NEIGHBORS AND CO-WORKERS:
THE IMPORTANCE OF RESIDENTIAL LABOR MARKET NETWORKS*
Judith K. Hellerstein
Department of Economics and MPRC,
University of Maryland,and NBER
Melissa McInerney
Department of Economics,
College of William and MaryandDavid Neumark
Department of Economics,
UCI,
NBER, and IZA
July 2009
Abstract
We specify and implement a test for the importance of network effects in determining theestablishments at which people work, using recently-constructed matched employer-employee
data at the establishment level. We explicitly measure the importance of network effects forgroups broken out by race, ethnicity, and various measures of skill, for networks generated byresidential proximity. The evidence indicates that these types of labor market networks play animportant role in hiring, more so for minorities and the less-skilled, especially among Hispanics,
and that these networks appear to be race-based.

*

This research was supported by NICHD grant R01HD042806. We are grateful for thecomments of Joel Elvery, Mark Granovetter, Lisa Hellerstein, Jed Kolko, Andrew Noymer,
Michael Ransom, Cliff Stein, Etienne Wasmer, and participants in “Social Networks and Peer
Effects: Theory and Applications, A Conference in Memory of Antoni Calvó-Armengol,” theWestern Economics Association meetings, the All-UC Labor Conference, the Society of Labor
Economists Annual Meetings, and seminars at ERMES, IZA, NYU, UCI, and USC. The
research in this paper was conducted while McInerney was employed by the U.S. Census
Bureau. This paper has undergone a Census Bureau review more limited in scope than thatgiven to official Census Bureau publications. It has been screened to ensure that no confidential
information is revealed. Research results and conclusions expressed are those of the authors anddo not necessarily indicate concurrence by the Census Bureau. The authors’ email addresses, inorder, are hellerst@econ.umd.edu, mpmcinerney@wm.edu, and dneumark@uci.edu.

I. Introduction
Racial and ethnic disparities in labor market outcomes are well documented. There isalso strong evidence of residential segregation in the United States by race and ethnicity (e.g.,
Iceland and Weinberg, 2002), and this segregation is correlated with poor labor market outcomesfor minority groups, especially the low-skilled among them (e.g., Cutler and Glaeser, 1997).
Residential segregation is not itself an underlying economic mechanism for poor labor market
outcomes for minorities. But there are at least two theories that can explain how residential
segregation by race and ethnicity can contribute to these poorer outcomes.
Perhaps the most famous of these theories is the spatial mismatch hypothesis (e.g., Kain,
1968). The essence of the spatial mismatch hypothesis is that blacks disproportionately live inareas with poor access to jobs, and that this spatial isolation contributes to poor labor market
outcomes for blacks. In previous research, we report evidence suggesting that spatial mismatchis not the mechanism by which residential segregation leads to poor economic outcomes forblacks (Hellerstein et al., 2008a). In particular, we find that poor employment outcomes for lowskilled blacks are not a function of a lack of jobs per se where blacks live, but rather that local
blacks get these jobs only when local employers are hiring other black workers. We concluded
that network effects were an explanation consistent with our results because we found a similar
relationship for whites to that which we found for blacks – with whites’ employment prospects
boosted by higher employment of whites where they live, but not by higher employment of
blacks. That is, the combined evidence for blacks and whites is consistent with race-based labormarket networks. Moreover, in other work we find evidence of substantial workplace
segregation by race and ethnicity, evidence that is also consistent with race- and ethnic-based1labor market networks (e.g., Hellerstein and Neumark, 2008).1
Indeed, theoretical models of labor market networks can be shown to formalize the link
between residential segregation and labor market outcomes, when networks are partially or fully
described by links between residential neighbors. Underlying all network models is some formof information imperfection where networks serve at least partially to mitigate theseimperfections. The specific ways in which networks do this, however, vary across models. For
example, Calvó-Armengol and Jackson (2007) analyze a labor market where workers face a costof obtaining information on vacancies. Vacancy postings are known by only some individuals in
the labor market, and those individuals then selectively pass the information on only to others intheir own networks (if they do not want the job themselves). The model also generates
predictions for how the existence of labor market networks can translate initial differences in
employment rates and wages between two groups into persistent differences. It does not specify,
however, how initial information about vacancies is transmitted to individuals, and in particular
there are no firms per se in the model.
Montgomery (1991), on the other hand, specifies a labor market where the information
imperfection is on the employer side, and where the role of the employer is explicit. Firms withvacancies cannot observe the underlying ability of a potential worker, but firms can infersomething about a potential worker’s ability if (and only if) the firm currently employs

1

Kasinitz and Rosenberg (1996) provide compelling case study evidence consistent with theresults of our previous work and this interpretation of them. They study the Red Hook section of
Brooklyn, an area of high unemployment that is populated largely by low-income blacks (and tosome extent Hispanics), but with a large number of local jobs in the shipping industry. They
document that many employers hire workers almost exclusively from outside of Red Hook,
recruiting employees via social networks within specific (non-black) ethnic groups.
2individuals from that worker’s social network, where social networks are at least partiallystratified by ability.2 Hence, networks act at the establishment level to reduce employer searchcosts. In equilibrium, individuals are more likely to receive and accept wage offers from firmsthat employ others in their social network, creating stratification across firms on the basis ofsocial networks. In Montgomery’s framework, if social networks are at least partially race- or
ethnic-based – which may be due to residential segregation – and white workers are initiallyemployed at higher rates than blacks and Hispanics, then the existence of a larger network ofwhite workers will lead to more job referrals at high wages for whites searching for jobs,
creating wage disparities between whites and other groups. Although Montgomery’s model does
not build in a reservation wage, having an option for remaining out of the labor market would, in
his framework, lead to employment differentials across groups as well.3
Our goal in this paper is to provide evidence on the importance of residentially-basedlabor market networks in determining the assignment of workers to establishments, based on alarge-scale data set covering most of the U.S. economy. In that sense, our paper is motivateddirectly by theoretical models of networks like Montgomery’s, in which networks yield
information for (or about) specific employers. We measure the extent to which the importanceof these labor market networks varies across whites, blacks, and Hispanics, as well as subgroupsof Hispanics. Moreover, we provide suggestive evidence that more directly addresses thequestion of stratification of networks, asking, in particular, whether the networks we identify arerace-based, operating more strongly within than across races.
To achieve this goal, we specify and implement a test for the importance of residence2

See also Simon and Warner (1992).

3

Similarly, a natural application of Calvó-Armengol’s model is one where race-based networks

lead to persistent differences between blacks and whites in wages and employment.
3

based labor market networks in determining the establishments at which people work, using
recently-constructed matched employer-employee data at the establishment level. We measure
the importance of network effects for groups broken out by race (black vs. white), by ethnicity(Hispanic vs. white), and by various measures of skill (education, English language proficiency,
and immigrant status). Our data and methods imply that we are testing for a particular form oflabor market networks – namely, networks that are generated by residential proximity. In thatsense, our analysis has some parallels to Bayer et al. (2005a). We recognize that our measure ofthis particular type of labor market network underestimates the overall importance of networks –in that networks may be based on more than just residence, and they may serve to help workers
find employment in more than one specific establishment – and that these networks may be more
important for labor markets are more local.
Our measure of labor market networks captures the extent to which employees of abusiness establishment come disproportionately from the same sets of residential neighborhoods(defined as census tracts), relative to the residential locations of other employees working in thesame census tract but in different establishments. This type of segregation of employees acrossestablishments would arise from residential proximity capturing the “network connectedness”
that is important to the flow of labor market information between specific employers, theiremployees, and potential hires. In particular, we first identify all establishments within each
census tract in our sample. Because we have matched employer-employee data, we have asample of workers in each establishment, and we know the census tracts in which they live. We
then compute the share of an individual’s co-workers who are his or her residential neighbors,
relative to the share that would result if the establishment hired workers randomly from thegeographic areas where all individuals who work in the census tract reside. Residence-based

4

networks would predict that the share of neighbors among a worker’s co-workers would behigher – and possibly much higher – than would result from the random hiring process. Thus,
the difference between these two shares provides a measure of the importance of residence-basedlabor market networks, which we rescale into an “effective” measure of the importance of
networks by comparing this difference to the maximum possible extent of networks that we
calculate could arise in the data (again, relative to randomness), given the distributions ofworkers across establishments and across residential neighborhoods. We of course consider
influences other than networks that could give rise to this evidence.
The data we use for this study come from the 2000 Decennial Employer-Employee
Database, which we have constructed at the U.S. Census Bureau. The DEED is a large dataset
consisting of workers matched to their establishment of employment. The employer-employee
matches enable us to study directly whether workers employed in the same establishment arelikely to live in the same neighborhoods.
Overall, we find evidence that residence-based labor market networks play an importantrole in hiring. For blacks, we find that the grouping of workers from the same neighborhoods inthe same business establishments is about 9 percent of the maximum grouping that could occur.
These networks are also important for whites, although somewhat less so than for blacks. For
blacks and whites, residence-based labor market networks affect employment patterns more forless-skilled than for more-skilled workers, and because these networks are also important withinskill groups, the findings do not simply reflect residential segregation by skill. Moreover, theevidence indicates that the networks we study are partly race-based, operating more strongly
within than across races. We also find that residence-based networks are even more significant
for Hispanics, for whom the grouping of workers from the same neighborhoods in the same

5

business establishments is about 21 percent of the maximum. And among Hispanics, these
networks play a larger role for immigrants and those with poor English skills. Finally, results
conditioning on industry indicate that the network effects we find are largely due to theassignment of workers to specific establishments rather than simply to industries. This evidence
therefore provides support for theoretical models in which networks serve to match workers tospecific employers.
II. Relation to Existing Literature
A large body of existing research points to the potential importance of labor market
networks. Granovetter (1974) is one of the early and most-cited sources of evidence on theimportance of informal contacts in finding employment. Ioannides and Datcher Loury (2004)
provide a recent review of evidence that indicates widespread reliance on friends, relatives, andacquaintances to search for and find jobs. As in Granovetter’s work, much of this evidence isbased on surveys of workers (e.g., Corcoran et al., 1980; Blau, 1992), although evidence from astudy of one firm also suggests that many hires are referred by friends (Petersen et al., 2000).
Based on the survey evidence from workers, Ioannides and Datcher Loury conclude that there isvery little difference between blacks and whites in the use of informal contacts in job search, butthat rates of use of informal contacts are higher for low-educated workers compared to higheducated workers, and that there are substantially higher rates of use of informal contacts for
Hispanics than for other groups. Subsequent work has noted the potential for labor market
networks to be race- (or ethnic-) based so that, for example, reliance on informal referrals in a
predominantly white labor market benefits whites at the expense of other groups.4
Bayer et al. (2005a) present an analysis that moves beyond survey evidence, testing more

