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The Effects of Gentrification on the Well-Being and
Opportunity of Original Resident Adults and Children∗
Quentin Brummet†^ and Davin Reed‡
July 2019
Abstract Gentrification represents a striking reversal of decline in many US cities, yet it is controversial because of its perceived negative consequences for original neighbor- hood residents. In this paper, we use new longitudinal census microdata to provide the first causal evidence of how gentrification affects a broad set of outcomes for incumbent adults and children. Gentrification modestly increases out-migration, though movers are not made observably worse off and aggregate neighborhood change is driven primarily by changes to in-migration. At the same time, many original resident adults stay and benefit from declining poverty exposure and rising house values. Children benefit from increased exposure to neighborhood character- istics known to be correlated with economic opportunity, and some are more likely to attend and complete college. Our results suggest that accommodative policies, such as increasing housing supply in high-demand urban areas, could increase the opportunity benefits we find, reduce out-migration pressure, and promote long-term affordability. JEL Codes: J62, R11, R21, R23, R Keywords: Gentrification, neighborhood change, migration, mobility
∗We particularly thank Ingrid Gould Ellen, Sewin Chan, and Katherine O’Regan for their support. We also thank Vicki Been, Devin Bunten, Robert Collinson, Donald Davis, Jessie Handbury, Daniel Hartley, Jeffrey Lin, Evan Mast, and Lowell Taylor for helpful comments and suggestions. Reed thanks the Horowitz Foundation for Social Policy and the Open Society Foundation for financial support while at New York University. This research was conducted as part of the Census Longitudinal Infrastructure Project (CLIP) while Brummet was an employee of the US Census Bureau. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the US Census Bureau, the Federal Reserve Bank of Philadelphia, or the Federal Reserve System. All results have been reviewed to ensure that no confidential information is disclosed. †
‡Brummet: NORC at the University of Chicago. brummet-quentin@norc.org Reed: Corresponding author. Federal Reserve Bank of Philadelphia, Community Development and Regional Outreach Department. davin.reed@phil.frb.org
1 Introduction
Over the past two decades, high-income and college-educated individuals have increasingly chosen to live in central urban neighborhoods (Baum-Snow and Hartley 2017; Couture and Handbury 2017; Edlund et al. 2016; Su 2018). This gentrification process reverses decades of urban decline and could bring broad new benefits to cities through a growing tax base, increased socioeconomic integration, and improved amenities (Vigdor 2002; Di- amond 2016). Moreover, a large neighborhood effects literature shows that exposure to higher-income neighborhoods has important benefits for low-income residents, such as im- proving the mental and physical health of adults and increasing the long-term educational attainment and earnings of children (Kling et al. 2007; Ludwig et al. 2012; Chetty et al. 2016; Chetty and Hendren 2018a,b; Chyn 2018). Gentrification thus has the potential to dramatically reshape the geography of opportunity in American cities. However, gentrification has generated far more alarm than excitement. A key concern is that the highly visible changes occurring in gentrifying neighborhoods are driven by the direct displacement of original residents, making them worse off and preventing them from sharing in the aforementioned benefits. These concerns are central to current de- bates about the distributional consequences of urban change and about policies associated with those changes. More specifically, they have emerged as an obstacle to building more housing in high-cost cities and have helped fuel support for policies like rent control, both of which could have large, unintended welfare costs.^1 Thus, understanding how gentrifi- cation actually occurs and whether it harms or benefits original residents is of primary importance for urban policy. Yet despite its importance, there is little comprehensive evi- dence on this question. Largely because of data limitations, previous research has focused on particular outcomes, specific cities, or relied on purely descriptive approaches. In this paper, we provide the first comprehensive, national, causal evidence of how gentrification affects original neighborhood resident adults and children. For adults, we estimate effects on a number of individual outcomes that together approximate well-being. For children, we estimate effects on individual exposure to neighborhood characteristics known to be positively correlated with economic opportunity and on educational and labor market outcomes. We focus on original residents of low-income, central city neighborhoods of the 100 largest metropolitan areas in the US and explore heterogeneity along a number of dimensions. Three innovations are central to our approach. First, we construct a unique data set (^1) Ganong and Shoag (2017) and Hsieh and Moretti (2018) show that local housing supply restrictions have reduced regional convergence and national economic growth. Diamond et al. (2018) show that rent control in San Francisco benefits controlled residents at the expense of uncontrolled and future residents.
