This week, you will submit an annotated bibliography for two (2) of your sources for the final project papr.?Both sources should be scholarly (peer-reviewed) and from the APUS

Wealth, Race, and Place

Author(s): Brian L. Levy

Source: Demography , February 2022, Vol. 59, No. 1 (February 2022), pp. 293-320

Published by: Duke University Press on behalf of the Population Association of America

Stable URL: https://www.jstor.org/stable/10.2307/48687237

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Demography (2022) 59(1):293–320 DOI 10.1215/00703370-9710284 © 2022 The Author This is an open access arti cle dis trib uted under the terms of a Creative Commons license (CC BY-NC-ND 4.0).

ELECTRONIC SUPPLEMENTARY MATERIAL The online ver sion of this arti cle (https: / /doi .org /10.1215/00703370 -9710284) con tains sup ple men tary mate rial.

Published online: 18 January 2022

Wealth, Race, and Place: How Neighborhood (Dis)advan tage From Emerging to Middle Adulthood Affects Wealth Inequality and the Racial Wealth Gap

Brian L. Levy

ABSTRACT Do neigh bor hood con di tions affect wealth accu mu la tion? This study uses the National Longitudinal Survey of Youth 1979 cohort and a coun ter fac tual esti ma tion strat egy to ana lyze the effect of prolonged expo sure to neigh bor hood (dis)advan tage from emerg ing adult hood through mid dle adult hood. Neighborhoods have siz­ able, plau si bly causal effects on wealth, but these effects vary sig nif cantly by race/ eth nic ity and homeownership. White homeowners receive the larg est pay off to reduc­ tions in neigh bor hood dis ad van tage. Black adults, regard less of homeownership, are dou bly dis ad van taged in the neigh bor hood–wealth rela tion ship. They live in more­ dis ad van taged neigh bor hoods and receive lit tle return to reduc tions in neigh bor hood dis ad van tage. Findings indi cate that disparities in neigh bor hood (dis)advan tage fg ure prom i nently in wealth inequal ity and the racial wealth gap.

KEYWORDS Neighborhood effects • Wealth • Inequality • Race

Introduction

Wealth is a key mea sure of well­being and pre dic tor of life chances in the United States (Spilerman 2000). It plays an impor tant role in edu ca tional, labor mar ket, and health out comes (Killewald et al. 2017) and serves as both a safety net in eco nomic down turns and a means for upward mobil ity (Shapiro 2006). Wealth is also one of the most unequally dis trib uted resources and a prominent fea ture of U.S. racial inequal­ ity. In 2016, median house hold wealth of Whites was 10 times that of Blacks and 8 times that of His pan ics (Dettling et al. 2017). Wealth’s mobil ity­gen er at ing and safety net func tions make it crit i cal to racial strat i f ca tion (Shapiro 2006), and wealth is a cen tral deter mi nant of racial disparities in edu ca tional attain ment and wel fare receipt (Conley 1999, 2001). Thus, many con sider wealth the “sine qua non indi ca tor of mate rial wellbeing” (Oli ver and Shapiro 2006:203).

I argue that neigh bor hoods are an overlooked driver of wealth inequal ity. For many, homes are a key source of wealth (Shapiro 2006), and home val ues are closely related to the neigh bor hoods in which they are located (Galster et al. 2008). Neigh­ borhoods also affect edu ca tional attain ment, employ ment, income, and other fac tors

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294 B. L. Levy

that impact wealth (Chetty et al. 2016; Vartanian and Buck 2005; Wodtke et al. 2011). Despite neigh bor hoods’ the o ret i cal impor tance, I am aware of no rig or ous causal anal y sis of neigh bor hood effects on wealth. Whereas meso­level char ac ter is tics, such as neigh bor hoods, are underexamined, research on macro­ and micro­level causes of wealth inequal ity is much more com mon (Keister 2005; Keister and Moller 2000). Still, our knowl edge of the sources of wealth inequal ity remains lim ited (Pfeffer and Schoeni 2016), and most ana ly ses of the racial wealth gap leave a siz able por­ tion unex plained (e.g., Campbell and Kaufman 2006; Herring and Henderson 2016; Maroto 2016; Oli ver and Shapiro 1995).

