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Recruitment of Seemingly Overeducated Personnel: Insider-Outsider
Effects of Fair Employee Selection Practices
Oliver Fabel*
Department of Business Administration
University of Vienna, Austria
Razvan Pascalau**
School of Business and Economics
State University of New York-Plattsburgh
November 2009
*Prof. Dr. Oliver Fabel, University of Vienna, Faculty of Business, Economics and Statistics,
Department of Business Administration, Chair for International Personnel Management, Brünner
Str. 72, 1210 Vienna, Austria; Tel: 43-1-4277-38161, Fax: 43-1-4277-38164, E-mail:
**Ass. Prof. Razvan Pascalau, PhD, SUNY College at Plattsburgh, School of Business and
Economics, 101 Broad Street, Plattsburgh, NY 12901, United States; Tel: +1-518-564-4193,
E-Mail: [email protected].
Abstract
We analyze an employee selection model given two constraints: first, professional experience
substitutes lacking formal education for insider candidates while this substitution is imperfect
for outsiders. Second, the respective substitution rate increases with the advertised educational
requirement due to legal risk. Optimal selection implies that the expected level of formal
education is higher for outsider than for insider recruits. Moreover, this difference in
educational attainments increases with lower educational job requirements. Investigating data
of a large US public employer confirms our theoretical implications. Generally, we find strong
insider-outsider effects on recruitment decisions.
Keywords: recruitment model, utility analysis, overeducation, fair employment policy, insiders
vs. outsiders.
1 Introduction
In the US public sector, screening applicants is subject to a set of institutional
constraints: first, fair employee selection rules imply that professional experience can substitute
formal educational job requirements. However, since job descriptions contain firm-specific
elements, the recruiter can accept less substitution when screening applications from outside
the firm. Second, screening on grounds of formal education risks that requirements may be
judged excessive and trigger disparate impact charges. Reflecting this legal risk, the recruiter
must accept more substitute professional experience when setting higher educational job
requirements.
Using a standard recruitment model, we characterize the optimal selection policy given
three predictors: formal education, professional experience, and test scores. Given the
institutional constraints on the selection policy, we derive two testable hypotheses: first, the
expected educational level of outsider recruits exceeds that of current employees. Second, this
wedge between the two groups' expected educational levels widens with decreasing
educational job requirements. Since the institutional rules constrain the use of informative
signals, the firm's outsider recruits are only seemingly overeducated - i.e. without the
constraints the firm would have advertised higher standards.
To our knowledge, we are the first to use actual recruitment data to test for
overeducation effects. The data is supplied by a large US public employer. The data set
comprises 33780 observations on individuals who entered recruitment processes to fill 1244
positions. We control for characteristics that are specific to a particular job-opening, selection
process, and individual applicant. The empirical analysis shows strong insider-outsider effects on
the hiring decisions. At the same time, our results do not confirm the implications of the career
mobility theory. This theory constitutes the dominant approach to motivate an
efficiency-enhancing virtue of preferential treatment of insider applicants. However, our
findings support the two hypotheses regarding the wedge between the two groups of recruits'
educational levels derived from our recruitment model. We conclude that future research
should investigate the effects of institutional constraints imposed on employee selection
processes more thoroughly.
The remainder of this paper is organized as follows: the next section motivates our
approach by discussing the existing literature on the overeducation phenomenon and
institutional rules that apply to recruitment processes. The third section develops the
theoretical model and derives testable hypotheses. Section 4 provides a description of the data,
develops the econometric approach, and reports our empirical findings. The finally section
summarizes our discussion.
2 Motivation
2.1 Overeducation effects in employment relations
Since the seminal work of Freeman (1976) and Duncan and Hoffman (1981), numerous
empirical labor market studies confirm that overeducation increases employment probabilities.
At the same time, overeducated employees earn less than the members of their educational
peer group. One set of explanations emphasizes matching problems in labor markets. Following
Spence's (1973) theory of labor market signaling, Thurow's (1975) theory of job competition,
and Sattinger's (1993) assignment theory, overeducation reflects inefficient investments in
formal education to produce ability signals [see the surveys in e. g. Hartog (2000), Bauer (2002),
and Chevalier (2003), and McGuinness (2006)]. Büchel and Mertens (2004) show the persistent
nature of the overeducation phenomenon. Then, following De Grip et al. (2008), employment in
jobs that demand less than the acquired education contributes to a worker’s cognitive decline.
Theories of labor market mismatching compete with the career mobility or insertion
theory originating in Sicherman and Galor (1990), Hersch (1991), and Sicherman (1990): here,
education is only an imperfect ability signal and the employer learns about the employee’s
actual productive ability on the job. Individuals accept underqualified work at early career
stages to be promoted into adequate jobs with higher wages at later stages. Hence, according
to the career mobility approach, overeducation on the first job marks the beginning of an
efficiency-enhancing internal matching process. Specifically testing this approach, Groeneveld
and Hartog (2004) finds that overeducation accelerates promotions and increases the wages of
employees that are sheltered in the still protected internal labor market of a former exclusively
public employer. However, no such effect can be found where privatization has introduced
labor market competition for jobs. Tsai (2009) shows that overeducation comes with deficits in
other skills and abilities which drive individual productivity. Controlling for employee and job
fixed effects, the overeducation effect on wages nearly vanishes.
Green and McIntosh (2007) conclude that both the mismatching and the career mobility
theories apply: hiring overeducated personnel may initially reflect labor market mismatching
and, at the same time, the beginning of an efficiency-enhancing internal learning process. Then,
the meta-analysis in Groot and Maasen van den Brink (2000) reveals wide variations of the
estimates of returns to overeducation. To a considerable extend, these differences reflect the
different approaches used to infer educational job requirements from observations on current
employees. Research has therefore shifted towards studying the incidence of overeducation in
particular social groups such as e. g. women [Büchel and Battu (2003)] or individuals with low
spatial mobility due to family responsibilities [Green et al. (2002)]. Then, Belfield (2009), among
other findings, confirms that the incidence of overeducation is strongest in shift-work,
part-time, and manual jobs characterized by low educational job requirements.
Our study controlls for a large number of both workplace and employee characteristics.
Hence, this result does not reflect that there are more and more distinct ways to observe
overeducation relative to such low requirements than for jobs with higher qualification
standards. Generally, it is is empirically well-documented that overeducation is higher in
low-skill jobs. Yet, this fact has not received much attention. Similar to Groeneveld and Hartog
(2004), Belfield (2009) also shows that overeducation is more widespread in protected, public
employment.
2.2 Fair employee selection policies
Following Clardy (2003), a US employer must provide job security and career
development for current employees. Consistent with the career mobilty approach, fair
employee selection rules are therefore alternatively phrased in terms of formal educational
requirements and substitute professional experience criteria. Public sector employees are
additionally covered by the Fourteenth Amendment right to Equal Protection [Carlson (2005, p.