rents for more-educated renters but not for less-educated renters, suggesting the former may be more willing or able to pay for neighborhood changes associated with gentrifica- tion.^3 We find few effects on other observable components of adult well-being, including employment, income, and commute distance. Given the importance of neighborhood quality for children’s long-term outcomes (Chetty et al. 2016; Chetty and Hendren 2018a,b; Chyn 2018; Baum-Snow et al. 2019), we also study how gentrification affects original resident children. We find that on average, gentri- fication decreases their exposure to neighborhood poverty and increases their exposure to neighborhood education and employment levels, all of which have been shown to be corre- lated with greater economic opportunity (Chetty et al. 2018). We also find some evidence that gentrification increases the probability that children of less-educated homeowners attend and complete college, with these effects driven by those endogenously staying in the origin neighborhood.^4 Taken together, the results for children and adults show that many original residents are able to remain in gentrifying neighborhoods and share in any neighborhood improvements, answering a key unresolved distributional question. At the same time, gentrification increases out-migration to any other neighborhood by 4 to 6 percentage points for less-educated renters and by slightly less for other groups. However, these effects are somewhat modest relative to baseline cross-neighborhood mi- gration rates of 70 to 80 percent for renters and 40 percent for homeowners. Importantly, we find no evidence that movers from gentrifying neighborhoods, including the most dis- advantaged residents, move to observably worse neighborhoods or experience negative changes to employment, income, or commuting distance. Our model shows that the key remaining channel through which gentrification may cause harm is through unobserved costs of leaving the origin neighborhood. These may be small given the high rates of baseline mobility we find and existing structural estimates of the value of community attachment.^5 We provide additional evidence that the highly visible changes associated with gentrification are driven almost entirely by changes to the quantity and composition (^3) This is consistent with recent findings on differences in preferences for urban consumption ameni- ties by skill (Couture and Handbury 2017; Diamond 2016; Su 2018) or some degree of rental market segmentation. (^4) We find no effects on educational attainment or labor market outcomes for other children, though they may nevertheless benefit in non-economic ways from living in lower-poverty neighborhoods (Katz et al. 2001; Kling et al. 2007). (^5) Costs may be pecuniary (time and money spent finding and moving to a new location) or nonpe- cuniary (loss of proximity to friends, family, networks, or other neighborhood-specific human capital). Diamond et al. (2018) structurally estimate cross-neighborhood moving costs of $42,000 on average, which increase by $300 per year of living in the origin neighborhood. High baseline mobility suggests that gen- trification may simply move up the date at which individuals decide to move, rather than causing them to make a move they would otherwise never make. Thus, $300 per year of residence may be closer to the unobserved cost than $42,000.
of in-migrants, not direct displacement. Our results have important implications for how policymakers should respond to con- cerns about gentrification. Foremost, they should weigh the benefits of gentrification that accrue to original residents, including less-advantaged residents, against any harms. Moreover, neighborhoods are far more dynamic than typically assumed, with high baseline migration allowing them to change quickly without the wholesale direct displacement of original residents. Instead, neighborhood demographic changes are driven almost entirely by changes to those willing and able to move into gentrifying neighborhoods. Thus, pre- serving and expanding the affordability and accessibility of central urban neighborhoods should primarily take a forward-looking approach that seeks to accommodate increasing demand for these areas. A growing recent literature suggests that building more housing (whether market-rate or affordable) is a promising way of maintaining and expanding housing affordability (Mast 2019; Nathanson 2019; Favilukis et al. 2019). It would also maximize the integrative and opportunity benefits we find. These policies could be com- plemented with rental subsidies or other inclusionary policies carefully targeted to the relatively small population of the most disadvantaged original residents, for whom out- migration effects are highest. Additionally, targeting inclusionary policies to low-income families with children could encourage them to stay in neighborhoods improving around them, complementing existing programs like Moving to Opportunity (MTO) that seek to increase