This study ana lyzes how neigh bor hood (dis)advan tage in adult hood relates to wealth at age 50 and helps explain the racial wealth gap. It makes three con tri bu tions to research on neigh bor hood effects, racial inequal ity, and wealth inequal ity. First, it identifes neigh bor hoods as an impor tant fea ture of wealth inequal ity. Second, it reveals two ways that neigh bor hoods con trib ute to the racial wealth gap: through (1) large disparities in neigh bor hood dis ad van tage (ND) and (2) Whites dis pa rately beneft ting from reduc tions in ND. Third, it responds to the recent call (Killewald et al. 2017) for research on the wealth of groups besides Blacks and Whites. Beyond these con tri bu tions, this study advances research on neigh bor hood effects by focus­ ing on an understudied period of the life course (adult hood), ana lyz ing het ero ge neous effects, and using coun ter fac tual meth ods with a con tin u ous treat ment.

Literature Review

Wealth Inequality in America

Wealth inequal ity in the United States is extreme and wid en ing, eclips ing even the lev els seen dur ing the Roaring Twenties (Piketty 2013/2014; Saez and Zucman 2016). The richest 1% now own 40% of wealth (Saez and Zucman 2016), and the share of house holds with no or neg a tive wealth is ris ing (Keister and Moller 2000; Pfeffer and Schoeni 2016). The mid dle of the dis tri bu tion also shows diver gence, with house hold wealth grow ing faster at the 75th per cen tile than at the median or the 25th per cen tile (Pfeffer and Schoeni 2016).

Research on wealth inequal ity has iden ti fed two broad types of deter mi nants: struc tural (macro­level) fac tors, includ ing the hous ing and stock mar kets, asset and tax pol i cies, and rac ism; and indi vid ual or fam ily (micro­level) driv ers, includ ing age, fam ily struc ture, edu ca tion, income, and inher i tances (Keister and Moller 2000). Research on the United States and 15 other high­income countries has found that income and inher i tances are the stron gest pre dic tors (Semyonov and Lewin­Epstein 2013). Several reviews offer fur ther insight into wealth inequal ity (Keister 2005; Keister and Moller 2000; Killewald et al. 2017), but nota bly absent from research on the causes of wealth accu mu la tion are meso­level fac tors, such as neigh bor hoods.

As is the case with over all wealth inequal ity, the racial wealth gap is sub stan tial and grow ing (Conley 2010; Oli ver and Shapiro 1995). The richest 100 U.S. house holds have as much wealth as all Blacks plus one third of His pan ics com bined (Collins and Hoxie 2015). The deter mi nants of the racial wealth gap vary from the deter mi nants of gen eral wealth accu mu la tion. Racial wealth inequal ity fol lows cen tu ries of rac ist

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295Wealth, Race, and Place: Neighborhood (Dis)advan tage and Inequality

pub lic pol icy (Conley 2010; Oli ver and Shapiro 1995) that “sys tem at i cally prevented [Black Amer i cans] from accu mu lat ing prop erty” (Conley 1999:611). Homeowner­ ship is foun da tional for wealth accu mu la tion given that owning a home and dura tion of homeownership are pos i tively asso ci ated with wealth (Di et al. 2007; Turner and Leua 2009). Disparities in the rate and dura tion of homeownership can explain a large por tion of the racial wealth gap (Oli ver and Shapiro 1995; Shapiro 2006; Shapiro et al. 2013)—much more than income and edu ca tional attain ment explain (Sullivan et al. 2015). Still, non­White homeowners have lower equity and equity con di tional on socio eco nomic sta tus than do White homeowners (Killewald and Bryan 2016; Krivo and Kaufman 2004). Whites start with homes that have higher val ues, and their homes appre ci ate faster (Flippen 2004). One poten tial expla na tion for this is racial­ ized neigh bor hood access; non­Whites, par tic u larly Blacks, dis pro por tion ately reside in dis ad van taged neigh bor hoods (Massey and Denton 1993; Newman and Holupka 2016).

