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Using Sibling Data to Estimate the
Impact of Neighborhoods on Children's
Educational Outcom es
Daniel Aaronson
Working Papers Series
Macroeconomic Issues
Research Department
Federal Reserve Bank of Chicago
November 1996 (W P -96-19)
FEDERAL RESERVE BANK
OF CHICAGO
l i b r a r y
NOV 0 5 1996
F D R L R SE V
EEA E RE
B N O C IC G
AK F H AO
Using Sibling Data to Estimate the Impact of Neighborhoods on
Children’s Educational Outcomes
Daniel A aronson
daaronson @ frbchi.org
Federal Reserve B ank o f C hicago
D raft O ctober 1995
Revised O ctober 1996
Abstract
Studies that attem pt to measure the im pact o f neighborhoods on children’s
outcomes are susceptible to bias because fam ilies choose w here to live. As a result, the
effect o f fam ily unobservables, such as the im portance parents place on their children’s
welfare, and other unobservables that are com m on to geographically clustered
households, may be m istakenly attributed to neighborhood influences. Previous studies
that attem pt to correct for this selection bias have used questionable instrum ental
variables.
This paper introduces an approach based on the observation that the latent factors
associated w ith neighborhood choice do not vary across siblings. Therefore, fam ily
residential changes provide a source o f neighborhood background variation that is free o f
the family-specific heterogeneity biases associated w ith neighborhood selection. U sing a
sam ple o f m ultiple-child families w hose kids are separated in age by at least three years, I
estim ate fam ily fixed effect equations o f children’s educational outcomes. The fixed
effect results suggest that the im pact o f neighborhoods exists even when fam ily-specific
unobservables are controlled. This finding is robust to m any changes in estim ation
techniques, outcom e measures, neighborhood m easures, variable definitions, and
samples.
My thanks t Joe A t n i Becky Blank, Greg Duncan, Judy H l e s e n Sandy Jencks, Bruce Meyer, and
o
loj,
elrti,
Lauren Sinai for h
elpful suggestions. All e r r and omissions are mine. The views expressed i t i paper
ros
n hs
are those of t e author and are not nec s a i y those of th Federal Reserve Bank of Chicago or the Federal
h
esrl
e
Reserve System.
L Introduction
T here is substantial evidence that family background characteristics play an im portant role
in th e educational development o f children. H owever, how neighborhoods, schools, and peer
g roups affect children remains unsettled. Although a fairly large cross-disciplinary literature on
neighborhood effects has emerged over the last fifteen years, th e empirical findings are not robust
to data issues, outcom e measures, and estimation techniques . 1 T h e lack o f consistent evidence
could stem from a number o f factors.
Perhaps survey data cannot adequately represent the
com plex nature o f how communities influence children. A nother possibility, often discussed in
the literature, is the role o f bias in the estimating equations.
Bias may arise because families are not randomly placed in neighborhoods but rather
choose their location based on a variety o f factors, including th e im portance they put on their
children's developm ent . 2 F or a variety o f reasons, the result is th at neighborhoods are stratified
along socioeconom ic lines. This is reflected in the fact th at key family characteristics, such as
household income, th e proportion o f single parent families, and average education levels vary
substantially across neighborhoods.
Studies that ignore the endogeneity o f neighborhood
selection risk over- or understating the importance o f neighborhoods for children's outcomes.
T he direction o f the bias is related to the w ay the unobservables associated w ith neighborhood
selection are correlated with the unobservables associated w ith children's outcomes.
I t is
generally thought th at this bias is positive, reflecting th e potential o f attributing family
characteristics, such as parental competence, tastes fo r education, o r tim e spent w ith their
1Significant neighborhood effects have been found i , among others, Summers and Wolfe (1977), Case and Katz
n
(1991), Crane (1991), Brooks-Gunn e a . (1993), Duncan (1994), andBoijas (1995). However, other s u i s most
tl
tde,
notably Evans, Oates, and Schwab (1992) and Corcoran e a (1992), have found no evidence t neighborhoods
t l
hat
matter. While Jencks and Mayer (1990) conclude t a no robust evidence of neighborhood e f c s e i t , t e r
ht
fet xss h i
extensive cross-disciplinary summary points to a number ofstudies where neighborhoods seem t matter.
o
2 To mini ize the self-selection issues involved in residential location choice, this research has wisely
m
concentrated on children and t
eenagers. A notable exception, which uses quasi random assignment, i the work by
s
Rosenbaum and Popkin (1991) on Chicago's Gautreaux program.
Other econometric problems discussed in the l t r t r are measurement error and the ' e l c i n problem*
ieaue
rfeto
(Manski 1993). The r f e t o problem concerns the p s i i i y t a individuals a f c or are d r c l part of the
elcin
osblt ht
fet
iety
neighborhood aggregate and therefore i i d f i u t t discern cause from e f c . This problem i p r i u a l
t s ifcl o
fet
s atclry
severe in cases l k Case and Katz (1991) where neighborhood variables are aggregates of a small number of
ie
individuals. In t i paper, neighborhoods const t t roughly 4,000 individuals and therefore should not suffer
hs
iue
from t i endogeneity problem.
hs
children, to th e neighborhood measures.
The bias might be enhanced if th e unobserved
heterogeneity th at is com m on to a group o f clustered individuals is correlated w ith m easured
neighborhood characteristics.
Studies th a t attem pt to correct for this selection bias have used an instrum ental variables
approach. H ow ever, this m ethod requires use o f a variable th at is a determ inant o f neighborhood
choice but n o t o f th e outcom es o f children.
Such an instrum ent is difficult to find. Only three
papers th at I am aw are o f attem pt to do so.
C ase and K atz's (1991) study o f peer influences surmise th at children are influenced by
peers in th eir ow n neighborhood and surrounding neighborhoods but n o t directly by those living
in noncontingent communities. This assumption allows them to use neighbors' neighbors as an
identifying variable but is susceptible to the criticism that children are likely to be influenced by
peers at school w ho do not necessarily live in the same or adjoining neighborhoods. A second
paper by Evans, O ates, and Schwab (1992) employs a similar strategy, using a geographically
different m easure o f the community-level variable to identify th e selection process.
They use
m etropolitan area unem ployment, median family income, poverty, and educational attainm ent
characteristics as instrum ents, arguing that these variables are likely to be correlated w ith their
peer group variable, the log o f the number o f disadvantaged children in a teen's school, b u t do not
directly affect their child outcom e measures. To make this assum ption, the authors m ust believe
that m etropolitan area characteristics do not affect schooling choice and that parents ta k e their
m etropolitan area as given w hen choosing a particular neighborhood o r school. T here are reasons
to question b o th assum ptions . 3
Finally, Duncan, Connell and K lebanov (1994) use th e
neighborhood th at th e m other lives in after all the children leave the parental hom e as an
instrument, under the prem ise th at once children move into their ow n households, parents'
residential choice is no longer based on concern about neighborhood influences on their ow n
children. H ow ever, inertia in residential choice makes these instrum ent choices suspect.
3 Defending their choice of instrument, EOS note that two-thirds of family moves in a five year period were
within the same metropolitan a e . However, t i evidence, which i also borne out in the PSID, suggests that
ra
hs
s
families are quite mobile and thus may be s lecting i t metropolitan as well as census t a ta e s
e
no
rc ra.
An alternative approach pursued in this paper is based on the observation th at the latent
factors associated w ith neighborhood choice are sibling-invariant; households rarely m ove due to
the differential ability o f their children. As a result, family residential changes provide a source o f
neighborhood background variation within families that can be used to identify neighborhood
influences. T he key advantage to using sibling neighborhood background differentials is it offers a
natural w ay to eliminate the family-specific heterogeneity biases associated w ith neighborhood
selection. Furtherm ore, unlike other studies that use twins o r siblings, such as th e rate o r return
to education o r teenage m otherhood debate, the potential endogenous variable is n o t being chosen
by the individual.
H ow ever, sibling-based fixed effect m odels are no panacea.
They may
accentuate problem s w ith measurement error and still leave open th e possibility o f omitted
variable bias
due
to
unobserved
time-varying
family
characteristics
and
within-family
heterogeneity. U sing a sample from the Panel Study o f Incom e D ynam ics (PSID ) o f multiplechild families w hose kids are separated in age by at least three years, I estim ate family fixed effect
equations o f children's educational outcomes. The fixed effect results suggest th a t th e im pact o f
neighborhoods exists even w hen family-specific unobservables are controlled.
This finding is
fairly robust to changes in estimation techniques, outcom e m easures, neighborhood m easures,
variable definitions, and samples.
T he paper is organized as follows. Section II explains the empirical strategy used in this
paper.
First, a m odel w here communities m atter to childhood educational opportunities is
introduced to show how endogeneity, heterogeneity and functional form assum ptions m ight
influence empirical results.
Some concerns about the fixed effect estim ates are explained,
including th e critical notions o f measurement error and within-family heterogeneity. Section HI
describes the data used to develop the sample and the neighborhood characteristics. Section IV
discusses the results, including linear probability, logit, instrum ental variables, and fixed effects
estimates o f neighborhood influences on the likelihood o f children graduating from high school.
Section V outlines a number o f tests o f the robustness o f the findings. The models are rerun using
different neighborhood proxies, different outcom e measures, different samples, and different
neighborhood variable definitions. M any o f these tests are used to reconcile th e different findings
o f this paper and Plotnick and Hoffinan (1995). Plotnick and Hoffman also use PS1D siblings to
identify neighborhood effects but conclude th at family fixed effects eliminate th e im pact o f
neighborhoods o n post-secondary schooling, teen births, and welfare recipiency. O u r results can
be partly reconciled by differences in sample and variable definitions.
Som e concluding rem arks
are offered in th e last section.
n. Empirical S tra te g y
M odel
T o help clarify these issues, I present a simple variant o f Becker's child quality m odel w ith
the additional assum ption th at com munities influence a child's future outcom es . 4 Suppose a parent
maximizes a CES production function over her m children's future outcom es (education, say),
k t+1, current consum ption, c*, and the quality o f the neighborhood, %
(l)
u(k
+rV
^ , 6 i * v + 1+ 52c? + 53n?
1= 1
Children are indexed by i and tim e by t.
wherep<1
Quality o f neighborhood enters the utility function
independently and additively to account for th e im portance that households place o n crime,
ethnicity, services, housing conditions, and other neighborhood-specific factors. The parent faces
a budget constraint o f the form I(n t )= c { + Pn n t -
Neighborhood conditions are allow ed to
influence the household's income. The parent uses income to purchase consum ption item s and
better neighborhood conditions at a price relative to consumption o f Pn. Finally, each child's
future education is determ ined by a production function o f the form
(2 )
lo g k . t + 1 = P g l o g G + P a l o g a it + P n l o g n t w here Pg,P „ P ne [ 0 ,l) .
Family-specific variables are captured in the G term.
This w ould include parents' interest in
education, and any family background characteristic and ability com ponent th at is constant across
siblings. The v ecto r a.^ m easures any variables th at exhibit heterogeneity betw een siblings, m ost
4 For examples of more complicated models with peer e fects, see deBartolome (1990) and Epple and Romano
f
(1993). Many other l t r t r s have argued that production functions should include c a a t
ieaue
h r c eristics of the
population, including growth theory (Jacobs 1969, Romer 1986, Benabou 1993) and lo a public finance
cl
(Brueckner and Lee 1989).
notably differences in ability, ambition, or, for siblings separated by age, family conditions.
w ould also include differences in parental expectations o r treatm ent o f siblings.
It
A v ector o f
neighborhood and peer measures also enters the production function.
An important element o f this model is the reason for family moves. W hen a family m oves
into a community, it has an expectation about the quality o f the neighborhood. H ow ever, ,there is
also uncertainty about th e caliber o f the family-neighborhood m atch since the neighborhood good
is, at least in part, an experience good. Therefore, th e actual neighborhood good is a sum o f the
random error com ponent that measures uncertainty in m atch quality and the expectation o f the
neighborhood prior to th e move. An unexpected negative shock in the match quality param eter
may cause households to migrate to a new neighborhood. This uncertainty is used to obtain the
variation in sibling background that is needed for the m odel to work. H owever, another source o f
neighborhood migration may be due to changes in the family's background. Changes in marital,
income, o r employment states may cause families to reevaluate their neighborhood choice. In the
empirical w ork, it will be im perative th at these family changes are controlled in o rd er to
decom pose the effect o f neighborhood change from other family changes that m ight affect
children’s outcomes.
T he empirical strategies used to do this are discussed m ore fully below.
