The Research Of The Factors Affecting Behavior Of Engagement With Advertiseme...
Honor Thesis Marketization of Incentives
1. Wen 1
Marketization of Incentives: An Analysis on the U.S. Online Job
Search System and Its Impact on the Labor Market Outcome
Undergraduate Honor Thesis of Zhengyang Wen
B.A. Economics & B.S. Mathematics Senior at University of California, Irvine, 2016
Director: Professor Daniel Bogart; Professor Michael McBride
2. Wen 2
Table of contents
Abstract…………………………………………………………………………………………...3
Introduction………………………………………………………………………………………3
Literature review………………………………………………………………………………...7
Qualitative discussion: job search in an online environment………………………………..11
Qualitative discussion: criterions for well-functioning online job search systemfor
employed users………………………………………………………………………………….14
VAR model: assumptions and model constructions………………………………………….16
Data……………………………………………………………………………………………...19
Quantitative results and interpretation………………………….............……………………23
Conclusion………………………………………………………………………………………29
Reference………………………………………………………………………………………..30
Appendixes……………………………………………………………………………………...31
3. Wen 3
Abstract
This research paper focuses on online job –search during the period after the global financial
crisis of 2007. I conduct a thorough analysis on the effectiveness of the online job search system
for three types of users: the tentative job searchers that are out of labor force, the unemployed job
seekers, and the employed job searcher aiming for turnover in terms of the improvements on
their labor market outcomes including: labor force participation rates, unemployment rates and
of labor turnover rates. The analysis is based on VAR time series model. The results indicate that
the effectiveness of online job search system for the tentative job seekers out of labor force and
the unemployed job seekers is different across demographic groups, and the effectiveness for the
employed users aiming for turnover is generally less strong as anticipated by previous scholars.
Introduction
U.S. has witnessed a significant growth in the users of online job search systems in recent
decades1. As a result of this growth, labor market participants have greater information regarding
not only their local labor markets but also the domestic and even the international labor markets.
There are two important concurrent changes that accompany this newly emerged informational
endowment. Firstly, there is an increase in the general accessibility of online professional
education2. Secondly, there is a large decrease in the cost of professional certification due to
standardization and the wide assimilation of online testing and verification system3. All together
these events would seem to create some influential evolvements for the labor market.
1 Accordingto Pew Research Center on Internet Science & Tech, 79% of the Americans who have looked for job in
the lasttwo years have used onlineresources and information,and 34% confirm that these onlinejob search
websites arethe most important resources.
2 Accordingto E-learningmarket onlinestatistics,the global E-learningmarketis expected to reach $ 107 billion in
2015,the global self-paced E-learningmarket reached $ 32.1 billion in revenue in 2010, with a five year compound
annual growth rate of 9.2%., which is expected to reach $ 49.9 billion in 2015.
3 Although there is no direct data for this observation,this can be derived from and perceived as a natural
outgrowth from the popularity of onlinelearningmarkets,as certificationsareintegral parts of every holistic
learningprogram.The commercialization of onlinetrainingand education programs would providecostbenefits
for certification as a resultof the economies of scaleand scope.
4. Wen 4
Shifts essentially occurred in the ways that potential workers perceive, conceptualize, and
comprehend their roles in the labor market. As the most immediate way of labor market
participation for potential workers is the job searching process, a modern view point to
understand the behaviors of labor market participants is to stress on the functionality of the job-
search systems in terms of their impacts on the workers’ welfare and career developments. (Peter
Kohn and Mikai Skuterud, 2011) Given the influence that the popularization and evolution of the
internet technology has exerted in recent changes of labor market, a natural step further is to
analyze the specific influence of online job search system on the labor market outcomes for its
various users.
There are generally three types of users of online job search system, including the
tentative job seekers out of labor force, the unemployed job seekers and the employed users
aiming for turnover. The main goal of this paper is to examine the effectiveness of online job
search system for all of these three types of users in terms of its improvements on their labor
market outcomes. After presenting the background knowledge and qualitative thinking based on
the findings of previous scholars, I’ll make an empirical quantitative analysis by constructing a
VAR time series model and make qualitative interpretations of the regression results.
Due to the complications of the labor market structure and the lack of sufficient data to
describe every possible variation in its relevant conditions, much of the former researches of the
online job search and labor market outcome have been restricted to theoretical not empirical
models4. In this research paper, I intentionally fill this gap by analyzing an empirical model
based on the construction of a brand new variable measuring the online job search activity,
4 Some of the data regardingworkers and job conditions held by privatefacilities arealso confidential and arenot
revealed to the public.
5. Wen 5
which is the log sum of the monthly website search engine traffic of the three largest online job
search website of U.S.
