tions 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 envi-

ronments 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 neigh-

borhoods 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:

vshi = β0hZi + β1hpi + β2hdhi + 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.

πhi =exp(vhi)∑i′ exp(vhi′)

17

Inverting this expression, we solve for vhi up to a scale from conditional choice probabilities:

vhi = lnπhi − ln

(∑i′

exp(vhi′)

)

This suggests the possibility of recovering estimates of β0hZi + β2hdhi + 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 = Aipθii . To close the model, we impose the equilibrium condition that supply

equals demand in the housing market: Hi =∑h

Nhπhi. Because Nh is exogenous, it is possible to

simultaneously recover information about supply elasticities θi (assuming they are fixed over time)

and the combination β0hZi + β2hdhi + 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 Nrej , we have all the information we need to recover estimates of β0hZi +β2hdhi + 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

Total CBSA ShareTercile Criterion Fraction White Frac College Ed Mean HH Income SES Index

(1) (2) (3) (4) (5) (6)

1970 19,382,696 0.237 0.138 0.251 0.133 0.1791980 17,332,137 0.190 0.140 0.268 0.093 0.1541990 16,973,575 0.167 0.125 0.271 0.110 0.1652000 16,967,954 0.149 0.125 0.270 0.117 0.1682010 16,846,052 0.136 0.151 0.315 0.153 0.208

1970-1980 -2,050,559 -0.047 0.002 0.016 -0.040 -0.0251980-1990 -358,562 -0.023 -0.015 0.003 0.017 0.0121990-2000 -5,621 -0.018 0.000 -0.001 0.007 0.0032000-2010 -121,902 -0.013 0.026 0.045 0.036 0.0401980-2000 -486,085 -0.053 0.011 0.048 0.060 0.054Note: Numbers are calculated using data at the census tract level in each year. For Columns 2-6, each tract is weighted by tractshare of CBSA population, such that each CBSA is equally weighted. Similar patterns exist for those living within the largest city ineach CBSA.

Table 1: Trends in Downtown Population and Demographic Composition

Panel A: Levels

Panel B: Decadal Changes

Share of Pop Within 5km of CBD that Lives in a Top CBSA Tercile TractPopulation Within 5 km of a CBD

up down up down

1970-1980 8.3% 11.9% 11.8% 13.8%1980-1990 5.1% 8.3% 6.8% 9.8%1990-2000 6.9% 5.7% 9.4% 9.3%2000-2010 9.3% 5.4% 13.5% 9.0%1980-2010 9.2% 5.5% 25.3% 19.8%

1970-1980 6.9% 8.7% 9.8% 7.8%1980-1990 4.8% 5.0% 6.4% 6.7%1990-2000 4.1% 4.8% 5.2% 6.3%2000-2010 9.3% 3.9% 12.5% 5.3%1980-2010 10.1% 3.8% 19.7% 14.4%

1970-1980 1.7% 9.2% 12.9% 14.8%1980-1990 5.2% 2.1% 9.4% 12.6%1990-2000 5.3% 2.4% 13.8% 6.2%2000-2010 8.3% 3.4% 15.5% 11.8%1980-2010 8.5% 3.5% 24.1% 20.3%

1970-1980 2.8% 7.2% 5.1% 8.6%1980-1990 2.9% 3.2% 3.5% 3.9%1990-2000 3.1% 2.5% 5.3% 3.6%2000-2010 6.8% 2.0% 9.1% 3.4%1980-2010 7.0% 1.9% 20.0% 13.2%

Notes: Numbers are calculated analogously to those in Table 1. Each tract isweighted by its share of CBSA population.

Panel D: SES Index

20 Percentile Points 1/2 Standard Deviation

Table 2: Share of Population within 5km of CBDin Tract Changing by at Least

Panel C: Average Income

Panel B: Fraction College Educated

Panel A: Fraction White

Fraction Fraction Mean HH SESPeriod White College Ed Income Index

1970-1980 Constant 1.001 1.114 0.883 0.869(0.014) (0.008) (0.017) (0.008)

1980-1990 ∆Ln(Employment), -0.080 -0.036 -0.115 -0.038 standard devs. (0.032) (0.013) (0.055) (0.013)Constant 0.976 1.109 0.910 0.963

(0.011) (0.005) (0.025) (0.004)

1990-2000 Constant 0.934 1.056 0.896 0.946(0.012) (0.006) (0.006) (0.005)


(0.011) (0.005) (0.009) (0.004)


(0.021) (0.012) (0.026) (0.007)

Inequality Criterion

Notes: Each column in each block reports coefficients from a separate regression of our CBSAgentrification index on the indicated variables and share married, share of population that arechildren, share college and share white in the base year. ∆ln(Employment) is expressed instandard deviation units and is instrumented with a Bartik quantity instrument, as explained inthe text. Only periods with sufficiently strong first stages have reported employmentcoefficients. Reported coefficients on the constant can be interpreted as the mean index acrossCBSAs. Each regression has 118 observations and are weighted by initial year tract populationshare of the CBSA. First stage F statistics are 19.99 (1980-1990), 22.19 (2000-2010) and 20.87(1980-2010).

