MIT Sloan School of Management
MIT Sloan School Working Paper 4778-10
Creating Connections For the Disadvantaged: Networks and Labor Market Intermediaries at the Hiring Interface
Roberto M. Fernandez
© Roberto M. Fernandez
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Creating Connections For the Disadvantaged:
Networks and Labor Market Intermediaries at the Hiring Interface
Roberto M. Fernandez
MIT Sloan School of Management
robertof@mit.edu
Word count: 14,981
March 22, 2010
Key words: Race, poverty, networks, brokerage, intermediaries
Creating Connections For the Disadvantaged:
Networks and Labor Market Intermediaries at the Hiring Interface
Abstract
Scholars interested in race inequality have been particularly attracted to network accounts of the
stratifying effects of social networks in the labor market. A recurring theme in policy‐oriented research on poverty
is that institutional connections can be engineered to create connections between job seekers and employers in
ways that parallel social network processes. Yet, there has been little empirical research on how such linkages
work across the various steps of the recruitment, screening, and hiring process. We examine how labor market
intermediaries can serve as functional substitutes for social network processes for disadvantaged workers.
Consistent with policy arguments about the desirability of creating connections to employers, applicants who are
connected to this employer via formal labor market intermediaries exhibit a number of the advantages
experienced by those applying to the firm via social network ties. Across the two stages of the hiring process, the
net result is that applicants with such “created connections” are more likely to be offered jobs, and ultimately
hired than other groups of applicants. We conclude with a discussion of the implications of these findings for
research on labor market intermediation and other forms of brokerage and the feasibility of policy efforts to
“create connections” in the labor market.
There is by now a rich tradition of research on the role of networks in job‐person matching processes.
Scholars interested in race and gender inequality have been particularly attracted to network accounts of the
stratifying effects of social networks in the labor market (for a recent review of the role of networks and race in the
labor market, see Fernandez and Fernandez‐Mateo 2006; for a review of gender and labor market networks, see
Fernandez and Sosa 2005). Scholars interested in poverty, too, have made research on networks a key research
focus (e.g., Elliott 1999; Fernandez and Harris 1992; Newman 1999; Reingold 1999; Royster 2003).
Another literature concerned with understanding how persons and jobs are matched is the scholarship on
formal labor market intermediaries (e.g., Kazis 2004; Osterman 2004). Similar to the research on social networks in
the labor market, studies of labor market intermediaries have also examined the stratifying consequences of labor
market intermediation (e.g., Fernandez‐Mateo 2009; Vosko 2000). As with the study of social networks in labor
markets, students of poverty have also been examining how labor market intermediaries can help the plight of
poor and disadvantaged job seekers (e.g., Burtless 1985). Interestingly, a number of these suggestions are explicitly
modeled on how social network processes work in the labor market (e.g., Granovetter 1979; Melendez and
Harrison 1998; Wilson 1996). Numerous scholars have argued for poverty reduction programs to remediate the
poor’s presumed network deficits (see Fernandez and Fernandez‐Mateo 2006) and use labor market
intermediaries to “create connections”—i.e., to use social programs to engineer relationships between poor and
disadvantaged job seekers and employers (Granovetter 1979; Gueron and Pauly 1991; Holzer 2009; Wilson 1996).
While the social network and labor market intermediary literatures are parallel in their conceptualization
of job‐person matching, their emphases have been different. Studies have appeared examining informal social
network processes from the perspective of all three actors involved: 1) potential job‐seekers (e.g., Bridges and
Villemez 1986; Lin 2001); 2) the perspective of the people connecting the job and the person (Smith 2005, 2007;
Fernandez and Castilla 2001; Fernandez and Fernandez‐Mateo 2006: 52‐58; Fernandez and Sosa 2005: 876‐880);
and 3) the firms’ hiring agents (e.g., Fernandez and Weinberg 1997; Fernandez et al. 2000; Petersen et al. 2000).
So, too, research on labor market intermediaries has been conducted from multiple perspectives. For example,
George and Chattopadhyay (2005) have looked at the phenomenon from the contractor’s point of view, examining
their divided loyalties between the client and the contracting firm. Research by Fernandez‐Mateo (2007, 2009)
adopted the point of view of the labor market intermediary, focusing on the intermediary’s brokerage role. Other
research using the broker’s perspective has specifically targeted disadvantaged populations (Granovetter 1979).
Another strand of research uses data on samples of firms to examine which organizations use temporary and
contract labor (e.g., Davis‐Blake and Uzzi 1993; Houseman et al. 2003; Kalleberg et al. 2003; Uzzi and Barsness
1998). Other studies have focused on low‐wage workers using broad samples of job seekers to assess the
effectiveness of policy programs that use temporary help agencies (Autor and Houseman 2010; Houseman 2001).
Less well‐understood, however, is how employers make choices among candidates from brokered and
non‐brokered sources. While social network research has numerous employer studies of hiring network applicants
in comparison with candidates from other sources (e.g., Fernandez and Weinberg 1997; Fernandez et al. 2000;
Petersen et al. 2000, 2005), parallel studies of employer’s choices between candidates from intermediated and
non‐intermediated sources are missing from the research on intermediaries. This limitation is unfortunate.
Without counterfactual information on other candidates the employer might have chosen‐–the options the
employer has between intermediated and non‐intermediated candidates‐–it is difficult to determine the value that
labor market brokers provide. Moreover, since labor market intermediaries can deliver benefits through multiple
mechanisms—providing a higher quality applicant pool, information advantages in recruiting, signaling and
screening (see below)—data on the employer’s choices is crucial for distinguishing among the various processes at
work at the hiring interface (for a similar argument, see Fernandez and Weinberg 1997).
In this paper, we seek to fill this gap in our understanding by examining unusually rich data on the hiring
process for entry‐level jobs at one employer. We compare candidates who are connected to this employer via
social network ties with those who have been referred to the employer by formal labor market intermediaries.
This latter group consists of people who have been sent to the focal firm by public and private labor market
intermediary organizations that have been charged with “creating connections” between the poor and local
employers. Rare among studies of screening among job candidates, we are able to identify the race, gender, and
poverty status of the applicants to this firm, thus we are able to study the implications of using “created
connections” for poor and minority populations. We determine whether and to what extent “created connections”
serve the same functions as social networks in matching people to jobs, and how these processes differ by race,
gender and poverty. We conclude with a discussion of the implications of these findings for research on labor
2
market intermediation and other forms of brokerage and the feasibility of policy efforts to “create connections” in
the labor market.
Creating the Connections for the Disadvantaged
Numerous scholars have examined the various forms of labor market intermediation (e.g., Houseman
2001; Kazis 2004; Osterman 2004). While there are many forms of labor market intermediaries—ranging from
public job placement agencies (Marano and Tarr 2004), temporary help firms (Autor 2001; Davis‐Blake and Uzzi
1993; Houseman et al. 2003; Vosko 2000), contract staffing agencies (Fernandez‐Mateo 2007), and executive
search firms (Finlay and Coverdill 2000, 2002)—these organizations share the feature that they broker between
the supply and demand sides of the labor market, matching people to jobs (Autor 2009; Fernandez‐Mateo 2007).
While much of this research has tended to look at markets for high skilled and higher paying jobs (Barley
and Kunda 2004; Benner 2002; Finlay and Coverdill 2000, 2002; Khurana 2004), scholars concerned with poverty
have especially been interested in the role of labor market intermediaries for poor and disadvantaged job seekers
(e.g., Anderson et al. 2009; Burtless 1985; Melendez and Harrison 1998). Osterman (1999) is explicit in this
diagnosis: “…too often training programs are isolated from employers and are not linked to clear paths of job
mobility. Making these connections is the job of labor market intermediaries…” (p. 133). Other scholars too have
called for poverty reduction programs to “create connections” between employers and poor and disadvantaged
job seekers (e.g., Granovetter 1979; Wilson 1996).
While labor market intermediaries of all types aim to place workers with employers, especially with
respect to poor populations, there is some disagreement about how these linkages work. Although strengthening
connections being poor job seekers and employers is often seen as desirable, past research has questioned
whether labor market intermediaries actually perform this function for those most in need. Recently, Autor and
Houseman (2010) have are argued that in the low wage sector temporary services can help workers in the short‐
term, but is not helpful in the longer‐term because temp employment weakens workers’ search efforts for direct
hire jobs. On the employer’s side, a number of studies have shown that employers often stigmatize low wage
workers who are sent to them by public and private labor market intermediaries (e.g., Laufer and Winship 2004).
In general, employers are concerned that since intermediaries targeting poor populations specialize in hard‐to‐
employ populations, candidates referred by these organizations will be adversely selected, constituting the labor
3
market “left‐overs” who could not find a job through other means (Autor 2009; Burtless 1985; Van Ours 1994).
Other scholars have argued that, perhaps in response to employers’ concerns, these agencies tend to cream the
best job candidates, avoiding those who are most in need of assistance and (e.g., Anderson et al. 1993; Gueron and
Pauly 1991; Thomas 1997).
From the perspective of the hiring firm, such creaming suggests that one of the functions of the
intermediary firm is to deliver candidates that have been pre‐screened for positions at the firm, and thus, are of
higher quality than candidates from other sources (see Houseman [2001] and Autor [2001] for evidence that many
employers use agency temporaries to pre‐screen candidates). This screening argument is exactly parallel to one of
the reasons offered for why employee referrals are often preferred over other applicants, i.e., that referred
applicants have been pre‐screened by the referring employee, and thus provide a “richer pool” of higher quality,
more hirable candidates than other recruitment sources (Fernandez and Weinberg 1997; Fernandez et al. 2000).
Indeed, employers’ complaints about the ineffectiveness of employment agencies often center on intermediaries
not doing enough to screen candidates (e.g., see Rees 1966:564; Laufer and Winship 2004:230).
In addition to screening benefits, another alleged function of intermediaries is to increase employers’
flexibility in responding to variability in workload and labor market conditions. In the research on labor market
intermediaries, this discussion has been centered on employers’ use of temporary help (Autor 2001; Autor and
Houseman 2010; Houseman 2001). More generally, however, employers have been found to shift among
recruitment sources as a way of managing fluctuations in labor market conditions (Gorter and van Ommeren
1999), and previous research has studied the use of employee referrals in this manner (Fernandez and Weinberg
1997; Fernandez et al, 2000). To our knowledge, no studies have examined whether relationships with labor force
intermediaries are used in a similar way to provide a flow of candidates at times of relative labor shortage. To the
extent that labor market intermediaries direct applicants to firms when competition for job vacancies is lower,
then such intermediary‐referred candidates will experience greater success at the hiring interface.
Even if labor market intermediaries do direct applicants to firms at more favorable times, the question
remains as to the nature of the connection beyond the initial contact with the firm. As mentioned above, to the
extent that intermediaries pre‐screen applicants, then such candidates will be more likely to advance in the hiring
process. However, since there are often multiple steps involved in hiring, with the first step being one in which
4
screeners decide who to interview, different kinds of information are relevant for the different stages. Although
HR recruiters generally attempt to reflect the wishes of line managers (Fernandez et al. 2000), it is line managers
and not HR personnel that set job requirements (Fernandez and Mors 2008; Rynes and Boudreau 1986). While
intermediaries might do well screening on relatively easily measured paper criteria that will impress HR screeners,
they might do less well in screening for harder to measure “soft‐skills” (Moss and Tilly 1996) that are more likely to
be revealed in interviews with hiring managers (for evidence of this, see Manning 2000:766‐67). For this reason,
distinguishing among the multiple steps of the hiring process is important for understanding the consequences of
connections created by labor market intermediaries.
While there have been a few employer‐side studies addressing the question of intermediaries (Houseman
[2001] and Houseman et al. [2003] are exceptions), what is crucially missing from extant studies is information on
how applicants with “created connections” fare in comparison to other non‐brokered candidates. While the issue
of alternative paths around the broker is a general issue (Ryall and Sorenson 2007), understanding employers’
options is of particular concern in the case of labor market since intermediaries. Without information about the
employer’s choice set, it is difficult to determine the particular functions served by intermediaries, or the value
that these functions provide for employers. Since these functions might work through multiple mechanisms at the
hiring interface, data on the employer’s choice set is crucial for distinguishing among these (for a similar argument,
see Fernandez and Weinberg 1997).