753 - 756)]. Hence, the federal government commits to a set of rules laid out in the Operating
Manual: Qualification Standards for General Schedule Positions.1 This manual lists the required
educational degree and substitute professional experience that qualify for employment in
specified positions. Other public employers specify similar substitution rules.
Generally, qualification requirements must be reasonable to avoid disparate impact
charges under Title VII of the Civil Rights Act of 1964. As discussed e. g. in Carlson (2005, p. 126 -
132), such requirements can be ruled “excessive” – i.e., intentionally set to preclude successful
appplications by women, disabled persons, members of minority groups or other individuals
that have been socially excluded from formal education.2 The Civil Rights Act of 1991
introduces the right to a jury trial and to rely on statistical information as evidence. Also,
punitive damages in addition to back-pay increase the potential costs per case [Oyer and
Schaefer (2002), Gutman (2003, 2004)].
The disparate impact issue has received early and repeated attention by economists,
socioligists, and legal scholars [see e. g. Welch (1981), Ashenfelter and Oaxaca (1987), Abram
(1993), Coate and Loury (1993), and Betsey (1994)]. These analyses typically address the effects
1
The manual is available online from the US Office of Personnel Management, Washington D. C.:
http://www.opm.gov/qualifications/standards/group-stds/GS-PROF.asp. 2In the original case, Griggs v. Duke Power Co., 401 U.S. 424 (1971), the company required a High School diploma
and a certain score on a general aptitude test to qualify for internal promotion. The court found that these
requirements disparately impacted ethnic minority groups. Specifically, African-Americans were less likely to hold a
High School degree and averaged lower test scores and were, therefore, selected at a much a lower rate.
on groups that are protected under this legislation. Oyer and Schaefer (2002) show that, since
population groups differ in their propensities to press legal charges, the Civil Rights Act of 1991
also induces distributional effects. Kalev and Dobbin (2006) find that internal compliance
procedures prove to be even more effective drivers for implementing equal opportunity
employment policies than the threat of lawsuits.
However, following Lindbeck and Snower (2004), such institutional constraints induce
labor turnover costs that constrain hiring choices. Initially implmented to overcome the problem
of hidden ability information, such rules can develop to adversely impact equal employment
opportunities by merely protecting current employees from job competition. We analyse the
effects of jointly implementing two fair employee selection rules: first, the educational
standards must be reasonable such as not to exclude qualified individuals. Second, professional
experience gained in similar jobs within a firm can substitute for a lack of formal education. We
show that the induced differential treatment of applications from within and outside the
organization implies that successful outsiders appear overeducated. Moreover, this effect
increases with lower educational job requirements.
3 "Utility analysis" of recruitment decisions
3.1 Basic assumptions and notations
We have conducted extensive interviews within the organization to verify the following
sequence of screening and selection activities: all applicants who pass the advertised
educational and professional selection criteria are pooled and subjected to the same set of
job-specific ability tests. These tests always include job interviews with the department of
employment. Conditional on the job type, other tests of cognitive abilities and/or non-cognitive
skills may be added. Appreciating the results of these tests, the department of employment
makes its hiring choices to be implemented by the human resources department. Before a
candidate is hired, human resources reviews every recruitment process to ensure compliance
with legal standards.
We assume that the organization rationally seeks to maximize the expected productivity
of its workforce. Given this assumption, the so-called "utility analysis" developed in assessment
psychology allows to characterize the outcome of this process [Holling (1998), Schmidt and
Hunter (1998)]: let on-the-job ability � be identically and independently distributed (�, ��)
over the two populations of applicants denoted insiders and outsiders. The degree of formal
schooling � , professional experience � , and potential test scores � are known to be
identically, independently, and standard normally distributed over these two populations. As
usual, Φ(�) and ϕ(�), � ∈ {�, �, �}, denote the standard normal distribution and density
functions, respectively.
The human resources department has carried out pre-tests to validate that
� = � + ��� + ��� + � � + ! (1)
where !~(0, #�) is a measurement error with $%&(!, �) = 0 for � ∈ {�, �, �}. Realistically,
the predictors are correlated [Anderson et al. (2004)]. In our subsequent empirical investigation,
we account for such correlations by including a large set of control variables. Thus, to keep the
theoretical analysis simple, we assume $%&(�, �) = $%&(�, �) = $%&(�, �) = 0. In contrast,
'�( ≥ 0 denotes the coefficient of correlation between ability and the predictor � , � ∈{�, �, �}. Then, � = � and �( = *+,-+
-, . To save space and notation, let '�� = '�� = .. Hence,
the two signals regarding educational and professional qualifications serve equally well as ability
predictors. Also, let '� = '.
The advertised job description may specify minimum educational and professional
experience levels, denoted / and 0. However, professional experience can substitute for
insufficient formal education. Again only for the purpose of simplifying the theoretical analysis,
we assume that this substitution is perfect for insider applicants. Thus, let 12 ≡ � + � and
Ω ≡ / + 0. Note that 12~(0,2) and denote the respective distribution and density functions
by Ψ2(12) and 62(12). Given that applicants whose test score satisfies � ≥ 7 are hired, the
expected ability of insider recruits can be derived as
82{�; /, 0, 7} = (2)
� + �:'��8{�|12 ≥ Ω} + '��8{�|12 ≥ Ω} + '� 8{�|� ≥ 7}< =
� + � =√2. ? @
Ω 12 ABC(DC)EFGBC(Ω)H + ' ?
@I � AJ( )
KFGJ(I)LM.
In principle, the above substitution rule applies to all applications. Yet, job descriptions
contain firm-specific elements. Thus, the recruiter can apply a discount factor O ∈ (0,1) when
evaluating the professional experience claimed by outsiders. Recall that educational
requirements may be judged excessive. The human resources department uses internal
compliance reviews to minimize the respective legal risk. As a result, with increasing educational
job requirements, outsiders can also increasingly substitute these requirements by documenting
professional experience.
Thus, O = O(/). The specific functional form depends on human resources' perception
of the legal risk. Generally however, OP(/) > 0 and limU→@O(/) = 1. Since 1W = � + O(/)�,
1W~(0,1 + KO(/)L�. Denote the respective distribution and density functions by
ΨW(1W; O(/)) and 6W(1W; O(/)). Then, the expected ability of outsider recruits can be
obtained as
8W{�; /, 0, 7} = (3)
� + �:'��8{�|1W ≥ Ω} + '��8{�|1W ≥ Ω} + '� 8{�|� ≥ 7}< =
� + � X �YZF[(\(U))] ?
@Ω 1W AB^(D^;\(U))
KFGB^(Ω;\(U))L + ' ? @
I � AJ( )KFGJ(I)L_ .