F o r simplicity, assume neighborhood location does not affect parents' income (dUdo. = 0 ) . 5*
Also assume there are tw o siblings that are separated in age by one period. Sibling / lives during
period 1 and sibling./ lives during period 2. Maximizing U (ct ,k t + j,n p subject to th e budget
constraint and equation ( 2 ) leads to equilibrium ratios o f future outcom es and neighborhood
conditions that are a function o f the heterogeneous com ponents between siblings and the
functional form assumptions associated w ith p .
(3)
(4)
nl
----- logi L
=2 - P d + Pn) %
n 2
'
P_(2-p)
a.
log 1 2 =
log- il
k .^
2 - p ( l + Pn)
j2
J3
log
1
5 This assumption pertains to the spatial mismatch hypothesis. Most researchers believe that the affect of
neighborhoods on a u t s income i n g i i l . Regardless, the assumption does not a f c the main r s l s of the
dl'
s elgbe
fet
eut
paper.
B asing th e estimating equations on within-family differences provides th e empirical
advantage o f eliminating the im pact o f any family-constant com ponent, including decisions about
neighborhood selection. Selectivity has an im pact only if parents choose neighborhoods based on
th e differential ability o f their children (equation 3 ) . 6 I f this selection process exists, th e sign o f
th e bias is dependent on th e functional form assumptions.
I f p > 0, parents u se a reinforcing
strategy in their choice o f neighborhood by taking special effort to live in b etter neighborhoods
fo r m ore able children.
A s p approaches zero (Cobb-Douglas), ability is independent o f
neighborhood choice. Finally, if p < 0, parents adopt a com pensating strategy w here the parents
try to place kids w ith lesser ability in better communities.
Econom etrically, this preference can be seen m ore clearly by looking at th e error term o f
an educational production function w here I rew rite ( 2 ) as:
(5)
k .r . , . = B - x - ,+ 6 x.~ +B a . « + B n . « + e . A
v '
i f , t + l Kx f f * x iff ' a iff * n in
iff
w here / indexes families. H ere the family and individual factors in the educational production
function are separated. The x term s represent family characteristics th at vary over tim e and
siblings (xjft) and those that are tim e and sibling invariant (xf).
The error term is broken into
three com ponents
(6 )
e jft =
"*"n if t<pf + M ift*
(Pf is a sibling invariant error com ponent. I f <p^ is correlated w ith family residential preferences,
then first differencing equation ( 6 ) across i will eliminate selectivity concerns. H ow ever, if the
selection o f neighborhood is correlated w ith the individual-specific error com ponents, then
selection bias remains a problem. In the results presented below, child ability is assum ed to be
6 This d f erential s l c i i ycould be due to other factors relatedto neighborhood choice. For example, suppose a
if
eetvt
parent decides whether to work based on the a i i y of th i c i
blt
e r h ldren. In t i ca e the budget constraint and the
hs s,
educational production function includes the parent's decision on how much e f r to put into t e r work and t e r
fot
hi
hi
children.
(2a)
(l-Sjt)Rku = c t + Pn nt
(21,
l o g k i t + 1 = P g G + P a l o g a i t + P s logsi t k i t + P n I og nt
The parent with human c p t l k t chooses whether t work a wage R or to put more e f r and time in o the
aia j
o
t
fot
t
production of the c i d s human c p t l I t i work e f r i used t buy b tter neighborhood goods fo th i
hl'
aia. f hs
fot s
o
e
r er
children, then the a t v t e are s b t t t s Because of the endogeneity of S t we would need t worry about
ciiis
usiue.
j,
o
another simultaneous equation that maps the d f e e t a e f r ofthe parents as a function of the d f e e t a a i i y
ifrnil fot
ifrnil blt
ofthe chi d e .
lrn
independent of neighborhood choice. This seems to be an innocuous assumption, although I hope
to better understand the correlation between the neighborhood good and the error term and its
im portance in this system in future work.
First differencing equation (5) eliminates th e family-specific unobservable and leaves a
reduced form equation o f the general form
(7)
v'
A .k.„ =B A .n.ft+B A .x .« + B A .a .^ + A s .^
1 iff Kn i lft “ x l lft Ka i lft
tft
w here A differences across siblings. W ithout differential neighborhood selectivity, then A . n ^
can be thought o f as an element o f the a.t /a jt v ector in equation (4). Equation (7) is th e main
estimating equation used in this paper.
H ow ever, at least four main com plications rem ain in th e estim ation o f equation (7):
unobserved heterogeneity within families, m easurem ent bias, th e discrete nature o f th e outcom e
measures used, and complications w ith the sample due to age restrictions placed o n the siblings.
The latter tw o estimation problems are discussed first as they are handled by conventional
m ethods . 7 *
Complications w ith the Fixed Effect E stim ator
The first complication is that the outcom e variable used in much o f this analysis (high
school graduation) is discrete.*
In a linear regression fram ew ork w ith continuous dependent
variables, one can handle family fixed effects by applying OLS to the data after taking deviations
from group m eans . 9 H owever, the nonlinearity o f discrete choice models excludes this strategy.
Furtherm ore, the asymptotic properties o f the logit m odel depend on the num ber o f observations
per group increasing. Therefore, as shown by Chamberlain (1980), discrete choice fixed effect
equations w ith small numbers o f observations p er group are inconsistent. Instead, h e proposes a
logit fixed effect model which is estimated using conditional likelihood functions. A n alternative
approach that uses within-group variation employs a specification suggested by M undlak.
He
7 A f f h complication arises from the effect of neighborhood specific error components on the standard errors.
it
This i accounted forusing Huber's formula.
s
* In the f n l s c i n Irun the fixed e f c estimator on a continuous variable-grades completed—as w l .
ia eto,
fet
el
9 Of course, the linear probability model introduces other well-known problems, including heteroskedasticity and
predicted probabilities that are not constrained to the zero-one interval.
allows individual effects to enter the probit model by simply specifying separate within-family and
across-family variables
(8)
k^ =
+P2xft +P3aift +P4aft +<l>
1nift +<l>
2nft +8ift
w here variables w ith b ars represent family averages and <> is the within-family neighborhood
fx
m easure o f interest. R esults are presented using conditional logit, M undlak probit, and linear
probability models.
H ow ever, it appears to m ake little difference w hether logit o r linear
probability techniques are used in the fixed effect models. Furtherm ore, because coefficients from
conditional logit equations are in different units than th e simple logit coefficients, they are not
comparable.
A s a result, because o f their ease o f comparability and use, linear probability
equations are em ployed to conduct much o f the estim ator comparisons.
A second concern is sampling restrictions.
In order to get meaningful variation in the
residential location o f siblings, th e sample includes only individuals w ith a sibling th ree o r m ore
years younger o r older than themselves . 10 M ost o f the difference in neighborhood background
betw een siblings w ho are close in age is likely to be com posed o f measurement noise. T he further
siblings are apart in age, the m ore likely they will experience tru e differences in neighborhood
com position and enable m e to identify real differences in background influences. Choosing the
age restriction is a b it ad-hoc, w ith sample size considerations balanced against the advantages o f
using m ore age-separated siblings. I use a cu to ff o f th ree years. Som e experimentation suggests
that using fo u r o r five years does not m ake a significant difference but decreases th e precision o f
the estim ates due to the smaller sample sizes.
R egardless o f th e cu to ff choice, this sampling
restriction com plicates th e com putation o f the fixed effect estim ator since one family fixed effect
will com bine siblings th at do not fit the age criterion. Therefore, I construct four fixed effect
estim ators to see how assumptions about grouping observations affect the evaluation.
The first three estim ators are constructed by pairing siblings th at fit the age criterion for
selection into th e sample. F o r example, in a family w ith three kids, aged x , x + 2 , and x + 5 , 1 w ould *
9
1
1 Other important restrictions are: the individual turn 18 by 198S, the individual be in the PSID sample for two
0
years between the ages of 10 and 14, and be in the sample for one year a t rage 18 so that high school graduation
fe
,
can be ascertained. I i i not possible to ascertain grades completed and the individual has data only through age
fts
1 , then he i dropped from the sample.
9
s
include tw o pairs o f siblings in the sample: the oldest w ith th e third child (5 years apart in age),
and the oldest w ith the second child (three years apart in age).
This setup might oversample
certain individuals and therefore could introduce bias to th e estimates.
Therefore, a second
estim ator w eights the variables by the number o f times each individual in a sibling pair is in the
total sam ple o r ( l / n i + l / 1 1 2 ), w here nj is the number o f tim es individual i is included in any sample
pair. I f there is concern that this weighting procedure will not com pensate for the over sampling
o f individuals, as a third alternative, I select one pair o f siblings—
the oldest and youngest—
from
each family. Finally, as a fourth option, I estimate an equation th at includes a single fixed effect
for each family. This alternative eliminates the multiple sampling problem but is problem atic in
my exam ple because it contains groupings o f siblings w ho do not fulfill the age criterion . 11 1 I f
2
inference is similar for all four estimators, I can be more confident in the robustness o f th e results.
U nobserved Heterogeneity within Families
A m ore serious concern relates to the reasons for neighborhood change and th e individual
error com ponents that describe the unobservable differences betw een siblings. In particular, tw o
problem s could potentially complicate the interpretation o f the sibling difference estim ator. First,
a t som e level, siblings may differ in, among other factors, ability, ambition, o r parental
expectations and treatment; these unobserved factors m ay be correlated w ith neighborhood
characteristics . 13
However, this is likely to be a serious concern only if parents choose
neighborhoods based on these sibling differentials, an unlikely scenario. W hile there appears to be
scant evidence on the impact o f siblings' differential ability on neighborhood choice, the research
that exists places little significance on differential selection. Altonji and D unn (1995) estim ate the
effect o f IQ scores on school choice and find little evidence th at ability m atters to this decision
w ithin families.
1 For example, child x and x+2 would be included in the same family fixed e f ct, which i inconsistent with the
1
fe
s
r s r c i nthat only s b i g threeyears apartbe compared inthe fixed e f c models.
etito
ilns
fet
1 For example, Summers and Wolfe (1977) find t at lower s i led students are more affected by classmates and
2
h
kl
school quality than t e rmore able p ers. Other omitted differences between s b i g , such as parents expectations
hi
e
ilns
or treatment, might have opposing e f c s Plomin and Daniels (1987) review the genetics and psychology
fet.
l t r t r and find a wide range of components—including the closeness to the mother, the f iendliness of the
ieaue
r
s b i g , the r of s b i g i family decision making, and parental expectations-that might a f c the outcomes
ilns
ole
ilns n
fet
ofs b i g , parti u a l those with d ffering a i i i s in d s i c ways.
ilns
clry
i
blte,
itnt
T o be safe, it w ould be useful if some m easure o f sibling differences could b e controlled.
U nfortunately, the PSED has no test score reports o r other useful childhood m easure. A potential
partial solution to th e missing ability m easure is to use w hether th e child w orks during his youth
as a m easure o f unobservable ambition or drive. H ow ever it is also possible th at this variable
w ould pick up th e availability o f jo b s 13 or itself be a com ponent o f o ther neighborhood influences.
I f this latter interpretation is correct, including the 'w hether w orked' variable will bias the
neighborhood coefficient downward.
O ne characteristic w here sibling differentials might m atter is age. I f parents learn h o w to
care for their children over time, it is possible that their younger children will benefit by being
placed into b etter neighborhoods than their older siblings. Fortunately, this possibility is easily
observed and controlled for in th e analysis by including th e birth order o f the children.
A second com plication arises because siblings separated by age may experience different
family environm ents due to, say, marital changes, different family income circum stances, o r less
m easurable changes in household characteristics.
A s m entioned above, this heterogeneity is
particularly w orrisom e if the neighborhood variable is simply picking u p changes in family
conditions th at precipitate changes in residential location. In o ther w ords, sibling differences in
neighborhood conditions may be a function o f changes in unm easured family conditions and n o t
changes in community influences.
T o provide insight into w hether this issue is im portant empirically, table 1 rep o rts
measurable changes in to tal family income, labor income, marital status, and em ploym ent status
for residential stayers and m overs, by type o f geographic m ove , 14 to see if residential m oves are
correlated w ith changes in family conditions.