We address these issues by examining unusually rich data on the hiring process for entry‐level jobs at one
employer. This firm has established a relationship with a number of public and private intermediaries which
channel job applicants to this firm. We compare candidates who are connected to this employer via social network
ties with those who have been referred to the employer by formal labor market intermediaries. This latter group
consists of people who have been sent to the focal firm by public and private labor market intermediary
organizations that have been charged with “creating connections” between the poor and local employers. Rare
among studies of screening among job candidates, we are able to identify the race, gender, and poverty status of
the applicants to this firm, thus we are able to study the implications of using “created connections” for poor and
minority populations. We compare both those connected to employers via social networks and referrals from labor
market intermediaries with candidates that sought out this employer on their own (i.e., non‐referred, non‐
5
networked candidates). We examine these three groups at initial contact with the firm, and at the interview and
job offer steps of the screening process. Specifically, we measure the degree to which the processes followed by
intermediary‐referred candidates parallel those of networked candidates. Thus, we determine whether and to
what extent these labor market intermediaries serve as functional substitutes for social network processes in
matching people to jobs, and how these processes differ by race, gender and poverty. We conclude with a
discussion of the implications of these findings for research on labor market intermediation and other forms of
brokerage.
Data and Setting
We study these issues with unique data collected on the hiring process for entry‐level jobs at one
company site. The company we are studying is a private, medium‐sized (approximately 1,200 person) firm located
in the western United States. While the company employs people at numerous locations, we focus here on its
headquarters and main production facility. The company hires workers for full‐time jobs with benefits, is not
unionized and does not employ temporary workers. These entry‐level jobs are factory‐production are for
occupations that fall into the broad category of “Other Production Occupations” (Bureau of Labor Statistics SOC
code 51‐9000; see www.bls.gov/soc/home.htm).
As a case study, we can make no claims with respect to the broad representativeness of the plant under
study. However, we do know that this firm does maintain relationships with a number of public sector labor
market intermediaries, and has hired some people—including candidates from poverty backgrounds—and has also
been the recipient of financial incentives provided by the Federal government designed to encourage the hiring of
poor and disadvantaged workers. Over the course of our study, the company received a total of $19,200 from
these agencies to subsidize the training of 16 new hires. The firm under study, therefore, is an example of a firm
that is both aware of, and open to the overtures of labor market intermediary organizations that are attempting to
create connections to employers. Additionally, the in‐depth information on the hiring process we have available
here makes this an ideal setting for studying a number of the mechanisms by which the poor and minorities are
matched to jobs via social networks and labor market intermediaries. Thus, the theoretical significance of this case
is that it provides a window through which one can view the operations of a set of processes that are normally
hidden from view.
6
Although we do not have the benefit of random assignment of candidates in this field setting, we have
collected and triangulated numerous sources of data to address as many of the internal threats to validity as
possible. We have coded an unusually rich set of variables measuring background variables that the firm’s hiring
agents use to screen, as well as some longitudinal information for repeat applicants to address unmeasured fixed
characteristics of applicants (see below). We also have the benefit of screeners’ reports of how they deal with
applicants from different recruitment source. In contrast to audit studies (e.g., Bertand and Mullianathan 2004;
Pager 2003; Pager et al. 2009) that are limited to examining only the initial stages of contact with employers (Blank
et al. 2004), we not only study the initial point of contact with the firm, but also the reactions to intermediary‐
referred, employee‐referred, and “Other” candidates by different organizational actors – HR screeners and hiring
managers – at later steps in the hiring process. As we show below, the reactions of the organization’s screeners
vary significantly by stage.
We assembled data on the pool of all applicants, interviewees, job offers, and hires for all production
floor jobs. While it is very rare to have information of the race of the applicant (other examples are Petersen et al.
[2000] and Petersen and Saporta [2005]), the human resources department’s standard operating procedure at this
company is to record applicants’ apparent race and gender. In order to apply, all job candidates must come into
the receptionist’s area to turn in a completed and signed application form. After accepting the application form,
the receptionist logs the receipt of the application and records the applicant’s apparent race and gender.
We matched the race and gender information gleaned from the logs to paper application materials (a
standardized application form and, occasionally, resumes) for 1,582 applications. A total of 88 percent of these
applications (1,389) arrived over the 29 month period July 1, 1998 – November 30, 2000.1 We obtained an
additional 193 cases that were made during the prior ten‐month period. We matched these 193 applications to
repeat applications from the same people who applied during the period during which the receptionist coded race.
1
During the period September 1, 1997 – June 30, 1998, the receptionist checked off the apparent race and gender of each
applicant, but did not record the specific name of the applicant in the log. For the 29 month period of this study, however, the
receptionist recorded the name of each individual applicant in the log. The receptionist was very consistent in her coding of
race. Looking at the 29 months during which receptionist coded race, 119 people generated 146 pairs of multiple applications.
The receptionist coded 89 percent of these pairings as the same race (for more details, see Fernandez and Fernandez‐Mateo
[2006: Appendix A]).
7
We then coded race for these applications based on how the receptionist coded race for the later application.2
From the paper application forms, we coded a number of human capital factors that company personnel
say they screen for from candidates’ applications. Because we also had access to the names and signatures on the
original application forms, we did not need to rely solely on the logs to identify gender. We were successful in
coding gender for 97.2 percent of the applications. HR personnel said in interviews that they do not consider years
of formal education a very important screening criterion for these factory jobs. However, there is often a gap
between what employers say and what they do (Pager 2005), and since it is the quintessential measure of human
capital (e.g., Becker 1993), we tested the degree to which education is important for screeners’ actions. The
application form explicitly asked candidates to list their educational background, and we coded the number of
years of education from these responses.
In contrast, the HR screeners said that labor force experience was quite important. Applicants are asked
to list their past employment experience, including dates and wages. From this, we coded years of labor force
experience. However, HR screeners say they tend to avoid “job hoppers”—people who frequently change jobs.
Consequently, we also coded the applicant’s number of jobs. In addition, we coded a dummy variable for whether
the applicant reported any work experience in the firm’s specific industry based on the applicant’s response to the
question: “Have you ever worked in the [NAME OF INDUSTRY] industry?”. As a measure of their labor market
value, we also coded the applicant’s hourly wage on their last job. For applicants who had never before been
employed, the last wage is coded zero. Only 2.4 percent of all applicants had never been employed at the time of
application. As has been found in other settings (e.g., Fernandez et al. 2000; Manning 2000), company’s HR
personnel say that they prefer applicants who are currently employed. We coded a dummy variable indicating
whether the applicant was employed at the time of application. Finally, we coded a dummy variable measuring the
response to the question: “Have you ever been convicted of a felony or job related crime (civilian or military)?”
While this self‐report question might lead to underreporting of convictions, it is important to note that the
application form states explicitly that the company conducts background checks as a part of screening (see Finlay
2
These repeat cases are helpful for checking whether the key results are due to unmeasured fixed characteristics of applicants.
Eliminating these 193 cases does not change the other substantive results.
8
2009).3
Another very special feature of this setting is that the company’s standard operating procedures include
asking every applicant to identify their poverty status at the point of application. In addition to the paper
application, all applicants submit to the receptionist IRS form 8850 “Pre‐Screening Notice and Certification Request
for the Work Opportunity Credit” (see Figure 1 for an anonymized sample form from the study). This form asks a
series of questions which identify whether the person comes from a welfare or poverty background. If the person
is hired, and is confirmed as being eligible, the hiring firm may then receive monies from the Work Opportunity Tax
Credit (WOTC) or Welfare to Work (WtW) programs to subsidize the new hire’s training costs up to approximately
$1,200.4 These monies are normally disbursed by local public sector organizations, which often also serve as
vendors for training services. While many companies routinely ask new hires to fill out the 8850 form as a part of
routine paperwork during the intake process for new hires, this company is rare in asking for this information at
the point of initial application. We coded positive answers to any of the four questions listed on the form as
evidence that the applicant came from a poor background. A total of 13.4 percent of applicants (14.3 percent of
females, and 11.8 percent of males) had a poverty background using these criteria.
We also used the applications to distinguish among three groups of applicants: networked candidates,
candidates who have been referred by labor market intermediaries, and candidates applying from any other
recruitment sources (e.g., newspaper ads, walk‐ins). The application form has an item that explicitly asks about
recruitment source: “How did you find out about the job?” and lists several boxes to check. One of the boxes is
“Current [NAME OF COMPANY] employee,” and includes space for the name of the referrer. Of the applications for
3
HR personnel said that they do screen on this factor, but unlike the case in the Fernandez et al. (2000), a felony conviction
does not automatically disqualify a candidate from further consideration at this company. Indeed, ex‐offenders are one of the
subcategories listed on the WOTC‐WtW eligibility form that the company collects (see below and Figure 1). A total of 31
applicants responded yes to the question on convictions. 19.3 percent (6 of 31) of people responding yes to the question on
convictions were interviewed. Half of those (3 of 31) were offered a job and ultimately hired, an overall rate of 9.7 percent for
convicted applicants. Contrary to the employers studied in Pager (2003) and Pager et al. (2009) did, these rates of success in the
hiring process are not statistically different from those who do not report a conviction (i.e., 17.7 percent of non‐convicted are
interviewed; 7.5 and 7.0 percent of the non‐convicted are respectively offered a job and ultimately hired).
4
While many companies collect such information once a person is hired, unique in our experience, this company asks all
applicants to fill out the welfare‐to‐work form and to sign it. Once they have hired a welfare‐to‐work person, a background
check is done to confirm that the person is indeed eligible for the training credit. Although they have made filling out this form
part of the standard operating procedures for hiring, the company HR personnel says that is does not use the form itself as part
of the screening process. The firm does not reap any direct benefit from the $1,200 even in the event they were to hire a
welfare‐to‐work person. They never see the check—it is absorbed by the central human resources office, in a far away office
located in another state. In interviews with HR personnel on the topic, they expressed that they consider the $1,200 per hire
trivial in comparison to the costs associated with making a hiring “mistake.”
9
whom we could identify recruitment source (1,551 of 1,582), 30.2 percent were employee referrals. However,
there were also applications that simply listed “friend,” or the name of someone not employed at the company.5
We combined these non‐employee referrals (an additional 5.4 percent of applicants) with employee referrals to
define a dummy variable for “network” job candidates.
Most important for the purposes of this paper, we identified 148 candidates who listed intermediary
organizations as their recruitment source. The vast majority of these applications (94.6 percent) were from public
(e.g., “Job Corps”) or private non‐profit agencies, the latter often simply listed as “PIC” (short for “Private Industry
Council”). Private Industry Councils (PIC) are private non‐profit corporations that provide services, usually at the
county level. PICs usually receive federal money for training and placing people into local jobs. In a very real sense,
these organizations seek to create connections between poor, often displaced job‐seekers, and local employers.
PICs employment counselors will often send applicants to particular employers, sometimes providing the applicant
with a “Referral Card” (see Figure 2) introducing the applicant to the firm’s screeners. As noted above, PICs often
manage the reimbursement for training costs when the firm hires WOTC or WtW eligible trainees. In our
discussions with the company’s HR managers, they indicated that they were well aware of these potential benefits,
and that they had established good working relations with staff at many of these intermediary organizations. A few
additional applications (6 in total) listed private for‐profit temp or job search firms as their job source. We
combined these cases by coding a dummy variable distinguishing applications from any labor market intermediary
from other applications. The remaining 53.4 percent of applications—828 of 1,551—were applicants from non‐
network, non‐intermediary sources.
Analysis
Table 1 presents some background information on the company and the local setting. Columns 1 and 2 of
Table 1 show the race distribution of the firm’s employees for both genders. Women constitute 63.2 percent of the
workforce at the plant. For both sexes, the populations are racially diverse, with minorities accounting for over
50% of those employed at this site. The third and fourth columns show that the race distribution of the pool of
5
Note that this company does not pay employees for their referrals (cf. Fernandez and Weinberg 1997; Fernandez et al. 2000).
Thus, workers do not have any financial incentive to ensure that applicants fill in their name as referrers on the application
form (see Neckerman and Fernandez [2003] and Fernandez and Castilla [2001] for analyses of referring incentives). This fact is
likely to account for the relative laxity these applicants show in providing the name of their referrer.
10
applicants roughly matches that of the employees for both sexes. The last two columns, however, show that Asian
Americans are clearly overrepresented, both among employees and applicants to this company.6 While African
Americans are well‐represented in both the firm and the application pool compared with the metro area data,
non‐Hispanic whites are somewhat underrepresented in both the firm and application pool. This relative paucity of
whites is probably due to the fact that whites are less likely to be attracted to these low‐wage jobs than are
minorities. Analysis of the PUMS data for the non‐college population in the local area (i.e., the same population
analyzed in columns 5 and 6 of Table 1) shows that the company’s offered hourly wage of $8.05 falls at the 19th
percentile of the local wage distribution for white males, and the 31st percentile for white females. The
corresponding figures for minorities are 35th percentile for males and 42nd percentile for females. Thus, the
company’s offered wages are relatively low compared to whites’ wages in the local labor market, but for minorities
the offered wage is much more attractive when compared with their wages in the open labor market.
Although differences in definition make the comparison only approximate, Table 2 shows the
representation of poor applicants to the firm compared with the official poverty definition by race and sex. By
these definitions, the firm is attracting a greater percent of poor whites and African Americans of both sexes than
their representation in the metro area. In contrast, poor Hispanics of both sexes are well‐represented, and poor
Asian Americans are somewhat underrepresented in the firm’s application pool.