For further analytic simplicity, we assume that the two groups of applicants are of
identical size F� . Given that there are a openings, the recruitment process must ensure that
K1 − Φ(7)Lc∑ ef2,W (1 − Ψe(Ω))g = hi , (4)
where hi < 1.
The department of employment's objective is to maximize the expected ability
8k{�; /, 0, 7} = ∑ lmC,^ KFGBl(Ω)Lnl{�;U,o,I}∑ lmC,^ KFGBl(Ω)L (5)
of its new employees net of the costs $ associated with the ability tests. These costs are fixed
and reflect the choice of the test design. If recruitment decisions are based only on the
educational and professional information documented by the applicants themselves, the costs
of screening equal zero.
3.2 Screening and testing with homogeneous groups of applicants
Focussing on recruiting from only one group of applicants serves to highlight the
selection mechanism: suppose that there are only internal applications. Hence, ΨW(Ω) = 1 in
(5) and (4) above. The respective Lagrange-function can be derived as
Ł2 = q(7):82{�; /, 0, 7} − $< + (1 − q(7)) E limI→G@82{�; /, 0, 7}H
−r2 sK1 − Φ(7)L(1 − Ψ2(Ω)) − hit (6)
where
q(7) = u1 if Φ(7) ∈ (0,1<0 if Φ(7) = 0 w (7)
denotes an indicator function that captures the opportunity cost nature of $.
The first-order conditions yield:
q(7): 82{�; /, 0, 7} − $ − limI→G@82{�; /, 0, 7} y=≤{ 0, (8)
if Φ(7) y≥={ 0;
| ∈ {/, 0}: r2K1 − Φ(7)L + }(I)~C(Ω) $ = (9)
-+√�Y
EFGBC(Ω)H =Ω − ? @
Ω 12 ABCKDCLEFGBC(Ω)HM ;
7: r2K1 − Ψ2(Ω)L = (10)
-+*
KFGJ(I)L �7 − ? @
I � AJ( )KFGJ(I)L� , if q(7) = 1 .
The conditions reveal two important properties: first, according to (9), recruitment will not be
subject to separate educational and professional requirements if there are only insider
applications. Second, only if the human resources department decides on additional testing, the
optimal recruitment policy balances out the marginal returns from setting application and
testing standards. Otherwise, expected ability is simply determined by choosing Ω such as to
satisfy (4) for Φ(7) = 0.
Investigating (8) reveals
Δ82 ≡ 82{�; /, 0, 7} − $ − limI→G@82{�; /, 0, 7} = (11)
−$ + �' ? @
I � AJ( )KFGJ(I)L − �√2. ?
Ω�Ω 12 ABCKDCL
BCKΩ�LGBC(Ω)
where Ω� is defined by K1 − Ψ2(Ω�)L = hi. Accounting for (4), limI→G@Δ82 = −$ < 0. Thus,
additional testing is optimal if the respective costs are low and the coefficient of correlation
between ability and the test score, ', is large relative to ..
Job interviews are likely to qualify in this respect [Dakin and Armstrong (1989),
Robertson and Smith (2001)]. For the remainder, we will therefore assume such an interior
solution. This solution implies
*XIG?
�� ��(�)K���(�)L_
Y=ΩG? �Ω DC ��CK�CL
E���C(Ω)HM= 1 − �EFGBC(Ω)H
~C(Ω)-+=ΩG? �Ω DC ��CK�CL
E���C(Ω)HM> 1. (12)
The costs of the ability tests induce a distortion: selection according to test scores is
"over-restrictive."
Setting Ψ2(Ω) = 1 in (5) and (4) allows characterizing the alternative scenario of hiring
from a pool of outsiders. Only exchanging superscripts, the first-order conditions with respect to
q(7) and 7 restate (8) and (10) from above. Yet, (9) is replaced by
0: rWK1 − Φ(7)L + }(I)~^(Ω) $ = (13)
�-+Y
EFGB^(Ω)HZF[(\(U))] =Ω − ? @
Ω 1W AB^KD^LEFGB^(Ω)HM ;
/: rWK1 − Φ(7)L + }(I)~^(Ω) $ = (14)
�-+Y
EFGB^(Ω)HZF[(\(U))] =Ω − ? @
Ω 1W AB^KD^LEFGB^(Ω)HM
+ �-+Y\�(U)\(U)~^(Ω)(F[(\(U))])�]
? @
Ω 1W AB^KD^LEFGB^(Ω)H .
Taking the limit / → ∞ of the RHS of (13) and (14), the corner solution violates (4),
since all applicants would be screened out. At the same time, / → −∞ implies that the
expected signal values are zero; the applicants' documents would not be used for screening at
all. However, the information is costless for the firm. Hence, this corner solution can also be
ruled out. The required level of formal education must therefore satisfy 0 < O(/) < 1, given
that it is optimal to carry out ability tests.
Then, with decreasing discount factor O(/), the two signals � and � contained in 1W
are increasingly used separately to predict on-the-job ability: discounting professional
experience increases the precision of the ability signal 1W. At the same time, however, lower
O(/)-values imply that professional experience receives less weight as a predictor of ability. The
optimal selection policy balances these two counteracting effects. Comparing (13) and (14) with
(9), the interior solution therefore implies distinctly separate minimum educational and
professional experience requirements in the outsider-recruitment case.
3.3 Recruiting from pools of insiders and outsiders
Since the organization is subject to mandatory public job advertisements, the
recruitment decisions maximize (5) subject to (4). Yet, characterizing this solution does not add
analytic insights. The respective first-order conditions with respect to the minimum educational
and professional experience requirements, / and 0, contain weighted sums of the terms in
(13), (14), and (9). The respective weights can be obtained as ∑ ef2,W 6e(Ω)/ ∑ ef2,W (1 −Ψe(Ω)).
Hence, the above characterizations carry over in the following sense: given that test
scores are used for selection, the solution balances the marginal returns to increased precision
from using all three signals. Separate educational and professional experience standards are
advertised. For 0 < O(/) < 1, the analysis implies the following hypothesis for empirical
testing:
H1: Outsider recruits are characterized by higher educational levels than insider recruits.
Since insider applications reflect the structure of formal education within the firm's
current labor force, new employees appear to be overeducated. Specifically, the
insider-outsider difference mirrors that the firm is more strongly restrained in screening on
grounds of formal education when dealing with insider applicants than when evaluating
outsider candidates.
Recall that lower minimum educational requirements / increase the possibility of
discounting the professional experience of outsiders. This is optimal because it improves the
precision of the screening process. The effect specifically concerns outsider candidates: when
applying for jobs with low educational requirements, successful outsiders must be even more
overeducated relative to their insider competitors. Hence, we state the following hypothesis:
H2: The overeducation effect on the group of outsider recruits increases with lower
minimum educational standards advertised by the firm.