moves from low- to high-opportunity neighborhoods. Our work builds on a broad existing literature studying the effects of gentrification across many disciplines. Ellen and O’Regan (2011a), Rosenthal and Ross (2015), and Vigdor (2002) provide thorough reviews of this literature. Most previous studies focus on displacement as the primary outcome of interest and, using descriptive approaches, find little evidence of more moving in gentrifying neighborhoods (Freeman 2005; McKinnish et al. 2010; Ellen and O’Regan 2011b; Ding et al. 2016; Dragan et al. 2019). Concurrent work by Aron-Dine and Bunten (2019) uses annual migration data and finds causal evi- dence that gentrification increases out-migration in the short term, similar to our findings of out-migration effects in the medium-to-long-term. We expand on these papers by tak- ing a comprehensive approach toward understanding how gentrification causally affects well-being overall, not only displacement, and by exploring heterogeneity. In this sense, our paper is similar to Vigdor (2002) and Vigdor (2010), which provide the earliest ap- plications of spatial concepts to understanding how gentrification might affect residents. They find no evidence of large negative effects and some evidence that neighborhood im- provements increase welfare. We build on those papers by using longitudinal individual microdata on many outcomes and estimating causal effects. Finally, concurrent papers by
(PIKs).^7 We use PIKs to match individuals responding to both the Census 2000 long form and the 2010-2014 American Community Survey (ACS) 5-year estimates.^8 Approxi- mately 10 percent of the Census 2000 long form sample matches, yielding around 3 million matched individuals. We observe in both years each individual’s block of residence and block of work (if working), employment and income, homeownership status, rent paid or house value, and demographic characteristics. Key demographics include education, age, race/ethnicity, and household type. We define neighborhoods as census tracts and assign each individual in each period to a geographically consistent neighborhood of residence, neighborhood of work, and metropolitan area (Core-Based Statistical Area (CBSA)).^9 The resulting data set is unique to the gentrification literature and central to our paper. It allows us to identify original residents of neighborhoods, to follow their locations and other outcomes regardless of their choice to stay or leave, and to do so by many different individual characteristics. Our focus on changes from 2000 to 2010-2014 allows us to study medium-to-long-term effects.^10
2.2 Adult Sample and Characteristics
We define original residents as all individuals living in initially low-income, central city neighborhoods of the 100 most populous metropolitan areas (CBSAs) in the year 2000. These are “gentrifiable.” Low-income neighborhoods are census tracts with a median household income in the bottom half of the distribution across tracts within their CBSA. Central cities are the largest principal city in their CBSA.^11 We focus on these neigh- (^7) PIKs are assigned to individuals by the Census Bureau’s Person Identification Validation System (PVS). The PVS uses probabilistic matching algorithms to match individuals in a given Census Bureau product to a reference file constructed from the Social Security Administration Numerical Identification File and other federal administrative data. Matching fields include social security numbers, full name, date of birth, and address (Alexander et al. 2015). 8 We assess match quality by ensuring that certain individual characteristics change in expected ways or do not change in unexpected ways. For example, age should change 10 years from 2000 to 2010, plus or minus one due to the exact timing of the survey interview. We therefore drop individuals with unexpected changes in age and similar characteristics. They are a small share of our total matched sample. (^9) We observe each year 2000 observation’s block of residence. We therefore construct a crosswalk from 2000 blocks to 2010 tracts using Census Bureau maps and geographic information system (GIS) software and use it to assign all year 2000 observations precisely to 2010 tracts. (^10) Most previous research on gentrification also studies decadal changes. The exceptions are Ding et al. (2016) and Aron-Dine and Bunten (2019), which use annual frequencies from the Federal Reserve Bank of New York (FRBNY) Consumer Credit Panel (CCP). Aron-Dine and Bunten (2019) find that the onset of gentrification increases subsequent out-migration by around 4 percentage points (hastening a move by 1.5 years). The estimate is similar to ours and suggests we may not be missing important short-term out-migration effects. 11 All results are robust to different samples of metropolitan areas (10, 25, or 50 most populous), definitions of low-income (bottom quartile of the CBSA distribution), and definitions of central city (within some distance of the central business district).