Table 3 shows that the three recruitment channels are delivering applicants with different racial
compositions (females: p < .003; LR chi‐square = 19.96 with 6 d.f.; males: p < .0001; LR chi‐square = 27.76 with 6
d.f.). In particular, white females are underrepresented among intermediary‐referred applicants (40.0 percent vs.
49.8 of the overall population). Also noteworthy is the fact that Asian males are 7.8 percent more plentiful among
networked candidates than one would expect on the basis of their representation in the applicant pool (38.0 vs.
30.2 percent). However, both male and female African Americans are represented among the intermediary‐
referred population at rates that are considerably higher than their representation in the applicant pool
6
While the company does ask for proof of citizenship and immigration status at hire, the application does not ask whether the
candidate is an immigrant. There is, however, some information on the application form (e.g., address of previous employers,
address of schools attended) that gives yields some information about the immigrant composition of the applicant pool. Using
these criteria, 23.7 percent of applicants are immigrants (22.9 percent of females and 25.0 percent of males). Only 2.5 percent
of African Americans are immigrants, but the percentages are much higher for Hispanics (17.2 percent) and Asian Americans
(54.3 percent).
11
(respectively, 13.4 and 13.7 percent of intermediary‐referred applicants, compared with 5.3 percent of the overall
applicant pool for both sexes).
As would be expected if public and private labor market intermediaries are performing their mission of
connecting poor people to jobs, poor applicants are a higher proportion of applicants applying agencies than those
applying via other means (22.3 percent of intermediary referrals vs. 12.2 percent for network applicants and 14.7
percent for applicants applying via all other means). This overall pattern is statistically significant (p < 0.011; LR X2 =
9.0 with 2 d.f.). So too is the contrast between intermediary‐referred and “Other” applicants (p < 0.019; LR X2 =
5.53 with 1 d.f.). As noted above, it is not a foregone conclusion that public labor market intermediaries will
actually perform this function for the poor. With respect to sex differences, the pattern of poor applicants being
more common among agency referrals is also found within sex (females: 25.0 percent for agency referrals vs. 11.9
percent for network applicants and 16.8 percent for “Other” applicants; males: 19.1 percent for agency referrals
vs. 12.3 percent for network applicants and 11.0 percent for other applicants). While the overall pattern for
females is statistically reliable (p < 0.012; LR X2 = 8.89 with 2 d.f.), the pattern is not statistically significant for
males (p < 0.209; LR X2 = 3.12 with 2 d.f.). The specific contrast between intermediary‐referred and “Other”
applicants, however, is reliable for both male and females at the .10 level of significance (males: p < 0.077; LR X2 =
3.12 with 1 d.f.; females: p < 0.051; LR X2 = 3.82 with 1 d.f.).
Examining race differences, none of the recruitment source by poverty contrasts are statistically reliable
at the 5 percent level. For whites and African Americans, however, the specific contrasts between intermediary‐
referred and “Other” applicants are statistically reliable at the 0.10 level. For whites, 22.9 percent of intermediary‐
referred applicants are poor compared with 14.0 percent of “Other” applicants (p < 0.084; LR X2 = 2.99 with 1 d.f.).
The contrast in the percent poor between these two recruitment sources is even greater among African Americans
(45.0 percent of intermediary‐referred African Americans are poor, compared with 20.6 percent of “Other” African
American). Despite the fact that the analysis is based on very few African American applicants (i.e., 20
intermediary‐referred and 34 “Other” applicants), this contrast is sufficiently large that it is statistically significant
at the .10 level (p < 0.060; LR X2 = 3.53 with 1 d.f.).7 As noted in Table 2, the poverty rate for African Americans in
the local area is substantial. The fact that poor African Americans are well‐represented among intermediary‐
7
For completeness, there are 27 African American networked applicants, and 22.2 percent of these applicants are poor.
12
referred applicants is additional evidence that the local labor market intermediary organizations are serving the
function of connecting poor people to job openings.
Better‐Qualified at Application
The question remains as to how these organizations’ connections work. To the degree that these labor
market intermediaries serve as functional substitutes for social network processes in matching people to jobs, the
recruitment and hiring processes followed by intermediary‐referred candidates should parallel those of networked
candidates.
The first network‐based process we examine is what has been termed the “richer‐pool” mechanism
(Fernandez et al. 2000; Fernandez and Weinberg 1997). In particular, employers often infer that applicants who
apply via social networks will be more qualified than those applying through other means. Fernandez et al. (2000)
identify a number of the ways by which this result might come to pass. In particular, Fernandez et al. (2000: 1291)
identify three mechanisms: M1, expansion of the recruitment horizon, M2, homophily between the referrer and
the referral, and M3, reputation protection of the referrer. With the possible exception of homophily,8 we would
expect parallel processes to be at work for labor market intermediaries charged with placing candidates at firms.
For these reasons, the pool of candidates referred by intermediaries should show a more qualified profile of
background characteristics at the application phase than candidates applying via “Other” means. To the extent
that M1‐M3 are at work for candidates referred by social ties, we would expect the candidate profile for
networked‐referred applicants to also be more qualified than “Other” candidates.
Table 4 shows data on how applicants from the three sources—intermediary‐referred, network‐referred,
and “Other”—compare along dimensions that the human resources personnel use to screen. Intermediary
referrals are somewhat better educated than “Other” applicants, but this difference is quite small (12.34 vs. 12.20
years of education) and is not statistically significant (one‐tailed test p < .208, t‐value = 0.81). Intermediary‐
referred applicants show higher average wages in their last job than “Other” applicants, but here too the
difference is not statistically reliable ($8.22 vs. $8.13; one‐tailed test p < .361, t‐value = 0.36). Applicants sent by
8
While it is possible that staff at labor market intermediaries might prefer to recommend candidates who are socially similar to
themselves (for some German evidence, see Behncke et al. 2008), we do not have data on the race or gender of which specific
employee of the intermediary firm referred the candidate. As described above, however, the organizational incentives are for
all of these people to place whomever they can at hiring firms.
13
intermediary organizations are more likely to have industry‐specific experience than “Other” applicants (14.4 vs.
12.3 percent), but this difference is not statistically significant (p < .499, LR Chi‐square = 0.45 with 1 d.f.).
More reliably, however, intermediary referrals are worse than “Other” applicants on several other
dimensions. In general, HR recruiters prefer people who are employed at the time of application, and have had
fewer jobs since they want to avoid what they describe as “job‐hoppers.” But perhaps not surprising in light of
their charge, intermediary‐referred candidates are less likely than “Other” candidates to be employed at the time
of application (15.1 percent for Intermediary‐referred candidates vs. 29.6 percent for “Other” applicants), and this
difference is statistically reliable (p < .001, LR Chi‐square = 13.71 with 1 d.f.). Intermediary‐referred candidates
have also had more jobs than “Other” applicants (3.36 vs. 3.14), and thus are at greater risk of being classified as
“job hoppers” by the firm’s screeners than are “Other” applicants. This difference is in the opposite of the
predicted direction and is statistically significant (one‐tailed test p < .036, t‐value = ‐1.80). Also noteworthy,
intermediary‐referred applicants report a significantly higher rate of having been convicted of a “felony or job
related crime” than “Other” candidates, 6.5 vs. 1.4 percent (p < .002, LR Chi‐square = 10.37 with 1 d.f.). Overall,
there is little evidence that intermediary organizations are producing a richer, more hirable pool of applicants.
The evidence with respect to network candidates is more mixed, however. Network candidates are
somewhat better educated than “Other” applicants (12.32 vs. 12.20), but this contrast is not statistically significant
(one‐tailed test p < .142, t‐value = 1.07). Network candidates are significantly more likely than “Other” applicants
to be employed at application (36.3 vs. 29.6 percent; p < .012; LR chi‐square 6.37 with 1 d.f.). Although this does
not control for years of work experience (see below), networked candidates also have had significantly fewer jobs
than “Other” applicants (one‐tailed test p < .001, t‐value = ‐4.81). Network applicants are not better than “Other”
applicants on any other measure. Indeed, network candidates are significantly worse than “Other” candidates on
years of work experience (6.08 vs. 7.17, one‐tailed test p < .002, t‐value = ‐2.92) and wage on last job ($7.67 vs.
$8.13, one‐tailed test p < .003, t‐value = 2.75).
While it is informative to look at each of the background factors separately, this ignores the degree to
which applicants from each of the three sources form distinct profiles. This is especially important to consider since
the value of some of these background factors (e.g., years of work experience and number of past jobs) are likely
to be seen as interdependent by screeners. Table 5 reports the results of a multivariate analysis designed to
14
identify which factors distinguish among the three recruitment sources, while controlling for each of the other
background factors. The coefficients in the table show the relative risks associated with each independent variable
of being referred by intermediary sources (columns 1) and network sources (columns 2). The coefficients can be
read as the odds of people with higher values on the background variable being in a category of the recruitment
source (either intermediary‐ or network‐referred), relative to the excluded category (“Other”).
For intermediary‐referred applicants, the profile that emerges by considering these background factors in
a multivariate framework is decidedly negative. Intermediary‐referred applicants are clearly less qualified than
“Other” applicants. A Wald test rejects the hypothesis that intermediary‐referred applicants have the same profile
as “Other” applicants (p < .0003; Chi‐square 26.94 with 7 d.f.). Controlling the other factors, intermediary‐referred
applicants are less likely to be employed (relative risk compared with “Other” applicants of .408), over 4 times
more likely to have had a felony conviction (relative risk compared 4.274), and more likely to be “job hoppers”
(i.e., those with a greater number of jobs, even after controlling for years of work experience; relative risk of 1.149)
than “Other” applicants. The first two of these factors are clearly understandable in terms of the charge of these
public sector labor intermediary organizations: these organizations are dedicated to helping hard‐to‐employ
populations.
The profile that emerges for networked candidates is more positive, however. Compared to “Other”
applicants, these candidates are somewhat better‐educated (relative risk = 1.054), 1.45 times more likely to be
employed (relative risk = 1.45), and 82 percent as likely to be “job hoppers” (relative risk = .821). Here, too, a Wald
test of equality of the Network and “Other” applicants’ profiles is rejected (p < .0001; Chi‐square 41.27 with 7 d.f.).
In this respect, these results replicate the findings of two previous studies examining the profiles of networked
applicants, Fernandez and Weinberg (1997) and Fernandez et al. (2000).
Most important for the goals of this paper, however, these results offer little support for the idea that
intermediary organizations produce better applicants than candidates who are applying via other means. At least
along these observed dimensions, not only are the results inconsistent with the “richer‐pool” hypothesis, but it
would be more accurate to say that intermediary‐referred applicants are significantly worse than other applicants.
If intermediaries are engaged in creaming from among their clientele when sending candidates to this employer,
then it is important to understand that the net result of those efforts does not out‐perform the firm’s other
15
options. In this respect, the results support employers’ concerns about public sector intermediaries’ ability to
supply employable candidates (e.g., Laufer and Winship 2004). In contrast, the results for networked applicants are
more consistent with the “richer pool” hypothesis. All totaled, applicants produced by intermediary organizations
are not working in a parallel manner to naturally occurring networks in this setting.9
Better Timing of Applications
Another mechanism by which networked applicants are often theorized to be advantaged in the
recruitment process is by means of their having better information about the firm and its hiring practices
(Fernandez et al. 2000). Here, we focus specifically on the tendency for networked candidates to apply at times
when their chances of being hired are more favorable (Fernandez and Weinberg 1997; Fernandez et al. 2000;
Manning 2000). In parallel fashion, intermediary organizations can provide this form of job search benefit for their
job‐seeking clientele. By routinely communicating with recruiters about their needs, these intermediary
organizations have the capacity to direct people to apply at times when their chances of success are higher than
usual. The fact that local labor market intermediaries can serve as a buffer to labor market volatility and supply
applicants at times of labor shortages is also seen as an advantage from the employer’s perspective (Laufer and
Winship 2004:228).
We examined this hypothesis by measuring the degree to which demand (number of job openings)
exceeded supply (number of applicants) on the day the candidate applied to the firm. We subtracted the number
of applicants from the number of job openings on the day that each candidate applied. Thus, positive numbers
indicate that there is a shortage of applicants relative to the number of job openings, and negative numbers show
that the number of applicants exceeds the number of available jobs.10
Table 6 shows the results of regression analyses distinguishing the state of the market by recruitment
channel for various populations of applicants. The column labeled “Other” shows all negative coefficients that are
significantly different from zero. This indicates that on average, for all race and poverty groups, the “Other”
candidates applied on days when there were significantly more candidates than there were job openings.
9
For completeness, we also tested whether the difference between networked and intermediary‐referred applicant profiles is
statistically reliable. A Wald test rejects the hypothesis that these two profiles are the same (p < .0001; Chi‐square 53.06 with 7
d.f.).