The last four columns explore family changes
corresponding to m oves into better (columns 6-7) and w orse (colum ns 8-9) neighborhoods as
crudely m easured by th e poverty rate in the origin and destination neighborhoods. T he sample
includes multiple child families in the PSED during 1971-1974 or 1980-1983. T hese tim e periods
1 See Holzer (1991) fora good summary ofthe spatialmismatch lit r t r .
3
eaue
1 Moves are categorized by geographic l v l s a e county, neighborhood, and residence. Neighborhood is the
4
ee: tt,
census t a t I the person does not l v in a census t a t then enumeration d s r c ( h ru a equivalent of census
rc. f
ie
rc,
itit t e r l
t a t ) i used. I the person does not l v i an enumeration d s r c ,then f v d g t zip codes are used.
rcs s
f
ie n
itit
ie ii
w ere chosen so th at it w ould be clear w hen moves occur.
B ecause the geocode database is
missing addresses fo r 1969, 1975, 1977, and 1978, the level o f a m ove cannot be identified for
these years and the year th at follows w ithout making som e ad hoc assum ptions about timing.
A s can be seen in th e income, marital status, and employment status variables, longer
distance m oves are m ore likely to occur among better-off families. State and county m overs look
like stayers w ith regard to income, marital status and employment status. B u t neighborhood and
residential m overs are poorer, less attached to the labor market, and less likely to be married.
M ost telling fo r this paper is the w ay these variables change in relation to moves. In the
row s titled “Change (t-i,t)n, I calculate the difference betw een the year after th e m ove (t) and the
tw o years preceding the m ove ( t-l,t-2).
Asterisks represent w hether these changes are
significantly different from th e changes prior to stayer years. A pound sign represents w hether
changes preceding m oves to better neighborhoods a re significantly different from changes
preceding m oves to w orse neighborhoods at the five percent level. I also calculate transitions
between marital and employment states in the tw o years before a m ove in th e row s directly below
the “change” row s. These transitions represent the percentage o f family-year m oves (o r stays)
that are prefaced in th e tw o previous years by that particular change. F o r example, 11.4 percent
o f state changers experience a divorce (married—
>divorced) in the previous tw o years before the
state move.
The results suggest som e significant change in observable family environm ent preceding
moves, although these changes vary by the distance and type o f mobility.
T he tw o income
categories show fluctuations in years before moves but these changes are generally not
significantly different from th e years before stays. There is a decline in total income and labor
income preceding moves to neighborhoods w ith higher poverty rates (colum n
6
) and an increase
in total income but not labor incom e in years preceding moves to neighborhoods w ith low er
poverty rates (colum n 8 ). M oves into higher poverty neighborhoods are also preceded by a small
spike in the variance o f total and labor income, suggesting th at income instability could be a factor
in these community changes. H ow ever, the overall picture from these income changes seems to
suggest little relationship betw een family changes and moves. O ther w ork using larger samples is
consistent w ith this conclusion.
T here is m uch m ore activity in marital and employment status changes prior to m oves.
Transitions into and o u t o f m arriage are significantly different for m ove years th an stay years
am ong every m ove category.
Changes preceding moves into high poverty neighborhoods are
especially noticeable, w ith strong evidence o f transitions into divorce but much less evidence into
marriage. Transition into m arriage is often followed by moves into low er poverty neighborhoods
and different counties and states.
H ow ever, there also appears to be high levels o f recently
divorced households m oving into low er poverty neighborhoods. Em ploym ent status changes play
an im portant role in sh o rt distance m oves (residential and neighborhood), particularly by those
moving to low er poverty neighborhoods.
M oves into low er poverty neighborhoods are often
preceded by transitions into retirem ent from full-time employment and into em ploym ent from
unemployment o r tem porarily laid off. Transitions into and out o f retirem ent are often related to
moves to higher poverty neighborhoods.
In sum, the inform ation in table
1
offers evidence that moves are associated w ith changes
in family background. H ow ever, the evidence is fairly w eak in tw o ways. First, incom e changes
are not highly correlated w ith moves. Second, negative shocks to family com position are as likely
to be followed by m oves into better neighborhoods as w orse neighborhoods. A s a result, there
does not appear to b e a consistent pattern in th e relationship betw een changes in observable
family environment and changes in neighborhood choice.
N evertheless, because a large part o f the variation used to identify the neighborhood effect
is from moves, th e analysis m ust control as much as possible for these different circum stances.
One im portant strategy is to directly control for family mobility. A nother promising feature o f the
fixed effect estim ates is th at if the unobserved family change is constant across siblings, then the
fixed family effect should eliminate this concern.
H owever, if these changes affect siblings in
different ways, w hich is possible given the age differences in the sample that I will w o rk with, then
some discretion m ust be used in interpreting the results as caused by changes in com m unity
influences rather than differing latent family conditions.
M easurem ent E rror
Since the neighborhood inputs, nj, are imperfect measures of the true effects o f
communities, a final concern w ith the fixed effect (and simple linear probability and logit)
*
neighborhood equations is classical measurement error. In particular, assume th a t n. = n + v . ,
w here vj is an iid random variable and n* is the tru e community measure. D ifferencing across
siblings exaggerates the measurement bias by creating a correlation betw een th e differenced
inputs, n, and the differenced idiosyncratic shocks, v.
eliminated while the noise remains.
A s a result, much o f the tru e variance is
The direction o f this measurement error bias is to w ard zero.
The size o f the bias would be proportional to th e difference betw een the signal to noise ratio in
9
9 9
the estim ator w ithout fixed effects ( a /(or + o % ) and the fixed effect signal to noise ratio
v
v
n
9
9
9
o ^ v/ ( a ^ v + a
*). A com mon solution to this problem is to estim ate fixed effect, instrum ental
An
variable (FE-IV ) equations. B u t this reintroduces the problem o f finding believable instrum ents
that are related to neighborhood differences but not to differences in outcom es betw een siblings
that arise from other sources. I tried some specifications using a differenced version o f Evans,
Oates, and Schwab’s county-level measures, although this instrum ent is susceptible to th e same
criticisms as before. I f this is a classic m easurem ent error story, one neighborhood m easure could
be used as an instrument for another, but this relies o n the precarious assum ption that the
measurement error is uncorrelated between variables. Fortunately, the results presented in the
next section show no evidence that first differencing increases the im portance o f classical
measurement error . 13 In fact, m ost o f the fixed effect results are o f the same size o r larger than
the simple OLS or logit models w ithout fixed effects.
1 Measurement error in the other right hand side variables could introduce upward bias in the neighborhood
3
estimates if there is a correlation between these measures and the neighborhood variables. For example, if there is
measurement error in the change in family income and a correlation between family income changes and
neighborhood changes, then the neighborhood measures will be positively biased. In most cases, however,
several years of family data are available before and after the moves. Under the assumption that measurement
error is iid, one could exploit this fact to construct an IV estimator.
m
Data
T he data used in this pap er are from the Panel Study o f Incom e Dynamics (PSID ) and its
accompanying geocode file for 1968-1985. Individuals are included in the sibling sam ple if they
have a sibling at least three years apart in age and are in a respondent household fo r a t least tw o
years while they are. betw een ages 10 and 14. Furtherm ore, the individual must have at least one
year o f d ata after age 18 so that high school graduation can be ascertained. These constraints
result in a sample o f 2,178 individuals from 742 families.
A problem w ith using the age restriction is th at th e sample is com posed solely o f children
from larger families.
excluded.
This can bias th e results in an unknow n direction w hen fixed effects are
In this paper's model, parents invest less p er child in large families, resulting in a
positive bias in the neighborhood effects measure.
H ow ever, if there are spillovers from large
families th a t m ake it easier for children to relate to community externalities, the bias m ight w o rk
in the opposite direction. Fixed effect specifications sweeps out this family-specific heterogeneity.
N evertheless, to gauge th e im portance o f this nonrandom sampling on the model w ithout fixed
effects, I also construct a sample that includes all individuals th at fit the data demands except th e
sibling requirements. The all-youth sam ple includes 4,410 people from 1,199 families.
Table 2 includes descriptive statistics on th e main variables used in the analysis fo r th e
sibling sample and the all youth sample. All statistics are w eighted using the PSDD-constructed
probability o f selection into the sample.
T he sibling sample appears to be roughly com parable to
the all youth sample. Some small differences reflect th e larger family sizes o f the sibling sample.
In particular, the sibling sample has low er education levels for the parents and children, low er
household income, low er mobility, m ore minorities, and m ore tw o parent families.
The N eighborhood M easure
The believability o f the neighborhood proxy is key to the measurement o f neighborhood
effects. Previous studies have used many different measures, including the fraction o f
disadvantaged students in the individual's school (Evans, Oates, and Schwab 1992), the
percentage o f families living in th e neighborhood w ith incomes below $10,000 and above $30,000
(B rooks-G unn et al. 1993), the percent o f families on w elfare (Corcoran et al. 1992), the
percentage o f female-headed households (C orcoran et al 1992), and racial com position (Summers
and W olfe 1977). D uncan (1994) tests many o f these m easures within the same data set and
specification. Case and K atz (1991) aggregate household data to derive neighborhood averages
o f the num erous outcom es they study.
I concentrate the analysis on tw o variables that should pick u p many o f the dimensions
hinted at in the above analyses. First, because th e outcom e m easure o f interest is high school
graduation, I use a similarly defined variable th at m easures the percentage o f young adults in a
census tract w ho w ere aged 16 to 19 in 1980 (16 to 21 in 1970) and w ho had not graduated from
high school and w ere not in school. This variable can be thought o f as an extremely rough proxy
for peer effects. Second, I use a variable that m easures the percentage o f households below the
federal poverty threshold. This variable might be thought o f as a proxy for the effect o f adult
neighbors and relative neighborhood conditions on youth achievement.
I m ake no claims that
these tw o variables will pick up all community-level influences. H ow ever, as much as the various
influences are highly correlated, these tw o measures should be representative o f th e size o f the
neighborhood effects on educational achievement.
In the final section, I also report some
preliminary results on three other neighborhood proxies — percentage o f female heads, average
family income, and percentage o f population that is w hite -- to see if any patterns em erge when
using these different measures.
The data for these neighborhood variables com e from tw o sources. Geographic identifiers
are reported in the PSID's geocode file. The geocode file is a set o f addresses collected from
mailings to respondents.
F rom these addresses, identifiers are assigned for various levels o f
geographic aggregation.
The smallest geographic area classified by the Census bureau is the
census tract or block numbering area (BNA), w hich is the basis for the neighborhood measure
used in this paper. A census tract is an area of, on average, 4,200 people that local authorities
deem to be a 'neighborhood.' B N A s are the equivalent o f census tracts for untracted urban areas.
W hen census tracts are unavailable, enum eration districts (ED), the rural equivalent o f census
tracts, are used. W hen tracts, BN As, and E D s are unavailable, I employ five digit zip codes,
w hich tend to encompass a larger area than the other identifiers.
T hese geographic identifiers are m atched to 1970 and 1980 Census identifiers and data o n
num erous area dimensions, including family structure, income, employment, race, education,
housing, and mobility. I linearly interpolate neighborhood variables for the years 1970 to 1978
and set 1980 to 1985 and 1968 to 1969 values equal to their closest census year. T he effect o f
this im putation schem e is examined in section V.
T h e main neighborhood measure used is an average o f th e community conditions that the
person lived in from ages 10 to 18. This averaging technique implicitly w eights each age equally
in th e neighborhood impact estimate. It does n o t pick up any additional effect th at m ay occur
from neighborhoods lived in prior to age 10 . 16 A s an alternative com putation, I also explore th e
robustness o f th e results to using the age 14 m easure o f neighborhood conditions.
This latter
m easure is m ore commonly used in the literature but does not describe th e full history o f
background influences that the person experiences.
Therefore, it may be m ore susceptible to
m easurem ent error relative to the averaged variable.
T he O utcom e and Control Variables
A lthough there are many dimensions in w hich peer and neighborhoods m ight be
influential, I concentrate on educational outcom es. M o st o f the analysis focuses on w hether the
individual graduated from high school. Section V reports som e findings using college attendance
and grades com pleted . 1 7 I chose these education variables because, unlike teenage pregnancy,
they do n o t limit the sample to a single gender.
O ther variables th at are often studied in this
literature, such as crime rates and drug use, are n ot available in the PSID .
1 For evidence ofneighborhood effects on younger children, see Brooks-Gunn et al (1993). No outcome measure
6
currently e i t for younger children in the PSID, which makes i d f i u t to determine neighborhood e f c s on
xss
t ifcl
fet
children prior to age 1 .