borhoods because they are where gentrification trends have been strongest (Couture and Handbury 2017; Baum-Snow and Hartley 2017) and where gentrification concerns have been greatest. To focus on adults capable of making move decisions and for whom educa- tion levels are mostly fixed, we restrict the sample to individuals 25 or older in 2000, not enrolled in school, not living in group quarters, and not serving in the military. We focus on education level and tenure status as essential elements of heterogeneity and therefore stratify all results by four key types of individuals: less-educated renters, more-educated renters, less-educated homeowners, and more-educated homeowners.^12 Appendix B pro- vides additional data details. Table 1, Panel A, describes baseline changes in a number of original resident adult outcomes from 2000 to 2010-2014 that together approximate changes in well-being. Out- migration captures potential unobserved costs of leaving the origin neighborhood, is cen- tral to gentrification debates, and has been the focus of previous gentrification research. We measure it in three ways: move to any other neighborhood, move at least one mile away, and exit the metropolitan area. We measure changes in observable well-being us- ing changes in self-reported rents for renters, self-reported house values for homeowners, neighborhood poverty rate, employment and income, and commute distance.^13 Among the patterns in Table 1, perhaps the most important is that migration for renters is high: 68 percent of less-educated renters and 79 percent of more-educated renters move to a different neighborhood over the course of a decade. This effectively places a limit on the potential for gentrification to cause displacement and makes it possible for neighborhoods to change quickly even without strong displacement effects. Table 2 describes the individual and household characteristics of original resident adults in 2000. We include these as controls in our regression models. Most are correlated with education level and tenure status in the expected ways.^14 It is worth emphasizing that the sample is evenly distributed across the four types of individuals, not overwhelmingly disadvantaged as is often implicitly assumed. In fact, the largest group is less-educated homeowners, who a priori could benefit from increased neighborhood demand through rising house values, an important component of household wealth. The distribution of (^12) We stratify by education level and tenure status in 2000, the start of our study period. Less-educated residents are those with a high school degree or less, and more-educated residents are those with some college or more. (^13) For the employment and income outcomes only, we further restrict the sample to individuals less than age 55 in the second period (working age). This is standard and aids interpretation but does not affect our regression results. 14 The sample counts are the rounded numbers of observations in our data set, while the means of each characteristic are weighted by census-provided person weights. The choice to weight or restrict to householders does not substantively alter any of the patterns described here or our regression results.
2.4 Defining Gentrification
Following the most recent research on the causes of gentrification, we conceptualize gentri- fication as an increase in college-educated individuals’ demand for housing in initially low- income, central city neighborhoods (Baum-Snow and Hartley 2017; Couture and Hand- bury 2017). We measure gentrification specifically as the change from 2000 to 2010- in the number of individuals aged 25+ with a bachelor’s degree or more living in tract j in city c , divided by the total population aged 25+ living in tract j and city c in 2000:
gentjc ≡ bachelors^25 jc, total^2010 25 −^ bachelors^25 jc,^2000 jc, 2000
We fix the denominator at its 2000 level to avoid mechanically correlating gentrification with less-educated population decline. Neighborhoods experiencing large positive changes in gentjc are said to gentrify more than those experiencing smaller or negative changes. Across all gentrifiable neighborhoods in our sample, the mean of gentrification is 0.06. We also model gentrification using a binary variable equal to one if a neighborhood is in the top decile of gentjc across all neighborhoods in our sample and zero otherwise. This picks up important nonlinearities in the effects and is our preferred specification.^20 The mean level of gentrification within the top decile of neighborhoods is 0.37. While we prefer our gentrification measure to alternatives based on increases in aggregate neighborhood incomes, rents, or house values, our main takeaways are broadly similar when using these other measures.^21 Figures 1 and 2 and Table A1 describe patterns of gentrification using our binary definition and suggest that it is in fact picking up the neighborhoods and cities where people talk about gentrification occurring.^22 Table 4 describes neighborhood characteristics in 2000 by gentrification status. The 10 percent of neighborhoods classified as gentrifying using our binary measure look quite different according to some measures yet very similar according to others. For exam- (^20) Results are robust to alternative nonlinear categorizations and are available upon request. We cal- culate percentiles using the distribution across all 10,000 neighborhoods in all 100 CBSAs in order to introduce an element of “absolute” gentrification into our definition. This allows, for example, a city like New York to have more than 10 percent of its neighborhoods defined as gentrifying. Results are similar when calculating gentrification percentiles within each CBSA. 21 We dislike using these alternative measures for our study in part because they take as given many of the outcomes we are interested in studying: what happens to neighborhood incomes, rents, and house values when neighborhoods experience high-skill housing demand shocks. 22 For example, the New York map in Figure 1 captures gentrification in north and central Brooklyn, the Lower East Side, and Harlem, among other places. Patterns in Figure 2 also match those discussed in popular media: areas north and east of the National Mall in Washington DC, areas north of downtown Portland OR, areas in downtown Seattle near Amazon, and areas south and east of downtown Atlanta near the BeltLine. The 10 most gentrifying central cities according to Table A1 are Washington DC, Portland OR, Seattle, Atlanta, Denver, Charleston, Austin, Boston, Raleigh, and Richmond.