Summarizing, new employees appear overeducated because the firm accepts less
substitute professional experience when evaluating the applications of outsiders. To pass the
screening process, outsiders must therefore document more formal education than insiders.
Increasing legal risk associated with setting excessive educational job requirements further
implies that this effect is stronger for jobs with low educational requirements.
However, for two reasons, outsider hirees are only seemingly overeducated: first, the
legal risk implies that educational standards are generally set too low. In absence of the
institutional constraints on employee selection, the firm would have set higher standards for
both insiders and outsiders alike. Second, the costs of testing induce over-restrictive selection
criteria in terms of test scores. Mirroring this effect, screening on grounds of educational and
professional qualification is too lax. Finally, recall that the overeducation effects are contingent
on the insider-outsider status of the applicant. This particular feature of our theoretical model
opens the possibility for empirical testing using recruitment data.
4 Empirical analysis
4.1 The data and definitions of variables
In May 2003, a large US public employer introduced an online recruiting system. Starting
with this date, all job applicants must (also) file electronic applications and obtain log-in
user-names and passwords. Our data covers the period from the introduction of this system to
February 2006. During this phase the human resources department assigned a team to provide
assistance for potential applicants.
The data set only contains information on rank-and-file employee and laborer positions;
recruitment processes to fill executive positions are excluded. It comprises 33780
observations on individuals who (a) filed a complete application during this time-span and (b)
entered a recruitment process that reached a final decision by the end of our observation
period. There are 1244 of such processes (see Table 1). The dependent variable in our
regression analysis, status, is equal to one if the applicant is hired and zero otherwise.
Applicant information includes age. As usual, our regressions also use the square of the
individual's age (age sq.) to allow for a non-linear age-productivity profile. Sex is set equal to
one if the applicant is male. The variable non-white captures the applicant's minority status.
More detailed ethnic classifications, although available, did not prove statistically significant in
our regression analyses. The total number of job-candidates (applications) for a job and the
number of applications using the same recruitment channel (applicants per recruiting channel)
reflect the individual's competitive environment.
Regarding a particular job opening, the data set provides the position title and type of
appointment (job type). The latter ranges from 1 for Contingent/On-Call Labor (no benefits) to
6 for Regular/Full-Time Employee (eligible for benefits). The Equal Employment Opportunity
(EEO) code number increases in steps of 10 points from 10 (executive, administrative and
managerial positions) to 70 (service and maintenance positions). The Fair Labor Standards Act
status (exemption status) is equal to one if the job is exempt (no overtime pay) and zero
otherwise.
The workplace score (grade) is calculated as the weighted sum of scaled indicators.
These indicators measure the necessary skills and experience, the complexity of the tasks and
creativity required in exercising them, the job's impact on the firm's mission, its exposure to
internal and external contacts, the degree of discretion in decision making, physical stress, and
working conditions. The human resources department and the department of employment
must agree on a weight associated with each indicator prior to advertising the job opening. The
workplace score determines the compensation range.
Regarding our key educational variables, all possible US formal educational degrees can
be found among both applicants and hirees (see Table 2); i. e., doctorate, master, bachelor,
some college education, high school degree, high school equivalent degree (GED), and only
some high school education. The variable education ranges from 0 for completed first grade to
19 for a doctorate degree. The coding mirrors the time spent in formal education. To account
for a possible non-linear education-productivity relationship, we include the square of this
variable (education sq.) in the regressions below. Unfortunately, the data only allows identifying
whether the individual possesses adequate professional experience as judged by the recruiter.
In this case, we set experience equal to one. Otherwise the variable equals zero.
The top part of table 3 reports the distribution of applications by recruitment channels:
11.4% of all applications have been forwarded by direct contact from a department of
employment (DCD) or some other internal reference (IR). Jointly, these two categories define
the insider status of the applicant. Given this definition, we distinguish the following
outsider-recruitment channels: web-based job postings including those on the firm's own
website (web recruiting), newspaper advertisements (newspaper ad), job notices sent to
colleges or universities (job notice) or to the state employment office (SEO), or some other
channel (ORC).
In the following, exactly educated is equal to one if the applicant possesses the
advertised required educational degree. The overeducated and undereducted status is defined
relative to this advertised level. In the subsequent analyses, overeducation serves as the
reference category. Recall that the insider effect reflects that professional experience
substitutes for a lack of formal education. In our regression analyses, we therefore include the
respective interaction variables, experience insiders and education insiders, between experience
and education and the insider status.
4.2 Characterizing the applicants
The bottom part of Table 3 reveals that the majority of the hirees (58% ) are
overeducated, i. e., they possess an educational degree that is higher than the advertised
required degree. 34% possess just the required degree. Insiders form the largest group among
undereducated applicants who are selected for the new jobs. In contrast, outsiders constitute
the largest group among the overeducated hirees. However, these observations may only reflect
the relative scarcity and abundancy of the respective insider and outsider groups among the
applicants. Before subjecting the data to testing, we therefore characterize the group of
applicants in more detail.
Table 4 reports the distribution of outsider and insider applicants across the various
educational job requirements. In total, there are 29,925 (88%) outsider applicants and
3,855 (12%) insider applicants. The abolute numbers and shares of outsider applicantions
exceed those filed by insiders across all levels of advertised educational requirements. Note
that, for jobs that require only reading and writing skills, 92% of the outsiders compared to
only 86% of the insider applicants are overeducated. Also, 15% of insiders relative to only 8%
of the outsiders possess just the required degree. Similarly, for jobs that require a High-School
Diploma 83% of the outsiders and 76% of the insiders are overeducated. At the same time,
23% ( 0.70% ) of the insiders possess only the highschool degree (are undereducated)
compared to 17% (0.44%) of the outsiders.
These findings generalize to all other educational requirements. In fact, for jobs
characterized by the highest possible required degrees, i.e., doctorate and J.D., the gap between
the shares of undereducated insiders and outsiders widens: here 67% and 28 % of the outsider
compared to only 54% and 18% of the insider applicants are overeducated, respectively.
Table 5 illustrates the distribution of the undereducated, exactly educated, and
overeducated applicants across the advertised positions: the overeducated applicants
outnumber the exactly educated applicants for conceivably lower-skilled jobs (i.e., assistant,
associate, cashier, clerk, operator, professional, representative, and secretary positions). In
contrast, the upper-level positions (i.e., analyst, attorney, assistant director, associate director,
consultant, director, manager, programmer, and specialist type jobs) primarily attract exactly
educated applicants; specifically, overeducation is rare among the applicants for such jobs.
4.3 Regression analysis
Given the size of our data set, table 6 investigates the data using the linear probability
model. This model has the advantage that the coeffcients can be directly interpreted as
marginal effects. Generally, the effects appear plausible: non-white and age exhibit negative
impacts where the latter effect levels out at higher ages. The insignificant coefficient on sex
suggests that the organization does not discriminate on grounds of gender. Recall that the value
of job type decreases with more attractive hierarchical positions. Accordingly, the coefficient is
negative.