0
1 Using the PSID education variables might cause an attrition problem because grades completed are not
7
reported until an individual has finished f ll-time schooling. As a r s l , a number of individuals over age 18
u
eut
leave the sample without ever reporting data on grades completed. The high school graduation variable i coded
s
as 1 i the individual ever reports completing 12 grades or the individual remains in the sample a t r age 20 but
f
fe
si l reports being a student Individuals who a t i e from the sample before age 20 without reporting grades
tl
trt
completed are excluded. The analysis using college attendance and grades completed includes only individuals
who report grades completed a t rage 1 . Grades completed are the greatest number ofgrades reported by age 25,
fe
9
unless the individual has not reported a grade by age 25. In t i c s , the f r treported grade completed i used.
hs ae
is
s
Covariates in the basic regressions include gender, race, parental education, household
income, parents' marital status, the number o f children living in the household, w hether the
teenager w orked during her youth, and year b o m and region dummies.
The income measure
includes all labor income, transfers and asset income, n et o f th e teenager's income.
Like the
neighborhood variables, the time-vaiying family background variables are averaged over the years
from age 10 to 18 th at the youth lives at home.
This averaging technique results in m ore
'permanent' measures o f these variables. H owever, if tem porary changes, such as large income
fluctuations, marital changes, or residential moves, are im portant, then this averaging might miss
im portant factors in the likelihood o f continuing schooling. Therefore, I also include controls for
a num ber o f transition variables that measure instability in th e family environment, including the
variance o f income during the youth's years in the household, th e percentage o f years that the
household moves, detailed marital transitions, and detailed em ployment transitions.1 I also
*
experiment w ith controls for birth order, whether the individual has an older sibling th at graduated
from high school, and w hether the teenager moves into their ow n household by age 18 to see if
these m easures o f individual heterogeneity change the estim ate o f com munity influences . *19
IV. Results
H o w M uch Variation Is There in the W ithin-Familv M easures?
B ecause I elect to use within-family variation rather than across-family variation to
identify community influences, I first report some findings in table 3 on th e am ount o f variance
that exists within families for four variables: grades com pleted, family income, and the tw o
neighborhood variables. The first tw o rows display the m ean and standard deviation for th e entire
sample. The third row gives the standard deviation w ithin families.
W ithin family standard
1 The employment transitions a e: employment to unemployment, employment to r t r d employment to
1
r
eie,
temporarily l i o f unemployment t employment, r t r d to employment, and temporarily laid off t
a d f,
o
eie
o
employment. The marriage t a s t o s a e marriage t divorce, marriage t widow, divorce to marriage, single t
r n i i n r:
o
o
o
marriage, and widow to marriage.
1 I include the l t e variable only as a t s of the robustness of the results. Because of the endogeneity of the
9
atr
et
measure, i i probably best not included.
ts
deviation estim ates adjust fo r degrees o f freedom lost in taking deviations from family m eans . 30
The fourth ro w rep o rts th e fraction o f total variance th at is within-family.
T here appears to be plenty o f variation in the education variable, w ith approxim ately 56
percent o f th e to tal variance in grades completed com ing from within-family differences . 31
U nfortunately, th ere is m uch less variation in the neighborhood variables.
A bout 7.5 to 13.6
percent o f the to tal variance in the time-averaged neighborhood variables is attributable to withinfamily differences.
Interestingly, this fraction o f variance is consistent w ith other time-varying
family variables, such as total averaged family money income, w here 10.3 percent o f th e variance
is from within-family differences. Therefore, family changes are n o t likely to dom inate changes in
neighborhood background characteristics.
This finding, combined w ith those from table
1
on
incom e and family com position changes before moves, makes it less likely th at family changes will
drive the neighborhood change param eters. As a result, w hile I rem ain cautious about th e effect
o f family changes, there is reason to be optimistic that neighborhood effects can b e reliably
estimated, especially if observable family changes are controlled.
W hen th e ag e 14 m easure o f the neighborhood variable is used, the within-family standard
deviation rises to 0.057-0.060 (from 0.034-0.036 w ith th e averaged neighborhood m easure), and
the fraction o f th e to tal variance due to within-family deviations is 18.2 percent fo r th e poverty
rate and 30.6 percent for the dropout rate, approximately tw ice as high as th e averaged
m easures . 33 H ow ever, this difference is most likely picking up additional
m easurem ent error
because o f the shorter and less reliable window it measures. R esults reported in the next section
that use these m easures are consistent with this measurement error story.
2 The within family standard deviation i calculated as
0
s
1-1
f I-F
where N.~is the value of
if
the neighborhood variable for individual iin family f N^is the family mean of the neighborhood varia l , I i
,
be
s
the number of individuals in the sample, and F i the number of f m l e .
s
aiis
The correlation between s blings in grades completed, high school graduation, and college attendance i
i
s
.approximately . 5 . 8
3-3.
22 The correlation among a l siblings i around . for the two neighborhood measures. Correlation using the age
l
s
9
14 neighborhood measures are .85 for the poverty r t and . 5 f r the dropout r t .
ae
7 o
ae
2
1
T he bottom o f table 3 presents m ore information on the am ount o f differentiation that
exists betw een siblings using the averaged neighborhood variables.
E ach ro w displays the
percentage o f sibling pairs w hose average neighborhood background m easure differs by 5, 10, 20,
30, o r 50 percent.2* Approximately 71 (76) percent o f th e sibling pairs live in an average
neighborhood during ages 10-18 that is at least 5 percent different ia poverty (dropout) rates.
The percentage th at lives in neighborhoods differentiated by at least 10 percent rem ains fairly high
at 58 percent fo r the poverty measure and 62 percent for the dropout measure.
At
2 0
, 30 and 50
percent differentiation, few er pairs qualify: 35 to 41 percent o f th e pairs g ro w u p in 20 percent
different average community environments and 7 to 11 percent g ro w up in 50 percent different
average com munity environments. So while the majority o f the variation is clearly across families,
a small am ount still exists across siblings.
N ow , I tu rn to the estimation. First, I present som e base case linear probability and logit
models using high school graduation as the outcom e measure.
N ext, I estim ate tw o stage
equations th at are similar in spirit to Evans, Oates, and Schw ab’s (EO S from here on)
instrumental variables technique for correcting neighborhood selectivity. I then present the main
part o f the analysis, the fixed effect equations. All results to this point use high school graduation
as the outcom e m easure and poverty and dropout rates as th e neighborhood proxy.
T he next
section tests th e robustness o f th e findings to changes in th e neighborhood proxy, outcom e
measure, and sample. Some further tests to determine the im portance o f d ata variability are also
reported in the following section.
Single Stage Estim ates
Table 4 displays neighborhood coefficients from simple, one-stage ordinary least squares
and logit high school graduation equations using the 2,178 children in the sibling sample.
Full
linear probability and logit regression results for a few selected equations are reported in
appendices l a and lb . The appendices include findings from the sibling sam ple and the all youth
sample but table 4 reports results only for the sibling sample.
2 This i not an absolute deviation but r ther a r l t v d v a i n Therefore, a poverty r t of 13 percent for one
3
s
a
eaie eito.
ae
s b i gversus 10 percent f r another i reported as a 30 percent difference in table 3
iln
o
s
.
The top row of table 4 reports the r sults from regressions that allow the neighborhood
e
variable to enter log-linearly.2 For both the neighborhood poverty and dropout r t s higher
4
ae,
values signify a reduced probability of graduating from high school. As an interpretation of the
size of these (linear probability) coeffi i n s a 10 percentage point increase in the neighborhood
cet,
poverty rate would reduce the likelihood of graduating from high school by 2.1 percent.
The
corresponding impact of a 10 percentage.point increase in the dropout rate i 3.6 percent. These
s
effects seem f i l large, so I reran the regressions using the all-youth sample of 4,410 children
ary
that does not require the existence of a 3 year age-separated s b i g This sample produces 35
iln.
percent smaller point estimates than the s b i g sample, but t s s of the sample coefficients show
iln
et
that these differences are not s a i t c l y s g i i a t The all-youth sample results are in line with
ttsial infcn.
the small neighborhood effects findings from previous work. Therefore, i should be kept in mind
t
that the estimates presented below are representative of the impact of neighborhood, family, and
individual characteristics on the educational attainment of children from la r g e f m l e .
aiis
These regressions include controls for race, gender, household income, parents' marital
s a u , whether the father or mother graduated from high school, whether the kid worked, the
tts
number of kids in the household, and the county unskilled wage r t . Because these variables are
ae
mostly averages over the c i d s youth from ages 10 to 18, they may not capture important
hl'
fluctuations in environmental conditions. These fluctuations may be c i i a i changes in
rtcl f
neighborhood conditions are picking up unobserved heterogeneity in family or individual
background rather than the true effects of the community. Therefore, 1 experimented with a
variety of such measures to see i omitting them affects the magnitude of the neighborhood
f
c e f c e t In particular, I t
ofiin.
ried the percentage of years that the household moved, the variance
of family income over the youth’ data, detailed transitions into employment, detailed transitions
s
into marital s a u , whether the individual moved into her own household by age 18, birth order,
tts
and whether the individual has an older s b i g that graduated from high school. The results i
iln
n
table 4 include the marital and employment t a s t o s Many of these factors are important
rniin.
determinants in the probability of graduating from high school, particularly the mobility measures
24 The analyses to follow are similar if the neighborhood measure is specified linearly.
20
and whether the individual moved into her own household. However, none signifi a t y affect the
cnl
magnitude of the neighborhood c e f c e t While I may not be picking up other important
ofiin.
factors that might be correlated with the neighborhood variable, I am reassured that adding these
factors, especially the indicator variable for moves into own household, do not affect the
neighborhopd parameters.
The bottom of table 4 reports the neighborhood coefficients when they are allowed to
enter nonlinearly. A spline i created at the 25th, 50th, and 75th percentile of the neighborhood
s
measure and also at only the 90th percentile to determine i there are nonlinear slopes i the
f
n
neighborhood coefficient depending on the "quality" of the neighborhood. In both the poverty
rate and dropout rate cases, there does not appear to be much evidence that such a nonlinearity
e i t . A notable exception i that the neighborhoods i the bottom decile of dropout rates
xss
s
n
(above 28 percent) exhibit stronger effects in the linear probability case. However, no such
pattern i detected in the l g t model. Therefore, the remaining analysis ignores any possible
s
oi
spline e f c s 2
f e t .3
A BriefNote on IV Estimates
Before presenting the fixed effect estimates, I t
ried to replicate E O S ’ two stage estimator
s
that uses a variety of metropolitan area characteristics to instrument for neighborhood selection.
They find that single stage equations show a s g i i a t impact ofneighborhoods on teen birth and
infcn
high school dropout rates but modeling the selection process using IV eliminates t i entire effect
hs
Although I use a different data set than EOS, the re u t are quite similar when using the
sls
neighborhood poverty r t . 4 This i reassuring since t e r peer variable i similar to the poverty
ae2
s
hi
s
rate variable used here. However, when the dropout rate i used as the neighborhood measure, the
s
23 I also looked at interactions between the neighborhood variables and a number of the family and individual
characteristics to see if nonlinearities enter this way. The importance of these nonlinearities is sensitive to the
choice of the neighborhood measure. For the poverty variable, only the gender interaction is significant Females
are less likely to be affected by high community poverty rates. The dropout rate interactions appear to be more
important Income and 'whether worked1 interactions are positive and significant at the one percent level; the
number of kids in the household is negative and significant at the one percent level. These results suggest that kids
who do not work during their youth and are from lower income households with more children are more
susceptible to negative neighborhoods externalities when the youth dropout rate is higher.
24 The results are available upon request.
findings are di f r n . Using EOS's instruments, the neighborhood effects are sil of the expected
feet
tl
sign and the point estimate i bigger than the single stage r s l s although they are insignificant at
s
eut,
any conventional level due to a substantial increase in the standard err r Therefore, m y IV
o.
results suggest that controlling for s l c i i y can eliminate the s gnificant effect of neighborhoods
eetvt
i
on children's high school graduation. But t i conclusion i f i l sensitive and prone to substantial
hs
s ary
increases in imprecision.
Fixed Effect Estimates
As explained e r i r an alternative approach to correcting the s lectivity b a , or at least
ale,
e
is
the family-specific component of location decisions, i to estimate family fixed effect equations.
s
Table 5 reports these estimates using eight methods.2 In row one oftable 5 the 2,178 individuals
7
,
are paired off with siblings that meet the age c i e i n This leaves 1,892 sibling pairs that are
rtro.
differenced to eliminate the family constant error term. Full results of th s equation are reported
i
in appendix 2 This procedure has a large impact on the poverty rate measures. The point
.
estimate of -0.144 corresponds to a seven percent decrease in the likelihood of graduating when
the neighborhood poverty rate increases by ten percent. This result i significant at the two
s
percent l v l On the other hand, the dropout rate coefficient i not affected by the f r t difference
ee.
s
is
estimator. The coefficient increases s i h l from -0.061 i the linear probability model to -0.068
lgty
n
in the f r t difference model. However, a substantial increase in the standard error produces an
is
increase in the p-value to about the s percent significance l v l
ix
ee.