4See, e.g., the discussion and references in Kmec (2007).
6

directly for network effects. They use confidential data from the 2000 Long Form of the
Decennial Census and focus exclusively on workers in the Boston area. In a clever method ofinferring whether networks are important in explaining employment patterns, they find that two
individuals who live on the same census block are about one-third more likely to work on thesame census block than are two individuals who live in the same block group but not on the same
block.5 To the extent that informal networks are stronger within the block than within the blockgroup, this evidence is consistent with residence-based labor market networks affecting hiring.6
We regard the Bayer et al. paper as providing the most definitive evidence to datesuggesting that residential geographic proximity affects labor market outcomes, presumably
through networks that connect people living in close proximity (see also Ioannides and Datcher
Loury, 2004). Nonetheless, there are some limitations of their study, as well as the broader
literature, upon which we try to improve in the present study.7 First, most of the existing workon networks does not relate employment in the same business to network connections between

5

For the Boston metropolitan area data they study, a census block corresponds roughly to a city

block, and there are on average 10 blocks per block group. (They get similar results using the 10
closest blocks to each block based on physical distance.)6

Although the baseline rate at which these workers work together is very small to begin with –0.36 percent – this effect is estimated for any pair of workers; the authors suggest that theestimate implies a considerably higher probability that a worker works on the same block as atleast one person who resides on his block.
7

We readily note that we cannot definitively establish that the direction of causality runs fromplace of residence to place of work. Bayer et al. (2005a) examine this issue in detail andconclude that it is likely that networks operate to allow people who live together to find work
together. Like them, we largely treat place of residence as exogenous to the place of work
decision. However, we present one analysis later on that assesses whether the potential
endogeneity of residence likely drives any of our findings, and we conclude that it does not.
7

employees of that business. This is certainly true of most of the evidence based on surveys ofworkers. Similarly, the data used by Bayer et al. contain no information on the exact
establishment in which the workers work, so that two individuals who work in the same census
block may work for different employers, particularly for blocks in the central city that containmultiple employers.8 The Petersen et al. (2000) study, which analyzes data from one firm, does
report that a large share of hires indicate that a referral from a friend was responsible for thematch. However, the data do not indicate whether the friend who made the referral was a current
employee of the company, so there is no reason necessarily to infer that the company used thereferral method to obtain information about potential hires from current employees. One
exception of which we are aware, however, is the Granovetter study, which reports that informal
contacts were often employed in the company where the job held by a respondent had opened up.
The data and approach that we use permit us to tie network connections to employment in aparticular business establishment, and thus provide more direct evidence on the hypothesis thatlabor market networks reduce search frictions on the part of employers, as in Montgomery’s(1991) model.
Second, previous work is unable to speak convincingly about differences in theimportance of networks by race and Hispanic ethnicity, and among subgroups of Hispanics.9
There are theoretical reasons, though, to expect the importance of networks to vary across8

For example, focusing on central city areas in our sample of urban establishments and theiremployees, which is described below, we find that there are on average 2.58 establishments per
block, which is an undercount by what we estimate to be about a factor of four, given that weonly observe in our data a subsample of establishments. This raises questions about Bayer etal.’s assumption that workers employed on the same block “work with” one another or “worktogether” (e.g., 2005a, p. 26).
9

Bayer et al. (2005a) study data only for Boston and focus mostly on whites.
8

groups. For example, if labor market discrimination (either statistical or taste-based) by some
employers raises search costs for certain groups, such as blacks or Hispanics (Black, 1995),
networks may partially mitigate these search costs by helping to identify possible employers onwhom to focus a job search. And to the extent that employers stigmatize some groups ofworkers based on other characteristics associated with race (such as welfare use or a criminal
record) or ethnicity (such as language ability), informal networks may help to overcome
statistical discrimination.10 Similarly, race and ethnic differences in the importance of networks
may exist if networks reduce employer uncertainty about productivity when it is difficult forworkers to provide signals of productivity because of language, cultural, and educational
differences (e.g., Lang, 1986). In the case of Hispanic workers, in particular, networks maycompensate for less-developed formal hiring channels in predominantly immigrant communities,
attributable in part, perhaps, to hiring of undocumented workers. (Note that if residence-based
networks are important for a group, then some degree of residential segregation may be
advantageous.) Some of the survey evidence discussed in Ioannides and Datcher Loury (2004)
suggests differential use of networks across racial and ethnic groups, but a good deal of the mostcited evidence cannot be used to address this question because it either covers narrow subsets ofworkers (e.g., Granovetter, 1974 and Bayer et al., 2005) or a single firm (e.g., Petersen et al.,2000). Because of the large data set we construct, and the explicit linkage of workers toestablishments of employment that the data set enables, we can fully explore the importance of
networks that assign workers to establishments and differentials in the importance of networks
by race, by Hispanic ethnicity, and across subgroups of Hispanics.
The final advantage of our approach is that it lets us assess the extent to which networks

10

The latter explanation follows more directly from Montgomery’s (1991) model.
9are race-based – operating more strongly within than across races. Theoretical work hassuggested that racial stratification of networks may be important in perpetuating racialdifferences in labor market outcomes.
III. The 2000 DEEDThe analysis in this paper is based on the 2000 DEED, a data set that matches workers totheir establishments, which we have created at the Center for Economic Studies at the U.S.
Bureau of the Census. We have constructed a similar data set for 1990 and described in detailthe process of its construction elsewhere (in particular, Hellerstein and Neumark, 2003). The
construction of the 2000 DEED follows the same procedures. Thus, in this section we simply
provide a brief overview.
The 2000 DEED is formed by matching workers to establishments. The workers are
drawn from the Sample Edited Detail File (SEDF), which contains all individual responses to the2000 Decennial Census of Population one-in-six Long Form. The establishments are drawn
from the Census Bureau’s Business Register list (BR) for 2000; the BR is a database containing
information for all business establishments operating in the United States in each year, which iscontinuously updated (see Jarmin and Miranda, 2002). The BR lists all business establishmentswith one or more employees operating in the United States. The Census Bureau uses the BR as asampling frame for its Economic Censuses and Surveys. The BR contains the name and addressof each establishment, geographic codes based on its location, its four-digit SIC code, and aunique establishment identifier.
Households receiving the 2000 Decennial Census Long Form were asked to report thename and address of the employer in the previous week for each employed member of thehousehold. This employer name and address information is stored in the “Write-In” file, which

10contains the information written on the questionnaires by Long-Form respondents but not
actually captured in the SEDF. We use employer names and addresses for each worker in the
Write-In file to match the Write-In file to the BR. Because the name and address information on
the Write-In file is also available for virtually all employers in the BR, nearly all of theestablishments in the BR that are classified as “active” by the Census Bureau are available formatching. Finally, because both the Write-In file and the SEDF contain identical sets of unique
individual identifiers, we can use these identifiers to link the Write-In file to the SEDF. Thus,
this procedure yields a very large data set with workers matched to their establishments, alongwith all of the information on workers from the SEDF.
Matching workers and establishments is a difficult task, because we would not expect
employers’ names and addresses to be recorded identically on the two files. To match workers
and establishments based on the Write-In file, we use MatchWare – a specialized record-linkage
program. MatchWare is comprised of two parts: a name and address standardization mechanism(AutoStan); and a matching system (AutoMatch). We link records using MatchWare in two
basic steps. The first step is to use AutoStan to standardize employer names and addresses
across the Write-In file and the BR. Standardization of addresses in the establishment andworker files helps to eliminate differences in how data are reported. The standardization
software considers a wide variety of different ways that common address and business terms canbe written, and converts each to a single standard form. Once the software standardizes thebusiness names and addresses, each item is parsed into components. The value of parsing theaddresses into multiple pieces is that we can match on various combinations of thesecomponents.
The second step of the matching process is to select and implement the matching11

specifications. The AutoMatch software uses a probabilistic matching algorithm that accounts
for missing information, misspellings, and even inaccurate information. This software permits
users to control which matching variables to use, how heavily to weight each matching variable,
and how similar two addresses must be in order to constitute a match. Different match
specifications may produce different sets of matches. Matching criteria should be broad enoughto cover as many potential matches as possible, but narrow enough to ensure that only matches
that are correct with a high probability are linked. Because the AutoMatch algorithm is not exact
there is always a range of quality of matches, and we therefore are cautious in accepting linked
record pairs. We chose matching algorithms based on substantial experimentation and visualinspection of many thousands of records.
The final 2000 DEED is an extremely large data set containing information on 4.09
million workers matched to 1.28 million establishments, accounting for 29.1 percent of workers
in the SEDF and 22.6 percent of establishments in the BR.11 We impose additional sample
restrictions for our analysis, which we discuss following the explanation of our empiricalmethods in the next section.

11

For both the DEED and SEDF we have excluded individuals as follows: with missing wages;

who did not work in the year prior to the survey year or in the reference week for the Long Formof the Census; who did not report positive hourly wages; who did not work in one of the fifty
states or the District of Columbia (whether or not the place of work was imputed); who wereself-employed; who were not classified in a state of residence; or who were employed in anindustry that was considered “out-of-scope” in the BR. (Out-of-scope industries do not fallunder the purview of Census Bureau surveys. They include many agricultural industries, urban
transit, the U.S. Postal Service, private households, schools and universities, labor unions,religious and membership organizations, and government/public administration. The Census
Bureau does not validate the quality of BR data for businesses in out-of-scope industries.)
12

IV. Measuring the Importance of Networks
We measure whether and to what extent residential networks play a role in theassignment of workers to establishments via an analysis that is based on the percentage ofworkers in an individual’s establishment (i.e., workplace) that comes from the individual’s
residential neighborhood. We compute the importance of these networks across a variety of
subsamples, e.g., all blacks, low-educated blacks, all whites, and Hispanics who speak English
poorly. For explication, here we describe in detail how we measure the role of race-based
residential networks for black workers; the construction of the network measure for othersubsamples is identical.
We first compute for each black worker in our sample the percentage of black workers
with which that worker works who come from the same residential neighborhood as that worker.
We exclude the individual worker from this calculation, since it is meaningless to say that aperson is his or her own neighbor. This requires a sample restriction to establishments where weobserve at least two black workers. We then average these percentages across all the black
workers in our sample to create a “network isolation index,” denoted NIO, which measures theaverage fraction of a worker’s co-workers who are also residential neighbors of that worker. The
superscript “O” on the network isolation index emphasizes that this is the fraction of a black
worker’s co-workers who are observed in our estimation sample to be residential neighbors.12