Applications and applicants per recruiting channel proxy the intensity of job competition.
As expected, the latter exhibits a significant negative impact. For outsiders, referring to the web
in the application significantly enhances the hiring probability. In contrast, being sent by the
state employment agency shows a negative effect. More responsabilities and authority
associated with a job yield a higher workplace score. Then, the negative coefficient of grade
reflects that employee selection becomes more restrictive with increasing importance of the job
for the organization (i.e., in terms of our theoretical model, the cut-off criterion Z increases).
On average, being exactly educated increases the chances of success compared to
overeducated as the reference category. Yet, relative to the benchmark classification advisor,
applying for jobs that require more advanced and specific skills (e.g., analyst, coordinator,
director, manager, programmer positions etc.) also enhances the hiring probability. In contrast,
for jobs that do not require specific skills (e.g., cashier, custodian, and cook) the probability of
being hired is lower compared to the reference classification.
The disadvantage of the linear probability model is that estimates are poor at the
boundaries of the distribution of hiring probabilities. Thus, we also apply Probit and Logit
analyses. Since both approaches yield virtually identical results, we only report the marginal
effects obtained from the latter in table 7. Note that the calculation of the marginal interaction
effects follows Ai and Norton (2003). Compared to table 6, there is only one notable difference:
the coefficient on insiders becomes statistically significant and positive. Hence, insider
applicants generally (i.e., over all jobs and educational job requirements) experience a higher
probability of being hired.
Focussing on the qualification variables, both education and professional experience
exhibit positive impacts. As before, the coefficient on exactly educated is positive and highly
significant, while the coefficient on undereducated is not statistically different from zero. Thus,
over all recruitment processes, neither overeducation nor undereducation yields higher hiring
probabilities. However, the marginal effects of both formal education and professional
experience are stronger for insiders. Also, the marginal effects of the interaction variables are
highly significant. Since, on average, insiders possess lower educational degrees, these findings
provide that insiders receive preferential treatment regarding the eductional requirement in the
recruitment process. In particular, note that this effect is distinctly different from the general
preference for insider hiring.
Recall that the human resources department and the department of employment must
agree on the workplace score (grade). Since a higher workplace score is associated with a higher
salary range, the department of employment is generally interested to negotiate higher scores
to increase its chances to hire qualified personnel. Given the sequential nature of this
grade-decison and the subsequent recruitment process, expectations concerning the relative
scarcity of qualified applicants could affect the department of employment's efforts to
negotiate a higher score. Consequently, our regressions results could be subject to an
endogeneity problem. We therefore use several sets of instruments (e.g. departments of
employment and the number of overeducated applicants per job). In all cases, though,
Hausman endogeneity tests fail to reject the null hypothesis of no endogeneity.
Using actual recruitment data, we are interested in testing whether our model is
descriptive for organization’s screening and hiring decisions. With regard to screening on
grounds of formal education, we apply the goodness-of-fit test developed by Lemeshow and
Hosmer (1982): we divide the sample into six subsamples to compare observed and predicted
counts of outcome events. The number of subgroups corresponds to the number of advertised
educational requirements: jobs which require (1) the ability to read and write, (2) a high school
diploma, (3) a post-secondary (i.e. two-year college) degree, (4) a bachelor's degree, (5) a
master degree, and (6) a doctorate degree.
We assume that the first sextile corresponds to the 1/6-sample of applicants who are
characterized by the lowest probability to be hired. The sixth sextile relates to the subgroup
with the highest probability to be hired. The Hosmer-Lemeshow (��) statistic is calculated as
�� = ∑ ��fF s(�������� �¡¢£�(�) G ¤���¥ £�� �¡¢£�(�))] ¤���¥ £�� �¡¢£�(�) t. (15)
This test-statistic follows a ¦�-distribution with four degrees of freedom. For the logit model,
the value of this statistic is 4.77. Consequently, the hypothesis that our model provides a
“good fit” cannot be rejected; there is a clear (over)education effect on hiring probabilities.
Although the model appears to fit well, there may still be a large number of cases where
it fails to predict individual hiring outcomes. Hence, in a second step, we want to test the
predictive power of our model regarding the organization’s hiring decisions. Given that a
predicted hiring is defined by a predicted probability of being hired exceeding 0.5, we compare
our model predictions with the actual outcomes (“hired” or “not hired”) for every applicant. In
96.4% of all cases the predictions are correct. Specifically, the model predicts 99.82% of all
non-hiring cases correctly. However, only 6.75% of the hiring decisions are correctly
predicted.
The share of correctly predicted hirings can be increased by lowering the cut-off
probability defining this incidence. The functional relationship between the percentage of
correctly predicted incidences and the cut-off probability is denoted sensitivity. Yet, increasing
the cut-off probability comes at the expense of increasing the probability of predicting a hiring
when the actual outcome is “not hired.” The functional relationship between the percentage of
falsely predicted hirings and the cut-off probability is denoted specificity. Hence, a second
possibility to address the goodnes-of-fit issue is to calculate the so-called Receiver Operating
Characteristic (ROC) curve. This curve depicts the sensitivity-specificity trade-of [DeLong et al.
(1988)].
Specifically, the ROC-curve provides a benchmark: perfect predictive power implies that
the area under the curve equals one; the ROC-curve coincides with the 45-degree line if the
model would both correctly and falsely predict 50% of all hirings for all cut-off probabilities.
The above logit model yields an area under the ROC-curve of 0.7960. We also evaluate the
ROC-curve using out-of-sample forecasts. We randomly exclude 10% of the successful
applicants and reestimate the model. Given the newly estimated coefficients, we compute the
hiring probabilities for those hirees that we initially excluded. We recalculate the ROC-curve
based on these these probabilities. This curve is shown in Figure 1. Now, the area under this
curve equals 0.7963. We conclude that our model possesses rather strong predictive power.
4.4 Insider-Outsider effects on the screening mechanism
To gain more insight into the insider-outsider effects, table 8 reports three separate
regressions for overeducated (10923 observations), exactly educated (10945 observations), and
undereducated applicants (3362 observations). As before, the dependent variable is status. To
save space, we only list the estimates for the key qualification variables: first, professional
experience increases the probability of being hired for both insiders and outsiders. However,
the §-tests reveal that the coefficient on experience is (statistically significantly) higher for
insiders across all types of applicants. Second, once we pool the applicants according to their
educational level, education shows no impact on the recruting outcome for both outsiders and
insiders. Jointly, these results are perfectly consistent with our theoretical model.