These findings are robust to adding more controls to account for transitions that may
d f e across s b i g . Like the linear probability regressions, I add controls for the variance of
ifr
ilns
money income and labor income, the percentage of years that the household moves, and whether
the teenager moved into her own household by age 18. None of these variables, individually or as
a group, affects the neighborhood estimates using either neighborhood measure. Even the 'own
household' variable, which i highly significant i the regressions, has no indirect effect on the
s
n
magnitude of the neighborhood estimates.
27 The regressions include controls for employment and marital transitions that occur after the older child has left
the parents' home. Therefore, these variables should measure changes in employment and family states that are
experienced by the younger sibling but not the older sibling in the pair.
These findings form the basic r s l s However, as noted i section n, the inferences
eut.
n
would be more convincing i the estimates were consistent across a number of changes in the
f
specification and assumptions of the model. In the second row, I weight the sample as described
in section I using the inverse of the number of times each person i included in a sibling p i .
I
s
ar
This change has no effect on either neighborhood measure. In row t r a single fixed effect i
h ee,
s
employed for each family. This lowers the coefficients and standard errors on both measures, but
the findings remain similarto the pairwise estimates.
A simpler method to a l v a e concern about over sampling i to choose one random pair
leit
s
of s b i g from each family. This i shown i row four, where only the oldest and youngest
ilns
s
n
s b ings from the 742 families are used. The point estimate for the dropout variable remains the
il
same but the poverty rate estimate (standard error) i smaller at -.115 (.067) than the other f r t
s
is
difference estimates, although silremaining almost one standard deviation higher than the simple
tl
l n a probability estimates.
ier
All of these estimates use two sources of variation to identify the neighborhood
c e f c e t time and differences in resid n i l location. Time influences these results because the
ofiin:
eta
interpolation of the decennial census figures implies that neighborhood conditions will d f e
ifr
between s b ings of different ages even i they l v in the same neighborhood.2 To see whether
il
f
ie
*
t i time component i driving the r s l s i rows five and s x I reestimate the unweighted
hs
s
eut, n
i,
pairwise f r t difference estimator i row one using only those si l n pairs who lived in different
is
n
big
neighborhoods at age 14 (row f v ) and who have a d fferent average neighborhood during the r
ie
i
i
youth (row s x . The l t e restriction eliminates only those pairs who never moved.
i)
atr
Although
the point estimates in the age 14 estimator are very similar i magnitude to the other estimates i
n
n
table 5 the small sample from using the diff r n neighborhood age 14 estimator results in huge
,
eet
standard errors and thus i s
n ignificant estimates. The d fferent average neighborhood re t i t o
i
srcin
r
esults i an increase i the point estimate ofthe poverty rate and no change i the dropout r t .
n
n
n
ae2
22 Secular trends in high school graduation rates are controlled with year turned age 15 dummies. Here, I use time
to refer to within-neighborhood changes over time (as opposed to across neighborhood changes due to household
moves).
In rows 7-9,
I return to the l g s i framework.
oitc
R o w 8 reports unweighted logit
estimates using Chamberlain's fixed effect l g t model. Because only pairs of siblings that had
oi
different educational outcomes contribute to the likelihood function, 441 of the 1,892 pairs are
usable. The conditional logit coefficients are in different units and therefore are not comparable
to the single stage l g t But the estimates, l k the linear probability estimates in row one, are
oi.
ie
signi i a t and of the expected sign for both neighborhood measures. Other coefficients in the
fcn
model seem to react similarly in the conditional l g t and linear probability equations.
oi
F n l y in row 9 l
ial,
, ogit equations were estimated using the Mundlak formulation where
separate within-family and across-family variables are defined. For the poverty rate measure,
(the within-family neighborhood coefficient) i -0.872 (0.432) and
s
dropout r t ,
ae
the findings on
i -0.407 (0.357) and
s
<x
j
>
<2
f
>
<2
j
>
$x
i 0.415 (0.456). For the
s
i -0.436 (0.402). Not surprisingly given their s m l r t ,
s
iiaiy
are very similar in terms of t-values to the one family fixed effect estimates in
row 3 In terms of other si l n or time-varying variables, the within-family coefficient i
.
big
s
signi i a t for the female indicator, marital s a u , and whether worked dummy. Family income
fcn
tts
and marital status are highly significant in the across-family point estimates.
V. Robustness Checks
Using Different Neighborhood Measures
H o w sensitive are the results to the choice and computation of the neighborhood proxy?
To test t i measurement i s e I reran the models using different proxies and aggregations of the
hs
su,
neighborhood variable, a l of which have appeared in the l t r t r i some form. Tables 6 and 7
l
ieaue n
report the re u t of th s investigation.
sls
i
In table 6 I examine whether the way the neighborhood measure i calculated has any
,
s
bearing on the findings. In p r i u a , t i table looks a the eff c of the imputation scheme and
atclr hs
t
et
the averaging of the neighborhood v riable. Linear probability and unweighted fixed effects
a
estimates are reported for four variants of the neighborhood measure. F r t i row one, I report
is, n
the basic results from e r i r tables that use an imputed, time-averaged neighborhood measure.
ale
As described in section ID, the imputation r f to the l n a interpolation between 1970 and
e ers
ier
1980 ofthe decennial Census variables, which allows some variation i the neighborhood measure
n
from time. The time averaging refers to the averaging of variables from ages 10 to 18 for each
s b i g In row two, I allow no imputation, setting the neighborhood measure for each year equal
iln.
to the 1980 Census report for that neighborhood.
Therefore, a l si l n differences in
l
big
neighborhood measures w l be from neighborhood moves. The res l of t i change i s
il
ut
hs
s mall.
The fixed e f estimator even increases for both the poverty and dropout equations. Therefore,
f ect
biases caused by the current imputation scheme, i anything, dampen the size of the neighborhood
f
e f c , suggesting that t i data assumption i probably not a problem.
fet
hs
s
In rows three and f
our, I use the age 14 neighborhood measure instead of the average
neighborhood c a a t r s i . I run t i experiment because one-year windows are a common way
hrceitc
hs
to measure the influence of neighborhoods and schools on children. However, t i variable may
hs
not be a r l a l measure of the true effects of neighborhoods (or any other time-varying
eibe
covariate) since i i ignores the re t of the individual’ neighborhood history and thus potentially
ts
s
s
introduces more measurement noise into the estimation. However, as a methodological and
comparative point, i seems to be an useful exercise to compare the results using th s measure
t
i
with the averaged measure.
The magnitude of the age 14 estimates i quite different from the averaged variable. In
s
row three, the census imputation i allowed.
s
Both the fixed effect and linear probability
estimators are i s g i i a t for the poverty measure; the dropout measure shows significant effects
ninfcn
with the l n a probability estimate but s i h l smaller and i s
ier
lgty
n ignificant results using the fixed
e f c equation due to a doubling of the standard e
fet
rrors. Furthermore, the size of the linear
probability coefficients i smaller, although sil s g i i a t R o w four drops the imputation and
s
tl i n f c n .
finds that the fixed eff c estimator i signi i a t a the 10 percent level for the dropout rate but
et
s
fcn t
not s g i i a t y d fferent from zero with regard to the poverty r t . I would be comforting to
infcnl i
ae t
find that the r
esults using the age 14 measures match the findings from the averaged variable
measures. That t i i not the case i not a f t l contradiction. F r t the findings are somewhat
hs s
s
aa
is,
supportive of the main conclusion of the fixed e f c estimates; correcting for s l c i i y and
fet
eetvt
unobserved family heterogeneity does not completely eliminate the p s i i i y of community
osblt
influences. While three out of four of the findings are insign f c n a even the 10 percent l v l
iiat t
ee,
the point estimates are in l n with the linear probability c e f c e t , just much le s precisely
ie
ofiins
s
estimated. Second, I would expect that the age 14 measure i not as good a proxy of the youth's
s
f l history of neighborhood background influences and thus i more prone to measurement e r r
ul
s
ro.
Therefore, while i would comforting to find that the age 14 measure and the averaged measure
t
come to exactly the same conclusions, i i not surprising that they do not.1
ts
9
As a second t s , the linear probability and unweighted fixed effect estimators were rerun
et
for three other neighborhood variables commonly used in the l t r t r : the percentage of
ieaue
households headed by females, the percentage of the population that i white, and average
s
household income. The findings are reported in table 7 Column one reports linear probability
.
estimates using each of the five neighborhood variables. To gauge the r l tive size of these
ea
coefficients against each other, column two displays derivatives calculated at the mean for each
measure. The size of these derivatives i f i l stable across the variables, with the exception
s ary
being the white composition, which i about the same size as the other variables for white students
s
but zero for nonwhite students.
Column three reports the fixed effect estimates. The three variables not discussed above
have point estimates very similar to the linear probability model, but with standard errors roughly
three times as l r e In a l three cases, the increase i standard errors res l i insi n f c n
ag.
l
n
ut n
giiat
neighborhood e f c s However, no cases show the dramatic changes i magnitude, much l s
fet.
n
es
sign switches, that are reported i EOS. However, the lack of precision of the within-family
n
estimates does not discount t i p s i i i y
hs osblt.
Using Different Outcome Measures
In table 8 I explore how the fixed effect estimator influences two other education
,
outcome variables — whether the individual attended college and the number of grades completed
by age 25. Two points need to be made about variable definitions and the sample. F r t in order
is,
to maximize sample size and to avoid problems due to a t i i n I include a l individuals who were
trto,
l
19 These findings on single age windows versus averaged values is consistent with those reported in An, Ha ve ma n
and Wolfe (1992).
26
in the sample and had a grade completion report after age 19, much l k the high school
ie
graduation equations.
However, t i may cause problems, particularly in the grades reported
hs
variable, since differences i grades reported may be partly due to differences in age. Therefore, a
n
variable that measures the l s age in the sample used to determine grade completed i included.
at
s
Second, I drop 41 individuals for whom high school graduation was inferred from t
heir status as
students well into t
heir twenties but who never report a grade completed due to a t i i n or the
trto
end ofthe sampling period. This sampling alteration makes l t l difference to the findings.
ite
Li rows 1 2 and 4,1 report the neighborhood coefficients for equations that employ high
,,
school graduation, college attendance, and number of grades completed as the outcome measure.
The regressions were also run for l g t models with identical implications. The findings suggest
oi
that the effect of neighborhoods on college attendance i much smaller than on high school
s
graduation, especially when looking at a subsample of siblings who graduated from high school
(row 3 . Once again, the neighborhood proxies seem to be acting quite d f e e t y Fixed e f c
)
ifrnl.
fet
estimates using the poverty rate measure suggest that unobserved heterogeneity does not
eliminate the e f of communities on educational attainment. In a l three outcome measures,
f ect
l
point estimates (and standard error) increase in magnitude, but remain relatively constant in
significance. The dropout rate shows strong support for neighborhood effects in the l
inear
probability and l g t specific t o s but no evidence of neighborhood effects in the fixed e f c
oi
ain,
fet
models, especially with regard to the college attendance and grades completed outcomes. Further
analysis using the percentage of households over $30,000 in 1979 dollars as the neighborhood
proxy finds some support for the importance of neighborhood conditions on college attendance
decisions.
Using Different Samples— Separating the Responses Bv Race. Gender, and Income
I may also be of i t r s to see how s r t f i g the sample by race and gender affects the
t
neet
taiyn
magnitude and significance of the neighborhood estimates. Table 9 reports these r
esults using the
high school graduation rate as the dependent variable. The f r t rows c a sify the sample by race.
is
ls
The nonwhite sample experiences larger neighborhood influences than the white population as
measured i the single stage framework when either the poverty rate or dropout rate i used as the
n
s
neighborhood variable. However, the f r tdifference estimator suggests larger effects in the white
is
sample for both neighborhood measures. In the nonwhite sample, no s a i t c l y significant effect
ttsial
i found.
s
None of these results are s a i t c l y different across groups at conventional
ttsial
significance l v l .
ees
When s s e s and brothers are s r t f e i rows three and four, the results are rather
itr
taiid n
surprising. R o w three (four) include a lfemales (males) in the linear probability estimates but only*
l
s s e (brother) pairs in the fixed effect regressions. The s s e s do not seem to react to poverty
itr
itr
conditions, especially relative to the brothers. However the s s e s have a strong response to
itr
neighborhood dropout r t s The brothers respond strongly to both neighborhood conditions,
ae.
although heterogeneity corrections have quite different effects depending on the neighborhood
proxy used. With regard to the poverty r t , the point estimates are extremely large (although so
ae
are the standard errors), while the dropout rate parameters are similar to those found in the
aggregate. Again, although some differences a
rise between the two groups, these differences are
not s a i t c l y s g i i a t
ttsial infcn.