12

The phrase “network isolation index” borrows from the sociology literature that measuresresidential segregation (often by race) by defining the “isolation index” to be the fraction of a
black person’s residential neighbors who are themselves black. “Segregation” and “isolation”
have the same meaning – that members of a particular group tend to interact with other membersof the same group, rather than members of other groups. Of course, many other segregation
measures have been used to measure the agglomeration of individuals of similar (usually two)13

Formally, the observed network isolation index is:

NIO =

N

∑ I ( j) ⋅ I ( j)1j≠ i
N i =1

∑

R
i

W
i

∑ I ( j)
W
i

,

j≠ i

where there are N workers indexed by i (and j). IiR(j) is an indicator for whether worker j lives inthe same residential neighborhood as worker i, and IiW(j) is an indicator for whether j works inthe same establishment as worker i. The sums in the numerator and denominator are taken over
all workers other than the worker i. Their ratio is the share of co-workers with whom eachworker is co-resident. This ratio is then averaged over all workers.
To operationalize our network isolation index, we need to define what it means forworkers to be residential neighbors. We define residential neighborhoods to be census tracts.
There are a few reasons why this definition seems sensible to us. First, census tracts define theboundaries that are traditionally used to measure residential segregation (see, e.g., Iceland andWeinberg, 2002). Second (and related to the first), census tracts are defined by the U.S. Census
Bureau to ensure that the tracts are “as homogeneous as possible with respect to population
characteristics, economic status, and living conditions,”13 which in itself is a reasonabledefinition of a neighborhood and might make it more likely that co-residents interact. Third,
most census tracts are relatively small, so it is reasonable to think that it is quite possible thatmany census tract residents have contact with each other, if not “over the back fence,” then at
types together in society. Measuring network segregation via the simple concept of isolation istransparent and compelling in our view. Hellerstein and Neumark (2008) measure workplace
segregation in a similar manner by considering the fraction of an individual’s co-workers who
are of a specific race, ethnicity, or skill group. There we provide a detailed discussion of theadvantages of measuring segregation this way rather than via other indexes such as the Duncan
Index, but we show that results are robust to using this alternative measure.
13

See http://www.census.gov/geo/www/GARM/Ch10GARM.pdf (viewed April 21, 2008).
14

parks, schools, churches, stores, etc.
To provide some idea of the size of census tracts, detailed maps of these tracts for
Chicago are reproduced in Figures 1 and 2. For the PMSA (Figure 1), the median census tract
was 0.57 square miles; the mean was 2.75 square miles. The smallest census tract was 0.02
square miles, and the largest (in the most outlying areas of the PMSA) was 151 square miles.
For the city itself (Figure 2), the median was 0.17 square miles, the mean was 0.26 square miles,
the minimum was again 0.02 square miles, and the maximum was 8 square miles. The larger
tracts are not problematic. For the city, the two largest tracts are O’Hare Field (the airport) andanother tract at the southern edge of the city that is mainly industrial.14 After these two, the next
largest tract is 3.5 square miles. The much larger census tracts in outlying areas, which are morerural, do not necessarily imply fewer social contacts, because the density of schools, churches,etc., is of course much lower. In addition, these tracts have few residents or establishments andhence contribute little to the overall findings.
There are three reasons why we might observe residential neighbors working in the same
establishment that have nothing to do with residentially-based labor market networks. First,
access to mass transportation alone may lead residential neighbors to work in the samegeographic place, although not necessarily to work in the same establishments within that place.
Therefore, in establishing boundaries for the local area in which workers might be employed, we
again use census tracts. Census tracts are generally small enough that it is possible, particularly
within urban areas, for individuals to be able to walk from any establishment to any other
14

The latter large tract represents the industrial South Deering community. The census tract isnearly 8 square miles, but Lake Calumet, wetlands areas, and the Calumet River comprise over 2
square miles. The area is largely industrial; the region’s first steel mill was located there(http://www.fieldmuseum.org/calumet/SouthDeering.html, viewed May 8, 2008).
15

establishment, so in that sense transportation differences should not materially affect the
distribution of workers across establishments in the tract.
Second, people may have heterogeneous tastes such that those who like similar
workplaces also like to live in similar neighborhoods. If this were the case, we would expect that
individuals who have the resources to better integrate into the economy are able to exercise morechoice about both where to live and where to work. We therefore are careful to disaggregate ouranalyses across groups that a priori have differential abilities to make choices about where to live
and work, and we show that those who should be most integrated into the economy are least
likely to work with their neighbors.
Third, some clustering of residential neighbors into establishments can occur even if
workers are assigned randomly to establishments, and we are of course interested in workplaceclustering of residential neighbors that occurs systematically – i.e., more than would be expected
to result from randomness.15
We therefore consider deviations of NIO from what would be expected if workers fromthe same residential neighborhood were randomly assigned to any establishment within thecensus tract in which they are employed. In particular, we compute the extent of network
isolation that would occur due to randomness, denoted NIR, by simulating random allocation
through Monte Carlo methods. Within a census tract, we randomly assign workers to
15

For example, consider a census tract that has two establishments in it, each of which isobserved to employ two black workers. Assume that two of the black workers come fromneighborhood A and two from neighborhood B. Even if the four black workers are randomly
allocated across the two establishments without regard to the neighborhoods in which they live,
1/3 of the time they will be working with a neighbor, so that NIR will be 1/3. This is a point that
we discuss in some detail in the context of measuring workplace segregation (Hellerstein and
Neumark, 2008), and was noted previously by others (see, e.g., Carrington and Troske, 1997).
16

establishments, ensuring that we generate the same size distribution of establishments (in termsof matched workers) within a census tract as we have in the sample. We do this simulation 100
times, and then compute the random network isolation index, NIR, as the mean over these 100
simulations. Not surprisingly, in our very large sample, the random network isolation measuresare very precise; in all cases the standard deviations were trivially small. We then focus on the
difference NIO − NIR, measuring network isolation above and beyond that which occurs
randomly; we refer to this as the “network isolation difference.” This computation requires asecond sample restriction – that census tracts of employment include at least two establishmentswith two black workers. Otherwise, if the only black workers in a census tract work together in
one establishment, we cannot distinguish the effect of residence-based labor market networks
from random clustering.
The network isolation difference NIO − NIR does not, in and of itself, give us a full senseof the importance of networks in generating the observed spatial pattern of neighbors working
together in establishments. In particular, while NIR gives us a measure of the minimum amountof clustering by neighborhoods that would happen with random assignment of workers toestablishments within a census tract, it is also important to know what the maximum possible
network isolation could be in our data, given the distribution of individuals across residential
census tracts and the size distribution of establishments in each census tract. That is, we would
like to know the maximum network isolation that could occur if workers were systematicallyassigned along with their neighbors to the maximum extent possible to establishments within thecensus tract in which they are actually observed to work.
While there may be individual instances of workplace census tracts in our data where we
could compute the exact maximum network isolation index, we do not know of a general method

17for solving for the maximum index in all cases in our data.16 We instead approximate themaximum network isolation through a “greedy” algorithm.17 For a given census tract in which
we have establishments represented in our sample, we order the neighborhoods in which workers
live by the number of workers from each neighborhood. Beginning with the neighborhood withthe greatest number of workers, we assign as many workers as possible to one establishment, andany workers who are not assigned to that establishment are grouped together and treated as a“new” neighborhood. We then move to the second largest neighborhood from which workers
originate (which could be the “new” neighborhood left from the previous pass), and assign
workers from that neighborhood to the establishment that holds the maximum number of these,
again therefore keeping neighbors working together in establishments as much as possible. We
continue moving down the list of neighborhoods, from those with larger to smaller numbers ofworkers, assigning workers to establishments until all workers are assigned. We would expect
this maximum network isolation index to be less than one.18

16

It turns out that computing the maximum network isolation that could occur for an arbitrarygroup of workers, residential census tracts, and workplaces falls into a well-known class ofproblems in computer science called “n-p complete” problems. This means that computerscientists think that there is no algorithm that will solve all potential instances of the network
isolation index in time polynomial in the size of the problem. (In other words, there is unlikely
to be any algorithm that will solve all potential realizations of the network isolation index infeasible computational time, particularly given the very large number of individuals in our data.)
Moreover, as far as we are aware, there is also no approximation algorithm to compute themaximum network isolation that is known to be arbitrarily close to the exact maximum(something that does exist for other n-p complete problems).
17

For more on n-p completeness and greedy algorithms, see, for example, Cormen et al. (2001).

18

For example, consider a census tract that employs nine workers across three establishments, A,

B, and C. Establishment A employs five workers, while establishments B and C each employ
18

This algorithm assigns workers to establishments in a way that simulates the maximum
possible neighborhood isolation, by mechanically ensuring that it is more likely that workers
from large neighborhoods will work together. After assigning workers from the largeneighborhoods, it often is still likely that we can assign workers from small neighborhoods towork together in establishments, filling in the remaining slots in establishments that are not filledby the workers from large neighborhoods. In contrast, if we instead started with smallerneighborhoods, we would be more likely to end up having to distribute workers from a largeneighborhood across many establishments. We do this for every census tract in our sample
where workers work, and then compute the weighted average of the maximum network isolationin each census tract of employment, weighting by the number of workers we observe to beworking in that census tract. We label this maximum network isolation index NIM.
Finally, we turn back to (NIO − NIR), the difference between our observed network
isolation index and the random isolation index, and we scale it by the maximum network
isolation that can occur beyond randomness, or (NIM− NIR), yielding
two workers. Six of the workers live in one census tract 1, two live in tract 2, and one lives intract 3. Our algorithm proceeds as follows. We first take the workers from the largest
neighborhood, tract 1, and put five of them in establishment A, forming a new “neighborhood”(call it tract 1A) consisting of the one leftover worker from tract 1. We then take the twoworkers from tract 2, the next largest neighborhood, and put them in establishment B (the same
result occurs if we put them in establishment C). At this point we are left with the single worker
from tract 3 and the single leftover worker from tract 1A, who have to be assigned in thisexample to establishment C so as to preserve the size distribution of establishments. In this case,the maximum network isolation index is {(5/9)·1 + (2/9)·1 + (2/9)·0} = 7/9. (The five workers inestablishment A are all from the same tract, with the share of co-workers from their census tract
equal to one. The same is true for the two workers in establishment B. And this share is zero forthe two workers assigned to establishment C.)
19