Table 9 reports the estimates obtained from three alternative regressions: we estimate
the hiring probabilities for applicants for jobs that require reading and writing abilities, a high
school, and a bachelor diploma, respectively. These three job categories comprise the largest
pools of applicants with 607 applications in the first, 18,412 applications in the second, and
10,923 applications in the third case. The coefficient on the outsiders' education is positive and
significant for the first and second job types. For the jobs that require a bachelor degree, it is
not statistically different from zero. Moreover, the partial effect of education is stronger for jobs
which require less education (i.e., 0.074 for jobs requiring the ability to read and write versus
0.019 for jobs that require a high-school diploma). This result confirms hypothesis H2. The
respective §-tests reveal that the positive impact of professional experience is stronger for
insiders than for outsiders. However, contrasting with the career mobility theory, the former
therefore not previously accept underqualified work.
Table 10 provides an analysis of the predicted hiring probabilities. Again, we distinguish
overeducated, exactly educated, and undereducated applicants. Recall that OLS, logit, and probit
estimates are almost identical. Hence, we use the OLS-estimates to calculate average hiring
probabilities conditional on whether the job has been advertised to require either a High School
diploma or a bachelor degree. We compute these probabilities for the full sample and for a
sample excluding all insider applications. Across both job-categories, the decrease in the
predicted average hiring probabilities induced by the presence of insiders is largest for
undereducated applicants. Again, when competing for the same job, successful outsider hirees
must document a higher degree of formal education. Yet, if they can provide these documents,
they are also more successful. Hence, while again supporting H2, the career mobility argument
does not apply.
5 Summary
Our data covers 1244 recruitment processes organized by a US public employer and
finalized between 2003 and 2006. The empirical analysis reveals strong insider-outsider effects
on hiring probabilities. Upon closer inspection, these effects are not consistent with the
dominant theory, the career mobility approach, that suggests an efficiency-enhancing virtue of
such differential treatment of applicants from inside and outside of the organization: hence, the
marginal effects of both professional experience and a higher educational level are stronger for
insiders. Also, we cannot confirm that insider applicants have previously accepted
underqualified work. Finally, given that they are able to document a higher educational level
than their insider competitors, outsiders are actually more successful in being hired. We
conclude that the preferential treatment of insider applicants does not reflect that the
organization has obtained better ability information by observing their past on-the-job
performances.
Thus, we propose an alternative recruitment model that explictly accounts for two
institutional constraints found in the US public sector: first, acknowledging the career mobility
argument, the organization accepts that professional experience can, in principle, substitute for
a lack of formal education. Yet, due to orgainzation-specific job descriptions, this substitution is
imperfect when appreciating outsider applications. Second, reflecting that internal compliance
reviews seek to minimize the risk of disparate impact charges, the organization accepts more
substitute professional experience when advertising higher educational job requirements. As a
result, the expected level of formal education is higher for outsider than for insider hirees.
Further, this wedge between the two groups' educational attainments widens with lower
educational job requirements. Yet, given our theoretical model, the new employees are only
seemingly overeducated: without the constraints on the use of educational signals, the
standards would be set higher and identically equal for both groups.
Groenveld and Hartog (2004) warn that findings from a single case-study should not be
generalized. Obviously, we agree. Yet, this previous study finds that the overeducation effects
on promotion decisions and wages are confined to the still protected internal labor market of a
former public utility. As is well-known, promotions and inter-temporal increases in wages can
also serve to provide effort incentives rather than reflecting a learning process regarding
on-the-job abilities. In contrast, we analyse data on actual recruitment processes and cannot
confirm career mobility effects. Note that our results do not imply that there is no
ability-learning during the employment relationship. Yet, this learning process does not appear
to hinge on being initially hired for underqualified work. Given that governments world-wide are
actively promoting life-long learning schemes, we therefore suggest that the personnel
selection of public employers should receive more attention: government’s own recruitment
processes could provide disincentives to acquire education.
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Appendix: Tables and Figures
Table 1: Descriptive Statistics of the Online-Recruitment System
Total number of
Applications
33780 100%
Qualified applicants
forwarded to departments
26641 78.86%
Disqualified applicants 4469 13.22%
Applications cancelled 828 2.45%
Applications withdrawn 837 2.47%
Applications filed but failed
to maintain contact
1005 2.97%
Number of jobs filled
using on-line system
1244 3.68%
Table 2: Educational Attainments of Original and Selected Employees
Year
Doctorate
Masters
Bachelor
Some
College
Highschool
Some
Highschool
GED
n.a.
Total
Applicants
2003
135 883 2208 2615 789 42 116 13
6801
2004
274 2068 4031 3850 1031 55 165 9
11483
2005
410 2271 4648 4804 1338 30 156 9
13666
2006
30 255 714 589 201 8 32 1
1830
Total
849 5477 11601 11858 3359 135 469 32
33780
Hirees
2003
7 24 51 84 29 1 3 0
199
2004
13 73 158 143 57 2 6 0
452
2005 19 84 181 180 50 1 7 0
522
2006
1 16 29 20 5 0 0 0
71
Total
40 197 419 427 141 4 16 0
1244
Table 3: Recruitment Channels and Insider-Outsider Distinction
a) Use of recruitment channels
Direct contact from
department (DCD)
1.38%
Internally Referred (IR) 10.02%
Web Recruiting 77.20%
Newspaper Ad 7.76%
Job notice 0.39%
State Employment
Office
0.24%
Other Recruiting
Channels
3.00%
b) Hirees: Outsiders vs Insiders
Qualification All Hirees IR DCD Insider Hirees (IR and DCD)
Undereducated 8.03% 20% 31% 51%
Exactly educated 33.52% 15.58% 24.7% 40.28%
Overeducated 58.44% 13.75% 29.02% 42.77%
Table 4: Insider vs. Outsider Applicants Across Job Requirements
Required
Degree
Ability to
read and
write and
follow
simple oral
and written
instructions
High
school
diploma
or GED
Post-
secondary
education
Associate
degree
Bachelor
degree
Master
degree
Docto-
rate
J.D.