Table 3: CBSA Demand Shifts and Neighborhood Inequality

1970-1980 1980-1990 1990-2000 2000-2010 1980-2010

1(< 5km to CBD) -0.081 -0.047 0.053 0.063 0.081(0.023) (0.019) (0.017) (0.021) (0.038)

Employment Bartik * 1(< 5km to CBD) -0.053 0.003 0.043 0.007 0.100(0.022) (0.014) (0.017) (0.019) (0.040)

Spatial Employment Bartik * 1(< 5km to CBD) 0.036 0.008 0.004 0.034 0.042(0.021) (0.009) (0.017) (0.016) (0.031)

N 12,004 11,992 12,245 12,236 11,979R-Squared 0.108 0.073 0.097 0.062 0.136

1(< 5km to CBD) -0.167 -0.074 -0.066 -0.022 -0.162(0.026) (0.022) (0.017) (0.019) (0.046)

Employment Bartik * 1(< 5km to CBD) -0.017 -0.001 0.021 0.006 0.098(0.018) (0.017) (0.020) (0.019) (0.046)

Spatial Employment Bartik * 1(< 5km to CBD) -0.015 0.036 0.015 0.046 0.082(0.018) (0.018) (0.027) (0.021) (0.041)

N 11,882 11,879 12,310 12,304 11,865R-Squared 0.106 0.065 0.087 0.068 0.127

1(< 5km to CBD) -0.048 0.011 -0.033 0.009 -0.012(0.041) (0.023) (0.020) (0.020) (0.044)

Employment Bartik * 1(< 5km to CBD) -0.058 0.003 -0.024 -0.025 -0.002(0.041) (0.023) (0.017) (0.022) (0.049)


N 12,016 12,009 12,322 12,316 11,990R-Squared 0.120 0.070 0.064 0.048 0.089

Notes: Each column in each panel reports results from a separate regression of the tract SES gentrification index onindicated 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 thenearest highest quartile SES index tract as of 1970. The Bartik variables are standardized to be mean 0 and standarddeviation 1. Regressions are weighted by share of 1970 tract population in 1970 CBSA population.

Table 4: Patterns in SES Index Gentrification of Tracts within 5 km of CBDs

Panel A: Bottom Tercile Neighborhoods

Panel B: Middle Tercile

Panel C: Top Tercile

1970-1980 1980-1990 1990-2000 2000-2010 1980-2010

1(< 5km to CBD) -0.133 0.015 -0.002 0.042 0.060(0.025) (0.019) (0.015) (0.023) (0.029)

Employment Bartik * 1(< 5km to CBD) -0.028 -0.011 0.006 0.010 0.023(0.018) (0.013) (0.013) (0.017) (0.028)


N 9,426 11,217 11,640 11,265 10,813R-Squared 0.117 0.092 0.089 0.057 0.090

1(< 5km to CBD) -0.049 -0.038 -0.045 -0.021 -0.107(0.020) (0.016) (0.012) (0.014) (0.024)

Employment Bartik * 1(< 5km to CBD) -0.008 0.004 0.014 -0.014 0.032(0.022) (0.013) (0.012) (0.014) (0.023)


N 10,377 11,719 12,177 12,084 11,605R-Squared 0.047 0.038 0.106 0.038 0.057

1(< 5km to CBD) 0.037 -0.075 0.023 -0.041 -0.093(0.030) (0.034) (0.035) (0.015) (0.031)

Employment Bartik * 1(< 5km to CBD) 0.014 -0.014 -0.002 -0.003 -0.003(0.025) (0.014) (0.014) (0.014) (0.024)


N 10,373 11,687 12,080 12,040 11,596R-Squared 0.071 0.078 0.126 0.094 0.092

Table 5: Patterns in Regression Adjusted Owner-Occupied Housing Cost of Tracts within 5 km of CBDs

Panel A: Bottom Tercile Neighborhoods

Panel B: Middle Tercile

Panel C: Top Tercile

Notes: Each column in each panel reports results from a separate regression of the tract owner occupied housing cost indexon 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 thenearest highest quartile SES index tract as of 1970. The Bartik variables are standardized to be mean 0 and standarddeviation 1. Regressions are weighted by share of 1970 tract population in 1970 CBSA population. The housing cost index isformed 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.