A Test ofData Variability
Given the small variation that I rely on to identify the point estimates, i might be useful t t
o
redo the analysis using si l n pairs that experience larger neighborhood d f e e t a s 3 There may
big
i f r n i l .0
be i t rest i sibling pairs with larger neighborhood differences i there i concern that those with
ne
n
f
s
small differences are especially noisy. However, there i a trade-off as the sample with larger
s
differences i more susceptible to bias from l t family characteristics that may have caused
s
a ent
large changes in neighborhood location.
Furthermore, a p i r , i i not clear whether the
roi t s
neighborhood influence should be l r e , smaller, or the same size with greater differences in
agr
sibling neighborhood backgrounds.
If families are selecting neighborhoods based on the
d f e e t a a i i y of their children, then larger changes in the quality of the neighborhood would
ifrnil blt
3aThis issue is essentially one of measurement error. Since, classical measurement error is likely to lead to a larger
downward bias in the fixed effect estimates, it is not of great concern in this case. I did run some FE-IV models to
formally account for measurement error in the difference neighborhood input, but because of the difficulty in
finding a reliable instrument for this model, it is not clear that these techniques will solve any problem. Further,
the results are hard to interpret The standard errors increase dramatically with the loss of efficiency
overwhelming any information that might allow better estimates of the neighborhood parameters.
28
show bigger effects on the outcome measures i the family i following a reinforcing strategy
f
s
(moving to better neighborhoods to accommodate the more able child) and smaller effects i the
f
family i following a compensating stra e y Ifthere i no d f e e t a s l ction, then the estimates
s
tg.
s
ifrnil ee
should be roughly the same magnitude regardless of the size of the neighborhood characteristic
difference.
Table 10 gives the r sults when the high school graduation model i remn on samples that
e
s
are s r t f e based on the percent difference in the neighborhood characteristics between the
taiid
sbig.
ilns
In row zero, the unweighted pairwise estimator from table 5 i reported as the base
s
case. Rows one through four break down the s b i g sample into those pairs with 5,10, 20, and
iln
30 percent differences in t
heir neighborhood measures. The results are consistent, although far
from conclusive, that parents do not sel c neighborhoods based on the a i i y of their children.
et
blt
There does not appear to be much difference in the point estimates across the categories. For the
dropout r t , a Wald te t ofthe equality of coefficients overwhelmingly shows no difference i the
ae
s
n
magnitude of the neighborhood impact even when the sample i limited to only those pairs whose
s
average neighborhood dropout rate i 30 percent d f e e t As for the poverty r t , the larger
s
ifrn.
ae
differences, especially the 30 percent l v l have somewhat smaller e f c s but these differences
ee,
fet,
are s a i t c l y indistinguishable from a l the other categories. When the sample excludes the
ttsial
l
largest s b i g d f e e t a s the results are also comparable, suggesting that outliers are not driving
iln ifrnil,
these f ndings. Therefore, the r
i
esults seem robust to the neighborhood d f e e t a used.
ifrnil
Reconciling The Findings With Plotnick and Hoffman
Plotnick and Hoffinan (1995) use the same family fixed effect approach and find no
evidence that neighborhoods matter.
The discrepancy between our r
esults i partly due to
s
differences in variable and sample d f n t o . This i exemplified in the robustness checks of
eiiin
s
tables 6 to 9 Many of t e r specification and variable definition choices are shown in these tables
.
hi
to res l i insigni i a t neighborhood c e f c e t . In p r i u a , I highlight four i s e .
ut n
fcn
ofiins
atclr
sus
F r t Plotnick and Hoffman's neighborhood measures are composed of averages over
is,
three years (age 16 to 1 ) Results reported i table 6 suggest that shorter time frames can lead
8.
n
to a reduction i parameter estimates. An, Haveman, and Wolfe (1992) argue that these shorter
n
29
windows are consistent with added measurement e r
r or, which i l k
s i ely to bias estimates
downward. Furthermore, Plotnick and Hoffman acknowledge that the use of a 16 to 18 window
ignores potential effects a e r i r ages or the accumulation of neighborhood significance over
t ale
many years. Second, several of the neighborhood measures employed in table 7 some of which
,
are used in Plotnick and Hoffman’ paper, display i s
s
n ignificant neighborhood e f c s Therefore,
fet.
the results are sensitive to the precise neighborhood measure chosen. Third, their education
dependent variable i post-secondary schooling, which I show in table 8 to display a much weaker
s
impact from neighborhood conditions.
The stronger effects arise in high school graduation
outcomes. Fourth, Plotnick and Hoffman include only s s e p i s which I find in table 9 to
itr ar,
display smaller neighborhood effects than brothers and brother-sister pairings. Some of these
specification, sample, and variable definitions are a b t a y especially the choice of neighborhood
rirr,
measure. However, the averaging problem seems to be an important measurement issue where
more ‘
permanent’ covariates are preferable.
Other is u s such as the choice of dependent
se,
variable and the sampling of s s e s versus brothers, suggests that neighborhoods could matter in
itr
certain cases.
V L Conclusions
A well-known complication of estimating the influence of neighborhoods on children's
outcomes arises because families are not randomly assigned to neighborhoods but rather choose
t
heir location based on many factors, including the importance they place on their children’
s
welfare. As a r s l , the effects of family unobservables, such as parental competence, taste for
eut
education, and time spent with their children, and other unobservables that are common to
geographically clustered households, may be mistakenly attributed to the neighborhood measures.
Previous studies that attempt to correct for t i selection bias have used questionable instrument
hs
varia l s
be.
This paper introduces an approach that r l e on the observation that the latent factors
eis
associated with neighborhood choice do not vary across s b i g . Therefore, family residential
ilns
changes provide a source of neighborhood background variation within families that i free of
s
family-specific heterogeneity biases associated with neighborhood se e t o .
lcin
This approach i
s
feasible because of the high levels of r s d n i l migration i the United S ates. Using a sample of
eieta
n
t
multiple-child PS1D families where the kids are separated i age by at l a t three years, I estimate
n
es
family fixed effect equations of children's educational outcomes. The fixed effect results suggest
that the impact of neighborhoods exists even when family-specific unobservables are controlled.
In f c , fhmily fixed effect regressions that use the neighborhood poverty rate as the proxy for
at
community conditions show even larger community ef e t on high school graduation and grades
fcs
completed compared with the models without fixed e f c s When the neighborhood dropout rate
fet.
i employed, there appears to be l t l difference i point estimates between the fixed effect high
s
ite
n
school graduation equations and the simple l n a probability or l g t r s l s but the effect on
ier
oi eut,
college attendance and grades disappears. Other neighborhood proxies show similar patterns.
When s r t f e by race and gender, whites and males are impacted the most by neighborhood
taiid
conditions i the fixed eff c sp cifications. Therefore, the re u t suggest t a , contrary to
n
et e
sls
ht
Evans, Oates, and Schwab's findings, corrections for neighborhood selection biases do not
necessarily eliminate the potential for signi i a t community e f c s
fcn
fet.
However, the findings are tempered to some degree by large standard errors due to small
sample s
izes and noise that might a
rise i there i not enough variation in the differenced
f
s
neighborhood variables. While attempts to control for family environment are introduced, there i
s
also the p s i i i y that the empirical models have not adequately isolated latent changes in family
osblt
background or individual s b i g heterogeneity. This i exemplified by the surprising finding that
iln
s
parameters sometimes increase when moving from the single stage models to the fixed e
ffect
models. Therefore, in future research, I hope to replicate the r
esults on a different sample of the
PSID (younger children using grade retention data currently being collected) or a different data
s t such as the National Longitudinal Survey of Youth.
e,
Table 1
Changes in Household Income, Employment Status and Family Composition Preceding a Residential Mov$ (1,2
1971-1974,1980-1983
Stavers
d)
Family years (3
Families
State
Movers
(2)
County
Movers
(3)
3,705
911
44
39
109
82
32.95
0.57
0.99
32.42
-0.66
•0.58
24.16
0.05
-0.11
26.20
-206
•3.29*
Parents' marital status
in move year
Change (t-1 ,t)
Change (t-2,t)
Married->divorced
Divorced->married
0.737
-0.015
-0.024
0.030
0.014
0.727
-0.046
-0.046
0.114 •••
0.114 —
0.798
0.055 ***
0.027 **
0.064 **
0.110***
Head's employment status
in move year
Change (t-1 ,t)
Change (t-2,t)
Employed->unempl.
Employed->retired
Employed->laid off
Unemployed->empl.
Retired->emptoyed
Laid off->employed
0.800
-0.014
-0.021
0.022
0.018
0.020
0.013
0.022
0.018
0.818
-0.023
0.000
0.023
0.023
0.068 **
0.023
0.045
0.045
0.844
0.046 **
0.018
0.037
0.037
0.064 ***
0.055 ***
0.028
0.018
Family money income (4
in move year
Change (t-1,t)
Change (t-2,t)
Head and wife labor income (4
in move year
Change (t-1,t)
Change (t-2,t)
Residence
Movers
(4)
Neighborhood Movers
Absolute poverty rate relative to previous neighborhood
Hloher
5% Higher
Lower
5% Lower
(6)
(7)
(8)
(9)
m
(5)
589
371
387
260
164
143
30.50
-0.80
-0.40
24.44
-0.42*
0.31
25.42
-0.57*
0.30
25.29
-0.67
-0.39
17.09
-059
-0.18
102
94
205
174
147
127
25.51
-1.86 **
•0.59
22.55
•0.64
-0.53
24.96
0.28
1.09
24.97
0.88
1.27
17.42
-0.86*
-0.47
18.19
-1.46 **
•0.98
13.62
-1.33
•0.99
16.32
-0.58
•0.26
15.59
-0.02
0.26
0.565
-0.014
-0.051 **
0.087 ***
0.058 ***
0.553
•0.018
-0.059 **
0.093 ***
0.054 **
0.530
-0.019
-0.055
0.091 ***
0.037
0.402
•0.049*
-0.069*
0.098 ***
0.020#
0.566
•0.014
-0.063 **
0.102 ***
0.073 ***
0.551
0.014
-0.020
0.082
0.095 •**
0.696
0.012 **
-0.014
0.034*
0.051 ***
0.044 ***
0.031 ***
0.037 **
0.026
0.703
0.026 **
-0.008
0.036*
0.054 ***
0.036**
0.034 ***
0.041 **
0.018
0.677
0.012
-0.030
0.037
0.043 **
0.037
0.012 #
0.043*
0.012
0.588
0.010
•0.039
0.039
0.039
0.049 **
0.020
0.059 **
0.020
0.727
0.049 ***
0.020*
0.034
0.068 ***
0.039
0.054 ***
0.039
0.024
0.721
0.061
*
0.048 *•
0.020
0.088 ***
0.020
0.048
0.020
0.027
Notes:
1) Asterisks represent significance levels from mean tests of the mover groups against the stayer group. *(**,***)«mean of the movers' characteristics is
different from the stayers at the 10% (5%,1 %) level. # means that columns (6) or (7) are significantly different from columns (8) or (9) at the 5 percent level.
2) The sample includes households that have a child under age 17 living in the household. Only years 1971-1974 and 1980-1983 are used to avoid
difficulties in determining when geographic moves occurred during periods when geocode data is missing (1969,1975,1977,1978).
3) Family year observations are included for all moves where that period's income can be determined. Since the PSID does not report income until
the following year, geographic moves during the final year of a household's response are not included.
4) Income is in thousands of 1982-1984 dollars.
Table 2
Descriptive Statistics of Main Individual, Neighborhood and Family Variables (
1
Weighted by PSID Sample Weights
Sibling sample_____
Mean
Std. Dev.