In this VAR model, I examine the responsiveness of labor force participation rates and
unemployment rates of different demographic groups to the online job search activities. The
VAR model shows that there is generally a negative response of civilian labor force participation
rates across demographic groups to online job search activities; the unemployment rates of high
school graduates above 16, of women and of college graduates above 25 respond negatively to
online job search activities. There is no statistical evidence that labor turnovers are significantly
influenced by online job search activities.
Also, as a test for the effectiveness of the U.S. online job search systems in its facilitation
for job turnover, I adopted another brand new approach by examining the responsiveness of the
Non-Farm Job Openings and the Non-Farm Job Quits which serve as a gauge of job turnover in
non-farming sectors to the online search activities5. The observed results demonstrate that the job
turnover does not respond to random shocks in online job search activities, which indicates that
the online job search system did not provide an effective facilitator for better job matchings, at
least during the period after the global financial crisis from 2009, 12 – 2016,1.
Similar to any form of internet-based operation of business activities, the source of
market value of the online job-search provision system arises from its capitalization on the
economics of scale of information processing and dissemination. As these websites are organized
5 Both of these measurements come from the Job Openings and Labor Turnover Survey from the Bureau of Labor
Statistics.JOLTS define Job Openingas all positions on the payroll who worked duringor received pay for the pay
period that includes the 12th of the month. When job openings are plentiful,resignation and recruitment rates
tend to be high, reflecting of movements of workers between jobs.JOLTS define Job Quits as employees who left
voluntarily.Exceptions include:retirements or transfers to other locations arereported with other separation.
6. Wen 6
to automatically gather together the previously scattered and discontinued labor market
information and represent them to the general public in a systematic and continuous way, the
users of these services gain an aerial view and comprehensive picture of the fluctuations and
changing nuances in the ongoing market activities, and are endowed with a better apparatus to
make assessments and judgments on the pros and cons between the various potential outcomes of
their labor market participation.
In regards to the contemporary level of development of the online job search system,
users are free to access thousands of well-categorized vacant job positions on these websites with
their proper entry of key words, and could even upload their personal resumes and credentials
through their registered accounts. Some of these job vacancies are provided directly by the
employers’ offering through their online accounts and some are gathered through the portals of
these websites over other online information windows6.
With the rapid introduction of a comprehensive and timely responsive online system of
databases on the listings of the potentially available job vacancies, the job applicants and job
seekers are equipped with an expanded range of informational parallels and conceptual
identifications of the feasible choices of their life career by actively comparing the required or
prevalent job market criterions with their own human capital qualifications7.
6 The functionalitiesof onlinejob search systemare twofold: itacts as a medium of labor market information
exchange between employers and employees who both hold accounts on the job websites; it also serves as an
information gatherer which pools the scattered labor market information for convenient references of the users.
7 Responsiveness of the onlinejob search systemis exemplified by its ability to provideinteractivesearching
experience: the user’s viewing history is generally recorded and is used as an analytical basisfor job information
recommendation, user categorization,and indicator of user preference. Subsequently, after each searching,the
system would providecustomized feedback information for its users etc.
7. Wen 7
Literature Review
According to John Horton’s research paper Online Labor Market, the original
informational frictions due to the geographically segmented nature of the job market has been
substantially reduced by the indiscriminative pooling of relevant resources of online job search
services. Job applicants are freed from the limitations of the traditional professional
communication network based on interpersonal relationships8. Also, they are no longer required
to have long-term experience in certain industries in order to be able to verify the authenticity of
the related job market information9.
Horton says that: the transparency of the online job search service essentially lowers the
previously prohibitively high entrance cost into labor market due to the lack of accurate
information. These high entrance costs also result from the prevalence of job related information
channels that are restricted only to insiders. Moreover, online job search system provides a much
higher level of liquidity for the flow of human resources of potential labor market participants in
accordance with the diversification of both the population of labor force and the market demand
from the employers’ perspective10.
Deduce naturally from Horton’s argument, with these informational advantages due to
the lowered entrance cost and enhanced liquidity of human resources, an individual worker, after
an adequate amount of investigation into her interested labor market sector and an accurate
8 The professional circles thatincorporatemembers based on the recommendations and references from insiders
and are exclusiveto the general public.This definition could beextended to more ordinary circumstances when
people establish their professional relationships on their familiarity and acquaintancewith each other.
9 For example, especially theindustries of credence goods likethe consultation industry,the area of market
strategy analysis and theinsuranceindustry etc.
10 This statement is based on the factthat onlinejob search systems often customize the searchingresults for their
various users based on the users’historical activities,which relies on the feedback system as explained earlier.