ple, gentrifying neighborhoods started with higher education levels (21 percent college- educated vs. 13 percent), higher self-reported house values ($225,000 vs. $160,000), and lower minority shares (51 percent vs. 56 percent). Yet both types of neighborhoods had similar initial median household incomes ($41,000), median rents ($800), and share poverty (24 percent). These mixed differences suggest some neighborhoods may already have begun gentrifying before 2000, which is supported directly by the fact that gen- trifying neighborhoods also experienced higher levels of gentrification over the previous decade. Gentrifying neighborhoods also had much lower initial populations (2,500 vs. 3,400), potentially allowing them to absorb new demand and helping explain our modest out-migration effects. Consistent with previous research on the causes of gentrification, gentrifying neighborhoods were also closer to the central business district, closer to other high-income neighborhoods, had a larger share of old housing (built before 1940), and were more likely to be near a coastline, providing additional support for the validity of our definition. We control for all of these characteristics, as well as changes from 1990 to 2000 for those that vary over time, in our regressions.
3 Model of Gentrification, Location, and Well-Being
The previous section shows that gentrifiable neighborhoods are quite dynamic (cross- neighborhood migration is high) and diverse (more- and less-educated homeowners each compose about one quarter of the population), suggesting the well-being and distributional effects of gentrification may not be clear-cut. In this section, we therefore develop a simple neighborhood choice model to highlight how gentrification affects original resident well-being through the various outcomes explored above and to anchor our empirical approach. Intuitively, it captures the idea that in any given neighborhood, over the course of a decade some original residents will choose to move and some will choose to stay. Gentrification affects the overall well-being of these original residents through its effect on two margins: the number of individuals choosing to move instead of stay (out-migration) and changes in the observable outcomes of both movers and stayers. The out-migration margin includes both the pecuniary costs (time and money spent finding and moving to a new location) and nonpecuniary costs (loss of proximity to friends and family, networks, or other neighborhood-specific human capital) of leaving the origin neighborhood. While we do not observe these, the total unobserved costs to original residents are increasing in the out-migration effect. We begin with a standard model of neighborhood choice similar to those in Moretti (2011), Kline and Moretti (2014), and Busso et al. (2013). Individuals i choose a neigh-
Equation 4 makes clear why out-migration itself is not evidence of harm. It is not evidence of harm for those who out-migrate, since their observable outcomes may be unchanged and unobserved migration costs may be small. It also not evidence of harm for the average original resident, as even if if out-migrants are in fact made worse off, stayers might be made better off. Thus, determining whether gentrification actually harms or benefits original residents requires estimating its effects on both out-migration and other important observable outcomes, among both those who choose to move and those who choose to stay. The first two terms of equation 4 are straightforward. The last term, the effect on induced movers, captures utility changes that accrue to individuals on the margin of moving.^24 These individuals are induced into moving from their original neighborhood by gentrification. We can estimate the first part of this margin, the effect of gentrification on the probability of moving, directly with our data. The second part, (∆ uijj ′ − ∆ uijj ), captures the change in utility among those moving from j to j ′^ minus the change in utility among those staying in j. It includes an observed part (∆ w , ∆ r , ∆ κ , and ∆ a ) that we can estimate directly in our data and an unobserved part ( ^2010 ij ′ − ^2000 ij ) that we cannot. This captures a key idea about moving. Moving affects residents’ utility not only through observed changes in neighborhood characteristics but also in proportion to the potential loss of unobservable fixed, idiosyncratic benefits of living in the origin neigh- borhood j instead of the next-best neighborhood j ′. These might include the benefits of living near friends and family and other forms of neighborhood capital or community attachment. If these are small or zero, then conditional on changes in observable utility we measure, evidence of out-migration may not be a concern. However, if they are siz- able, then the unobserved harms from gentrification are increasing in the out-migration effect. Given the importance of displacement in gentrification debates, we do not make assumptions about the strength of these unobserved costs. More work is needed to better understand the pecuniary and nonpecuniary costs of moving across neighborhoods.