[(NIO − NIR)/(NIM − NIR)]·100,
which we call the “effective network isolation index.” It measures the share of the maximum
possible network isolation difference that is actually observed, and provides a natural scaling forthe importance of networks formed by residential networks in determining the establishments inwhich people are employed.
The greedy algorithm we use is bounded from above by the true maximum, but the extent
to which it is an understatement is unknown. There are two reasons why this does not concern
us much. First, from an empirical standpoint, the conclusions we draw by comparing effective
network isolation across subsamples (e.g., whites vs. Hispanics) are almost always the sameconclusions that we draw simply by comparing the network isolation difference (NIO − NIR)
across subsamples – that is, by comparing the numerators of the effective isolation indexes.
Second, while the theoretical maximum network isolation index is the one that would arise if asocial planner had full control of the assignment of workers to establishments,19 the greedy
algorithm we employ is one that would actually bound from above what might happen in the realworld if employers each individually tried to hire to the maximum extent possible from only asmall set of neighborhoods – and where large employers might have more resources andtherefore more ability to do so. In that sense our algorithm for computing maximum network
isolation may actually be a very reasonable practical measure to use.
Like any measure that tries to operationalize the concept of job networks, ours is limited
to networks that operate among particular members, affecting employment in a particular set ofjobs. Specifically, our measure only captures the extent to which networks operate to increase

19

And, in the general case, the social planner would have to be able to solve the n-p complete

problem of computing the maximum isolation index.
20

the likelihood that census tract co-residents work in the same establishment. To the extent that
networks also increase the flow of information about jobs near the employers of network
members (or jobs in other places entirely), and to the extent that networks connect people wholive in different census tracts (perhaps because they participate together in other institutions ororganizations), we will understate the importance of labor market networks. At the same time,
the role of residence-based labor market networks is significant in its own right, in thinking
about how spatially-based labor market policies might either take advantage of or, instead,inadvertently weaken or sever, valuable network connections between neighbors.
V. Sample Characteristics and Restrictions
In Table 1 we provide descriptive statistics for the matched workers from the DEED as
compared to the SEDF. Column (1) reports summary statistics for the SEDF for the sample ofwhite, black, or Hispanic workers who were eligible to be matched to their establishments.
Column (2) reports summary statistics for all white, black or Hispanic workers in the full DEED
sample – that is, those who we successfully matched to their establishments. The individuals we
successfully match in the DEED are more likely to be female, to work full time, and to havemore education than those in the SEDF. These differences result in part from the matchingprocess, because there are many individuals who meet our sample inclusion criteria but forwhom the quality of the business address information in the Write-In file is poor. We suspectthat the differences in business address information partially reflect weaker labor market
attachment among less-skilled workers, suggesting that estimates of the importance of networks
we obtain might best be interpreted as measuring the extent of network isolation among workers
who have relatively high attachment to the labor force and to their employers. The last eightrows of the table report on the industry distribution of workers. There is some over-21

representation of workers in manufacturing in the full DEED, because larger establishments are
more likely to be matched, although the over-representation is not severe.20
We make several restrictions to arrive at the samples used for our analysis of network
effects. First, we only consider workers who live and work in the same Metropolitan Statistical
Area/Primary Metropolitan Statistical Area (MSA/PMSA). Second, in order to be able toidentify a race- or ethnic-based workplace network, we exclude from the sample workers thathave no co-workers in the same establishment who are of the same race or ethnicity as theworker. Finally, we retain only workers in establishments that are located in census tracts with atleast one other establishment having two matched workers of the same race or ethnic group, sothat we can meaningfully consider the distribution of workers across establishments in a census
tract. The means for the samples of white, black, and Hispanic workers that we analyze areshown in columns (3)-(5).
In addition to comparing worker-based means, it is useful to examine the characteristics
of establishments in the DEED once we make our sample restrictions. Table 2 shows descriptive
statistics for establishments in the full DEED and for each of our final analysis samples.
Because only one in six workers is sent the Decennial Census Long Form, it is more likely thatlarge establishments will have two matched workers, especially for smaller racial or ethnic
groups. One can see evidence of the bias toward larger employers by comparing the medians
across the columns for total employment in the establishment as recorded in the BR. (This bias
presumably also influences the distribution of workers and establishments across industries,
where, for example, the DEED itself, and the final analysis samples for blacks and Hispanics
even more so, over-represent workers in manufacturing establishments.) The establishments

20

For more information on the DEED versus the SEDF, see Hellerstein et al. (2008b).
22

corresponding to the full 2000 DEED sample of workers have median employment of 15
workers. Once we restrict attention to establishments in metropolitan areas with at least two
matched white workers, and with at least two such establishments in the census tract, as incolumn (2), the median employment level rises to 35. Restricting our attention to thoseestablishments in census tracts with another establishment having at least two black workers
matched, in column (3), median employment is even higher, at 154; and in column (4), when we
consider the sample of establishments employing two matched Hispanics in census tracts with atleast two such establishments, median employment is 84. Later in the empirical analysis we
consider the possible ramifications for the estimates of these consequences of our sample
selection rules.
VI. Network Isolation Results
VI.1. Results for Whites
Table 3 presents results for white workers, who make up by far the largest subsample ofworkers that we use. Column (1) reports results for all approximately 1.7 million white workers
in our sample. These workers work in 26,470 unique census tracts and live in 46,764 census
tracts.21 The mean number of establishments in each census tract for which we observe at least
two white workers in two establishments is 130; the mean number of residents from the sameneighborhood working in the same census tract is 9.4.
The observed network isolation index for the full sample of whites is 7.87, indicating
that, on average, 7.87 percent of a white worker’s white co-workers live in the same census tract
as that worker. When we randomize workers in this sample across establishments within census
tracts, we recover a random network index of 2.97, less than 40 percent of the observed index,

21

This is out of a total of about 65,000 census tracts in 2000.
23

and the network isolation difference (the difference between the two) is 4.90. The maximum
possible network isolation that could be observed in the data for all white workers is 54.84. Thisis well below 100 (perfect sorting of workers by residential neighborhoods into establishments),
because in many of our census tracts there are establishments with more workers observed towork in them than are drawn from any particular census tract residential neighborhood. The
maximum is also considerably above the observed network isolation measure.22 As a result,when we scale up the network isolation difference by the maximum network isolation that occurs
beyond randomness, we recover an effective network isolation index of 9.45. That is,approximately 9.5 percent of the maximum amount to which residential networks (at the census
tract level) could contribute to the sorting of workers into establishments is actually observed in
the data. Whether this is a large number or a small one is a subjective matter, of course, andthere is little with which to compare it given the sparseness of empirical evidence on theimportance of labor market networks. To us, however, it seems like a large number suggestingthat labor market networks are quite important.
An alternative explanation for the apparent importance of networks in column (1) is that
there is sorting of workers by both neighborhoods and establishments according to skill. For
example, in a census tract with two establishments employing workers from two neighborhoods,
22

Bootstrap methods show that all of the effective network isolation measures we report are

statistically significantly different from zero. Indeed, the estimates of the effective network
indices are quite precise, so that, in general, substantive numerical differences across columns
and tables in reported effective network isolation measures are also statistically significant. (Theconfidence intervals are constructed from bootstrap replications in which we bootstrap the entire
sample, and then compute each of the measures in the tables that follow. Thus, we obtainbootstrap replications of the differences between any pair of effective isolation measures withinor across tables.)24perhaps one establishment hires only less-skilled workers (for example, grocery store cashiers)
who tend to live together in a neighborhood where housing is cheap, and the second
establishment hires only more-skilled workers (for example, lawyers) who tend to live together
in a different neighborhood with more expensive housing.23 Such sorting on skill potentiallyinvalidates a network-based interpretation of evidence that people who live near each other tend
to work together.24
In order to evaluate whether the results are driven by skill differentials that lead to bothresidential and workplace skill-based segregation across establishments, in columns (2) and (3)we report results separating whites by education level. Column (2) reports the results for whites
with at most a high school education. That is, we take the sample of white workers from column(1) who have no more than a high school education and compute the network isolation measures
for those workers. Specifically, we first calculate the observed network isolation index (NIO) by
averaging across the sample of low-educated white workers the fraction of each individual’s
white co-workers who live in that individual’s residential census tract, regardless of the coworkers’ education levels.25 This number is 10.56, somewhat higher than the 7.87 number in

23

In Hellerstein and Neumark (2008) we report evidence of some segregation of workers acrossestablishments based on skill levels. Bayer et al. (2005b) provide evidence of residential
segregation by education for blacks.
24

The Bayer et al. (2005a) study addresses this issue directly, arguing that the sorting ofindividuals on residential location is at the block group level (roughly speaking, 10-block areas),so that a finding that those living on the same individual block are more likely to work on thesame block (albeit a different one) than are those living on the same block group reflects
geographic proximity but not sorting. Evidence they present based on observables suggests that,in their analysis, this assumption is likely to be valid.
25

We could do a more extreme version of this calculation where we also restrict the sample of25

column (1). We then calculate the network isolation index for low-educated white workers using
the simulated sample from column (1). The random network isolation index is 4.06, also higher
than its counterpart in column (1), so that the network isolation difference is 6.50. Finally, tocalculate the maximum possible network isolation index, we calculate what the index would be if
low-educated white workers were able to work to the maximum extent possible in establishmentswith their neighbors of any education level, given the size distribution of establishments in our
sample, and the size and residential distribution of workers in them. This number is 61.62,
which is again somewhat higher in column (1). Taking all of these together, the effective
network isolation index is 11.29, 19 percent higher than the corresponding figure in column (1).
We perform the same exercise for whites with more than a high school degree and report
the results in column (3). Across all rows, the resulting measures of network isolation aresmaller than in the full sample; the network isolation difference for this group is 4.10, and theeffective network isolation index is 8.21. Looking across columns (1) through (3), the results
suggest that networks are somewhat more important for low-educated whites, as has beensuggested in previous surveys of workers’ use of informal contacts by education. Of course, thisdifference by skill may arise because residence-based networks are more important for local
labor markets, which in turn may be more significant for low-skilled than for high-skilledworkers. An equally important conclusion from these results, however, is that effective network

co-workers to be those with low education. This approach yields qualitatively similar
conclusions for the various groups we study, but in some cases leads to rather small samples as it
causes us to discard some establishments and some census tracts in order to meet the datarequirements necessary to compute the indexes. Since we do not expect residential (orestablishment) sorting by skill to be perfect, there is no reason to preclude labor market
information flowing across workers of different skill levels.
26

isolation is about as high or higher once we disaggregate by education, implying that the network
results we report for the full sample in column (1) are not being spuriously driven by the joint
sorting of workers by education level into neighborhoods and establishments.
Another potential explanation of our results is that rather than residential neighborhood
influencing where one works – via residence-based networks – place of work determines where
one lives. If, for example, co-workers recommend neighborhoods or houses to which workers
then move, then we would see clustering of co-workers in neighborhoods, but this would not be
due to the operation of residence-based networks. We do not think this alternative explanation of
our findings is plausible, on a priori grounds, as job mobility is much higher than residential
mobility. For example, based on March CPS data for 1999-2000, the rate of within-county
residential mobility – which might roughly correspond to the type of move one would make to benear a co-workers – was 9 percent on an annual rate.26 But the monthly job-to-job mobility rateis in the range of 2.7-3.2 percent (Moscarini and Thomsson, 2008); if this is independent acrossmonths, it implies an annual job mobility rate of around 30 percent.
To answer this question definitively, we would need to know where workers lived when
they first began working (or applied for work) at a particular employer. We of course do nothave such data. However, in the Census of data we know whether a person has changed
addresses in the past five years. And in the Business Register we know establishment age. Thus,
if we restrict attention to residents who have not moved in the past five years who work inestablishments that are fewer than five years old, then we know that our measure of theimportance of networks has to be based on a predetermined choice of residential location, since26