10
The substantive results do not change if we define shortage at the level of the week. When shortage is defined at the level of
month, the results are consistently correct sign, but are not statistically significant.
16
Consistent with the idea that applicants referred by intermediary organizations are receiving information about
the nature of the competitive situation at the firm, the coefficients in the first column show that intermediary
candidates are applying when there is significantly less competition than that experienced by “Other” applicants.
Across all race and poverty groups, the coefficients for intermediary‐referred candidates are positive and
statistically reliable.
For example, looking at row 1 (“All Applicants”), “Other” candidates are applying when there are 5.614
more candidates than there are openings. The coefficient for intermediary‐referred applicants in row 1 is 6.458,
meaning that on average intermediary‐referred candidates are applying on days when the market conditions are
more favorable for the candidate than when “Other” candidates are applying. Comparing these two coefficients
shows that intermediary‐referred candidates are applying when the number of openings exceeds the number of
applicants by 0.844 (6.458 – 5.614). Except for whites and African Americans, comparisons of the coefficients show
that on average intermediary‐referred candidates apply when the number of job openings exceeds the number of
candidates. Similarly, candidates network referred applicants (column 2) also apply at times when their chances
are significantly better than “Other” applicants. Across the various populations, network candidates tend to apply
on days when the state of the market is better than the situation for “Other” applicants. With the exception of the
smallest subgroup—African Americans—this pattern is statistically reliable.
All totaled, there is strong support for the idea that intermediary organizations provide information for
applicants to apply at times when their chances of being hired are more favorable. While a comparison of the
point estimates show that intermediary‐referred candidates are applying at times that are more favorable than
when network candidates apply (cf. columns 1 and 2), the information in columns 4 and 5 show that the degree of
advantage is not statistically reliable. This suggests that the mechanism by which intermediary‐referred applicants
are advantaged in application timing is quite parallel to the way that networks produce such an advantage, and
that the degree of advantage in application timing is of comparable magnitude for all race and poverty subgroups.
Consequences for Hiring
The question remains whether intermediary‐referred and networked applicants experience superior
hiring outcomes than “Other” applicants. Although it is not likely to be a universal pattern (see Fernandez et al.
2000), a number of studies have shown preferences for networked candidates in the hiring process (e.g.,
17
Fernandez and Weinberg 1997; Fernandez et al. 2000; Petersen et al. 2000). The evidence on firms’ reactions to
public intermediary‐referred candidates is mixed (for evidence of positive effects, see Melendez and Harrison
1998; also see Holzer [2009] and Thomas [1997] and the studies reviewed therein). To the degree that local public
intermediary firms have “created connections” with this firm, we would expect these connections would result in
better hiring outcomes for intermediary‐referred applicants.
To address these questions, we developed predictive models of the chances of interview and job offer.
Table 7 presents the descriptive statistics of the background factors and application behavior variables we used to
predict success in the hiring process. The first column shows the figures for the overall applicant pool; the second
column reports the statistics for those who have been interviewed by personnel from the company’s human
resources department and are under further consideration for a job offer. In addition to the factors listed in Table
3, we added a squared term for years of labor market experience to measure decay in the value of experience over
time (Mincer 1974). We also control for the number of times an individual applies to the firm, since people were
free to apply multiple times over the course of the study. The number of applications per individual varied
between 1 (accounting for 76 percent of applications) and 6 (0.4 percent of applications).
Overall, the probability of being interviewed by HR is 18.3 percent (see the next to last row of Table 7).
However, the chances of a job offer are substantially higher for those who survive the interview phase since 42.3
percent of interviewees are offered jobs. Of particular interest for the purposes of this paper is the comparison for
the recruitment source variables. While applicants referred by intermediary organizations are about 10 percent of
the applicant pool, they comprise almost 16 percent of those being considered for job offers. Although we have
yet to control for other background factors, this is an initial indication that intermediary‐referred candidates are
indeed being given some preference in screening by HR personnel.
Interview Stage
We begin by examining the first step of the screening process—the interview decision—controlled by
personnel in the factory’s Human Resources (HR) department. Without controls, the interview rate of 29.5 percent
for intermediary referrals is 8.7 percentage points higher than the interview rate for “Other” applicants (17.8
percent). This contrast is statistically reliable (p < .002; LR chi‐square 9.306 with 1 d.f.). The interview rate for
networked applicants is only 16.0 percent, however, and is not statistically different from the rate of 17.8 percent
18
for the “Other” applicants (p < .381; LR chi‐square 0.766 with 1 d.f.). This suggests that, at least at this step of the
hiring process, intermediary organizations have successfully created connections to this firm.
The question remains as to the nature of these connections, however. As discussed above, although they
are applying at more favorable times, intermediary‐referred applicants are of significantly lower quality on several
dimensions that the recruiters screen for. Since these factors can be traded‐off,11 it is not clear what the net
impact of these factors is. The interview rates just presented do not control for any background factors, and it is
possible for these background variables to account for the higher interview rates observed for applicants referred
by labor market intermediaries.
Model 1 of Table 8 presents the results of a probit model predicting the chances of interview among all
applicants. The marginal effects for the human capital variables show that ceteris‐paribus HR screeners are
significantly more likely to grant interviews to candidates with more education, experience in the firm’s industry,
higher wages in their last jobs (a signal of applicants’ labor market value), fewer jobs (i.e., are not “job‐hoppers”),
and more general labor market experience, although the experience‐squared term is negative showing the
expected pattern of decay (Mincer 1974). Persistence also pays off with HR recruiters: applicants are 2.0 percent
more likely to be granted an interview each subsequent time they apply. Surprising in light of the results reported
in Table 6, applying when there is a shortage or surplus of applicants relative to job openings is not significantly
related to the chances of interview.
Turning to the demographic background variables, in contrast to studies in other settings, we find no
statistically reliable evidence that race (cf. Petersen et al. 2000) or gender (cf. Fernandez and Sosa 2005) affect the
chances of interview at this firm. Nor was there evidence of significant race or gender interactions with the
recruitment source variables (see below). However, applicants from poverty backgrounds are 6.5 percent less
likely to be interviewed than “Other” applicants by HR. This is after controlling for the other human capital factors,
and despite the potential of gaining training subsidies for the firm if poor applicants are hired. Consistent with the
views expressed in interviews (see note 4), the possibility of training subsidies apparently does not outweigh these
applicants’ other perceived liabilities in HR’s eyes.
11
See Rees (1966:562) for a discussion of employers’ willingness to relax hiring standards when there is a shortage of
applicants.
19
Most important, however, the results in Model 1 show that even after controlling the other factors in the
model, intermediary‐referred applicants are significantly more likely to be granted an interview than “Other”
applicants. The magnitude of the effect in Model 1 is 8.8 percent, which is virtually identical to the zero‐order
advantage noted above. It is clear that the observable factors included among the controls in Model 1 do not erase
the advantage that HR personnel are giving intermediary‐referred applicants at this stage of the hiring process.
Similar to the zero‐order difference, the effect in Model 1 for network‐referred applicants is small and statistically
insignificant.
Interaction Tests
While intermediary‐referred applicants are more likely to be interviewed than other applicants, the
analysis in Model 1 does not address how the effects of recruitment sources (intermediary‐referred and network‐
referred vs. “Other”) might affect the chances of interview in conjunction with other variables. This might be the
case if HR screeners were to adjust their standards for granting an interview depending on the applicant’s
recruitment source. To the extent that intermediaries supply applicants that are more likely to be eligible for
training subsidies associated with poverty background, the firm’s screeners might be tempted to relax hiring
standards. Especially in light of the findings showing that intermediary‐referred candidates are applying at times
when hiring conditions are more favorable (Table 6), we are also interested in exploring interactions of the other
predictor variables with applicant shortage. Such interactions would be expected to the degree that HR recruiters
raise or lower their standards in response to a surplus or shortage of applicants.
Table 9 reports the results of tests for various interactions. Looking at the figures in the first three
columns labeled “Model 1,” the first row shows the Likelihood‐ratio X2, degrees of freedom and level of statistical
significance for Model 1 in Table 8. This model has no interaction terms at all, and thus forms the baseline against
which the interaction tests reported in subsequent rows of the table are assessed. The second row labeled “B: All
Shortage Interactions” shows the improvement in LR X2 associated with adding all interactions between shortage
and the other independent variables in Model 1 of Table 8, a total of 16 interaction terms. The LR X2 improvement
of 29.04 with 16 d.f. is statistically significant (p < .031) indicating that the effect of at least some of these variables
differ depending on the level of applicant shortage.
20
The analyses reported in the subsequent rows of Table 9 are designed to isolate which variables are
interacting. The third row labeled “C: All Intermediary‐Referred Interactions” reports the improvement in LR X2
associated with interacting all the predictors variables with the dummy variable for Intermediary Referral. This
addition does not significantly improve the fit of the model (p < .223; LR X2 improvement = 18.81 with 15 d.f.). The
next row (labeled “D”) repeats this exercise for the dummy variable for Network Referrals. Here, too, the
improvement in fit is not statistically significant (p < .820; LR X2 improvement = 10.00 with 15 d.f.). Nor is the
improvement in fit statistically significant when both Intermediary‐ and Network‐referred sets of interaction terms
are included (the row labeled “E;” p < .474; LR X2 improvement = 29.83 with 30 d.f.).
However, adding two specific interactions with applicant shortage to Model 1—interactions for
Intermediary Referral and Poverty Background—does significantly improve fit over the baseline model (row
labeled “F;” p < .001; LR X2 improvement = 13.19 with 2 d.f.). In order to determine whether these two interactions
account for the significant LR X2 improvement in row B, we examined whether the addition of the other 14
interactions terms for shortage improve fit over the model with only the shortage x intermediary referral and
shortage x poverty interactions.
Column 2 of Table 9 reports the results of these tests. Row A shows the model fit for Model 2 of Table 8
containing the main effects plus the Shortage x Intermediary Referral and Shortage x Poverty Interactions. Thus,
compared to Model 1, Model 2 uses 2 additional degrees of freedom (19 vs. 17), and forms a new baseline for the
tests reported for Model 2. Row B of column 2 shows the LR X2 improvement of adding the 14 remaining
interaction terms for shortage over the new baseline model with only the Shortage x Intermediary Referral and
Shortage x Poverty interaction terms. Adding these interaction terms does not significantly improve the fit of the
model (p < .389; LR X2 improvement = 14.84 with 14 d.f.). Considered against this new baseline, adding the other
interaction terms for Intermediary Referral (row C), Network Referral (row D), or both sets of these interactions
(row E), does not significantly increase the LR X2. Consequently, we are safe in concluding that it is only the
Shortage x Intermediary Referral and Shortage x Poverty interactions that improve the fit of the model.
These interaction terms show that HR screeners apparently adapt to applicant shortages in two ways
when making decisions about whom to interview. First, the positive interaction term for Shortage x Intermediary
Referral (see Table 8, Model 2) shows that compared to those applying via other sources, the chances of being
21
interviewed for people referred by intermediary organizations improve as the number of job openings exceeds the
number of applicants. Second, HR screeners also appear to change their behavior with respect to poor applicants
under conditions of applicant shortage. In this case, however, the interaction term for Poverty is negative in Model
2 of Table 8.
Job Offer Stage
No one receives a job offer (exclusively given by hiring managers) without first being interviewed by HR.
Looking specifically at the actions of the hiring managers, overall, 42.3 percent of 260 interviewees are offered
jobs. The zero‐order comparisons shows that the offer rate is lowest for intermediary‐referred interviewees (i.e.,
39.0 percent), and highest for networked interviewees (46.4 percent), compared with 40.7 percent of “Other”
interviewees. None of these zero‐order differences, however, are statistically reliable (Intermediary‐Referred vs.
“Other” contrast, p < .844; LR chi‐square 0.039 with 1 d.f.; Network vs. “Other” contrast, p < .409; LR chi‐square
0.068 with 1 d.f.).
Model 3 of Table 8 shows the results of a probit regression model predicting job offer among the pool of
interviewees. The strongest effect in this model shows that hiring managers highly value previous industry‐specific
experience. Compared with those without experience in the firm’s industry, experienced candidates are 23.8
percent more likely to be offered a job. In contrast to the actions of HR recruiters during the interview step, hiring
managers appear to disfavor applicants with more education (‐3.8 percent), and those who have applied multiple
times (2.0 percent per application). Most interesting for our purposes here, unlike the actions of HR screeners,
there is no reliable evidence that hiring managers are more likely to offer jobs to intermediary‐referred applicants
(the marginal effect of 0.6 percent is not statistically significant).