Data 1980 ChoicesRace | X All All

(1) (2) (3) (4)

1980-1990 -0.023 -0.024 -0.020 0.0011990-2000 -0.018 -0.024 -0.021 0.0052000-2010 -0.013 -0.013 -0.011 0.0011980-2010 -0.053 -0.061 -0.052 0.006

1980-1990 0.010 0.025 0.001 0.0291990-2000 -0.020 -0.027 -0.014 -0.0072000-2010 -0.014 -0.017 -0.025 0.0151980-2010 -0.024 -0.019 -0.038 0.036

Table 6: Counterfactual Changes in Fraction of Population Share within 5 km of CBDs

Notes: Each entry in Column (1) is constructed with actual data. Remaining entries arecalculated using counterfactual data. Entries in Columns (2) maintain the 1980 CBSAfraction of the population in each cell of the race conditional on education or incomedistribution. Entries in Column (3) maintain the 1980 CBSA fraction in the raceXeducationor raceXincome joint distribution. Entries in Column (4) maintain the 1980 neighborhoodchoices in each raceXeducation or raceXincome cell. The race conditional on incomedistribution and raceXincome joint distribution use families in 1980 and households insubsequent years as the underlying unit of observation, thereby requiring a differentbaseline.

1980 Shares

Panel A: X=Education

Panel B: X = Income

Data All 1980 ChoicesRace | X All

(1) (2) (3) (4)

1980-1990 -0.015 -0.031 -0.040 0.0121990-2000 0.000 0.017 0.004 -0.0052000-2010 0.026 0.030 0.033 -0.0011980-2010 0.011 0.016 -0.002 0.006

1980-1990 -0.042 -0.046 0.023 0.0001990-2000 -0.001 0.005 0.030 0.0152000-2010 0.024 0.026 0.043 -0.0231980-2010 -0.019 -0.015 0.097 0.000

1980 Shares

Panel A: X=Education

Panel B: X=Income

Table 7: Counterfactual Changes in Fraction of Central Area Population Living in a Top Fraction White Tercile Tract

Notes: Columns are analogous to those in Table 6, except they describe the change inthe fraction of the population of central area tracts which live in a top tercile fractionwhite neighborhood. The top block examines the role of education composition andthe bottom block examines the role of income composition.

Data All 1980 Shares All 1980 Choices(1) (2) (3)

1980-1990 0.003 -0.013 0.0091990-2000 -0.001 0.001 -0.0012000-2010 0.045 0.032 0.0091980-2010 0.048 0.020 0.016

1980-1990 0.018 0.023 0.0001990-2000 0.010 0.030 0.0152000-2010 0.038 0.043 -0.0231980-2010 0.066 0.097 0.000

Notes: Columns are analogous to those in Table 6, except they describe thechange in the fraction of the population of central area tracts which live in a toptercile neighborhood, as defined by education composition in Panel A and incomecomposition in Panel B.

Table 8: Counterfactual Changes in Central Area Population Living in a Top Tercile Tract

Panel A: Fraction of Central Area Population within Top Education Tercile

Panel B: Fraction of Central Area Population within Top Income Tercile

−.8

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hite

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−.8 −.6 −.4 −.2 0 .2Share White Relative to Mean in 1980

Best Linear Fit 45 degree lineTracts within 5km of CBD Tracts more than 5km from CBD

Panel A

−.5

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hare

with

Col

lege

Deg

ree

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Mea

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−.2 0 .2 .4 .6 .8Share with College Degree Relative to Mean in 1980


Panel B

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−3 −2 −1 0 1Income Relative to Mean in 1980


Panel C

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Z−

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−4 −2 0 2 4Mean Z−Index in 1980


Panel D

Figure 1: Chicago Tract Dynamics 1980 - 2010.

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−3 −2 −1 0 1 2 3Mean Z−Index in 1970


Panel A

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Panel B

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Panel C

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−3 −2 −1 0 1 2 3Mean Z−Index in 2000


Panel D

Figure 2: Chicago Tract Dynamics Z-Index. By decade.

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

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Gary

Peoria

Madison Lansing

Kenosha

Chicago

Rockford

Milwaukee

DavenportFort Wayne

Grand Rapids

Figure 4: Chicago area close-up of map showing sample. Black dots indicate CBD location.Tracts in sample are shown in blueif farther than 5km from CBD and red if within 5km of CBD.

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−1 −.5 0 .5 1Income Relative to CBSA Mean in 1970

45 Degree Line Regression Line

Slope = 0.82 (0.04)

Panel A

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Panel B

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Panel C

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Panel D

Figure 5: Tract Income Dynamics. CBSAs given Equal Weight

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Inde

x R

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BS

A M

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−2 −1 0 1 2Z−Index Relative to CBSA Mean in 1970


Slope = 0.86 (0.01)

Panel A

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Panel B

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Panel C

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Slope = 0.96 (0.00)

Panel D

Figure 6: Tract Z-Index Dynamics. CBSAs given Equal Weight

gentrification and changes in the spatial structure of ... · women’s labor force participation...

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