Most research incor po rat ing a range of indi vid ual­level var i ables can not fully explain the racial wealth gap (e.g., Campbell and Kaufman 2006; Herring and Henderson 2016; Maroto 2016; Oli ver and Shapiro 1995). A nota ble excep tion is Killewald and Bryan’s (2018) anal y sis of median racial wealth gaps at age 50. They con cluded that fam ily social ori gins explain about half of the median wealth gap, income and edu ca tion explain another quar ter, and homeownership and other house­ hold fac tors explain the fnal quar ter. Still, Maroto (2016) found that the racial wealth gap is large and dif f cult to explain at the top end of the wealth dis tri bu tion. Thus, expla na tions for the median gap may not trans late to the full wealth dis tri bu tion, and fur ther research on the gap is crit i cal (Killewald et al. 2017). For a new expla na tion, I turn to a key meso­level fea ture of fam i lies’ homes: the neigh bor hoods in which they sit.

Residential Segregation

The United States has a long his tory of racial res i den tial seg re ga tion. Documented back to the nineteenth cen tury (Du Bois 1899), seg re ga tion has waned only some­ what and remains a prob lem by con cen trat ing non­White, par tic u larly Black, Amer i cans in less­advan taged neigh bor hoods (Lee et al. 2014; Logan et al. 2015; Massey and Denton 1993). Contemporary seg re ga tion results from his tor i cal inequal­ ities (Sharkey 2013), ongo ing dis crim i na tion in mort gages and hous ing (Fischer and Lowe 2014; Pager and Shepherd 2008; Rugh and Massey 2010), and Whites’ pref er­ ence for neigh bor hoods with few non­White res i dents (Krysan et al. 2009).

Unlike racial seg re ga tion, class­based seg re ga tion emerged more recently as an impor tant con sid er ation. Income seg re ga tion increased from 1970 to 2012, with nota­ ble increases in the 1980s and 2000s (Jargowsky 1996; Reardon et al. 2018). Class­ based seg re ga tion is par tic u larly salient for racial and eth nic minor i ties; low­income Blacks and His pan ics are often seg re gated into the most dis ad van taged neigh bor­ hoods (Jargowsky 1996). Together, race/eth nic ity and class con sti tute the two key fea tures of con tem po rary neigh bor hood seg re ga tion (Lee et al. 2015).

These pat terns sug gest that neigh bor hoods could affect wealth. Rusk (2001) pos ited a “seg re ga tion tax,” with dis ad van taged groups receiv ing lower returns to

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296 B. L. Levy

homeownership (see also Faber and Ellen 2016; Flippen 2004; Shapiro 2004). Denton (2001) fur ther spec u lated that the seg re ga tion tax is paid across the class dis tri bu tion. Still, this idea is not fully devel oped. Why might seg re gated neigh bor hoods reduce non­Whites’ wealth? Do highly seg re gated cit ies always have high wealth inequal ity? That is, does seg re ga tion lead to wealth disparities per se? Flippen (2010) found that met ro pol i tan racial seg re ga tion is asso ci ated with low rates of minor ity homeown­ ership, which is a poten tial mech a nism by which seg re ga tion could cause wealth disparities. Alternatively, do the char ac ter is tics of seg re gated neigh bor hoods drive wealth through dis pa rate access to advan taged, wealth­pro mot ing neigh bor hoods?

Neighborhood Effects

There are sev eral poten tial mech a nisms for neigh bor hood effects on wealth, which broadly fall into two groups: achieved sta tuses and hous ing. Considerable research has exam ined neigh bor hood effects on sta tus attain ment and behav ioral out comes. Neighborhood effects on edu ca tional and labor mar ket out comes are well established (Chetty et al. 2016; Sharkey and Faber 2014). Disadvantaged neigh bor hoods also increase the risk of crime and incar cer a tion (Hipp et al. 2010; Peterson and Krivo 2010), neg a tive behav ioral out comes (Sampson et al. 2002), and low lev els of health and well­being (Ludwig et al. 2012; Ross and Mirowsky 2001). Each of these out­ comes rep re sents a plau si ble path way for neigh bor hood effects on wealth. Achieved sta tuses seem espe cially likely to explain neigh bor hood effects on over all wealth inequal ity, whereas they may be less impor tant for neigh bor hood­based racial wealth disparities (Keister and Moller 2000; Semyonov and Lewin­Epstein 2013; Sullivan et al. 2015).