See http://www.census.gov/population/socdemo/migration/p20-538/tab01.txt (viewed July 8,2009).
27

that decision necessarily preceded the decision to work at a new establishment

This analysis isreported in Table 4. Column (1) shows the baseline estimates from Table 3. In column (2), we
make the additional restriction that all workers in the sample work in establishments that are lessthan five years old (as of the 2000 Census). The effective network isolation index quite a bit
higher – 15.61 versus 9.45 – this may be because newer establishments are smaller; as we show
later (in Table 8), in general the index is higher for small establishments. Finally, in column (3)we also restrict attention to those individuals who did not change residential addresses in the past
five years. When we do this, the effective index climbs substantially, to 23.53. Note that this isfor a sample of larger establishments than those in column (2), because imposing the requirementthat there be two matched workers per establishment with a more stringent criterion on workers
to begin with (in this case, non-movers) makes small establishments less likely to be included in
the sample. Thus, when we focus on those for whom the residential location decision isexogenous to the workplace location decision, we find stronger evidence of residence-based
networks. This evidence does not just rule out the possibility that the causality runs fromworkplace location to residential location rather than the other way around, but it actuallystrengthens the interpretation of the results as reflecting residence-based networks, because wewould expect such network connections to be stronger among those who have lived in theirneighborhoods for longer amounts of time, and who therefore are more likely to haveconnections to neighbors through any of a number of channels.
VI.2. Results for Blacks, and Black-White Differences
As noted earlier, the importance of network effects may differ for blacks and whites. On
the one hand, as Montgomery’s (1991) model suggests, because whites make up a greater
fraction of the working population, if networks are race-based one might expect white28

individuals searching for employment to be able to take advantage of a larger network of white
working neighbors, making it more likely that whites will work together in the same
establishment than will blacks, above and beyond what would be predicted by random
allocation.27 On the other hand, if labor market networks serve to overcome information
imperfections more for blacks than for whites, perhaps by helping to lower search costs forblacks related to finding non-discriminatory employers, one might expect network isolation to belarger for blacks than for whites. Ultimately, this is an empirical question.
In Table 5 we provide results for black workers. Column (1) reports results for the
sample of black workers. We estimate that the observed network isolation index (NIO) for all
blacks is 5.29, somewhat smaller than for whites, and the random network isolation index (NIR)
is 2.58, just slightly below that for whites; the network isolation difference is therefore 2.71.
However, for blacks the maximum possible network isolation index (NIM) is quite a bit belowthat for whites (31.60), with the net result that the effective network isolation index of 9.35 isvery close to what we find for whites. (In fact, the 9.35 estimated effective network isolationindex for all blacks is not statistically significantly different from the estimate of 9.45 for all
whites.) These full sample results therefore suggest little racial difference in the importance ofresidence-based networks in explaining the assignment of workers to establishments, once werescale the network isolation difference by the maximum.28

27

In our full samples of whites and blacks in columns (1) of Tables 3 and 5, blacks have an

average of 10 black working neighbors, whereas whites have an average of 64 white working
neighbors.
28

We noted earlier that we find only a few instances where comparisons of the network isolationdifference, NIO − NIR, and the effective network isolation index, [(NIO − NIR)/(NIM − NIR)]·100,
lead to qualitatively different conclusions. The results for blacks and whites discussed in this29

In column (2) of Table 5 we report results for blacks who have at most a high school
education, and in column (3) we report results for blacks who have more than a high school
education. The results by education are significant both to address the issue of sorting by skill,
and to examine whether black-white differences reflect race differences in education coupledwith variation across skill groups in the importance of residence-based labor market networks.
In both cases, each of the observed indexes is lower than for whites, but as with the full sample
results the overall effective network isolation indexes for blacks by education level are
substantively similar to those for whites (although in each case the network isolation differenceis smaller for blacks than for whites). Whether or not we scale by the maximum, though, theevidence suggests that residentially-based networks are more important for less-educated blacks
than for more-educated blacks. For example, for less-educated blacks networks generate 11.52
percent of the maximum amount of sorting by neighborhood that could occur (and this is notstatistically significantly different from the estimate of 11.29 percent for less-educated whites),
and for more-educated blacks, the effective index is 7.01 (compared to 8.21 for more-educatedwhites). On the other hand, conditioning on education has no substantive effect on the blackwhite comparison of the importance of labor market networks.
Overall, the results in Tables 3 and 5 are in line with other results from the literature,
based on quite different types of analyses, including: survey results or indirect evidence
indicating greater use of informal contacts among the less-educated (Ioannides and Datcher
Loury, 2004; Topa, 2001); evidence based on place of work and place of residence indicating

paragraph point are one such instance. Indeed, it is only for the black-white comparisons that theconclusions are ever sensitive to scaling by the maximum possible segregation.
30

stronger network effects among those with less education (Bayer et al., 2005a); 29 and an absenceof consistent evidence of race differences in the reported use of informal contacts (Ioannides andDatcher Loury, 2004).
Table 6 presents some additional analyses for black and white workers. One issue withdirectly comparing the full sample results for blacks and whites is that we know these groups are
not similarly distributed geographically in the United States. As a result, black and white
workers in our sample may work in different labor markets where the importance of networks
could differ as a result of labor market institutions, constraints, or other factors. Therefore, incolumns (3) and (4) of Table 6 we present estimates for whites and blacks restricting the samples
to workers working in census tracts that are represented by workers in both the white and black
samples.30 For comparison, columns (1) and (2) repeat the full sample results for whites andblacks from Table 3. For whites, the sample restriction reduces the sample by about one-half,
and the numbers of workplace census tracts and residential census tracts are also reduced
considerably. The results are different than for the full sample of whites. First, the observed
network isolation index is lower at 5.68, and interestingly is closer to that for blacks. Second,the effective network isolation measure is lower (7.00 vs. 9.45 in column (1)).31 Column (4)29Weinberg et al. (2004) study neighborhood effects on hours worked, as a manifestation of

network effects, and also find stronger effects for the less-educated.
30

That is, we restrict the samples of whites and blacks to those who work in census tracts inwhich we observe at least two establishments employing at least two white workers each, andtwo establishments employing at least two black workers each (where these latter two or moreestablishments could overlap with those for which we observe white employment as well).
31

The same is true of the network isolation difference. Note that the difference between theeffective network isolation measure in columns (1) and (3) is driven in part by the higher
maximum isolation measure in the latter case. This occurs because the observations in column
31reports results for blacks. The effective network isolation index for the restricted sample ofblacks is 9.08, quite similar to the figure of 9.35 in column (2), which is not surprising since
between columns (2) and (4) the sample is reduced by fewer than 4,000 workers.32 The random
network isolation measure is quite a bit lower for this sample of whites than for blacks (1.48 vs.
2.55), so that the network isolation difference is higher for whites than for blacks (4.20 vs. 2.71).
However, the maximum network isolation index for whites is almost double that for blacks, sothat when we scale by our measure of maximum possible isolation, we reverse the relative
magnitudes of isolation for blacks and whites. We conclude that the effective network isolation
measure for the consistent samples is actually higher for blacks than for whites, although not by
an amount that we deem important, especially given that the difference is driven by our
calculation of the maximum.
VI.3. Racial Stratification of Networks
The results we have presented thus far suggest that residence-based networks areimportant, and in that sense point to racially-stratified networks. After all, given that there ispervasive racial residential segregation in the United States (Iceland and Weinberg, 2002),
networks that are predicated on residential “connectedness” have to be partially race-based.

(3) come from a much smaller number of tracts with many more establishments making it
possible to achieve a higher maximum amount of network isolation.
32

We also computed results (but do not report them in a table) for the sample of black workers

who work in urban establishments (that is, in census tracts that are within the borders of the maincity included in the MSA or PMSA). The effective network isolation index is 8.18, a bit smaller
than for the full sample of blacks. Of course, one reason that the apparent network effects appear
a bit weaker in urban areas may be that residential neighborhoods in which people interact andexchange job market information are more likely to extend beyond the census tract, given how
small census tracts are in urban areas (Figure 2).
32However, it is important also to consider whether there is racial stratification of networks even
within neighborhoods – that is, the idea that labor market information and job referrals is lesslikely to flow between black and white co-residents than between co-residents of the same race.
Understanding the role of race in driving network effects is extremely important. Race-based
networks are central to the work of Calvó-Armengol and Jackson (2007) in deriving the resultthat networks can perpetuate and exacerbate initial differences in employment between blacks
and whites, although in their model racial stratification due solely to residential segregation is
sufficient to generate this result. In contrast, racial stratification of networks even withinneighborhoods is potentially more important. It can potentially explain the results in Hellerstein
et al. (2008a) that higher local job density for one’s own race affects employment probabilities,
but higher job density for the other race does not.33 More significantly, this type of racial
stratification would imply that policies that solely address spatial mismatch, by attempting tomove blacks to areas where more whites live and where more jobs (per person) are located, may
fail to help blacks precisely because network connections are severed, and are less likely to beestablished with white neighbors.34

33

In that paper, however, the network effects do not necessarily operate among those living inthe same neighborhood, as we estimated the effect of the density of jobs in a residential
neighborhood – whether or not held by neighbors – on residents’ employment.
34

The evidence from Moving to Opportunity (MTO) is consistent with this conclusion (see, e.g.,Turner et al., 2006). Qualitative interviews of experimentals and controls in MTO suggest thatboth groups rely heavily on network connections to find jobs, and that experimentals who moved
from public housing to lower-poverty neighborhoods had less access to neighbors with jobs inthe sectors in which they had been previously employed (largely retail and health care).
Interestingly, though, the connections through which both experimentals and controls reported
finding jobs were not “immediate neighbors,” but did include associates from school, church,
33