8. Wen 8
evaluation of her possession of human capital qualifications, could practically measure her
current personal value in terms of the best possible monthly income from her engagement into
the job market11. These theoretical developments generally indicate that online job search
activities should have a positive correlation with the labor force participation rates and a negative
correlation with the unemployment rates. This is precisely because that online job search system
not only lowers the costs for tentative job seekers’ entrance into the labor market, but also
increases the searching convenience and human resource liquidity for the unemployed users12.
However, as suggested by Mario kokkodis and Panagiotis G.Ipeirotis in their research
paper The Utility of Skills in Online Labor Market: a Research Agenda, the relationship of online
job search activities with the labor force participation rate and unemployment rate is rather
unclear. Because a rational worker would make sustainable efforts to maximize her own gains
not only based on her current occupation, but also on her expected revenue enhancement
resulting from turnovers to more desirable and lucrative positions in the labor market13. As a
result of these dynamic changes, potential workers might either spend more time out of the labor
market for human capital accumulation or keep looking for optimal occupations for an extended
period as a result of their active online job search efforts.
As shared by Betsey Stevenson and Peter Kuhn & Mikal Skuterud in their paper: The
Internet, Job Search, and Worker Mobility, employed users’ endeavors for job turnovers
constitute an important motivation for the adoption of online job search services. They largely
11 The accuratetime length and the amount of energy devotion required for the investigation diversify with
respect to different people, for now, we assumeit’s uniform. This difference woul d be taken into accountlater in
the interpretation of the quantitativeobservations.
12 These two effects are simultaneous and mutually supportive.
13A standard economic assumption which describethosepeople who purposefully and systematically pursuethe
maximization of their personal welfare.
9. Wen 9
capitalize on the informational convenience provided by online job search. Especially with
regards of the information updating functionality of the online job search systems in reflection of
the changes in labor market conditions, employed users would quickly discern the variations in
the labor market demands and make potential adjustments in preparation for their turnovers
when the opportunity comes.
However, these previous theoretical models have not been tested empirically in detail.
The researchers often examine only the short-term correlations between online job search and the
labor market outcome by establishing probability mathematical models to describe the involving
variables, and much of their conclusion has been based on mathematical hypothesis rather than
the empirical analysis of the first-hand data14.
The most important contribution and extension that I made in this paper to the former
researches is that I developed an empirical analysis based on a variable which directly gauges the
online job search activities in my regression model, which is the log sum of the monthly website
search engine traffic of the three largest job search website in the United States. Then, I appeal to
the VAR time model to elicit the impulse responses between this variable and various direct
measurements of labor market outcomes including labor force participation rates, unemployment
rates and the index of job turnover rate.
14 For Peter Kuhn, the research has only accounted the onlinejob search activities between 1998 and 2000, most
of the data is based on the CPS report from government. Betsey, however, even abandoned the dimension of time
flow of onlinejob search activities butadoptthe ownership rates of household applicants of 1960 as a static
indicator for the level of vitality of the onlinejob search activities.By mathematical hypothesis,I refer to the
general adoption of probability models in these previous researches.For example, the Hidden Markov Model that
adopted by John Horton in his PhD thesis Online Labor Markets, and the UnivariateDuration Model adopted by
Peter Kuhn in his thesis Internet Job Search and Unemployment Durations.
10. Wen 10
In the following section, I’ll propose a qualitative discussion in an effort to explain and to
some degree predict the patterns of online job search behaviors of the tentative job searchers out
of labor force and the unemployed job seekers from the perspective of the fitness of user’s
personal profile with the efficiency requirement of online job search.
11. Wen 11
Qualitative discussion: job search in an online environment
There are various methods of job search including Networking, Referrals, Job Boards,
Job Fairs, Online Job Search, Head Hunters and Recruitment Agencies, Tempting or
Internship15. Many of these job search methods possess their unique characteristics, and they
contrast with each other along a wide variety of dimensions. Among these several kinds of job
search methods, online job search is relatively new and innovative, and the others are basically
traditional and classic.
In this analysis, I examine four types of user requirements for each of these job search
method. They are the requirement for job seeker’s professional qualification, the requirement for
job seeker’s professional relationship, the requirement for the job seeker’s proficiency with
searching technicality and the requirement for the employer’s screening ability.
The requirement of professional qualification refers to the level of requirement of a
specific job search method for the amount of human capital accumulation of job searchers in
their interested industry. The requirement of professional relationship refers to level of
requirement of a given job search method for the interpersonal connections of job searchers with
related personnel. The requirement of proficiency in searching technicality of a particular job
search method is defined as the level of requirement for job seekers to master the searching tools
and their ability of information processing such as communication and reading skills etc. Finally,
the requirement for the employer’s screening ability of a given job search method is defined as
the level of requirement for the efficiency of the employers to sort out the most desired
15 Accordingto Investopedia,these job search methods have standard definitions in thefield of Business
Administration.The details of these job search methods are beyond the scopeof this paper and are thus omitted
here.