Neighborhood demo graph ics also cor re late with home val ues. Whites’ dis in cli­ na tion to move to low­income or non­White neigh bor hoods neg a tively affects home equity (Crowder and South 2008; Emerson et al. 2001; Galster et al. 2008; Krysan et al. 2009). Although both race (Anacker 2010; Coate and Schwester 2011) and class (Galster et al. 1999; Peng and Thibodeau 2013) are related to val ues, class is espe cially salient (Flippen 2004; Harris 1999). Because hous ing is a key source of wealth, the hous ing mar ket rep re sents a unique mech a nism for neigh bor hood effects on wealth—one not empha sized in most research on neigh bor hood effects.

Legacy and struc tural dis ad van tages in the hous ing mar ket imply that neigh bor­ hood effects oper at ing through hous ing may be salient for racial inequal ity. The his­ tory of redlining, block bust ing, and urban renewal (Faber 2020; Lipsitz and Oli ver 2010), cou pled with con tem po rary inequities in appraisal (Howell and Korver­Glenn 2021), mort gage lend ing (Korver­Glenn 2021; Stu art 2003), fore clo sure (Hall et al. 2015a, 2015b; Rugh et al. 2015), and sit ing of ame ni ties (Moore et al. 2008; Morland et al. 2002), dis pa rately concentrates value. Discrimination in the hous ing search pro cess (Fischer and Lowe 2014; Korver­Glenn 2021; Pager and Shepherd 2008; Rugh and Massey 2010), as well as long­stand ing pat terns of res i den tial seg­ re ga tion (Massey and Denton 1993; Reardon et al. 2018), restricts non­Whites’, espe cially low­income non­Whites’, access to neigh bor hoods with strong wealth advan tages. This research documenting the breadth of rac ism in hous ing sug gests that the seg re ga tion tax (Rusk 2001) results from spe cifc dis ad van tages in neigh bor hoods

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297Wealth, Race, and Place: Neighborhood (Dis)advan tage and Inequality

into which tra di tion ally dis ad van taged pop u la tions, espe cially Blacks (Newman and Holupka 2016), are seg re gated.

Because wealth deter mi nants are mul ti ple and vary by race, it is impor tant to con sider effect het ero ge ne ity (e.g., Levy 2019; Wodtke et al. 2016). Housing is an out sized com po nent of non­Whites’ wealth (Kuebler 2013), so neigh bor hood effects through hous ing may be par tic u larly impor tant for racial and eth nic minor i ties. Alter­ natively, with the increas ing con cen tra tion of wealth (Saez and Zucman 2016) and larger pay off to homeownership among Whites (Krivo and Kaufman 2004), wealth ben e fts may be con cen trated in the most-advan taged neigh bor hoods, among Whites, or among homeowners.

Data

I use the restricted­use National Longitudinal Survey of Youth 1979 cohort (NLSY79) to ana lyze neigh bor hood effects on wealth accu mu lated at roughly age 50. The NLSY79 is a nation ally rep re sen ta tive panel sur vey of nearly 10,000 indi vid u als aged 14–21 in 1979. The NLSY79 sur veyed respon dents annu ally from 1979 to 1994 and bien ni ally after ward. During the ini tial wave(s), the NLSY79 also col lected infor­ ma tion from par tic i pants’ par ents. The NLSY79 has sev eral use ful fea tures for this anal y sis. First, it includes a rep re sen ta tive sam ple of His pan ics in the ini tial sam pling frame, per mit ting anal y sis of an impor tant but understudied group in the neigh bor­ hood effects and wealth lit er a tures. Second, each wave col lects data on res i den tial neigh bor hoods and a range of var i ables pre dic tive of ND. Third, wealth inequalities and racial gaps sta bi lize when a cohort reaches age 50 (Urban Institute 2015), so the NLSY79 rep re sents a recent cohort at this age. For this anal y sis, I use the lon gi tu di nal sam ple of roughly 7,300 indi vid u als who com pleted a sur vey in 2012. This num ber rep re sents a 79% response rate for those alive from the main lon gi tu di nal sam ple, a low level of attri tion for a study span ning 33 years.1