In marked contrast to the analyses of the interview phase, hiring managers appear to prefer networked
applicants from among the candidates that HR has sent them. Among interviewees, after controlling for
background and other factors, network candidates are 13.6 percent more likely to be offered a job than “Other”
applicants. Despite the small numbers of interviewees, this marginal effect is statistically reliable.12 Also in contrast
12
This latter analysis, however, does not take into account the fact that the interviewees are a selected group, and thus subject
to possible selection bias (Winship and Mare 1992). Although we are limited in our ability to fully address this issue, the
substantive results are unchanged when we correct the analyses for selection bias (Appendix A). Indeed, some of the findings
22
to the results for the interview phase, we find no statistically significant evidence of interactions between any of
the background factors or the applicant shortage variables (see Table 9, columns for Models 3 and 4). Thus, we
have no reliable evidence that hiring managers are adjusting their criteria of evaluation either by the candidate’s
recruitment source or the degree of labor shortage.
Although we have no evidence of hiring managers giving the benefit of the doubt to applicants who have
been referred by local intermediary organizations, the question remains what are the net effects of HR’s and the
hiring managers’ preferences. Models 5 and 6 of Table 8 are designed to answer this question. It reports the
marginal effects of a unit change in each independent variable on the joint probability of being interviewed and
hired (for a similar analysis across two stages of a selection process, see Garip 2009). As such, this analysis reflects
the net impact of the screening choices of both HR and hiring managers.
The most important effect in Model 5 is that for industry experience: compared to applicants with no
industry‐specific experience, candidates with experience in the firm’s industry are 5.0 percent more likely to
successfully pass the two stages of screening and receive a job offer. General labor market experience matters as
well. Among the other statistically reliable effects in Model 5 are years of work experience (labeled “Experience,”
and its associated decay term “Experience – squared”). While HR managers rely on this factor relatively heavily
when deciding who to send on to the hiring manager (marginal effect of 2.3 percent per year, Model 1), work
experience is less important (point estimate of 1.5 percent) and not statistically different from zero at the offer
stage. The only other statistically reliable effect in Model 5 is for hourly wage on the last job. As we discussed
above, this variable is meant to tap applicants’ value in the open labor market. For every dollar increase in past
wages, the candidate’s chances of advancing past the two stages of the screening process increase by 0.3 percent.
Here, too, this factor appears to be more important for HR screeners than for hiring managers (cf. Models 1 and 3).
Most important for our purposes, however, there are important differences in the way HR and hiring
managers treat applicants referred by intermediaries and network sources. Intermediary‐referred applicants are
8.8 percent more likely to be sent to the hiring manager than other applicants (Model 1), but hiring managers are
relatively indifferent to these candidates (marginal effect 0.6 percent, Model 3). Instead, hiring managers strongly
with respect to intermediary‐referred applicants become stronger and statistically significant where they are marginal without
controlling for selection bias (see below).
23
prefer network applicants (marginal effect 13.6 percent, Model 3), a group that has failed to impress HR managers
(a not statistically significant marginal effect of ‐1.6 percent, Model 1). The net effects in Model 5 show that the
two steps apparently cancel out across the two stages of screening for these two groups of applicants.13
Interaction Tests
The analyses of the net effects across the two screening stages in Model 5 do not address possible
interactions, in particular, how the effects of recruitment sources (Intermediary‐Referred and Network‐Referred
vs. “Other”) might affect the chances of interview when viewed in conjunction with other variables. The column
labeled “Model 5” in Table 9 reports the results of tests for various interactions. The first row shows the
Likelihood‐ratio X2, degree of freedom and level of statistical significance for Model 5 in Table 8. Parallel to the
analyses for Models 1 and 3, we isolate which variables interact with the recruitment source variables. The second
row labeled “B: All Shortage Interactions” shows that the improvement in LR X2 associated with adding all 16
interactions between Shortage and the other independent variables in Model 5 of Table 8 does not significantly
improve the fit of the model (p < .211). Nor does the addition of interactions terms for Intermediary‐Referred
applicants (third row labeled “C: All Intermediary‐Referred Interactions”; p < .138), or Network‐Referred
applicants (fourth row labeled “D: All Network‐Referred Interactions”; p < .652) or both sets of interactions (the
row labeled “E;” p < .670).
Some subset of these variables might still produce significant interactions, however. Similar to the results
for the interview step (Model 1), adding the two specific interactions with Applicant Shortage—interactions for
Intermediary Referral and Poverty—does significantly improve fit over the baseline model (row labeled “F;” p <
.014). The last column of Table 9 examines whether the addition of the other 14 interactions terms for Shortage
improve fit over the model with only the Shortage x Intermediary‐Referred and Shortage x Poverty interactions.
Considered against this baseline, adding the other interaction terms for Intermediary‐Referred (row C), Network‐
Referred (row D), or both sets of these interactions (row E), does not significantly increase the LR X2. Parallel to the
results for Model 1, the evidence here shows that it is only the Shortage x Intermediary‐Referred and Shortage x
Poverty interactions that improve the fit of the model.
13
As noted above (see note 12), correcting for selection bias makes for estimates of marginal effects that are somewhat larger
and statistically significant (cf. Models 5 and 6 in Table 8 with Models A5 and A6 in Appendix A). In order to be conservative, we
proceed using the weaker results in Table 8.
24
Model 6 in Table 8 reports the marginal effects after adding these two interaction terms to the model.
The interaction term for Shortage x Intermediary‐Referred is positive and statistically significant. While the main
effect of being referred by an intermediary is not statistically different from zero, the net prospects for success in
the hiring process for intermediary referred applicants improves under conditions of labor shortage. Similar to the
results reported for the interview stage, the interaction term for the Shortage x Poverty interaction is negative,
indicating that poor people’s prospects for receiving a job offer worsen under during labor shortages. This latter
interaction effect, however, is smaller in magnitude than the effect at the interview stage (0.1 percent vs. 0.3
percent) and is not statistically significant.
Summary of Hiring Consequences
Taken together, these results have several important implications for understanding the nature of the
connections being created between intermediary organizations and the firm. First, the absence of significant
interaction terms between recruitment source and the human capital variables across these models suggests that
HR screeners are not adjusting their assessments of these jobs’ human capital requirements depending on the
source of the application. Consequently, the boosts in intermediary‐referred and networked applicants’ chances of
interview are occurring at the margin, and are not due to HR screeners’ relaxing of hiring standards. In addition,
the fact that we have no evidence of race or gender interactions with recruitment source suggests that HR
screeners are not using recruitment source as an information signaling device to statistically discriminate by either
race or gender.14 The absence of significant interaction terms between applicant shortage and the human capital
variables also suggests that HR screeners are not responding to fluctuations in the relative supply of applicants by
lowering or raising their assessments of these jobs’ human capital requirements (cf. Brencic 2010; Rees 1966). Nor
is there evidence of HR screeners responding to applicant shortages by altering their decisions of whom to
interview with respect to race or gender.
14
Statistical discrimination theories posit that disfavored groups should be disadvantaged because employers want to avoid
paying the costs of screening out “bad apples” (Arrow 1998; Phelps 1972). Rational employers will forego all “apples” from the
“barrel” that is perceived to be worse, treating all members of the group like the average member of their group. To the extent
that some recruitment sources can cheaply provide information about applicant quality (see the mechanisms reviewed in
Fernandez et al. 2000), then minorities and women from those recruitment sources should be preferred by statistically
discriminating employers over other minorities and women. Moreover, this effect should be greater for minorities and females
since the relative value of the recruitment source signal should be correspondingly less for non‐minorities and males.
25
HR screeners, however, do apparently adapt to applicant shortages when making decisions about whom
to interview in two ways. First, the positive interaction term for Shortage x Intermediary‐Referred (see Table 8,
Model 2) shows that compared to those applying via other sources, the chances of being interviewed for people
referred by intermediary organizations improve as the number of job openings exceeds the number of applicants.
Thus, this employer appears to be using the labor market intermediaries as a buffer against labor shortages (Laufer
and Winship 2004; Giloth 2004). The magnitude of this adjustment is considerable. Figure 3 plots the estimated
change in the probability of being interviewed (the y‐axis) as applicant shortage increases in percentile units (x‐
axis).15 While on average intermediary‐referred candidates are 8.8 percent more likely to be interviewed than
people applying via other means, Figure 3 shows that this difference varies substantially depending on whether or
not there is a shortage of applicants. The left side of the graph shows the situation when there are many applicants
per job opening. At the 13th percentile (when the number of applicants exceed the number of job openings by 16)
and below, the effect becomes negative meaning that intermediary‐referred applicants are being avoided by HR
screeners. However, the relative chances of interview improve above that point, and exceed the 8.8 percent figure
at the 57th percentile and above, i.e., when the numbers of openings is greater than the number of applicants by at
least 1.
The second way in which HR screeners change their behavior under conditions of applicant shortage is
with respect to poor applicants. In this case, however, the interaction term for Poverty is negative in Model 2 of
Table 8. Contrary to the received wisdom that a tight labor market is good for poor people’s employment
prospects, in this case HR screeners’ avoidance of poor applicants intensifies as the number of job openings
relative to applicants increases. Model 1 show that on average candidates with a poverty background are 6.5
percent less likely to be interviewed than people applying via other means. Figure 3 incorporates the negative
interaction in Model 2. At the median of applicant shortage (i.e., ‐1), poor applicants’ chances of interview are 8.7
percent less than non‐poor applicants’ chances of interview. However, the chances of being interviewed further
15
The Applicant Shortage variable varies from a low of ‐72 (meaning that the numbers of applicants exceeds the number of jobs
open by 72) to a high of 27 (where jobs open exceeds the supply of applicants by 27). The mean and the median of the Shortage
variable are ‐2.91 and ‐1. The deciles of the Shortage variable are: ‐18, ‐8, ‐3, ‐2, ‐1, 1, 3, 7, and 13. The steep changes in effects
for both intermediary‐referral and poverty shown on the leftmost side of the graph correspond to very few cases (i.e., the
bottom 4 percent of the Applicant Shortage variable with values ranging ‐59 to ‐72). In that range, the marginal effects for
intermediary‐referral drop precipitously from ‐.06 to ‐.35, and increase from ‐.008 to +.157 for the poverty variable.
26
drop as applicant shortage increases. At the 75th percentile of applicant shortage (+4), poor applicants’ are 10.5
percent less likely to be interviewed than non‐poor applicants; the figure drops to 13.6 percent at the 90th
percentile of applicant shortage (+13).
These analyses also give some insight into how the screening preferences of HR screeners and hiring
managers align (Fernandez and Mors 2008).16 Applicants with industry experience show a 7.1 percent greater
chance of being interviewed by HR than applicants with no industry experience (Model 1), and the chances of
hiring managers offering a job to interviewees with industry experience is 23.8 percent (Model 3). As noted above,
the net result of these two steps is that applicants with industry experience are 5.0 percent more likely to make it
past the two screening steps than applicants without such experience. Among the other statistically reliable effects
in Model 5 are years of general work experience. While HR managers rely on this factor relatively heavily in
deciding who to send on to the hiring manager (marginal effect of 2.3 percent, Model 1), work experience is less
important (point estimate of 1.5 percent) and not statistically different from zero at the offer stage. The only other
statistically reliable effect in Model 5 is for hourly wage on the last job. For every dollar increase in past wages, the
candidate’s chances of advancing past the two stages of the screening process increase by 0.3 percent. Here, too,
this factor appears to be more important to HR screeners than to hiring managers (cf. Models 1 and 3).
However, these analyses also reveal several examples where the screening preferences of HR screeners
and hiring managers are not in synch, and sometimes off‐set each other. HR screeners are more likely to interview
applicants with more years of formal education (marginal effect of 1.7 percent per year of education, Model 1),
but hiring managers show a strong preference for interviewees with less education (marginal effect ‐3.8 percent
per year of education, Model 3). Similarly, while HR managers reward persistence when granting interviews
(repeat applicants are 2.0 percent more likely to be granted an interview, Model 1), hiring managers apparently do
not like such repeated attempts (marginal effect ‐11.6 percent, Model 3). The fact that the effects of education
and repeat application in the offer step are strongly negative suggests that hiring managers are undoing at least
some HR managers’ work.
16
Such alignment need not be perfect to be effective, since it is possible—and perhaps desirable—for HR and hiring managers
to divide labor on screening tasks. Hiring managers might rely on HR managers for upstream screening on easily observable
factors like education, and thus not need to do any additional screening on this factor since the surviving candidates for job
offer are all above some qualification threshold. Such an arrangement might accomplish the desired screening goals while also
saving hiring manager’s time.
