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:

oliver.fabel@univie.ac.at.

**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: rpasc001@plattsburgh.edu.

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.

5 References

Abram, T.G. (1993). The Law, Its Interpretation, Levels of Enforcement, and Effect on Employer

Behavior. American Economic Review, vol. 83, p. 62-66.

Ai, C. and E.C. Norton (2003). Interaction Terms in Logit and Probit Models. Economics Letters,

vol. 80, p. 123-129.

Anderson, N., F. Lievens, K. van Dam and A.M. Ryan (2004) Future Perspectives on Employee

Selection: Key Directions for Future Research and Practise. Applied Psychology: An International

Review, vol. 53, p. 487-501.

Ashenfelter, O. and R. Oaxaca (1987). The Economics of Discrimination. American Economic

Review, vol. 77, p. 321-325.

Bauer, T. (2002). Educational Mismatch and Wages: A Panel Approach. Economics of Education

Review, vol. 21, p. 221-229.

Belfield, C. (2009) Over-Education: What Influence Does the Workplace Have? Economics of

Education Review, forthcoming.

Betsey, C.L. (1994). Litigation of Employment Discrimination under Title VII: the Case of

African-American Women. American Economic Review, vol. 84, p. 98-102.

Büchel, F. and H. Battu (2003). The Theory of Differential Overqualification: Does It Work?

Scottish Journal of Political Economy, vol. 50, p. 1-16.

Büchel, F. and A. Mertens (2004). Overeducation, Undereducation, and the Theory of Career

Mobility. Applied Economics, vol. 36, p. 803-816.

Carlson, R. (2005). Employment Law, New York, N.Y.: Aspen Publishers.

Chevalier, A. (2003). Measuring Over-Education. Economica, vol. 70, p. 509-531.

Clardy, A. (2003). The Legal Framework of Human Resources Development, Part II: Fair

Employment, Negligence, and Implications for Scholars and Practioners. Human Resources

Development Review, vol. 2, p. 130-154.

Coate, S. and G. Loury (1993). Antidiscrimination Enforcement and the Problem of

Patronization. American Economic Review, vol. 83, p. 92-98.

Dakin, S. and J. S. Armstrong (1989). Predicting Job Performance: Comparison of Expert

Opinions and Research Findings. International Journal of Forecasting, vol. 5, p. 187-194.

De Grip, A., H. Bosma, D. Willems and M. van Boxtel (2008). Job-Worker Mismatch and

Cognitive Decline. Oxford Economic Papers, vol. 60, p. 237-253.

DeLong E.R, D.M. DeLong and D.L. Clarke-Pearson (1988). Comparing the Areas Under Two or

More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach.

Biometrics, vol. 44, p. 837-845.

Duncan, G.J. and S.D. Hoffman (1981). The Incidence and Wage Effects of Overeducation.

Economics of Education Review, vol. 1, p. 75-86.

Freeman, R.B. (1976). The Overeducated American, New York: Academic Press.

Green, F. and S. McIntosh (2007). Is There a Genuine Under-Utilisation of Skills Among the

Overqualified? Applied Economics, vol. 39, 427-439.

Green, F., S. McIntosh and A. Vignoles (2002) The Utilization of Education and Skills: Evidence

from Britain. The Manchester School, vol. 70, p. 792-811.

Groeneveld, S. and J. Hartog (2004). Overeducation, Wages and Promotions Within the Firm.

Labour Economics, vol. 11, p. 701-714.

Groot, W. and H. Maasen van den Brink (2000). Overeducation in the Labor Market: A Meta

Analysis. Economics of Education Review, vol. 19, p. 149-158.

Gutman, A. (2003). On the Legal Front: Adverse Impact - Why Is It So Difficult to Understand.

The Industrial-Psychologist, vol. 40, p. 42-50.

Gutman, A. (2004). On the Legal Front: Ground Rules for Adverse Impact. The

Industrial-Psychologist, vol. 41, p. 109-119.

Hartog, J. (2000). Over-Education and Earnings: Where Are We and Where Should We Go?

Economics of Education Review, vol. 19, p. 131-147.

Hersch, J. (1991). Education Match and Job match. Review of Economics and Statistics, vol. 73,

p. 140–144.

Holling, H. (1998). Utility Analysis of Personnel Selection: An Overview and Empirical Study

Based on Objective Performance Methods. Methods of Psychological Research Online, vol. 3, p.

5-24.

Kalev, A. and F. Dobbin (2006). Enforcement of Civil Rights Law in Private Workplaces: The

Effects of Compliance Reviews and Lawsuits Over Time, Law & Social Inquiry, vol. 31, p.

855–903.

Lemeshow, S. and Hosmer, D. W. (1982). A Review of Goodness of Fit Statistics for Use in the

Development of Logistic Regression Models. American Journal of Epidemiology, vol. 115, p.

92-106.

Lindbeck, A. and Snower, D. J. (2004). The Insider-Outsider Theory: A Survey. Bonn, Germany:

Institute for the Study of Labor - IZA, Discussion Paper 534, rev. vers. October 24, 2004.

McGuinness, S. (2006). Overeducation in the Labour Market.: Reality or Fiction? Journal of

Economic Surveys, vol. 20, p. 387 - 418.

Oyer, P. and S. Schafer. (2002). Litigation Costs and Returns to Experience. American Economic

Review, vol. 92, p. 683-705.

Robertson, I.T. and M. Smith (2001). Personnel Selection. Journal of Occupational and

Organizational Psychology, vol. 74, p. 441-472.

Sattinger, M. (1993). Assignment Models of Distribution of Earnings. Journal of Economic

Literature, vol. 31, p. 851-880.

Schmidt, F.L. and J.E. Hunter (1998). The Validity and Utility of Selection Methods in Personnel

Psychology: Practical and Theoretical Implications of 85 Years of Research Findings.

Psychological Bulletin, vol. 124, p. 262-274.

Sicherman, N. and O. Galor. (1990). A Theory of Career Mobility. Journal of Political Economy,

vol. 98, p. 169-192.

Sicherman, N. ( 1991). Overeducation in the Labor Market. Journal of Labor Economics, vol. 9, p.

101-122.

Spence, M. (1973). Job Market Signaling. Quarterly Journal of Economics, vol. 87, p. 355-374.

Thurow, L. C. (1975). Generating Inequality: Mechanisms of Distribution in the US Economy. New

York, N.Y.: Basic Books.

Tsai, Y. (2009). Returns to Overeducation: A Longitudinal Analysis in the U.S. Labor Market.

Working Paper, Boston University (Rev. Version April 24, 2009). Available at SSRN:

http://ssrn.com/abstract=1087427.

Welch, F. (1981). Affirmative Action and Its Enforcement. American Economic Review, vol. 71, p.

127-133.

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