4 Empirical Approach
Given that gentrification is not randomly assigned, there are at least three major chal- lenges to establishing a causal effect of gentrification in our cross-sectional setting: selec- tion and omitted variables bias, spatial spillovers, and reverse causality. Omitted indi- vidual and neighborhood characteristics correlated with both gentrification and outcomes (^24) Gentrification could also reduce the probability of moving, so that “induced movers” would be more accurately described as “induced stayers.”
create a selection problem and will bias our estimated gentrification effects.^25 26 Spatial spillovers in how gentrification affects original residents could bias OLS estimates toward zero (when spillovers are from gentrifying to nongentrifying neighborhoods) or away from zero (when spillovers are from one gentrifying neighborhood to another), and Figures 1 and 2 suggest both could be present. Finally, reverse causality could arise if increasing out-migration from a neighborhood contributes to more college-educated in-migration to that neighborhood, perhaps through greater vacancy or falling rents. We address this concern by showing that our results are very similar when restricting the sample to in- dividuals who lived in their origin neighborhood in 1995, five years before we start to measure gentrification.^27 We address omitted variables bias and spatial spillovers using the following three methods, which rely on different assumptions and identifying variation to establish a causal effect. They yield similar results, thus providing plausible bounds for the causal effects of gentrification.
4.1 OLS Regression Model
To determine the effect of gentrification on original resident outcomes, we first estimate the following OLS models:
∆ Yijc = β 0 + β 1 gentjc + β 2 Xijc + β 3 Wjc + β 4 ∆ Wjc, 1990 s + β 5 gentjc, 1990 s + μc + ijc. (5)
The dependent variable ∆ Yijc is one of our individual observable well-being or out- migration outcomes. We estimate models with binary outcomes as linear probability models. We estimate models using our binary definition of gentrification, as described in Section 2.4, and include some results using the continuous measure in Appendix A. Xijc is a vector of detailed individual, household, and housing unit characteristics in 2000 described in Tables 2 and 3.^28 For models where the dependent variable is the change in self-reported rents or house values, employment status and income, commuting distance, (^25) This will create different directions of bias depending on the nature of selection and the particular outcome. For example, if individuals choose subsequently gentrifying neighborhoods because they antic- ipate changes they prefer or new job opportunities, this would bias effects on out-migration downward and bias effects on employment upward. If instead unobservably more mobile individuals select into subsequently gentrifying neighborhoods, this would bias effects on out-migration upward. 26 Post-2000 neighborhood changes, such as rezonings or new transit, that are caused by gentrification should be considered part of the treatment effect of gentrification and are not problematic. 27 These results are not included here but are available upon request. We remove sources of purely mechanical correlations when constructing our gentrification measure, as described before. 28 Though not shown in Table 2, in the actual regressions, we include age as fixed effects and break out the minority indicator variable into a set of more detailed indicators.
a gentrification coefficient and model R-squared with full controls. The Oster estimator uses as inputs the change in gentrification coefficient, the change in model R-squared, an assumption about the maximum possible R-squared in a model with all remaining unobservables ( Rmax ), and an assumption about the influence of remaining unobservables relative to the influence of full controls ( δ ). With these inputs, it provides a gentrification coefficient estimate that corrects for possible bias from remaining unobservables. We use Oster’s rule-of-thumb values of Rmax^ = 1.3 times the R-squared from our model with full controls and δ = 1.^32 33 The strength of this approach hinges on the quality of control variables available to the researcher. Given the large set of individual and household con- trols available in the census and the large set of neighborhood controls and pre-trends we assemble based on previous research, we believe this approach is particularly well suited to our setting.