Therefore, in column (5) of Table 6 we assess more directly whether networks are racebased. To do this, we carry out the same types of sampling and computational procedures used
before, except that we consider the relevant set of a black worker’s neighbors and co-workers toconsist of blacks or whites. We begin by constructing a sample of black workers and theirneighbors, regardless of race, who we observe to work in establishments where at least one otherblack or white worker is matched. We then further restrict the sample to those who work in a
census tract with at least two establishments that have workers in the sample.. We again
construct an effective network isolation index for black workers in this sample, but what isdifferent now is that we construct this measure by asking whether our sample of black workers
are more likely than would be predicted by randomness to work in the same establishment with aneighbor, regardless of the race of that neighbor. If networks among co-residents are raciallystratified, then the network isolation that results when we measure how likely it is that a blackworks with a neighbor regardless of race should be smaller than when we measure how likely itis that a black works with a black neighbor.
For each black worker in our sample, we first calculate an observed network isolationindex by averaging across the sample (of black workers) the fraction of each individual’s coworkers who live in that individual’s residential census tract, regardless of race. As shown in thelast column of Table 6, this number is 3.99, substantially lower than the 5.29 number in column(2). We then calculate the random network isolation index by taking all workers in this sample,
randomizing them across establishments in that sample, and calculating the network isolation
past jobs, etc. We take this to indicate that, for those represented by this sample, residential
networks may be better captured by broader geographic areas like census tracts than by blocks;
on the other hand, it also emphasizes that neighbors – however defined – are not the only sourceof network connections.
34

index for the black workers in this simulated sample. The random network isolation index is
2.00, leading to a network isolation difference of 2.00. The fact that this difference is lower thanwhen we restrict the sample of co-workers to blacks, in column (2), suggests that race is indeed
playing a role in driving the probability of working with a neighbor. To obtain a measure of the
maximum possible network isolation index, we again use the same greedy strategy thatapproximates what the index would be for blacks if the workers who make up this sample wereable to work to the maximum extent possible in establishments with their neighbors of any race,
given the size distribution of establishments in our sample, the residential distribution of workers
in them, and the workers’ races. The resulting maximum network isolation number is 30.74,
which is similar to that in column (2). Taking all of these together, the effective network
isolation index is only 6.94, which is about 25 percent smaller than in column (2), providing
evidence that residence-based labor market networks have a fairly strong race-based component.
Moreover, because in this column we only need one black worker in an establishment for theestablishment to be in the sample, as opposed to column (2), which requires two black workers,column (3) includes smaller establishments for which, as already noted, residence-based
networks are more important. Thus, on a comparable basis the difference between the effective
network isolation index in columns (2) and (3) would be even larger, bolstering the evidence thatthese networks are racially stratified.35
VI.4. Results for Hispanics
Survey evidence suggests that Hispanics use referrals in finding employment much morethan do blacks or whites (Ioannides and Datcher Loury, 2004). Immigrants and poor English
35

In future work, we plan on trying to determine the extent to which race-based networks are

attributable to residential segregation by race, as opposed to weaker network connections acrossthan within races for those residing in the same neighborhood.
35

speakers in particular may suffer from high search costs in the labor market, both because theirlimited understanding of U.S. labor markets and of English may make it hard for them to search
widely in the labor market, and because potential employers may have a difficult time inferring
the ability of these workers. Finding employment through informal networks of otherimmigrants and those who speak one’s native language may therefore be particularly important
for these groups. There is some indirect evidence consistent with this conjecture. For example,
evidence of “enclave effects,” such as the finding that Hispanics with poor English skills pay lessof a penalty for those poor skills when they live in a county or SMSA with a larger Hispanic
population (McManus, 1990), might reflect network effects, although it could also reflect higher
productivity from a greater ability to work with Spanish speakers in the enclave.36 Munshi(2003) presents a more-refined analysis of Mexican immigrants, tying labor market outcomes toa larger local population of immigrants from the same origin community. Patel and Vella (2007)
find that new immigrants work disproportionately in occupations held by previous immigrants
from the same country. And our previous work documents establishment-level segregation by
English language skills, and segregation of Spanish-speaking workers from non-Spanish
speaking workers among poor English speakers (Hellerstein and Neumark, 2008). Finally,
perhaps the most direct evidence of these types of networks for immigrants comes from the workof Massey et al. (1987), who document the importance of networks linking recent and earlier
immigrants from the same communities in Mexico.
In this section, therefore, we turn to an analysis of results for Hispanic workers, paying
particular attention to Hispanic workers who speak English poorly (or not at all) and Hispanic
workers who are immigrants. The results are presented in Table 7. Column (1) presents results

36

For a similar type of evidence for Sweden, see Edin et al. (2003).
36

for the full sample of Hispanic workers (again with the sample restrictions that allow us toconstruct the network isolation index). The observed network isolation index is 11.22, quite a bit
larger than for blacks or whites, and the random network isolation index is 3.08. Once we scale
the difference between these by the difference between the maximum possible network isolationindex and the random index, we find that the effective network isolation index for Hispanic
workers is 21.37, which is more than twice as large as what we find for blacks or whites.37
In column (2) we restrict the sample to Hispanics who self-report speaking English either“poorly” or “not at all” – which together we refer to, for simplicity, as the sample of poor
English speakers. For this sample, the observed network isolation index is 20.27. This is verylarge – it means that, on average, for a poor-English speaking worker, 20.27 percent of his or her
co-workers who also are poor English speakers live in the same census tract! The random
network isolation index is much smaller, at 4.99, and the maximum network index is 54.32.
Taken together, these numbers yield an effective network isolation index of 30.98, meaning thatover 30 percent of the maximum possible establishment network isolation by census tract ofresidence for Hispanics who speak English poorly is actually observed in the data. This is morethan three times larger than what we find for blacks and whites, and suggests to us that
residence-based labor market networks are extremely important for Hispanics who speak English
poorly. In addition, paralleling our results by education level for blacks, the fact that theimportance of networks goes up when we focus on those with poor language skills implies thatthe overall results for Hispanics are not driven by residential sorting on language skills.
37

The comparisons of Hispanics to whites and blacks, and between different groups ofHispanics, are similar for the effective network isolation index and the network isolationdifference (its numerator), so in the ensuing discussion we focus on the effective network
isolation index.
37

By way of contrast, in column (3) of Table 7 we report the results for the sample ofHispanics who report speaking English “well” or “very well.” The effective network isolationindex is 17.47, just over half as large as that for Hispanics who are poor English speakers. This
contrast is consistent with the idea that networks are extremely important in mitigating the highsearch frictions that exist for workers in the United States whose English language skills arepoor. In addition, the finding that the effective network isolation index is much higher for
Hispanics who speak good English than for whites suggests that the overall Hispanic-white
differences are not driven solely by skills.
In column (4) we report the results for Hispanic immigrants. The effective network
isolation index is 27.12, which is quite a bit higher than for all Hispanics.38 In contrast, incolumn (5) we report the results for non-immigrant Hispanics, for whom the effective network
isolation index is 13.62, smaller than for the full sample of Hispanics or any of the other
Hispanic subgroups. To the extent that the Hispanic workers in column (5) are most integrated
into U.S. society and have good English language skills, this provides further evidence that what
we are capturing in our measure of network isolation is, indeed, the important of residence-based
networks that reduce search frictions in the labor market.39
VI.5. Results for Small Establishments
We noted earlier that our sample selection rules lead to under-representation of small
establishments for blacks, and to a lesser extent for Hispanics. If network isolation differs in a

38

The results are similar for only Mexican immigrants, who represent the largest share of

Hispanic immigrants.
39

Immigrant status and language skills are strongly related. A bit under half of the immigrantsample consists of poor English speakers, while the non-immigrant sample is nearly entirely
good English speakers.
38

way that is related to establishment size, the different sample compositions of establishments
could bias our comparisons of the importance of networks across racial and ethnic groups. In
fact, other evidence indicates that smaller establishments rely more heavily on informal referrals(e.g., Holzer, 1998). To the extent that these referrals are associated with the types of network
effects we capture, under-representation of small establishments in our black and Hispanic
samples likely results in downward bias in our estimates of the importance of networks forminorities.
Table 8 reports evidence that addresses this type of bias. In particular, it reports ourbaseline analyses for whites, blacks, and Hispanics, but utilizing a restricted sample of workers
employed only at establishments with 50 or fewer workers. Two things are apparent. First,
compared to the corresponding estimates in columns (1) and (4) of Table 3 and column (1) of
Table 7, networks appear much more important when we restrict the analysis to small
establishments. Second, and more to the point, the racial and ethnic comparisons in Table 8,
which are based on much more homogeneous samples with respect to establishment size, suggestthat residence-based networks are much more important for blacks than for whites (an effective
network isolation index of 38.26 vs. 20.47,40 and even more so for Hispanics relative to whites.
Thus, the relative importance of networks for blacks and especially Hispanics compared towhites is greater than is suggested by the analyses of our full samples in Tables 3-5.41
VI.6. Network Isolation Conditional on Industry
In interpreting the results to this point we have presumed that the residence-based40

Paralleling the results in Table 6, columns (3) and (4), this conclusion for black-white

differences is driven by the scaling by maximum segregation.
41

The analysis of blacks and whites for these restricted samples also yields evidence indicatingthat networks are race-based, similar to that reported in column (5) of Table 6.
39

networks we are measuring operate to help workers find jobs in particular establishments.
However, networks may instead (or as well) serve to help job searchers learn of vacancies incertain industries, rather than reducing frictions that prevent workers from matching to specificestablishments within industries. For example, someone who works in a retail firm may tell aneighbor of job vacancy postings in other nearby retail establishments. Note that, in principle,
this kind of mechanism could underlie the results in Bayer et al. (2005a), since they only
establish that those who live nearby are likely to work in the same narrow geographic area, notthe same establishment.
If networks operate to increase the likelihood that census tract co-residents work in thesame industry, our calculations to this point might overstate the extent to which networks
determine the establishment of employment, because the clustering of workers from the same
census tract of residence in the same industry within a census tract of employment will inevitably
lead to some clustering in the same establishments. In this section we explore whether the
network effects we find reflect employment at the establishment level, or instead only at theindustry level. We do this by constructing “conditional” network isolation indexes, simulating
network isolation while holding the distribution of workers across industries fixed within acensus tract of employment. Intuitively, if a particular residential census tract has a lot of
workers employed in a specific industry, then the random allocation of workers in the simulation
will preserve that particular industry concentration, and by subtracting off the network isolationthat occurs randomly conditional on industry we will isolate the extent to which the clustering ofcensus tract co-residents in the same establishments exceeds the clustering that is driven by themworking in the same industry.42