()
2
(D
High school graduate
College attendance
Number of grades completed
Nonwhite
Female
Percent worked during youth
Number of kids i household
n
M o m high school graduate
Dad high school graduate
Household money income (82-84 $)
Parents' married a l years (2
l
Percentage of years that family
moved between ages 10 and 18
Whether ever moved, ages 10 to 18
Neighborhood Characteristics:
Percent households i poverty
n
Percent youth not employed or i school
n
Percent white
Percent female household heads
Average income
0.871
0.425
12.88
0.186
0.494
0.801
3.23
0.631
0.561
40,046
0.740
0.122
0.335
0.494
1.89
0.389
0.500
0.400
1.56
0.483
0.496
23,737
0.439
0.168
0.878
0.445
12.99
0.177
0.493
0.808
2.97
0.678
0.595
40,482
0.722
0.131
0.327
0.497
1.95
0.382
0.500
0.394.
1.60
0.467
0.491
24,452
0.448
0.175
0.485
0.500
0.514
0.500
0.125
0.134
0.852
0.134
40,761
0.097
0.094
0.252
0.088
14,051
0.127
0.131
0.856
0.132
41,309
0.097
0.095
0.240
0.087
14,802
0.359
0.220
0.288
0.110
0.145
0.315
0.243
0.298
0.146
0.284
0.252
0.320
0.169
0.166
0.055
0.114
0.013
0.026
0.120
0.073
0.093
0.028
0.091
0.071
0.110
0.031
0.372
0.228
0.318
0.116
0.159
0.325
0.257
0.291
0.165
0.288
0.257
0.313
0.174
Head experienced at least one transition during ages 10-18 of youth:
Married -> divorced
0.152
Mamed -> widowed
0.051
Divorced -> married
0.091
Single -> married
0.012
Widowed -> married
0.021
Employed -> unemployed
0.111
Employed -> retired
0.063
Employed -> temp, l i off
ad
0.098
Employed -> disabled
0.022
Unemployed -> employed
0.088
Retired -> employed
0.068
Temp, l i off-> employed
ad
0.115
Disabled -> employed
0.029
Number of unique individuals
Number of unique families
A l youth sample
l
Mean
Std. Dev.
()
3
(4)
2,178
742
4,410
1,199
Notes:
1 Sibling sample includes a l individuals with ( ) one sibling that i three years apart i age, ( ) two
)
l
1
s
n
2
years of data between ages 10 and 14, and one year after age 18 that can distinguish whether
the individual graduated from high school. A l youth sample does not require condition ( )
l
1.
Variables are averaged for each individual between ages 10 and 18. Family background variables
are averaged over the years that the person lived a home. Some 9% of the sample moved out of
t
their parents' household by age 18.
2) Equals one i the parents stay married while the child i l v n at home between ages 10 and 18.
f
s iig
Table 3
Within-Family Variance i Some Key Variables
n
-
Averaged Variable (
1
Mean of variable
Total standard deviation in sample
Standard deviation within families
Fraction of variance within families
Age 14 Variable (2
Mean of variable
Total standard deviation i sample
n
Standard deviation within families
Fraction of variance within families
Percentage of sibling pairs whose
neighborhood measures are different by:
>5%
>10%
>20%
>30%
>50%
Grades
Completed
(D
12.52
1.87
1.40
0.560
Neighborhood
Poverty
Dropout
Rate
Rate
()
2
0)
0.198
0.131
0.036
0.075
0.167
0.093
0.034
0.136
0.199
0.140
0.060
0.182
0.169
0.104
0.057
0.306
0.71
0.58
0.35
0.21
0.07
0.76
0.62
0.41
0.26
0.11
Notes:
1) Neighborhood and income variables are averaged over ages 10 to 18 for each s
ibling.
Education outcome variables are based on the highest reported grade completed from
age 19 to 25.
2) Variable i the measure at age 14 (orthe closest age to 14).
s
Family
Money
Income
()
4
30,433
19,975
6,403
0.103
Table 4
Effect of Neighborhood Poverty and Dropout Rate on High School Graduation Rates (1
Neighborhood Measures; logfpoverty rate) or logfdropout rate)
Dependent variable: 1 if high school graduate
(Huber standard errors in parentheses) (2
d)
Linear Specification
Neighborhood Measure: log(poverty rate)_______________
Linear Probability________
____________ Logit____________
(3)
(2)
(4)
(6)
(5)
-0.042 •••
(0.016)
-0.034 *
(0.019)
-0.042 •••
(0.016)
-0.543 —
(0.155)
-1.360 ***
(0.352)
-0.596 ***
(0.164)
________________Neighborhood Measure: log(dropout rate)
________ Linear Probability________
_____________Logit
(8)
(9)
(10)
(7)
(11)
-0.061 **•
(0.013)
-0.016
(0.013)
-0.047 •**
(0.012)
-0.805 •*•
(0.163)
-0.784 **
(0.371)
(12)
-0.749 ***
(0.178)
Spline Specification (3
Neighborhood measure
25th percentile
50 percentile
75th percentile
-0.002
(0.013)
0.001
(0.015)
-0.008
(0.016)
Sign, level from test of
nonlinear coefficients
All spline slopes=0
0.079
(0.116)
-0.003
(0.020)
90th percentile
0.943
•0.023 *
(0.014)
0.004
(0.017)
-0.034*
(0.018)
0.249 *
(0.132)
0.153
(0.109)
0.045
(0.096)
0.867
0.088
0.506
Notes:
‘“ -significant at 1% level
“ =significant at 5% level
^significant at 10% level
1) Sample size is 2,178. Regressions control for gender, race, parents' education, parents' marital status, household income,
number of siblings, whether the child worked, the year the child turned 15, five marital transition variables, eight employment
transition variables, and the wage for unskilled workers in the county of residence.
2) Standard errors corrected for clustering by 1968 neighborhood.
3) Regressions include a spline at the 25th, 50th, and 75th (or 90th) percentiles of the neighborhood measure. The breakpoints
for the poverty rate are 8.9,17.5,28.0, and 38.3%. Corresponding breakpoints for the dropout rate are 9.9,16.2,22.6, and 28.0%.
-0.053
(0.152)
0.127
(0.118)
-0.128
(0.121)
-0.045 **
(0.021)
0.009
0.035
-0.078
(0.120)
0.678
0.516
Table 5
Effect of Neighborhood Poverty and Dropout Rates on High School Graduation
Fixed Effect Estimates (
1
Neighborhood Measures: log(poverty rate) or log(dropout rate)
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
Estimators (2
Linear Probability Models
(0) Base case
loa (Dovertv)
()
1
loa (droDout)
(2)
Size
()
3
-0.042 * *
*
(0.016)
-0.061 * *
*
(0.013)
2,178
(1 Unweighted pairwise f r tdifference
)
is
-0.144 *
*
(0.060)
-0.068 *
(0.036)
1,892
(2) Weighted pairwise f r tdifference
is
-0.146 *
*
(0.060)
-0.068 *
(0.037)
1,892
(3) Single family fixed effect
-0.129 *
*
(0.052)
-0.045
(0.033)
2,178
(4 Oldest-Youngest pairs
)
-0.115 *
(0.067)
-0.068 *
(0.038)
742
(5) Unweighted pairwise f r tdifference
is
Different age 14 neighborhood variable
-0.110
(0.074)
-0.066
(0.064)
600
(6 Unweighted pairwise f r tdifference
)
is
Different average neighborhood variable
-0.166 * •
*
(0.060)
-0.069 *
(0.042)
1,554
-0.543 • *
*
(0.155)
-0.805 * *
*
(0.163)
2,178
(8 Conditional l g t pairwise sample
)
oi
-1.250 "*
(0.408)
-0.774 *
*
(0.390)
(9 Mundlak fixed effect l g t
)
oi
-0.872 "
(0.432)
-0.407
(0.357)
Loait Models
( ) Base case
7
441
2,178
Notes:
•••^significant at 1 % level
**=significant at 5 % level
*=significant at 10% level
1) Regressions control for gender, race, parents’education, parents’marital status, household
income, number of siblings, whether the child worked, the year the child turned 15, the wage
for unskilled workers i the county of residence, the variance of money income while the
n
youth lives at home, f v parent marital transition variables, and eight head employment
ie
transition variables. These transition variables are l s e i table 2 Only transitions that are
itd n
.
experienced by the younger child ( e after the older child has l f the household) are coded
i.
et
as 1 i the difference estimators.
n
2) See text for explanation of different estimators.
Table 6
The Effect of the Neighborhood Imputation and Averaging
on the High School Graduation Results (
1
Neighborhood Measures: log(poverty rate) or log(dropout rate)
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
log(poverty r te)_________
a
Unweighted
Fixed
Linear
Effect
Probabilitv
()
2
(D
_____ log(dropout rate)
Unweighted
Fixed
Linear
Effect
Probabilitv
()
4
()
3
Imputed, time averaged (3,5
-0.042 * *
*
(0.016)
-0.144 ~
(0.060)
-0.061 * *
*
(0.013)
-0.068 *
(0.036)
No imputation, time avg. (3,6
-0.044
(0.017)
-0.142 **
(0.064)
-0.055 * *
*
(0.014)
-0.081 **
(0.041)
imputed, age 14 (4,5
-0.024
(0.015)
-0.042
(0.037)
-0.048 ***
(0.012)
-0.037
(0.025)
No imputation, age 14 (4,6
-0.030 *
*
(0.015)
-0.020
(0.041)
-0.040 ***
(0.011)
-0.053 *
(0.028)
Notes:
***=significant at 1% level
t
"=significant a 5% level
*=significant at 10% level
1) See notes to tables 4 and 5 for list of control variables.
2) Instruments are county poverty rate, unemployment rate, average household income, and percentage of
adults who did not graduate from high school.
3) Neighborhood variables are averaged overages 10 to 18.
4) Neighborhood variables set to age 14 (or closest age to 14) measure.
5) Neighborhood measures are imputed between Census years (1969 and 1979) and held constant at 1969
(and 1979) values before1969 (after 1979)
6) No imputations are calculated. All neighborhood measures are from the 1980 Census reports.
Table 7
Linear Probability and Fixed Effect Estimates
of Neighborhood Impact on High School Graduation
Using Different Neighborhood Proxies (
1
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
Neiahborhood Measure (2
Linear
Probability
(D
Derivative
at Mean
()
2
Unweighted
Fixed
Effect
(3)
( ) Poverty rate
1
-0.042 ***
(0.016)
-0.0021
-0.144 ~
(0.060)
( ) Dropout rate
2
-0.061 ***
(0.013)
-0.0036
-0.068 *
(0.036)
( ) Percent white population
3
0.012
(0.012)
( ) Percent households
4
that are female headed
( ) Average income
5
0.0002
-0.001
(0.029)
-0.060 ***
(0.021)
-0.0029
-0.089
(0.066)
0.073 **
(0.038)
0.0021
0.051
(0.100)
Notes:
***=significant at 1% level
**=significant at 5% level
*=significant at 10% level
1) See notes to tables 4 and 5 for ls of controls.
it
2) Neighborhood measures are entered into the high school graduation equations
one at a time.
Table 8
The Effect of Neighborhoods on Different Educational Outcome Measures (
1
Neighborhood Measures: log(poverty rate) or log(dropout rate)
(Huber standard errors i parentheses)
n
Outcome Measure
Unweighted
Mean of
Outcome
()
1
log(poverty rate)
Unweighted
Linear
Probability
Fixed Effects
()
2
()
3
log(dropout rate)
Linear
Unweighted
Probability
Fixed Effects
()
4
()
5
( ) High school graduation
1
0.803
-0.038 **
(0.017)
-0.165 *"
(0.060)
-0.056 ***
(0.013)
-0.064
(0.040)
( ) College attendance
2
0.351
-0.037 *
(0.020)
-0.082
(0.050)
-0.081
(0.017)
-0.012
(0.039)
(3 College attendance
)
conditional on h s graduation
..
0.437
-0.027
(0.022)
-0.058
(0.067)
-0.068 ***
(0.018)
0.033
(0.048)
( ) Grades completed (2
4
12.52
-0.180 *
*
(0.080)
-0.519 * *
*
(0.199)
-0.362 ***
(0.068)
-0.082
(0.148)
Sample Size (3
Rows (1,2,4)
Row ( )
3
2,137
1,716
1,822
1,206
2,137
1,716
Notes:
***=significant at 1% level
**=significant at 5 % level
*=significant at 10% level
1 See tables 4 and 5 for l s of control variables. Regressions also include the maximum age
)
it
used to determine an individual's educational outcome measure.
2) Maximum number of grades reported from age 19 to 25. I no grades have been reported by
f
age 25 ( e the indiviudal i sil a student), then the f r tgrade report after age 25 i used.
i.
s tl
is
s
3) The sample includes only those siblings where grades completed are easily determined.