27
Most important for this paper, however, are the differences in the way HR and hiring managers treat
applicants referred by labor market intermediaries and network sources. Intermediary‐referred applicants are 8.8
percent more likely to be sent to the hiring manager than other applicants (Model 1), but hiring managers are
indifferent to these candidates (marginal effect 0.6 percent, Model 3). Instead, hiring managers strongly prefer
network applicants (marginal effect 13.6 percent, Model 3), a group that has failed to impress HR managers (a not
statistically significant marginal effect of ‐1.6 percent, Model 1). The net effects in Model 5 show that the two
steps apparently cancel out across the two stages of screening for these two groups of applicants.17
The results in Model 6, however, reveal that net effect across the two stages of screening of being
referred by an intermediary depends on whether or not there are applicant shortages. Although considerably less
dramatic than the results at the interview step, Figure 4 shows the same general pattern as Figure 3: the
intermediary effect on chances of job offer increase with applicant shortage from 1.6 percent at the 50th (applicant
shortage of ‐1) percentile, to 2.6 percent at the 70th percentile (+3), 3.7 percent at the 80th percentile (+7), and 5.2
percent at the 90th percentile (+13). Also similar to Figure 3, the effect of poverty declines with applicant shortage,
although the interaction term is not statistically significant.
Discussion
We have sought to understand how policy efforts to “create connections” between job seekers and
employers operate in an empirical setting where we can examine how such linkages provide value for the
employer. Using very rare data, we examined the extent to which the application process for job candidates
referred by intermediary organizations parallels that of network candidates. We looked at these questions in a
setting where the firm is well aware of the existence of public and private labor market intermediaries and the
potential benefits of hiring their referrals. In contrast to previous studies, we have studied this question across the
various steps of recruitment, screening, and hiring.
17
As noted above (see note 12), correcting for selection bias makes for estimates of these marginal effects that are somewhat
larger and statistically significant (cf. Models 5 and 6 in Table 8 with Models A5 and A6 in Appendix A), but are otherwise not
different from the uncorrected results. Fernandez and Weinberg (1997) argued in their hiring study that the selection
correction can provide indirect evidence of some applicants performing better than others in interviews. We find no such
evidence for interviewing differences here since the corrected and uncorrected results are very similar. Although speculative,
we conjecture that these “soft” skills are likely to be less important in this factory context where none of the jobs being
screened for are customer‐facing, whereas all of the retail bank branch jobs in the Fernandez and Weinberg (1997) study
involved working with customers.
28
The results here suggest several important lessons regarding this firm’s use of labor market
intermediaries. Consistent with these organizations’ charge to help the hard‐to‐employ, people referred by labor
market intermediaries are less qualified than others at the point of application. At least in terms of observable
factors that HR recruiters say they care about, we found little evidence that intermediary organizations provide the
firm with a “richer pool” of attractive candidates. In contrast, network applicants are somewhat better qualified
than other applicants, suggesting that networks are providing some benefits of up‐stream screening by those who
refer them to the firm. This finding serves to underscore the idea that the interests of intermediary organizations
need not align with the firm’s goals. (A similar point has been made with respect to referrals from employees; see
Fernandez et al. [2000]). In this low‐wage context, to the degree that intermediaries pursue their goal of finding
jobs for the hard‐to‐employ, their referrals will look relatively poor in comparison to the firm’s other candidates.
The interests of the firm’s HR screeners and the intermediary are better aligned in other respects,
however. Parallel to the labor market literature on one of the social network mechanisms (Fernandez et al. 2000),
we did find support for the idea that intermediary firms are guiding applicants to the firm’s threshold at times
when it is more favorable from the candidate’s perspective. In addition, we found that interviewers apparently
give candidates sent from intermediary organizations the benefit of the doubt when granting interviews. Although
the overall pool of intermediary referred applicants is not as good as those applying via other means, the firm’s HR
screeners do interview these applicants at higher rates than other applicants, and HR screeners particularly prefer
these intermediary‐referred applicants at times of labor shortage. However, we found no evidence that the firm’s
HR screeners changed their standards of evaluation when considering whether or not to interview intermediary‐
referred applicants: at least in terms of observable characteristics, the HR screeners appear to be treating these
applicants just as they do other applicants.
This raises the question as to whether HR’s apparent preference for intermediary‐referred candidates is
spuriously due to the influence of unobserved factors. Other evidence suggests that this preference is real,
however. In our field research in the firm, we found a number of applicant paper files which had communications
such as the “Referral Card” (see Figure 2) from local intermediary organizations attached to them. When we
inquired as to what these were, one of the HR recruiters responded, “Oh, that is a ‘get an interview for free’ card.”
The HR screener went on to explain that they maintain a relationship with these local organizations, and that they
29
honor referrals from local intermediary organizations. As we showed above, while the chances of intermediary‐
referred applicants being interviewed are certainly higher than for other candidates, interviews are far from
automatic for that group. Although exaggerated, the statement quoted above nevertheless serves to corroborate
that HR’s observed preference for intermediary candidates reflects an organizational policy.18
At the next step of the process, hiring managers are indifferent to intermediary‐referred candidates.
While for many purposes it might be analytically convenient to bracket the issue, this finding focuses attention on
organizational processes at work at the hiring interface. Although HR has established relationships with
intermediary organizations, they are only partly in control of the hiring process. In contrast, hiring managers show
a preference for network referrals at the job offer stage. As numerous scholars have noted (e.g., Bielby 2000), one
important role of HR is to increase the formality of the recruitment and hiring process. Considered from this
perspective, it is noteworthy that hiring managers’ turn toward networks is not mirrored by HR at the prior stage,
and might de facto undo some of HR work. We can only speculate whether hiring managers’ preference for
networks reflects something HR does not know, reflects shop‐floor patronage, or other factors. Irrespective of
which of these processes are at work, it is important to recognize that the actions of these two important sets of
actors are not fully aligned.19
While HR does not have final say on who is ultimately offered a job, HR’s involvement in hiring still has
important consequences. As Rubineau and Fernandez (2006, 2009) have argued, policies that stoke the hiring
18
While we do not have the benefit of random assignment to control for the impact of unobservable factors (Blank et al. 2004),
we are able to make some progress along these lines by exploiting some natural experiments produced by repeat applications
from the same person (for a similar approach, see Mouw 2002). A total of 188 people applied multiple times producing a total
of 407 applications. Some of these repeat applicants switched recruitment source, and thus are occasions where we can
observe the behavior of the HR screener when observing the same person when they apply through different recruitment
channels. We fit linear probability model with all the time‐varying variables in Model 1 of Table 8 including individual fixed
effects to control for any measured or unmeasured person‐specific factors that do not change across applications. Of course
applicants who are successful in their initial attempt—i.e., those who are interviewed, receive a job offer, and are hired—will
not be at risk of applying again. Therefore, analyses of these switchers are limited by their being conditional on not being hired
in their first attempt. Bracketing that issue, the results here show that applicants switching to intermediary referral are 22.0
percent more likely to be interviewed (p < .10, t‐value = 1.94) than when they apply as “Other.” Similar to the findings without
fixed effects, there is no reliable effect for those switching to networks (‐0.9 percent, p < .661; t‐value = ‐0.11). We replicated
these analyses with an alternative specification, the fixed effect logit model with person fixed effects (Chamberlain 1980). This
model however drops 253 applications produced by 131 people that do not vary on the dependent variable within person.
Replicating the analyses using this model for the remaining 154 applications (submitted by 57 people) yields even stronger
results: applicants switching to an intermediary source are 5.945 times more likely to be interviewed (p < .044; z‐value = 2.01),
but here too there is no reliable effect for those switching to networks (odds ratio of 1.715, p < .461; z‐value = 0.74).
19
We used the same strategy of person fixed effects to study people who switched recruitment source (see note 18). There
were very few cases available for this analysis among interviewees (i.e., 85 applications produced by 63 people) and neither the
intermediary or network effects were statistically significant (respectively, t‐values of ‐0.94 and ‐0.53).
30
pipeline can have indirect but still important downstream consequences in hiring. As gatekeepers (Fernandez and
Gould 1994) to the company’s hiring pipeline, HR’s actions have substantial leverage on hiring outcomes. Some
simple simulations are informative here. The top panel of Table 10 shows observed probabilities of applicants’
receiving an interview, a job offer conditional on having been interviewed, and an unconditional job offer20 for
applicants from each of the three recruitment sources. The second panel simulates what would happen if HR were
to end its relationship with intermediary organizations, but hiring managers were to continue to treat such
referrals as they have been in the past. We simulate the consequences of this in two ways, first by setting the
interview rate for intermediary‐referred applicants to be the same as that for “Other” applicants, and second by
setting to rate to be the same as network referrals. Both these changes would result in overall offer rates that are
substantially below that observed for intermediary‐referred candidates, i.e., .069 and .062 vs. .115. Moreover, the
right side of Table 10 shows that the reductions in offer rates would be even more acute under conditions of labor
shortage, i.e., .074 and .081 vs. .156. Absent HR’s policy, applicants with “created connections” would fare
substantially worse in terms of their ultimate chances of a obtaining a job offer.
In contrast, the bottom panel simulates changing hiring managers’ offer rates to referrals from
intermediaries while leaving HR’s interview rate unchanged. If such a change in hiring managers’ behavior could be
accomplished, it would result in offer rates that are similar to those observed for intermediary referrals, i.e., .120
and .137 vs. .115 for all applicants and .160 and .141 vs. .156 for people applying during times of applicant
shortage. While hiring managers may have the ultimate say over who is offered a job, the impact of their actions
on the company’s relationship with its intermediary organizations is less consequential than the actions of the
front‐line HR screeners. Their structural position upstream in the hiring process provides HR with important
leverage to affect hiring outcomes.
It is important to note that we found no evidence of race or gender differences in advancement through
the screening steps. In this respect, the results in this low‐wage setting do not follow the gender‐based cumulative
disadvantage pattern reported by Fernandez‐Mateo (2009). However, we did find stratifying effects for applicants
coming from a poverty background. Controlling observed factors, applicants who are poor were less likely to be
20
Of course, the there is an axiomatic relationship among these: the probability of a job offer (Pr[O]) is equal to the probability
of an interview probability of an interview (Pr[I]) times the probability of an offer given an interview (Pr[O|I]).
31
interviewed and offered jobs than non‐poor candidates. This is true even despite the extra financial incentives the
firm has for hiring people from a poverty background, and even under conditions of applicant shortage. To the
extent that connections have been successfully created between this firm and local intermediary organizations, the
benefits do not seem to ultimately flow to the poor.
In addition to lessons regarding this firm’s use of labor market intermediaries, this study has important
implications for more general models of brokerage. Studies of brokerage rarely adopt the perspective of the
receiver of the transaction, i.e., the employing firm in the case of labor market intermediaries. Instead, the analysis
is usually centered on the broker and its ability to monopolize access to needed goods or services. Numerous
studies (e.g., Burt 1992; Cook et al. 1983; Marsden 1983; Reagans and Zuckerman 2008) recognize that the power
of the broker diminishes to the degree that the brokered parties have available alternative means to accomplish
connections around the broker.21 To the degree that other parties in the transaction have alternatives, brokerage
should be a fleeting phenomenon since those receiving the good or service should seek disintermediating
alternatives to the service than those going through the broker (Ryall and Sorenson 2007:574). To a large extent,
the issue of the stability of brokerage has been side‐stepped by defining brokerage opportunities as those
situations when there are no available alternatives to the broker to the good or service.
Especially in the context of a free labor market, such a conceptualization of brokerage is implausibly
restrictive. A more complete understanding of brokerage emerges by asking under what conditions would
potential receivers of transactions chose to work through a broker, rather than work around them. Past empirical
research focusing on the role of the broker cannot address this issue. For example, while Fernandez‐Mateo (2007)
convincingly shows that the labor market intermediary in her study makes money by paying contractors less than
what they bill client firms, she cannot address the client firms’ or contractors’ alternatives. A continuing
relationship might lead us to infer that the broker is providing value to the brokered parties, but what that value is
is outside the scope of that study.
In contrast, this paper highlights why receivers might choose to work through brokers rather than eschew
them: actors will choose to work with brokers to the extent that brokers provide goods or services that are of
21
For this reason, Gould and Fernandez (1989) defined “partial” brokerage to capture the idea that the degree of the
intermediation is diminished by the degree to which options are available to the brokered parties (also see Fernandez and
Gould 1994).
32
greater value than those available through alternative means. Rare among studies of brokerage, this paper
provides data on the receiving actor’s alternatives, and provides empirical evidence of the value that the brokers
provide the receivers of these transactions. The interests of the firm’s HR screeners and the intermediary appear
to be well‐aligned and mutually beneficial in several respects. The firm faces considerable variation in labor supply,
and intermediary organizations help HR by insuring a stream of mixed‐quality candidates through times of labor
shortage. HR then applies a consistent screen and selects qualified candidates from amongst the entire pool of
applicants. However, at the margin, HR gives the benefit of the doubt to applicants referred by intermediaries. In
so doing, the firm helps intermediary organizations fulfill their charge to help place hard to employ job seekers.