4.3 Spatial First Differences
We also estimate spatial first differences (SFD) models as developed by Druckenmiller and Hsiang (2018), which leverage a different source of identifying variation and yield causal estimates for gentrification using a complementary and weaker set of assumptions than OLS and Oster. Intuitively, the model organizes all neighborhoods into a two- dimensional grid, with each neighborhood assigned a row and column index. Within each row, differences are taken across adjacent columns (neighborhoods). The estimating equation is a “spatially first differenced” version of equation 5:
∆(∆ Yirc ) = α 0 + α 1 ∆ gentrc + α 2 ∆ Xirc + ∆ υirc. (6) ∆(∆ Yirc ) = ∆ Yirc − ∆ Yirc − 1 is a vector of differences in how individual outcomes change from 2000 to 2010-2014 between adjacent neighborhoods (columns c ) within a row r. ∆ gentrc = gentrc − gentrc − 1 is a vector of differences in gentrification levels between adjacent neighborhoods, and ∆ Xirc = Xirc − Xirc − 1 is an optional vector of differences in individual and neighborhood controls between adjacent neighborhoods.^34 (^32) Oster develops these rule-of-thumb values through a re-analysis of results from randomized experi- ments. These values allow 90% of the results from randomized experiments to remain significant. We implement the estimator using the Stata package psacalc, available from the Boston College Statistical Software Components (SSC) archive. (^33) An alternative way to assess robustness is to assume values for one of Rmax (^) or δ and to “tune” the other until the Oster estimate equals zero (or until the OLS confidence interval includes zero). Though not included here, this exercise reveals that our key out-migration and poverty results are only truly zero for unlikely values for the sign and influence of remaining unobservables. Results are available upon request. 34 In practice, we first create means of individual outcomes and controls within each neighborhood, as
The estimator compares how outcomes evolve differently across the boundary of ad- jacent neighborhoods where one gentrifies (and the other does not) with how outcomes evolve differently across the boundary of adjacent neighborhoods where neither gentrifies or both gentrify. The identifying assumption is that unobservables are constant across adjacent neighborhood pairs. The assumption may be particularly plausible for individual unobservables: even if individuals select into general areas, whether they end up in one specific neighborhood as opposed to the adjacent neighborhood may be quasi-randomly determined by search timing, availability of vacancies, etc. Some version of this assump- tion is commonly used in spatial differencing approaches.^35 As described by Druckenmiller and Hsiang (2018), a priori SFD should work well when omitted variables are highly spa- tially correlated with the treatment of interest and observations are densely packed in space, both of which are likely true in our setting. Importantly, SFD also address the problem of spatial spillovers. By estimating the effect of gentrification using comparisons of adjacent neighborhoods, one of which gentri- fied and one of which did not, SFD restricts the source of bias to the scenario in which spillovers are from gentrifying to nongentrifying neighborhoods (removing the scenario in which they are from one gentrifying neighborhood to another). It thus restricts the sign of bias toward zero.^36
5 Effects of Gentrification on Adults
Table 5 shows OLS and Oster estimates of the effects of gentrification in our full sample of original resident adults. While effects in the full sample are most important for un- derstanding the overall effect of gentrification, we discuss them alongside estimates from Table 6, which we obtain by first stratifying our sample by the endogenous choice to move or stay. They help us understand what may be driving the overall effects and whether movers specifically may be observably harmed. We discuss robustness to SFD in a later subsection. described in Appendix D. (^35) Another way of thinking of identification in our setting is using the SFD equivalent of the standard difference-in-differences parallel trends assumption: absent gentrification, outcomes would have evolved similarly across neighborhood boundaries in adjacent pairs where one neighborhood gentrified and the other did not as in adjacent pairs where either both neighborhoods gentrified or neither did. (^36) While there may still be spillovers from other nearby gentrifying neighborhoods not in the specific pair, this should not bias our results. If both neighborhoods in the specific pair are near other gentrifying neighborhoods, the bias from spillovers cancels out. If only one of the neighborhoods in the specific pair is near other gentrifying neighborhoods, this is only problematic if nearness is systematically correlated with which neighborhood within the specific pair gentrified. Our results are robust to many different ways of constructing specific pairs, suggesting this is not the case.