Total
Outsiders
Under-
educated
1 (0.21%) 72
(0.44%)
349
(71.96%)
266
(67.34%)
1584
(16.16%)
558
(25.33
%)
55
(53.92
%)
33
(17.65%)
2918
(9.75%)
Exactly
educated 36
(7.68%)
2717
(16.69%)
0
(0%)
0
(0%)
5199
(53.05%)
1475
(66.95
%)
47
(46.08
%)
154
(82.35%)
9628
(32.17%)
Over-
educated
432
(92.11%)
13494
(82.87%)
136
(28.04%)
129
(32.66%)
3018
(32.66%)
170
(7.72%)
0
(0%)
0
(0%)
17379
(58.08%)
Total 469
(100%)
16283
(100%)
485
(100%)
395
(100%)
9801
(100%)
2203
(100%)
102
(100%)
187
(100%)
29925
(100.00%)
Insiders
Under-
educated 0 (0%) 15
(0.70%)
89
(83.96%)
79
(74.53%)
188
(16.76%)
55
(26.70%)
8
(66.67%)
10
(27.78%)
444
(11.52%)
Exactly
educated
20
(14.49%)
499
(23.44%)
0 (0%) 0 (0%) 631
(56.24%)
137
(66.50%)
4
(33.33%)
26
(72.22%)
1317
(34.16%)
Over-
educated 118
(85.51%)
1615
(75.86%)
17
(16.04%)
27
(25.47%)
303
(27.01%)
14
(6.80%)
0 (0%) 0 (0%) 2094
(54.32%)
Total 138
(100%)
2129
(100%)
106
(100%)
106
(100%
1122
(100%)
206
(100%)
12
(100%)
36
(100%)
3855
(100.00%)
Table 5: Under-, Exactly, and Over-Qualified Applicants Across the Job Positions
Advisor
Advocate
Accountant
Assistant
Associate
Alumni
Analyst
Attorney
Assistant
Director
Under-
educated
312
(14.18%)
18
(8.78%)
150
(20.08%)
166
(4.35%)
38
(0.55%)
30
(14.29%)
150
(35.05%)
47
(26.86%)
222
(15.48%)
Exactly
educated
1245
(56.59%)
93
(45.59%)
240
(32.13%)
865
(22.65%)
1193
(17.34%)
147
(70.00%)
217
(50.70%)
128
(73.14%)
733
(51.11%)
Over-
educated
643
(29.23%)
93
(45.59%)
357
(47.79%)
2788
(73.00%)
5648
(82.10%)
33
(15.71%)
61
(14.25%)
0
(0.00%)
479
(33.40%)
Associate
Director
Assist.
Manager
Coordinat
or
Consultant
Cashier
Custodian
Clerk
Cook
Designer
Under-
educated
80 (19.09%) 142
(57.26%)
286
(18.71%)
51
(11.21%)
13
(2.42%)
1
(0.22%)
36
(1.51%)
0
(0.00%)
32
(42.67%)
Exactly
educated
207
(49.40%)
46
(18.55%)
865
(56.57%)
238
(52.31%)
143
(26.58%)
48
(10.69%)
508
(21.37%)
3
(16.67%)
39
(52.00%)
Over
educated
132
(31.50%)
60
(24.19%)
378
(24.72%)
166
(36.48%)
382
(71.00%)
400
(89.09%)
1833
(77.11%)
15
(83.33%)
4 (5.33%)
Director
Editor
Officer
Operator
Manager
Nurse
Pilot
Physician
Programmer
Under-
educated
125
(15.86%)
6
(23.08%)
169
(43.78%)
10
(5.05%)
203
(18.08%)
156
(21.88%)
5
(38.46%)
5
(15.63%)
59 (27.31%)
Exactly
educated
375
(47.59%)
11
(42.31%)
102
(26.42%)
44
(22.22%)
629
(56.01%)
172
(24.12%)
7
(53.85%)
27
(84.38%)
111
(51.39%)
Over
educated
288
(36.55%)
9
(34.62%)
115
(29.79%)
144
(72.73%)
291
(25.91%)
385
(54.00%)
1
(7.69%)
0
(0.00%)
46 (21.30%)
Professional
Reporter
Representative
Specialist
Supervisor
Secretary
Technician
Instructor
Under-
educated
39
(4.50%)
2
(6.67%)
22
(6.81%)
486
(22.00%)
4
(2.25%)
9
(0.26%)
177
(36.72%)
102
(30.09%)
Exactly
educated
327
(37.76%)
22
(73.33%)
124
(38.39%)
1030
(46.63%)
43
(24.16%)
596
(16.95%)
71
(14.73%)
181
(53.39%)
Over-
educated
500
(57.74%)
6
(20.00%)
177
(54.80%)
693
(31.37%)
131
(73.60%)
2911
(82.79%)
234
(48.55%)
56
(16.52%)
Table 6: The Linear Probability Model
Variable Coefficient (Std. Err.) Variable Coefficient (Std. Err.)
Grade -0.005 ∗∗∗ (0.001) Analyst 0.024 ∗∗∗ (0.009)
Administrative
Dept.
-0.001 (0.004) Attorney 0.017 (0.018)
Academic Dept. -0.001 (0.004) AssistDirector 0.009 ∗ (0.005)
Number of
overeducated
per job
0.001 (0.001) AssocDirector 0.023 ∗∗ (0.009)
Applications per
job
-0.001 (0.001) AssistManager 0.017 (0.012)
Applicants per
recruiting
channel
-0.001 ∗∗∗ (0.000) Coordinator 0.017 ∗∗∗ (0.006)
Exactly Educated 0.010 ∗∗∗ (0.003) Consultant 0.021 ∗∗∗ (0.008)
Undereducated 0.003 (0.004) Cashier -0.036 ∗∗ (0.014)
EEO Scale 0.001 (0.001) Custodian -0.051 ∗∗∗ (0.013)
Exemption Status 0.001 (0.002) Clerk -0.001 (0.006)
Job Type -0.030 ∗∗∗ (0.002) Cook -0.098 ∗ (0.056)
State
Employment
Office
-0.520 ∗∗∗ (0.098) Designer 0.015 (0.022)
Job Notice -0.002 (0.025) Director 0.023 ∗∗∗ (0.008)
Other Recruiting
Channel
0.036 ∗∗∗ (0.012) Editor -0.019 (0.012)
Web Recruiting 0.615 ∗∗∗ (0.092) Manager 0.026 ∗∗∗ (0.007)
Insiders 0.013 (0.028) Nurse 0.042 ∗∗∗ (0.011)
Age -0.001 (0.001) Officer 0.035 ∗∗∗ (0.011)
Age Sq. 0.001 ∗ (0.000) Physician 0.088 ∗ (0.049)
Experience 0.013 ∗∗∗ (0.002) Pilot 0.054 (0.068)
Experience
Insiders
0.105 ∗∗∗ (0.012) Programmer 0.048 ∗∗∗ (0.015)
Education 0.005 ∗ (0.003) Professional 0.052 ∗∗∗ (0.011)
Education Sq. -0.001 (0.001) Representative 0.001 (0.009)
Education
Insiders
0.004 ∗∗ (0.002) Reporter 0.002 (0.034)
Sex -0.002 (0.003) Secretary 0.036 ∗∗∗ (0.007)
Non-white -0.017 ∗∗∗ (0.002) Specialist 0.017 ∗∗∗ (0.005)
Advocate -0.011 ∗ (0.007) Supervisor 0.040 ∗∗ (0.018)
Accountant 0.020 ∗∗∗ (0.007) Technician 0.044 ∗∗∗ (0.012)
Assistant 0.014 ∗∗∗ (0.005) Instructor 0.071 ∗∗∗ (0.015)
Associate 0.043 ∗∗∗ (0.009) Operator 0.028 (0.021)
Alumni -0.001 (0.010) Intercept 0.493 ∗∗∗ (0.074)
N 33780
R � 0.095
F (©ª,««¬�) 16.304
Significance levels : ∗ : 10% ∗∗ : 5% ∗∗∗ : 1%
Note: Robust standard errors in parenthesis.