Finally, this paper has important implications for policy efforts to help the disadvantaged find jobs. Policy
makers often recommend that labor market intermediaries “create connections” for disadvantaged job seekers in
ways that mimic social network processes. However, the various ways in which such social network connections
might work in the hiring process have not been fully appreciated. A key lesson of this study is that, created or not,
such connections need to provide value to the firm. While low‐wage employers generally stigmatize job‐seekers
sent to them from labor market intermediary organizations, the findings of this study show how it is that such
biases can be overcome. To the degree that intermediary organizations can help the firm address its recruitment
problems, “created connections” can serve as functional substitutes for social network processes in matching
people to jobs.
33
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38
Table 1. Racial and Gender Distributions of Workers Employed, Applicants, and Persons
Employed in the Metropolitan Area
All Plant
All
Employees
Applicants
2000 PUMSa
Female Male
Female Male
Female
Male
Non‐Hispanic
White
44.0
41.5
49.8
44.2
54.3
50.4
African American
3.1
5.9
5.2
5.2
5.0
4.0
Hispanic
28.7
24.9
23.6
19.3
29.8
35.3
Asian American
23.3
26.8
21.2
30.5
6.5
6.1
Native American
0.9
1.0
0.1
1.0
0.9
0.7
Other, Multirace
—
—
—
—
—
—
Total
100.0 100.0
100.0
100.0
100.0
100.0
Total N
352
205
909
659
174,838 208,174
a
Persons in the 5 percent 2000 PUMS who are at least 15 years of age and less than 16
years of education with positive wage and salary income in 1999. Data are weighted to
reflect the population.
Table 2. Poverty Rates for Applicants and Persons Employed in the
Metro Area by Race and Gender
Percent Poor
(Federal Definition)
Percent Poor
Metro Area
(8850 form)
2000 PUMSa
All Applicants
Female Male
Female
Male
Non‐Hispanic
White
17.4
10.0
6.0
3.8
African American
34.0
17.6
19.6
9.2
Hispanic
15.3
18.9
14.7
15.4
Asian American
6.7
10.4
12.1
11.5
Native American
0.0
0.0
11.3
9.0
Other, Multirace
—
—
7.2
7.9
Total
15.5
12.1
9.6
8.4
Total N
909
659
237,385 205,731
a
Persons in the 5 percent 2000 PUMS who are at least 15 years of
age and less than 16 years of education with positive wage and
salary income in 1999. Data are weighted to reflect the population.
Table 3. Racial and Gender Distributions of Applicants By Recruitment Channel
Intermediary‐
Referred
Non‐Hispanic
White
African American
Hispanic
Asian American
Total
Total N
Network‐
Referred
All Other
Sources
Total
Applicant Pool
Female
Male
Female
Male
Female
Male
Female
Male
40.0
13.7
22.5
23.7
100.0
43.3
13.4
22.4
20.9
100.0
46.6
4.0
23.5
25.9
100.0
43.8
5.8
12.4
38.0
100.0
53.6
4.8
24.3
17.3
100.0
46.5
3.3
23.7
26.4
100.0
49.8
5.3
23.8
21.0
100.0
45.2
5.3
19.3
30.2
100.0
80
67
328
242
481
333
899
642
Table 4. Means and (Standard Deviations) on Background Factors by Recruitment Source
Intermediary Source
Network Source
All Other Sources
Years of Education
12.34
(1.90)
12.32
(2.01)
12.20
(1.84)
Years of Work
Experience
7.11
(6.31)
6.08
(5.82)
7.17
(7.02)
Industry
Experience
(1=Same industry)
.144
(.352)
.099
(.296)
.123
(.328)
Wage in $ on Last
Job (0 if never
employed)
8.22
(2.78)
7.67
(2.98)
8.13
(2.89)
Employed
(1=Employed at
Application)
.151
(.359)
.363
(.481)
.296
(.457)
Number of Jobs
3.37
(1.34)
2.78
(1.37)
3.14
(1.33)
Felony Conviction
(1=Convicted)
.065
(.247)
.017
(.130)
.014
(.1201)
Number of Cases
139
526
757
Table 5. Multinomial Logit Model with Background Factors Distinguishing Among Recruitment
Sources (Coefficients are relative risk ratios, z‐values in parentheses; Excluded category is
“All Other Recruitment Sources”)
Intermediary Source
Network Source
Years of Education
1.043
(0.83)
1.054 *
(1.69)
Years of Work
Experience
.995
(‐0.34)
.985
(‐1.56)
Industry Experience
(1=Same industry)
1.136
(0.47)
.826
(‐1.02)
Wage in $ on Last Job
(0 if never employed)
.995
(‐0.15)
.967
(‐1.55)
Employed (1=Employed
at Application)
.408 ***
(‐5.21)
1.453 ***
(3.01)
Number of Jobs
1.149 *
(1.90)
.821 ***
(‐4.44)
4.275 ***
(3.12)
1.333
(0.62)
Felony Conviction
(1=Convicted)
LR Chi‐square (14 d.f.)
84.57
Number of Cases
1,422
Note: One tailed z‐tests; * = p < .10 ; ** = p < .05; *** = p < .01
Table 6. OLS Regression Analyses Predicting the State of the Market on the Day of Application (Number of Openings – Number of
Applications) by Race and Recruitment Source (t‐values in parentheses)
Intermediary‐
Network
Contrast
Significant?
Adjusted
R‐square
Number
of Cases
Network
Constant
(“Other”)
Intermediary vs.
Network
Contrast
6.458 ***
(4.57)
5.623 ***
(6.53)
‐5.614 ***
(‐10.16)
0.836
(0.57)
No
.031
1,544
Whites
4.726 **
(2.06)
4.357 ***
(3.29)
‐5.054 ***
(‐6.13)
.368
(0.16)
No
.014
731
African
Americans
8.618 *
(1.90)
6.802
(1.64)
‐8.618 ***
(‐3.13)
1.815
(0.38)
No
.031
81
Hispanics
8.173 **
(2.60)
6.997 ***
(3.48)
‐6.446 ***
(‐5.38)
1.176
(0.365
No
.039
335
Asian
Americans
7.956 **
(3.14)
6.402 ***
(4.53)
‐5.441 ***
(‐5.42)
1.554
(0.61)
No
.054
390
Non‐Poor
Applicants
6.083 ***
(3.89)
5.237 ***
(5.78)
‐5.152 ***
(‐8.79)
.845
(0.53)
No
.028
1,323
Poor
Applicants
8.910 **
(2.59)
7.821 **
(2.97)
‐8.364 ***
(‐5.20)
1.088
(0.29)
No
.044
221
All Applicants
Intermediary‐
Referred
Note: Two‐tailed tests: * = p < .10; ** = p < .05; *** = p < .01.
Table 7. Means and (Standard Deviations) of Variables Used to Predict Interview and Job Offer
Interview Stage
Job Offer Stage
.098
(.297)
.158
(.365)
.370
(.483)
.323
(.468)
.584
(.493)
.600
(.491)
.052
(.222)
.217
(.412)
.247
(.431)
.139
(.346)
.058
(.234)
.200
(.401)
.281
(.450)
.081
(.273)
‐2.905
(16.018)
‐4.023
(19.039)
1.451
(.863)
1.577
(.993)
12.256
(1.907)
12.742
(1.726)
Experience (in years)
6.762
(6.547)
9.510
(6.669)
Experience ‐ squared
88.555
(170.957)
134.742
(185.454)
Industry Experience (Yes = 1)
.116
(.320)
.181
(.386)
Hourly Wage in Last Job ($;
0 if never employed)
7.971
(2.920)
8.96
(2.744)
Employed (Yes = 1)
.307
(.461)
.323
(.468)
Number of Jobs
3.031
(1.361)
3.188
(1.294)
Felony Conviction (1=Convicted)
.020
(.141)
.023
(0.150)
Recruitment Source:
Intermediary
Network
Demographic Background:
Female
African American
Hispanic
Asian
Poverty Background
Application Behavior:
N of Openings – N of Apps
Number of Times Applied
Human Capital:
Education (in years)
Dependent Variables:
Interview
.183
(.387)
Job Offer
‐‐
‐‐
.423
(.495)
1,422
260
Number of Cases
Table 8. Marginal Effects of Probit Models Predicting Interview Among Applicants (Models 1 and 2), Job Offer Among Interviewees (Models 3 and 4), and Job Offer Among
Applicants (Models 5 and 6) (Coefficients are the change in the probability of interview for a unit change in the independent variable; z‐Values are in parentheses)
Model 1
P(Interview)
Model 2
P(Interview)
Model 3
P(Job Offer|Interview)
Model 4
P(Job Offer|Interview)
Model 5
P(Job Offer&Interview)
Model 6
P(Job Offer&Interview)
.088 ***
(3.07)
.084 ***
(2.81)
.009
(0.11)
‐.004
(‐0.05)
.023
(1.64)
.019
(1.19)
‐.016
(‐0.76)
‐.014
(‐0.69)
.137 **
(2.23)
.141 **
(2.28)
.008
(0.77)
.008
(0.82)
.009
(0.50)
.006
(0.31)
.047
(0.88)
.045
(0.82)
.007
(0.84)
.006
(0.65)
.020
(0.50)
.020
(0.86)
.010
(0.45)
‐.064 **
(‐2.07)
.018
(0.45)
.020
(0.83)
.008
(0.37)
‐.090 **
(‐2.54)
.124
(1.06)
.075
(1.08)
.067
(1.02)
.088
(0.86)
.117
(0.99)
.071
(1.01)
.063
(0.95)
.107
(0.91)
.016
(0.83)
.013
(1.11)
.007
(0.65)
‐.013
(‐0.84)
.016
(0.84)
.013
(1.11)
.006
(0.57)
‐.024
(‐1.28)
.016 ***
(3.10)
.023 ***
(5.08)
‐.0006 ***
(‐3.82)
.070 ***
(2.77)
.016 ***
(2.97)
.024 ***
(5.09)
‐.0006 ***
(‐3.85)
.071 ***
(2.74)
‐.038 **
(‐2.27)
.015
(1.20)
‐.0004
(‐0.90)
.239 ***
(3.18)
‐.038 **
(‐2.27)
.016
(1.25)
‐.0004
(‐0.97)
.243 ***
(3.20)
.002
(0.82)
.009 ***
(3.22)
‐.0002 **
(‐2.60)
.050 ***
(3.42)
.002
(0.71)
.009 ***
(3.23)
‐.0003 **
(‐2.61)
.051 ***
(3.39)
.011 ***
(3.32)
.011 ***
(3.41)
‐.005
(‐0.49)
‐.005
(‐0.48)
.003 *
(1.81)
.003 *
(1.89)
Employed (Yes = 1)
.011
(0.59)
.015
(0.75)
‐.083
(‐1.30)
‐.083
(‐1.30)
‐.008
(‐0.73)
‐.007
(‐0.63)
Number of Jobs
‐.005
(‐0.63)
‐.005
(‐0.73)
‐.014
(‐0.62)
‐.015
(‐0.68)
‐.002
(‐0.64)
‐.003
(‐0.70)
Felony Conviction
(1=Convicted)
.020
(0.32)
.018
(0.29)
‐.016
(0.08)
‐.040
(‐0.19)
.014
(0.48)
.014
(0.45)
Recruitment Source:
Intermediary‐Referred
Network‐Referred
Demographic Background:
Female
African American
Hispanic
Asian
Poverty Background
Human Capital:
Education (in years)
Experience (in years)
Experience ‐ squared
Industry Experience (Yes = 1)
Hourly Wage in Last Job ($;
0 if never employed)
Table 8 (continued). Marginal Effects of Probit Models Predicting Interview Among Applicants (Models 1 and 2), Job Offer Among Interviewees (Models 3 and 4), and Job
Offer Among Applicants (Models 5 and 6) (Coefficients are the change in the probability of interview for a unit change in the independent variable; z‐Values are in
parentheses)
Application Behavior:
Number of Times Applied
Shortage of Applicants
(N of Openings – N of Apps)
Interactions with
Applicant Shortage:
Shortage of Applicants x
Intermediary‐Referred
Shortage of Applicants x
Poverty
LR X2 (d.f.)
p – value <
Number of Cases
Model 1
P(Interview)
Model 2
P(Interview)
Model 3
P(Job Offer|Interview)
Model 4
P(Job Offer|Interview)
Model 5
P(Job Offer&Interview)
Model 6
P(Job Offer&Interview)
.021 **
(2.04)
.021 **
(2.01)
‐.120 ***
(‐3.68)
‐.120 ***
(‐3.66)
‐.009
(‐1.61)
‐.010
(‐1.59)
‐.0004
(‐0.83)
‐.0004
(‐0.74)
‐.0003
(‐0.02)
‐.0006
(‐0.34)
‐.0001
(‐0.52)
‐.0001
(‐0.41)
____
.006 ***
(2.67)
____
.004
(0.60)
____
.003 **
(2.02)
____
‐0.003 **
(‐1.99)
____
.002
(0.43)
____
‐.001
(‐1.57)
143.46 (17)
.0001
156.60 (19)
.0001
35.17 (17)
.006
35.71 (19)
.011
69.66 (17)
.0001
78.24 (19)
.0001
1,422
1,422
260
260
1,422
1,422
Two‐tailed tests: * = p < .10; ** = p < .05; *** = p < .01.