migration effects are similar across homeowners, renters, and education levels, despite these groups likely having different abilities to remain in their neighborhoods, suggests to us that idiosyncratic preferences for origin neighborhoods may not be very strong on average. Gentrification also increases the probability that less-educated renters leave the CBSA entirely by around 4 percentage points, on a much lower baseline move rate of 15 percent. Interestingly, this effect looks to be zero for all other types of adults, suggest- ing that less-educated renters are differentially more likely to leave a housing and labor market entirely when their neighborhood gentrifies.^39 Table 6, Panel A, provides additional evidence on how we should interpret the out- migration results. It shows that for all types of individuals, movers from gentrifying neighborhoods do not experience worse changes in observable outcomes than movers from nongentrifying neighborhoods. That is, they are not more likely to end up in a higher- poverty neighborhood, to become unemployed, or to commute farther than individuals moving from nongentrifying neighborhoods. This suggests that on average and over the course of a decade, gentrification does not appear to cause particularly constrained or otherwise suboptimal relocations. Though not shown here, the findings are the same for movers who exit the CBSA entirely.
5.2 Observable Well-Being
Neighborhood Poverty Neighborhood poverty is an important measure of neighbor- hood quality, and research has shown that the poverty rate of one’s neighborhood can affect the physical and mental health of adults and the long-run educational attainment and earnings of children (Kling et al. 2007; Ludwig et al. 2012; Chetty et al. 2016). While it may be expected that an influx of college-educated individuals would lower a neighbor- hood’s poverty rate mechanically, it is not guaranteed that it would reduce the poverty exposure of the average original resident.^40 Table 5 shows that gentrification does in fact decrease the average original resident’s exposure to neighborhood poverty, by around 3. percentage points for less-educated renters and owners and slightly less for more-educated individuals. The Oster estimates are again only about 1 percentage point away from the OLS estimates, and they again suggest that the OLS estimate for less-educated renters is a lower bound. The baseline change in poverty exposure for less-educated renters over (^39) This result is consistent with the findings from Diamond et al. (2018) that the introduction of rent control in San Francisco decreased, by similar amounts, both the probability that renters left their origin neighborhood and the probability that they left the city entirely. 40 For example, if all original residents were displaced, none would be exposed to the new lower poverty rate. Or if some did stay but others were displaced to higher-poverty neighborhoods, the overall effect could be to increase poverty exposure.
the decade was zero (Table 1), so gentrification appears to have led to an absolute decline in poverty exposure for this group. Table 6, Panel B shows that these overall effects are driven almost entirely by stayers: less-educated renters staying in gentrifying neighbor- hoods experience declines in exposure to poverty that are 7 percentage points larger than those staying in nongentrifying neighborhoods. Magnitudes are again similar across all types of individuals and very Oster-robust.
Rents Table 5 shows that somewhat surprisingly, gentrification has no effect on reported monthly rents paid by original resident less-educated renters. Rents increased on average for these individuals by $126 (Table 1), so gentrification simply did not increase rents paid by these individuals even further. Table 6 shows that the effect is also close to zero for less- educated renter stayers. By contrast, gentrification increases monthly rents paid by the average more-educated renter by around $50, with this effect driven by stayers ($90). The fact that we find large rent effects for more-educated renters, driven by stayers, but not for less-educated renters suggests that more-educated renters may have greater willingness to pay for neighborhood changes associated with gentrification or that there is some degree of rental market segmentation.^41 This is consistent with recent findings of differences in preferences for urban consumption amenities by skill and the increasing importance of these amenities in explaining the location choices of the college-educated (Couture and Handbury 2017; Diamond 2016; Su 2018). The small effects for less-educated renters could also be explained by sticky rents. Subsidized housing does not explain the result.^42 These results caution against using simple neighborhood median rents when studying gentrification, as is almost always done. Changes in median rents can miss important segmentation and heterogeneity, leading to incorrect conclusions about how the housing costs paid by different types of households are actually affected.
House Values Tables 5 and 6 also show that gentrification increases original resident house values and that these are driven by increases for stayers. Less-educated homeowners staying in their origin neighborhood experience increases in self-reported house values of around $15,000 on a baseline change of almost $40,000. Increases for more-educated homeowner stayers are slightly higher: $20,000 on a baseline of almost $60,000. While we find no effect here for movers (for whom we are simply comparing self-reported house (^41) If less-educated renters occupy lower-quality rental housing, that housing may be considered less of an option by college-educated in-migrants. (^42) We test the role of subsidized housing by matching our sample to Department of Housing and Urban Development (HUD) administrative data on rental assistance. Subsidized individuals are a small share of our less-educated renter sample, and dropping them does not substantially change the results.