Table 7: Logit - Marginal Effects
Variable Coefficient (Std. Err.) Variable Coefficient (Std. Err.)
Grade -0.003 ∗∗∗ (0.001) Analyst 0.027 ∗ (0.016)
Administrative
Dept.
-0.001 (0.004) Attorney 0.022 (0.018)
Academic Dept. -0.001 (0.004) AssistDirector 0.001 (0.006)
Number of
overeducated
per job
0.001 (0.001) AssocDirector 0.027 ∗ (0.017)
Applications per
job
-0.001 (0.001) AssistManager 0.021 (0.017)
Applicants per
recruiting
channel
-0.001 ∗∗∗ (0.000) Coordinator 0.021 ∗∗ (0.009)
Exactly educated 0.008 ∗∗∗ (0.002) Consultant 0.031 ∗ (0.016)
Undereducated 0.002 (0.003) Cashier 0.012 (0.009)
EEO Scale -0.001 (0.001) Custodian 0.001 (0.008)
Exemption Status 0.001 (0.001) Clerk 0.008 (0.007)
Job Type -0.008 ∗∗∗ (0.002) Cook -0.006 (0.016)
State
Employment
Office
-0.021 ∗∗∗ (0.001) Designer 0.026 (0.028)
Job Notice 0.014 (0.013) Director 0.025 ∗ (0.013)
Other Recruiting
Channel
0.034 ∗∗∗ (0.010) Editor -0.019 (0.012)
Web Recruiting 0.283 ∗∗∗ (0.047) Manager 0.037 ∗∗∗ (0.013)
Insiders 0.043 ∗∗ (0.021) Nurse 0.051 ∗∗∗ (0.017)
Age 0.001 (0.001) Officer 0.040 ∗∗ (0.019)
Age Sq. 0.001 (0.001) Physician 0.182 ∗ (0.107)
Experience 0.013 ∗∗∗ (0.002) Pilot 0.054 (0.068)
Experience
Insiders
0.057 ∗∗∗ (0.025) Programmer 0.067 ∗∗ (0.033)
Education 0.007 ∗∗ (0.003) Professional 0.060 ∗∗∗ (0.018)
Education Sq. -0.001 ∗∗ (0.001) Representative 0.006 (0.010)
Education
Insiders
0.058 ∗∗∗ (0.018) Reporter 0.009 (0.032)
Sex -0.001 (0.002) Secretary 0.032 ∗∗∗ (0.010)
Non-white -0.011 ∗∗∗ (0.001) Specialist 0.021 ∗∗∗ (0.008)
Advocate -0.013 ∗ (0.007) Supervisor 0.043 ∗∗ (0.024)
Accountant 0.028 ∗∗ (0.013) Technician 0.055 ∗∗∗ (0.019)
Assistant 0.017 ∗∗ (0.007) Instructor 0.088 ∗∗∗ (0.026)
Associate 0.045 ∗∗∗ (0.011) Operator 0.032 ∗ (0.017)
Alumni 0.006 (0.012)
N 33780
R � 0.095
F (©ª,««¬�) 16.304
Significance levels : ∗ : 10% ∗∗ : 5% ∗∗∗ : 1%.
Note: Robust standard errors in parenthesis.
Table 8: Education Effects Across the Three Types of Applicants
Applicant Type: Overeducated Adequately Educated Less Educated
Dependent Variables
Status
N=19473
Status
N=10945
Status
N=3362
Coefficient
(Std.
Err.)
Coefficient
(Std.
Err.)
Coefficient
(Std.
Err.)
Experience .011*** (.002) .018*** (.004) .012** (.006)
Experience Insiders .089***
(.016) .095*** (.021) .074** (.032)
Education .002
(.012) .023
(.019) .008 (.005)
Education Insiders .004
(.003) .004
(.003) .007 (.006)
Outsiders=Insiders F-stat p-value F-stat p-value
F-stat
p-value
Experience 22.20 0.0000 11.60 0.0007 3.31 0.0691
Significance levels: ∗ 10%; ∗∗ 5% level; ∗∗∗ 1%.
Note: Robust standard errors in parenthesis.
Table 9: Education Effects across Jobs Requiring Reading and Oral Abilities, High School
Diploma, and Bachelor Degree
Educational Job
Requirement Ability to read/write High School Diploma Bachelor Degree
Dependent Variables
Status
N=607
Status
N=18412
Status
N=10923
Coefficient
(Std. Err.)
Coefficient
(Std. Err.)
Coefficient
(Std. Err.)
Exactly Educated
.096∗
(.052)
.010∗
(.005)
.009∗
(.005)
Undereducated .457** .222) .003 (.024) -.001 (.012)
Experience .019 (.021) .014∗∗∗
(.003) .011∗∗∗
(.003)
Experience Insiders -.011 (.059) .128∗∗
(.017) .054∗∗∗
(.019)
Education .074** (.034) .019∗
(.008) -.003 (.006)
Education Insiders .010 (.012) .006* (.003) .007 (.005)
Outsiders=Insiders F-stat p-value F-stat p-value F-stat p-value
Experience 0.17 0.679 43.78 0.0000 4.80 0.0284
Education 3.53 0.061 1.23 0.2678 1.88 0.1706 ∗
Significance at the 10% level; ∗ ∗
Significance at the 5% level; ∗ ∗ ∗
Significance at the 1% level.
Note: Robust standard errors in parenthesis.
Table 10: Predicted hiring probabilities with and without insiders
Model Overeducated Exactly Educated Undereducated
All Applicants and Educational
Requirements
Hiring Probability Hiring Probability Hiring Probability
OLS 3.73% 3.80% 2.97%
Logit 3.73% 3.80% 2.97%
Probit 3.72% 3.83% 2.95%
(a) Full Sample: All Applicants
Jobs requiring a High-School Diploma 3.02% 1.19% 0.03%
Jobs requiring a Bachelor degree 0.42% 1.78% 0.89%
(b) Restricted Sample: Outsider Applicants Only
Jobs requiring a High-School Diploma 1.99% 0.87% 0.01%
Jobs requiring a Bachelors degree 0.23% 1.09% 0.40%
%Δ (a) vs. (b)
% of jobs requiring a Bachelor degree -34.17% -27.25% -51.62%
% of jobs requiring a Bachelor degree -45.10% -38.32% -55.24%
Figure 1: ROC Curve