42

Earlier, we noted that we have chosen a specific way to operationalize networks – as affecting40

To condition on industry, we modify the procedure used previously to construct the
random network isolation index (NIR). Instead of randomly assigning all workers in a census
tract of employment to establishments in the tract, holding the size distribution of establishments
fixed, we instead ensure that workers are also assigned to their industry of employment.43 We
then once again compute the average (across the simulations) simulated fraction of co-workers
who come from a worker’s own neighborhood, denoting this NIC, and we define the extent of“conditional effective network isolation” to be:[{NIO − NIC}/{ NIM − NIR}]×100 ,where NIR and NIM are defined as before, without regard to industry. A conditional effective
network isolation index of zero (when NIO = NIC) would imply that all of the effective network
isolation can be attributed to networks that help workers find employment in specific industries,
but not to establishments within industries; that is, above and beyond the clustering of
employment of neighbors in the same industry, there is no clustering in the same establishment.
Conversely, a network isolation index equal to that of the (unconditional) effective segregation
the establishments at which people work. Here we are making a different argument. In
particular, having chosen this definition of networks, we could be overstating the importance of
networks if networks affect the industry of employment, because workers employed in the same
census tract who work in the same industry are more likely to work in the same establishment
than are two randomly chosen workers employed in the same census tract.
43

The industry definitions that we use are the same eight industries reported in Tables 1 and 2:

mining; construction; manufacturing; transportation; wholesale trade; retail trade; FIRE; andservices. We do not want a more highly-detailed industry classification for this exercise. First,
we are interested in the flow of information about nearby jobs, which need not be in the samefinely-classified industry. Second, our test depends on having multiple establishments within thesame industry in a census tract, which obviously would occur less frequently the more we
disaggregate industries.
41

measure (when NIC = NIR) would imply that all of the effective network isolation comes fromnetworks helping individuals find jobs in specific establishments within industries, and thatindustry, per se, plays no role in sorting.
For brevity, we report in Table 9 results on conditional network isolation only for groups
that are “low-skilled” in the sense that they either have low levels of education (for whites andblacks) or poor English proficiency (for Hispanics). These are the groups for which residencebased networks appear most important. In column (1) we repeat the network isolation results forless-educated whites that we previously reported in Table 3, column (2). Then, in column (2),we report results where we condition the random network isolation index on industry. The
observed network isolation index, the (unconditional) random isolation index, and the maximum
network isolation index all remain the same, as the conditional random isolation index does notplay a role in these calculations. The simulated conditional index is 5.46, which means thatwhen workers are randomly assigned to establishments in the same industry in which they areobserved to work (and the same census tract), on average 5.46 percent of their co-workers will
come from the same residential neighborhood. This is higher than the unconditional random
isolation index of 4.06, so that that the conditional effective network isolation index of 8.86 is
somewhat smaller than the unconditional effective index of 11.29 reported in column (1). The
difference implies that assignment of workers from the same neighborhoods to specific industries
within a census tract can explain some of the assignment of workers to specific establishments.
However, even after conditioning on industry, effective network isolation is still relatively high,and dividing 8.86 by 11.29, 78 percent of the effective network isolation remains even after we
condition on a worker’s industry.44 This demonstrates that, at this level of industry detail, most44

In a sense, this is likely a lower bound for the percentage of the effective network isolation42

of the (unconditional) effective network isolation for less-educated whites cannot be explained
just by a mechanism whereby residence-based networks serve only to help workers find jobs inthe same industries as their neighbors.
Columns (3) and (4) of Table 9 explore this issue for less-educated blacks. Column (3)repeats the unconditional network isolation indexes from Table 3, column (5), and column (4)reports the indexes conditional on industry. The results are very similar to those for lesseducated whites. The conditional random network isolation index of 4.72 reported in column (4)is somewhat larger than the unconditional random index of 3.51, so that the conditional effective
network isolation index of 7.95 is somewhat smaller than the unconditional index of 11.52. And,as the last row of column (4) reports, assignment of workers to industry alone rather than to
specific establishments only explains slightly more than 30 percent of effective network
isolation.
Finally, columns (5) and (6) turn to estimates of network isolation for low-skilled
Hispanics, with low skill defined as poor English proficiency. The results parallel closely thequalitative results for less-educated whites and blacks. Column (5) repeats the unconditional
results for less-skilled Hispanics previously reported in Table 7, column (2). Column (6) reports
the conditional indexes, where the conditional effective network isolation of 21.86, whencompared to the (unconditional) effective network isolation index of 30.98, implies that 71

index that remains. If there is only a small number of establishments in an industry in aparticular census tract, then what this procedure treats as sorting on industry may in fact
represent sorting on establishments. In the limit, if there were only one establishment in theindustry in the census tract, we could not distinguish between sorting on industry or
establishment, whereas the conditioning procedure used in this subsection attributes the sorting
to industry first, and only the residual to establishment.
43percent of the assignment of workers from residential neighborhoods to establishments within acensus tract cannot be explained by industry alone.
We conclude from these analyses conditioning on industry that the network isolation
results we find are largely due to the assignment of workers to specific establishments, providing
evidence in support of theoretical models in which networks serve to match workers to specificestablishments.45
VII. Conclusions
We use matched employer-employee data for the United States to measure theimportance of residence-based labor market networks in the allocation of jobs. The core of ourapproach is to look at business establishments in a census tract, and to ask whether the workers at
each establishment are disproportionately clustered in particular residential neighborhoods,
relative to what we would expect to occur randomly given that most workers employed in aparticular census tract reside in a subset of nearby census tracts. Evidence of this kind of
disproportionate residential concentration of a business establishment’s workforce is consistent
with labor market networks that connect individuals residing in the same neighborhood tospecific business establishments. Because of recent research highlighting the potential

45

We also compute isolation indexes where we condition on a worker’s reported occupation,rather than industry. This is also useful if one is concerned that our unconditional effective
network isolation indexes are driven not by networks per se but by the sorting of workers ofdifferent skills into different neighborhoods and establishments, where occupation is a proxy for
those skills. For less-educated whites, occupation (broken out into six categories) explains lessthan 7 percent of effective network isolation; for both less-educated blacks and low-Englishproficiency Hispanics it explains somewhat more, approximately 17 percent. All in all, though,the results show that sorting by occupation does not come anywhere close to explaining oureffective network isolation indexes.
44

importance of labor market networks for less-skilled workers in the labor market, and moregenerally positing that labor market networks operate along the lines of race, ethnicity, and skill,
we consider separately the importance of these labor market networks for whites, blacks, andHispanics, and, within each group, the relative importance of these networks for workers withdifferent skills.
Our evidence is complementary to an existing body of research on labor market networks
and the use of informal labor market contacts that are thought to characterize networks. What is
unique about our evidence, however, is that it looks directly at potential network effects forworkers employed at the same business establishment. Given that many theories of theimportance of labor market networks emphasize the gains to employers from using their current
employees to refer other employees, it seems particularly useful to test whether network
connections among workers – in our case based on residential location – actually make it more
likely that workers are employed in the same business.
We interpret the evidence as indicating that labor market networks play an important rolein establishment-level employment. For both whites and blacks we find that the grouping ofworkers from the same neighborhoods in the same business establishments exceeds by a factor of
more than two what we would expect to occur randomly. For whites, we find that network
isolation is about 9.4 percent of the theoretical maximum amount of grouping that could befound in the data, and many of our analyses indicate that residence-based labor market networks
are more important for blacks than for whites. For both whites and blacks, these labor market
networks appear more important for workers who have low levels of education – a high school
degree or less – than for more-educated workers. There is also some evidence that networks are
more important in small establishments.

45

Our results also provide evidence that networks operate to some extent along racial lines,
above and beyond the racial stratification of networks that comes from residential segregation by
race. In particular, the link between residential location and the establishment of employment isstronger for blacks when we consider only co-workers of the same race, consistent with morelabor market information and referrals flowing across co-residents of the same race than of theother race. As emphasized in recent theoretical work by Calvó-Armengol and Jackson (2007),
race-based labor market networks may prevent the convergence of black and white labor market
outcomes – and can even exacerbate the differences. Moreover, race-based labor market
networks likely limit the ability of spatial policies – which encourage blacks to move to areas
where there are more jobs but also likely more white co-residents – to improve labor market
outcomes for blacks.
We also find that residence-based networks are more important for Hispanics than forblacks or whites, and among Hispanics, these networks are especially important for immigrants
and those with poor language skills. The results for Hispanics give credence to the idea that
informal labor market networks may be particularly important for those workers who are not as
well-integrated into the labor market, and for whom employers may have less reliable
information.46
As the discussion of the data requirements for this study indicates, it is difficult to obtainevidence on labor market networks. Although the notion of networks has been around for many46

As noted previously, the alternative interpretation of our results – that they reflect

heterogeneous tastes such that people who like similar workplaces also like similar
neighborhoods – is not consistent with the patterns of evidence that we find whereby residencebased networks are more important for those least integrated into the economy, such as thosewith less education, Hispanics, and especially Hispanic immigrants.
46

decades, there are only a handful of studies providing evidence that networks affect labor market
outcomes, and this study is the first to document the importance of labor market networks indetermining the establishments in which workers work. Aside from further attempts to construct
or obtain data to study the kind of network effects we examine in this paper, a number of otherimportant questions remain. First, what are the consequences of labor market networks thatmatch workers in a network to specific establishments? Do those who find employment inestablishments with others in their networks actually have better labor market outcomes (e.g.
higher wages or more job security) as a result? Second, are minorities who have network
relationships mainly with other minorities disadvantaged relative to those that have network
relationships with whites? The DEED is likely to prove useful in trying to address thesequestions in future research.
On the other hand, matched employer-employee data sets such as the DEED have some
limitations in terms of what they can teach us about networks. First, it is obviously important toconsider along what other dimensions of social interactions – aside from residence – networks
operate to cause individuals to work in the same establishment, and what types of networks aremost important. Among the possibilities are schools,47 religious institutions, and community
groups, as well as existing places of employment (from which workers may move to other jobs).
Second, the DEED provides little scope for understanding the dynamics of how networks
actually work. Are all members of the network equally important? What kinds of information
get shared within the network? The data demands for answering many of these questions aredaunting, but the answers can provide clues regarding how important it is for individuals,
47

Indeed Bayer et al. (2005a) show that the type of network effects they study appear to bestronger for those with children of similar ages, which could reflect social interactions offamilies in schools.
47communities, and other institutions to foster network relationships so as to improve economic
outcomes, and what types of networks are most effective.