This eliminates 41 individuals who were assumed to be high school graduates i previous
n
tables because they were sil students i their early 20s when they a t i e from the sample.
tl
n
trtd
This assumption makes no difference to the results. Row 3 includes only those siblings
who graduated from high school.
1,822
1,206
Table 9
Effects of Neighborhoods on High School Graduation, By Race, Gender, and Income
(Huber standard errors in parentheses)
Neighborhood Measures: log(poverty rate) or log(dropout rate)
Dependent variable: 1 if high school graduate
[sample size in brackets]
Sample
log(poverty rate)__________
Unweighted
Linear
fixed effect
orobabilitv
(2)
(1)
log(dropout rate)
Linear
Unweighted
Drobabilitv
fixed effect
(3)
(4)
White
-0.013
(0.017)
-0.204 ***
(0.073)
-0.031 ***
(0.012)
-0.077 **
(0.037)
Nonwhite
-0.055 *
(0.031)
-0.092
(0.086)
-0.097 ***
(0.032)
Female (4
-0.016
(0.019)
-0.030
(0.083)
Male (5
-0.057 "
(0.023)
-0.218 **
(0.087)
Sample
size f2
(5)
985
769
-0.040
(0.072)
1,193
1,123
-0.077 ***
(0.015)
-0.086 *
(0.051)
1,104
495
-0.039 **
(0.017)
-0.083
(0.074)
1,074
449
Notes:
***=significant at 1% level
**=significant at 5% level
*=significant at 10% level
1) Instruments are county poverty rate, unemployment rate, average household income,
and percentage of adults who did not graduate from high school.
2) Sample size for linear probability and IV models.
3) Sample size of sibling pairs for pairwise first difference model. See text for explanantion.
4) Fixed effect sample includes only those sibling pairs that are sisters.
5) Fixed effect sample includes only those sibling pairs that are brothers.
Sample
Dairs 13
(6)
Table 10
The Importance of Sibling Differences i Neighborhood Background
n
on High School Graduation Rates
Unweighted F r t Difference Equations
is
Neighborhood Measures: log(poverty rate) or log(dropout rate)
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
[Sample size i brackets]
n
Row
Neighborhood
Differential (
1
Poverty
Rate
(D
Dropout
Rate
()
2
( ) * 0%
0
-0.144 *
*
(0.060)
[1,892]
-0.068
(0.036)
[1,892]
( ) > 5%
1
-0.151 *
*
(0.059)
[1,350]
-0.070
(0.037)
[1,445]
( ) > 10%
2
-0.148 *
*
(0.059)
[1,101]
-0.070
(0.037)
[1.173]
( ) > 20%
3
-0.126 *
*
(0.058)
[657]
-0.070
(0.036)
[779]
( ) > 30%
4
-0.108 *
(0.062)
[398]
-0.072
(0.038)
[488]
Significance level from Wald test of
difference between row 0 and row 4
0.89
0.98
Notes:
***=significant at 1% level
**=significant at 5 % level
*=sign‘f c n at 10% level
riat
1 Neighborhood d f e e t a refers to the percentage difference between s
)
ifrnil
ibling pairs i
n
th i average neighborhood characteristic. For example, the >10% category includes
er
only those sibling pairs whose average poverty rate (or dropout rate) d f e s by more
ifr
than ten percent.
Appendix 1a
Linear Probability High School Graduation Regressions
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
Neighborhood measure: log(poverty rate)
Al youth
l
samDle
Sibling sample
(3)
(D
(2)
Intercept
Neighborhood variable
Whether female
Log(household income)
Whether nonwhite
Parents' married
Dad high school grad.
M o m high school grad.
No. kids in household
Whether kid worked
County unskilled wage
Variance of income
Parents divorced while
youth was aged 10-18
Percentage of years
moved, aged 10-14
Own household by 18
Adusted R-squared
Sample size
0.317
(0.298)
-0.042***
(0.016)
0.060 * *
*
(0.015)
0.056 *
*
(0.028)
0.086 ***
(0.028)
-0.012
(0.033)
0.063 * *
*
(0.023)
0.084 * *
*
(0.023)
-0.031 * *
*
(0.007)
0.068 * *
*
(0.020)
0.015
(0.010)
-0.020
(0.015)
-0.083 *
*
(0.036)
0.133
2,178
0.470 *
(0.287)
-0.041 **
(0.016)
0.079 ***
(0.015)
0.046 *
(0.027)
0.058 **
(0.028)
-0.020
(0.031)
0.059 ***
(0.023)
0.077***
(0.021)
-0.027 ***
(0.007)
0.053 ***
(0.020)
0.015
(0.010)
-0.015
(0.015)
-0.082 **
(0.035)
-0.202 ***
(0.047)
-0.225 ***
(0.037)
0.172
2,178
0.210
(0.199)
-0.024 **
(0.011)
0.061 ***
(0.011)
0.067 *
**
(0.018)
0.049 **
(0.020)
-0.016
(0.021)
0.060 ***
(0.017)
0.070 ***
(0.015)
-0.020 ***
(0.004)
0.043 ***
(0.014)
0.004
(0.007)
-0.004
(0.006)
-0.042
(0.026)
-0.152 ***
(0.034)
-0.195 * *
*
(0.024)
0.140
4,410
Neighborhood measure: log(dropout rate)
All youth
Sibling sample
sample
(4
)
()
5
(6)
0.320
(0.279)
-0.061 *
*
(0.013)
0.062 * *
*
(0.015)
0.060 **
(0.027)
0.079 * *
*
(0.027)
-0.010
(0.032)
0.057 *
*
(0.024)
0.081 * *
*
(0.023)
-0.031 ***
(0.007)
0.069 * *
*
(0.020)
0.017 *
(0.010)
-0.021
(0.016)
-0.080 **
(0.035)
0.139
2,178
0.424
(0.273)
-0.049 ***
(0.012)
0.081 ***
(0.015)
0.052 *
*
(0.026)
0.049 *
*
(0.026)
-0.019
(0.031)
0.055 *
*
(0.023)
0.076 * *
*
(0.021)
-0.027 ***
(0.007)
0.056 * *
*
(0.020)
0.017 *
(0.010)
-0.016
(0.016)
-0.080 *
*
(0.035)
-0.194 * *
*
(0.047)
-0.218 * *
*
(0.036)
0.175
2,178
Notes:
***=significant at 1% level
**=significant at 5% level
*=significant at 10% level
1) All regressions include 8 employment transition variables, 4 other m arital status transitions,
and region and age 15 dummies.
0.198
(0.187)
-0.034***
(0.008)
0.061 ***
(0.011)
0.070 ***
(0.018)
0.046 **
(0.019)
-0.016
(0.021)
0.057 * *
*
(0.017)
0.068 ***
(0.016)
-0.020 * *
*
(0.004)
0.043 ***
(0.014)
0.005
(0.007)
-0.006
(0.006)
-0.042
(0.026)
-0.148 ***
(0.034)
-0.191 ***
(0.024)
0.142
4,410
Appendix 1b
Logit High School Graduation Regressions
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
Neighborhood measure: iog(poverty rate)
All youth
samDle
Sibling sample
O)
(D
()
2
Intercept
*
Neighborhood variable
Whether female
Log(household income)
Whether nonwhite
Parents' married
Dad high school grad.
M o m high school grad.
No. kids i household
n
Whether kid worked
County unskilled wage
Variance of income
Parents divorced while
youth was aged 10-18
Percentage of years
moved, aged 10-14
Own household by 18
Log likelihood
Sample size
-1.470
(2.858)
-0.543 * *
*
(0.133)
0.463 * *
*
(0.124)
0.403 * *
*
(0.157)
0.765 * *
*
(0.185)
-0.093
(0.181)
0.663 *
*
(0.177)
0.705 ***
(0.144)
-0.221 * *
*
(0.039)
0.437 * *
*
(0.133)
0.144 *
*
(0.068)
0.019
(0.274)
-0.562 * *
*
(0.195)
-867.3
2,178
-0.309
(2.912)
-0.586 ***
(0.158)
0.659 ***
(0.133)
0.343
(0.280)
0.556 *
*
(0.222)
-0.153
(0.239)
0.630 * *
*
(0.213)
0.690 ***
(0.174)
-0.195 ***
(0.045)
0.348 * *
*
(0.138)
0.145 *
(0.087)
0.058
(0.264)
-0.593 * *
*
(0.223)
-1.329 * *
*
(0.282)
-1.323 * *
*
(0.202)
-827.8
2,178
-2.581
(1.859)
-0.348***
(0.116)
0.525 ***
(0.094)
0.511 ***
(0.176)
0.458 ***
(0.162)
-0.132
(0.159)
0.636 ***
(0.157)
0.581 ***
(0.123)
-0.145 ***
(0.028)
0.292 ***
(0.098)
0.028
(0.064)
0.227
(0.248)
-0.320 *
(0.167)
-0.964 * *
*
(0.211)
-1.169 * *
*
(0.134)
-1,678.2
4,410
Neighborhood measure: iog(dropout rate)
Al youth
l
Sibling sample
samDle
()
4
(5)
()
6
-1.611
(2.778)
-0.805 ***
(0.163)
0.495 ***
(0.125)
0.483 *
(0.277)
0.572 * *
*
(0.197)
-0.105
(0.244)
0.604 * *
*
(0.212)
0.667 ***
(0.178)
-0.215 ***
(0.045)
0.438 * *
*
(0.133)
0.174 **
(0.087)
0.045
(0.252)
-0.553 *
*
(0.218)
-854.9
2,178
Notes:
***=significant at 1% level
1) All regressions include 8 employment transition variables, 4 other marital status transitions,
and region and age 15 dummies.
-1.058
(2.889)
-0.726 * *
*
(0.161)
0.682 * *
*
(0.133)
0.449
(0.289)
0.340 *
(0.200)
-0.172
(0.248)
0.587 * *
*
(0.212)
0.662***
(0.173)
-0.190 * *
*
(0.045)
0.358 * *
*
(0.137)
0.172 **
(0.088)
0.074
(0.265)
-0.597 * *
*
(0.227)
-1.228 * *
*
(0.287)
-1.244 * *
*
(0.197)
-821.0
2,178
-2.959 *
(1.706)
-0.473
(0.103)
0.530
(0.094)
0.579
(0.170)
0.361
(0.149)
-0.137
(0.160)
0.604
(0.157)
0.561
(0.124)
*
-0.143 * *
(0.027)
0.283
(0.097)
0.046
(0.063)
0.197
(0.235)
-0.334
(0.166)
-0.919
(0.210)
*
-1.127 * *
(0.134)
-1,669.3
4,410
Appendix 2
Unweighted Pairwise Fi s Difference High School Graduation Regressions
rt
Neighborhood Measures: log(poverty rate) or log(dropout rate)
Dependent variable: 1 i high school graduate
f
(Huber standard errors i parentheses)
n
logfpoverty rate)_____
(2)
(D
Intercept
Neighborhood Var.
Whether female
Log(hh income)
Parents' married
Number of kids in hh
Whether kid ever worked
during youth
County unskilled wage
-0.057 *
*
(0.028)
-0.144 *
*
(0.060)
0.072 * *
*
(0.022)
0.003
(0.053)
0.004
(0.063)
-0.037 *
*
(0.019)
0.070 * *
*
(0.025)
0.020
(0.018)
Own household by 18
Adusted R-squared
0.058
-0.053 **
(0.027)
-0.142 **
(0.060)
0.087 ***
(0.021)
-0.003
(0.051)
0.011
(0.065)
-0.029
(0.018)
0.048 **
(0.024)
0.016
(0.018)
-0.217 * *
*
(0.048)
0.084
_____ log(dropout rate)
(3)
(4)
-0.052 ***
(0.028)
-0.068 *
(0.037)
0.070 ***
(0.021)
0.018
(0.055)
0.001
(0.063)
-0.036 *
(0.019)
0.066 **
(0.026)
0.022
(0.019)
0.054
Notes:
***=significant at 1% level
**=significant at 5 % level
*=significant at 10% level
1) A l regressions also include sibling differences i parents' marital and employment
l
n
status dummies, variance of household income, whether the child participated i
n
housework, and region and year turned age 15 dummies.
-0.057
(0.027)
-0.068
(0.036)
0.085
(0.021)
0.012
(0.054)
0.009
(0.065)
-0.029
(0.019)
0.044
(0.024)
0.019
(0.018)
-0.218
(0.049)
0.080
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