To merge data on par tic i pants’ neigh bor hoods, the restricted­use NLSY79 pro vi des wave-spe cifc res i den tial cen sus tract iden ti f ers using 2010 bound aries.2 I use neigh­ bor hood socio eco nomic data from the decen nial censuses and the fve-year Amer i can Community Survey (ACS) cen tered on 2010, which are pro vided by the Longitudinal Tract Database (LTDB) (Logan et al. 2014) and Social Explorer.3 I impute inter cen sal

1 Selecting 2012 respon dents as the ana lytic sam ple reduces miss ing data and allows the use of NLSY­ constructed sam pling weights that adjust for observed var i a tion in attri tion and account for oversampling in the ini tial frame. The ongo ing panel com prises 9,964 indi vid u als frst sur veyed in 1979. Since the ini tial sur vey, 689 respon dents were recorded as deceased. Of all other non re spon dents in 2012, 903 refused to par tic i pate, 466 could not be located, 125 were deemed too dif f cult to inter view, and 481 did not respond for other rea sons. Yearly attri tion for the NLSY79 is low compared with similar surveys, and evi dence sug gests that lon gi tu di nal panel stud ies can rea son ably esti mate cur rent pop u la tion sta tis tics decades after their incep tion (Schoeni et al. 2013). 2 Neighborhood clus ter ing is very low. For 84% of all per son-years in the sam ple, the respon dent is the only indi vid ual liv ing in their tract in that year. Ninety­nine per cent of all per son­years have four or fewer respon dents in a tract in a year. 3 Data from Social Explorer are avail able at https: / /www .socialexplorer .com /.

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298 B. L. Levy

data using lin ear inter po la tion, har mo nize data to 2010 bound aries using the LTDB, and merge this infor ma tion to respon dents.

The depen dent var i able is fam ily wealth in 2012, when respon dents were roughly age 50 (mean age = 51.5; range = 47–56).4 The NLSY79 cal cu lates wealth as total assets minus debts. Assets include homes, auto mo biles, businesses, estates, stocks, bonds, and cash. Debts include prop erty, mort gage, and other debts. The NLSY79 top codes assets for the top 2% of respon dents as their group mean wealth, which is a poten tial lim i ta tion for ana ly ses of the wealth i est but is unlikely to bias aver age neigh bor hood effects. This wealth mea sure was assessed after the Great Recession, which had out sized impacts on non­Whites’ wealth (Pfeffer et al. 2013). Although 2012 may rep re sent a high­water mark for racial inequal ity, it is impor tant ana lyt i­ cally. Non­White house holds recov ered hous ing wealth at slower rates (Bricker et al. 2014; Thomas et al. 2018), and their height ened vul ner a bil ity to eco nomic down turns rep re sents an impor tant aspect of inequal ity. Given its right skew, I trans form wealth using the inverse hyper bolic sine (IHS), which is akin to the nat u ral log with the excep tion that the IHS is defned for zero and neg a tive num bers. The IHS trans for- ma tion also guards against the undue influ ence of out li ers.

The pri mary inde pen dent var i able (“treat ment”) is neigh bor hood (dis)advan tage. I cal cu late ND using fac tor anal y sis of seven neigh bor hood char ac ter is tics: pov erty, unem ploy ment, female­headed house holds, wel fare receipt, adults with out a high school diploma, adults with a col lege degree (neg a tive load ing), and work ers hold ing man a ge rial or pro fes sional jobs (neg a tive load ing). I mea sure these at the tract level and use the frst com po nent’s score for ND, which aligns with recent neigh bor hood effects research (e.g., Wodtke et al. 2011). In addi tion to ND, I mea sure expo sure to met ro pol i tan or micropolitan area5 racial res i den tial seg re ga tion using an entropy index (Theil’s H) based on tract­level shares of the pop u la tion that are White, Black, Asian, His panic, and mul ti ra cial/other (see Reardon and Firebaugh 2002). If seg re ga­ tion causes wealth disparities per se, then this should explain any asso ci a tion between ND and wealth.