Note: Coefficients are the change in the probability of the dependent variable (i.e., interview or job offer) associated with a unit change in each independent variable, evaluated
at mean levels of education, experience, experience‐squared, last wage, number of jobs, applicant shortage, shortage x intermediary‐referred, shortage x network‐referred,
number of applications and at the following categories for the nominal variables: male, white, no poverty background, no industry experience, not employed, and not convicted.
Table 9. Tests of Interactions Between Predictor Variables and Applicant Shortage and Applicant Shortage and Recruitment Source
Model 1
LR X
2
Model 2
d.f.
p‐value
143.46
17
.0001
27.93
16
C: All Intermed‐
Referred
Interactions
18.83
D: All Network‐
Referred
Interactions
Model 3
2
d.f.
Model 4
p‐value
LR X
2
d.f.
Model 5
p‐value
LR X
2
d.f.
Model 6
p‐value
LR X
2
d.f.
p‐value
d.f.
p‐value
LR X
156.60
19
.0001
35.17
17
.007
35.71
19
.014
69.66
17
.0001
78.24
19
.0001
.032 *
14.79
14
.392
18.50
16
.293
18.00
14
.207
22.52
16
.127
13.93
14
.455
15
.221
9.54
14
.794
21.09
15
.134
20.56
14
.113
15.05
15
.448
9.66
14
.787
9.92
15
.825
11.30
15
.731
12.31
15
.655
12.44
15
.646
8.29
15
.912
7.55
15
.940
E: All Intermed‐
and Network‐
Referred
Interactions
29.79
30
.476
21.25
29
.850
32.37
30
.351
31.91
29
.324
26.21
30
.664
20.40
29
.880
F: Only Shortage
x Intermed &
Shortage x
Poverty
13.14
2
.001***
‐‐
‐‐
‐‐
0.54
2
.764
‐‐
‐‐
‐‐
8.59
2
.014 *
‐‐
‐‐
‐‐
A: Baseline
Models
Increment Over
Baseline Models:
B: All Shortage
Interactions
Number of Cases
1,422
LR X
2
1,422
2
Statistical significance of interactions based on LR X tests: * = p < .05; ** = p < .01;
260
260
1,422
1,422
Table 10. Simulations of Changes in Firm Policies Related to Referrals from Intermediary Organizations
Applicant Shortage
(N of Openings > N of Applications)
All Applicants
P(Interview)
P(Offer|Interview)
P(Offer)
P(Interview)
P(Offer|Interview)
P(Offer)
.295 ***
.390
.115 *
.328 ***
.476
.156 **
Network
Referred
.160
.464 *
.074
.170
.429
.073
Other
Applicants
.178
.407
.073
.155
.489
.076
Intermediary
= Other
.178
.390
.069
.155
.476
.074
Intermediary
= Network
.160
.390
.062
.170
.476
.081
Intermediary
= Other
.295
.407
.120
.328
.489
.160
Intermediary
= Network
.295
.464
.137
.328
.429
.141
1,422
260
1,422
614
110
614
Observed:
Intermediary
Referred
HR Simulations:
Hiring Manager
Simulations:
Number of
cases
Notes: Probabilities are not adjusted for covariates.
Two‐tailed tests of contrasts with “Other” applicants for observed data: * = p < .10; ** = p < .05; *** = p < .01.
Figure 1. Sample IRS 8850 Form Anonymized
Figure 2. Sample Referral Card From Private Industry Council Anonymized
Figure 3. Partial Effects of Recruitment Source and Poverty on Probability of Interview by
Percentiles of Application Shortage
0.35
0.30
0.25
0.20
0.15
Derivatives
0.10
0.05
0.00
‐0.05
‐0.10
‐0.15
Pr(Interview) for Intermediary‐Referred
Candidates
Pr(Interview) for Network Candidates
‐0.20
‐0.25
Pr(Iinterview) for Other Candidates
‐0.30
Pr(Interview) for Poor Candidates
‐0.35
0
10
20
30
40
50
Percentiles
60
70
80
90
100
Figure 4. Partial Effects of Recruitment Source and Poverty on Probability of Interview and
Job Offer by Percentiles of Application Shortage
0.20
0.15
0.10
Derivatives
0.05
0.00
‐0.05
0 05
‐0.10
Pr(Offer&Interview) for Intermediary‐Referred
Candidates
Pr(Offer&Interview) for Network Candidates
‐0.15
Pr(Offer&Interview) for Other Candidates
Pr(Ofer&Interview) for Poor Candidates
‐0.20
0
10
20
30
40
50
Percentiles
60
70
80
90
100
Appendix A
We address the issue of selection bias by estimating a bivariate probit model with selection, where the
selection step is whether applicants are interviewed, and the ultimate dependent variable is whether the
candidate is offered a job (see Fernandez and Weinberg 1997; Fernandez and Sosa 2005). Because the same
application materials that are used by HR to decide who to send on for an interview are also available to the hiring
managers as they make their decisions about which of the interviewed candidates will be offered a job, the same
predictors are used in both the interview and job‐offer phases of the analysis. Selection models with identical
covariates in the two stages are only weakly identified off of the non‐linearity of the selection effect, however.
Stronger identification can be achieved by defining instruments—factors that by assumption affect one stage, but
not the other (see Fernandez et al. 2000: 1329, fn. 32). Because the actors making downstream offer decisions
have available to them the same information that those making upstream interview decisions have, the way the
hiring process is organized here makes it difficult to identify such instruments.
Models A1, A3, and A5 in Table A1 report the marginal effects (i.e., the boosts in the probability of success
in the hiring process associated with a unit change in each of the independent variables) calculated from estimates
of the bivariate probit model. There are only very minor differences between these effects and those for the
corresponding univariate probit model results (Models 1, 3, and 5 in Table 8), with the only noteworthy differences
being that effect for intermediary‐referred is statistically significant at 0.10 level in Model A5, whereas it is not
significant in Model 5.
In order to replicate Models 2, 4, and 6 in Table 8, we attempted to incorporate the shortage x
intermediary and shortage x poverty interactions into both the selection (interview) and job offer equations.
Unfortunately, we could not get the model to converge. Such estimation problems are common, since the
bivariate probit model is only weakly identified without instrumental variables. However, we were able to obtain
estimates for Models A2, A4, and A6 by including the interaction terms in only the interview stage of the model.
This assumes that it is HR recruiters—and not hiring managers—who adjust their screening criteria with respect to
recruitment source and poverty background under conditions of applicant surplus or shortage. Under this model of
the process, the selection‐corrected results look very similar to the uncorrected results (cf. Model A2, A4, and A6
in Table A1 with Models 2, 4, and 6 in Table 8). However, in this case too, the main effect for intermediary‐referred
becomes significant in Model A6, while it is not significant in Model 6. Correcting for selection bias weakens the
effect for the intermediary‐referred x shortage interaction terms.
Table A1. Marginal Effects from a Bivariate Probit Model Predicting Job Offer Controlling for Selection at Interview Stage (Coefficients are the change in the probability of
interview for a unit change in the independent variable; z‐Values are in parentheses)
Model A1
P(Interview)
Model A2
P(Interview)
Model A3
P(Job Offer|Interview)
Model A4
P(Job Offer|Interview)
Model A5
P(Job Offer&Interview)
Model A6
P(Job Offer&Interview)
.088 ***
(3.09)
.085 ***
(2.82)
.018
(0.19)
.003
(0.04)
.027 *
(1.79)
.024 *
(1.67)
‐.016
(‐0.76)
‐.014
(‐0.67)
.142 **
(2.16)
.140 **
(2.18)
.015
(1.40)
.015
(1.45)
.007
(0.36)
.004
(0.20)
.053
(0.94)
.052
(0.93)
.009
(1.02)
.008
(0.91)
.019
(0.48)
.020
(0.84)
.010
(0.46)
‐.065 **
(‐2.09)
.018
(0.44)
.019
(0.82)
.008
(0.37)
‐.090 **
(‐2.53)
.128
(1.04)
.079
(1.07)
.069
(0.99)
.087
(0.82)
.127
(1.05)
.078
(1.09)
.067
(0.97)
.078
(0.69)
.023
(1.12)
.016
(1.39)
.012
(1.10)
‐.006
(‐0.36)
.023
(1.12)
.016
(1.41)
.011
(1.04)
‐.014
(‐0.73)
.016 ***
(3.09)
.023 ***
(5.11)
‐.0006 ***
(‐3.84)
.071 ***
(2.80)
.016 ***
(2.98)
.024 ***
(5.11)
‐.0006 ***
(‐3.86)
.072 ***
(2.75)
‐.039 **
(‐2.11)
.017
(1.25)
‐.0004
(‐0.96)
.260 ***
(2.80)
‐.039 **
(‐2.18)
.016
(1.23)
‐.0004
(‐0.92)
.247 ***
(3.19)
‐.0007
(‐0.24)
.009 ***
(3.10)
‐.0002 **
(‐2.53)
.055 ***
(3.16)
‐.0009
(‐0.31)
.009 ***
(3.16)
‐.0003 **
(‐2.58)
.054 ***
(3.36)
.011 ***
(3.30)
.011 ***
(3.39)
‐.004
(‐0.34)
‐.005
(‐0.42)
.002
(1.30)
.002
(1.35)
Employed (Yes = 1)
.011
(0.59)
.015
(0.75)
‐.084
(‐1.26)
‐.083
(‐1.26)
‐.008
(‐0.77)
‐.007
(‐0.70)
Number of Jobs
‐.005
(‐0.64)
‐.005
(‐0.74)
‐.014
(‐0.59)
‐.014
(‐0.61)
‐.003
(‐0.81)
‐.003
(‐0.89)
Felony Conviction
(1=Convicted)
.019
(0.31)
.018
(0.28)
‐.014
(0.06)
‐.007
(‐0.03)
.003
(0.10)
.004
(0.12)
Recruitment Source:
Intermediary‐Referred
Network‐Referred
Demographic Background:
Female
African American
Hispanic
Asian
Poverty Background
Human Capital:
Education (in years)
Experience (in years)
Experience ‐ squared
Industry Experience (Yes = 1)
Hourly Wage in Last Job ($;
0 if never employed)
Table A1. (Continued) Marginal Effects from a Bivariate Probit Model Predicting Job Offer Controlling for Selection at Interview Stage (Coefficients are the change in the
probability of interview for a unit change in the independent variable; z‐Values are in parentheses)
Application Behavior:
Number of Times Applied
Shortage of Applicants
(N of Openings – N of Apps)
Interactions with
Applicant Shortage:
Shortage of Applicants x
Intermediary‐Referred
Shortage of Applicants x
Poverty
rho
LR X2 test of rho = 0 (d.f.)
p – value <
Wald X2 (d.f.)
p – value <
Number of Cases
Model A1
P(Interview)
Model A2
P(Interview)
Model A3
P(Job Offer|Interview)
Model A4
P(Job Offer|Interview)
Model A5
P(Job Offer&Interview)
Model A6
P(Job Offer&Interview)
.020 **
(1.99)
.020 *
(1.95)
‐.120 ***
(‐3.39)
‐.119 ***
(‐3.40)
‐.011 *
(‐1.92)
‐.011 *
(‐1.93)
‐.0004
(‐0.84)
‐.0005
(‐0.77)
‐.00009
(‐0.06)
‐.0003
(‐0.02)
‐.0001
(‐0.52)
‐.0001
(‐0.48)
____
.006 ***
(2.70)
____
.002
(0.31)
____
.002 *
(1.74)
____
‐.003 *
(‐1.91)
____
‐.0007
(‐0.32)
____
‐.0009
(‐1.60)
‐.678
0.28 (1)
.594
‐.199
0.10 (1)
.7537
‐.678
0.28 (1)
.594
‐.199
0.10 (1)
.7537
‐.678
0.28 (1)
.594
‐.199
0.10 (1)
.7537
38.35 (17)
.002
31.59 (17)
.017
38.35 (17)
.002
31.59 (17)
.017
38.35 (17)
.002
31.59 (17)
.017
1,422
1,422
1,422
1,422
1,422
1,422
Two‐tailed tests: * = p < .10; ** = p < .05; *** = p < .01.
Note: Coefficients are the change in the probability of the dependent variable (i.e., interview or job offer) associated with a unit change in each independent variable, evaluated
at mean levels of education, experience, experience‐squared, last wage, number of jobs, applicant shortage, shortage x intermediary‐referred, shortage x network‐referred,
number of applications and at the following categories for the nominal variables: male, white, no poverty background, no industry experience, not employed, and not convicted.