48

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Figure 1: Census Tracts, Chicago PMSA

Source: U.S. Census Bureau, http://www.census.gov/geo/www.cob.tr2000.html (viewed May 5, 2008).
Note that Chicago city limits are highlighted.

Figure 2: Census Tracts, Chicago

Source: U.S. Census Bureau, http://www.census.gov/geo/www.cob.tr2000.html (viewed May 5, 2008).

Table 1: Individual-Level Means for Preliminary and Final Analysis Samples, White, Black, andHispanic Workers

Age
FemaleMarriedWhite
Black
Hispanic
Full-time
Number of kids (if female)High school diploma
Some college
BA
Advanced degree
Speaks English well
Immigrant
Log(hourly wage)
Hours worked in 1999
Weeks worked in 1999
Earnings in 1999
Industry:
MiningConstruction
Manufacturing
Transportation
Wholesale
RetailFIRE
ServicesN

2000 SEDF,
workers
eligible to bematched(1)
39.18(13.07)
.46
.58
.81
.09
.09
.78
.77
(1.06)
.31
.33
.15
.06
.97
.08
2.54
(.73)
40.22
(11.73)47.28(10.53)33,444(42,952)
.006
.081
.207
.075
.047
.210
.070
.304
13,456,402

2000 full
DEED
(2)
39.60
(12.54)
.50
.61
.86
.06
.07
.82
.76
(1.04)
.29
.36
.17
.07
.92
.06
2.62
(.69)
40.71
(11.08)48.43(9.22)
37,091(47,220)2000 DEED,
final analysis
sample forwhites(3)
40.14(12.37)
.50
.62
1.0
.83
.69
(.99)
.26
.36
.22
.09
1.00
.03
2.74
(.69)
41.04
(10.98)48.95(8.55)
42,669(53,413)

2000 DEED,
final analysis
sample forblacks(4)
38.32(11.49)
.63
.42
1.0
.84
.87
(1.12)
.28
.41
.14
.05
.99
.09
2.56
(.66)
40.39
(9.56)
47.64(10.15)31,090(31,108)

2000 DEED,
final analysis
sample for
Hispanics(5)
35.65(11.48)
.46
.57
1.0
.83
1.12
(1.22)
.25
.28
.07
.03
.80
.49
2.41
(.64)
40.58
(9.39)
47.01(10.54)
26,682(29,589)

.004
.048
.257
.052
.052
.212
.068
.306
3,924,714

.003
.041
.266
.053
.054
.195
.072
.316
1,675,412

.001
.007
.242
.074
.025
.146
.079
.425
97,967

.002
.040
.353
.052
.050
.212
.043
.249
110,235

Notes: In addition to restricting by race and ethnicity, the three additional restrictions imposed ingoing from column (2) to columns (3) through (5) are: the individual must live and work in same
MSA/PMSA; there must be at least two workers matched to establishment; and there must be atleast one other establishment with two matched workers in the census tract.

Table 2: Establishment-Level Descriptive Statistics for Preliminary and Final Analysis
Samples

Total employment
Total employment(approximate median)
Establishment size:
1-25
26-50
51-100101+
Industry:
MiningConstruction
Manufacturing
Transportation
Wholesale
RetailFIRE
Services
In MSA/PMSA
Census region:
North East
Mid Atlantic
East North Central
West North Central
South Atlantic
East South Central
West South Central
MountainPacific
Payroll ($1000)
Payroll/total
employment
Share employees
matchedMulti-unit
establishment
N

2000 full
DEED
(1)
49.82
(368.46)15

2000 DEED, final 2000 DEED, final 2000 DEED, final
analysis sample
analysis sample
analysis sample
for Hispanics
for blacks
for whites
(2)
(3)
(4)
102.82412.43258.09(344.85)(887.34)(670.16)
35
154
84

.65
.15
.10
.10

.39
.20
.17
.22

.11
.11
.14
.62

.18
.16
.20
.45

.004
.078
.133
.050
.067
.284
.081
.303
.792

.003
.070
.186
.052
.074
.265
.077
.272
1.0

.002
.013
.226
.077
.039
.231
.082
.331
1.0

.003
.053
.310
.051
.060
.266
.049
.209
1.0

.053
.135
.199
.092
.166
.050
.102
.061
.142
2,103
(146,515)
37.14(2,285)
.16

.048
.148
.232
.089
.157
.043
.090
.057
.135
5,303
(281,585)
47.69(2,716)
.14

.013
.122
.204
.039
.332
.081
.136
.012
.061
19,061(67,785)37.63(50.53)
.05

.015
.084
.088
.010
.052
.002
.218
.084
.448
11,905(56,649)35.16(77.83)
.07

.40

.51

.80

.61

1,254,718

329,943

21,87230,343

Notes: See notes to Table 1. The approximate median is an average of the median and someobservations to either side of the median, to preserve confidentiality.

Table 3: Network Isolation for Whites, Overall and by Education

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment
from same tract of residence

All
(1)
7.87
2.97
4.90
54.84
9.45

High school
degree or less
(2)
10.56
4.06
6.50
61.62
11.29

More thanhigh school
degree
(3)
6.51
2.41
4.10
52.32
8.21

1,675,412
26,47046,764
129.6
38.4
9.4

561,370
25,69043,469
114.5
24.4
11.2

1,114,042
26,29945,666
137.2
45.4
8.5

Notes: The calculation is described in the text. NIO is the average fraction of a worker’s
co-workers (i.e., excluding the worker) who reside in the same census tract as the worker,
averaged across all workers in the sample. NIR is the average fraction that is simulated tooccur randomly. NIM is the simulated average maximum fraction. “Effective network
isolation” therefore measures the fraction of the maximum that is actually observed.

Table 4: Network Isolation for Whites, with Residential Location Exogenous to Job Location

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment
from same tract of residence

Notes: See notes to Table 3.

All
(1)
7.87
2.97
4.90
54.84
9.45

Working inestablishmentsborn 1996 or after
(2)
8.68
3.07
5.62
39.03
15.61

Working in newer
establishments and did
not move 1995-2000
(3)
10.48
3.70
6.78
32.51
23.53

1,675,412
26,47046,764
129.6
38.4
9.4

206,261
8,692
38,855
38.0
18.4
2.8

65,2993,953
26,010
17.2
13.6
2.0

Table 5: Network Isolation for Blacks, Overall and by Education

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment
from same tract of residence

Notes: See notes to Table 3.

All
(1)
5.29
2.58
2.71
31.60
9.35

High school
degree or less
(2)
7.42
3.51
3.90
37.40
11.52

More thanhigh school
degree
(3)
3.90
1.97
1.93
29.55
7.01

97,967
4,490
21,623
22.2
18.6
2.6

38,754
4,301
13,598
19.8
15.6
2.9

59,213
4,350
18,299
23.8
20.5
2.4

Table 6: Network Isolation for Blacks and Whites, for Consistent Samples of Establishments, and Ignoring Race

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment
from same tract of residence

Notes: See notes to Table 3.

All whites
(1)
7.87
2.97
4.90
54.84
9.45
1,675,412
26,47046,764
129.6
38.4
9.4

All blacks
(2)
5.29
2.58
2.71
31.60
9.35
97,967
4,490
21,623
22.2
18.6
2.6

Establishments located intracts in both black andwhite samples
WhitesBlacks
(3)
(4)
5.68
5.25
1.48
2.55
4.20
2.71
61.43
32.35
7.00
9.08
845,290
4,122
42,533
229.7
46.6
11.4

94,2104,122
21,459
23.0
17.7
2.6

Network isolation basedon blacks and whites
All blacks
(5)
3.99
2.00
2.00
30.74
6.94
140,083
9,094
26,768
39.7
40.3
4.5
2.00

Table 7: Network Isolation for Hispanics, Overall and by Skill and Immigrant Status

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment
from same tract of residence

Notes: See notes to Table 3.

All
(1)
11.22
3.08
8.14
41.15
21.37

Poor
Englishskills
(2)
20.27
4.99
15.28
54.32
30.98

Good
Englishskills
(3)
8.89
2.59
6.30
38.65
17.47

Immigrant
(4)
15.27
3.78
11.49
46.14
27.12

Nonimmigrant
(5)
7.25
2.39
4.86
38.06
13.62

110,235
5,059
20,716
59.7
9.1
2.7

22,538
3,683
7,703
67.6
6.9
3.3

87,697
5,043
19,920
57.6
9.7
2.5

54,5294,680
13,802
66.1
7.5
2.9

55,706
4,754
16,548
53.4
10.7
2.5

Table 8: Network Isolation for Whites, Blacks, and Hispanics Working in Small Establishments (50 employees or
less)Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Network isolation difference, NIO − NIR
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
N# place of work tracts# residential tracts
Mean establishments/tract
Mean matched workers/establishment
Mean number of workers in tract of employment from same tractof residence

Notes: See notes to Table 3.

Whites
(1)
15.76
4.49
11.27
59.54
20.47

Blacks
(2)
12.76
4.43
8.33
26.20
38.26

Hispanics
(3)
22.79
5.74
17.05
40.71
48.75

527,430
22,162
43,700
66.0
3.3
3.9

8,706
1,097
5,308
7.4
2.7
1.4

21,952
2,093
8,019
23.0
2.7
1.8

Table 9: Network Isolation for Low-Skilled Workers, Conditional on Industry

Network isolation index, observed, NIO
Simulated random network isolation index, NIR
Simulated conditional network isolation index, NIC
NIO − NIC
Maximum possible network isolation index, NIM
Effective network isolation index,[(NI O − NI R)/(NI M − NI R)]·100
Conditional effective network isolation index,[(NI O − NI C)/(NI M − NI R)]·100
Share of effective isolation index unexplained

Whites, high school or
less
Uncond.
Cond.
(1)
(2)
10.56
10.56
4.06
4.06
5.46
5.10
61.62
61.62
11.29

Blacks, high school or
less
Uncond.
Cond.
(3)
(4)
7.42
7.42
3.51
3.51
4.72
2.69
37.40
37.40
11.52

Hispanics, poor
English skills
Uncond.
Cond.
(5)
(6)
20.27
20.27
4.99
4.99
9.48
10.78
54.32
54.32
30.98

8.86

7.95

21.86

78.46

69.01

70.57

See notes to Table 3. There are 561,370 whites with a high school degree or less; 38,754 blacks with a high school degree
or less; and 22,538 Hispanics with poor English skills. The industries are mining; construction; manufacturing;transportation and warehousing; wholesale trade; retail trade; finance, insurance, and real estate; and services.