Another focal inde pen dent var i able is race/eth nic ity. I use the racial/eth nic ori- gin with which the respon dent most closely iden ti fed in 1979 to code indi vid u als as His panic, non­His panic Black, non­His panic White, or non­His panic other race. I use screener-reported race/eth nic ity from 1978 to com plete miss ing or ambig u ous data. Screener­reported race is highly valid according to respon dent­pro vided race. Among nonmissing respon dents, 97.4% of indi vid u als have the same racial cat e gory for both mea sures.

This anal y sis includes con trol var i ables reflecting char ac ter is tics with a major effect on neigh bor hood attain ment (Harding 2003; Quillian 2003; Sampson and Sharkey 2008; Wodtke et al. 2011). Time-invari ant con trols include race/eth nic ity, sex

4 Wealth data are also avail able for 2016. Among those sur veyed in 2012 and 2016, wealth val ues and per­ cen tiles cor re late strongly between waves. Yet, attri tion due to death increased sub stan tially between 2012 and 2016; roughly 2.3% of 2012 respon dents were deceased by the 2016 wave. One quar ter of all attri tion due to death by 2016 occurred between the 2012 and 2016 waves. Given that wealth inequal ity sta bi lizes around age 50 and death is endog e nous to wealth, I use 2012 wealth as the out come. 5 I assign tracts to core­based met ro pol i tan or micropolitan areas using a Missouri Census Data Center cross walk: https: / /mcdc .missouri .edu /applications /geocorr .html.

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299Wealth, Race, and Place: Neighborhood (Dis)advan tage and Inequality

(male/female), nativ ity, for eign lan guage spo ken dur ing child hood (yes/no), paren tal edu ca tion, paren tal employ ment skill level, child hood fam ily struc ture, and base line mea sures of wealth and ND. Nativity is a dummy var i able distinguishing frst- and sec ond­gen er a tion ado les cents from third­plus­gen er a tion ado les cents. Parental edu­ ca tion mea sures the highest edu ca tional attain ment of res i dent par ent(s): less than a high school diploma, high school diploma, some col lege, or bach e lor’s degree or higher. Parental job skill is the highest job skill level of res i dent par ent(s): unskilled, clerk/sales, skilled man ual, or man ager/pro fes sional. Childhood fam ily struc ture is one of the fol low ing categories: always lived with two bio log i cal par ents, always lived with one and never the other bio log i cal par ent, or some other liv ing arrange­ ment. Family wealth was frst mea sured in 1985, so wealth and ND in 1985 are base- line con trols. I adjust all dol lar val ues to 2012 con stant dol lars using the con sumer price index.

Time­vary ing con trols are char ac ter is tics of the respon dent, their fam ily, and the head of their house hold, which can be the respon dent. Respondent con trols are edu­ ca tional attain ment (same categories as noted ear lier), mar i tal sta tus (never mar ried, mar ried, or other), and age. Family con trols are wealth, fam ily size, income­to­needs ratio, inher i tance value, home value, home debt, home equity, and dummy var i ables for inher i tance receipt, homeownership sta tus, mov ing since the prior sur vey wave, and pub lic assis tance receipt. Wealth is fam ily wealth at the prior wave. Income­to­ needs is the ratio of fam ily income to the fed eral pov erty thresh old. Inheritance value is the IHS of the total value of estates, trusts, and inher i tances that the respon dent or spouse received in the last year. Home val ues are respon dent­reported mar ket val ues of pri mary homes at the prior wave; for rent ers, home val ues are zero.6 Home debt is the total value of mort gages, back taxes, and other debts owed on the res i den tial home by the respon dent and their spouse at the prior wave. Home equity is the dif fer ence between home val ues and home debts. I trans form home value, debt, and equity using the IHS. Head of house hold con trols are the num ber of jobs worked, the per cent age of weeks worked, and the num ber of hours worked per week in the last year.

Whereas I use the 2012 wave of the NLSY79 to mea sure the depen dent var i able, I use most post-base line waves (1986–2010, exclud ing 1992, 2004, and 2008)7 for the ND treat ment. Cumulative ND is the aver age ND score across these years. I address miss ing data using mul ti ple impu ta tion with chained equa tions and 10 imputed data sets. I retain obser va tions with an imputed depen dent var i able (see Wodtke et al. 2016). Section A of the online appen dix sum ma riz