12. Wen 12
candidates from a pooling of applicants. In order to present a comprehensive picture over the
characteristics of all the job search methods, I summarize their input levels for four types of
requirements in the following chart.
Professional
qualification
Professional
relationship
Searching
technicality
Screening ability
Networking High High High Low
Referrals High High Low Low
Job Boards Low Low Low High
Job Fair High High High High
Online Job Search Low Low High High
Head Hunters and
Recruitment
Agencies
High Low Low Low
Tempting or
Internship
High Low Low High
Drawing conclusions upon the information presented above, for online job search, those
user populations of tentative job seekers out of labor force and unemployed job seekers with
personal profiles which have a low level of human capital accumulation and professional
connections, which have a high level of searching technicality resulting from high internet
penetration rate and an early exposure to the internet and computer technologies, which have a
high level of searching technicality due to a better educational attainment and which weigh the
13. Wen 13
cost saving and risk spreading effect of online job search more than its potential screening
inefficiency matches the requirements of online job search, and are thus the population of
tentative job seekers out of labor force and unemployed users that benefit most effectively from
the online job search systems.
In the next section, I present the second qualitative discussion regarding online job search
system from the perspective of dynamic human capital accumulation.
14. Wen 14
Qualitative discussion: criterions for well-functioning online job search system for
employed users
Employed users capitalize on the informational convenience of online job search systems
and make corresponding adjustments for their turnovers when the opportunity comes based on
the system’s timely updating functionality in reflection of changes in labor market conditions.
The effectiveness of online job search system for the employed users, however, depends on
several criterions.
Firstly, the effectiveness of online job search system for employed users depends on
authenticity of the job relating information posted on these systems. More specifically, it is
essential that the online job search system has an effective channel of gathering the volume and
verifying the validity of these information before making them public postings. The higher the
general level of authenticity of these job-related information, or the lower the level of the
discrepancy between the reality and the employed users’ conceptualization of the labor market
conditions, the more likely that the online job search provision system would properly function
for them.
Employed users surfing in the online information provision systems are generally
oriented to the information concerning a specific industry of interest. Employed users even desire
to perceive the detailed information regarding the specific part of value creations process of that
industry. Consequently, the more detailed the classification of job information categories that the
online job search system is able to provide, the more easily and fluently that the employed users
are likely to assimilate the information that they need. So the second determinant of the well-
15. Wen 15
functioning of the online job search provision is the level of detailed classification of the highly
diversified pool of job related information.
The most important criterion of the effectiveness of online job search systems for
employed users is the speed of their information updating functionality. If there is any significant
delay in these information updating functionalities, employed users who base their labor market
decisions on these systems would lose their timing accuracy and possibly miss the best
opportunity of turnover resulting from changing conditions in the labor market.
These three criterions: the authenticity of information made public by these websites, the
level of detailed classification of diverse job information, and the timeliness of the information
updating functionality in accordance with changes of the labor market conditions establish the
most essential determinants of the well-functioning of the online job search system for employed
users aiming of turnover,
In the next section, I would introduce the major assumptions of the VAR time series
analysis and the regression structures of my VAR models.
16. Wen 16
VAR model: assumptions and model constructions
Vector auto-regression time series models are generally developed as an extension of the
univariate auto-regression AR models in order to capture the linear interdependencies among
multiple time series. The flexibility of model construction for VAR model is that: the only prior
knowledge required to write a proper VAR model is that the endogenous variables can be
hypothesized to affect each other inter-temporally.
I am basically using unrestricted VAR model as a basis of my empirical analysis. This
kind of specific VAR model has been based on the assumption that; there are no additional
constraints on the sequence by which the endogenous variables under inspection would affect
each other. The assumption of the simultaneous mutual influence between several time-series
variables indicate that: the random shocks of one standard deviation in the error term of one
variable from the other variable is identical to the shock that this variable imposes on the other16.
The first regression model which tends to explore the mutual impacts between online job
search activities and the tentative job seekers out of labor force are summarized as follows.
LOG_SE t = log (SE_MONSTER t+ SE_INDEED t+ SE_ CAREERBUILDER t)
LOG_SE t= β0 + β1 * CLFPR X t-1 + Error 1
CLFPR X t = β0+ β1 * LOG_SE t-1 + Error 2
16 The absence of the constraintof sequential influencemeans that the random shocks between variables arenot
dynamically differentiated,and the causality is for both direction between two variables.
17. Wen 17
CLFPR X = civilian labor force participation rate; civilian labor force participation
rate of male; civilian labor force participation rate of female; civilian labor force
participation rate from 16 to 19; civilian labor force participation rate of high school
graduates above 25; civilian labor force participation rate of bachelor degree holder above
25
The second regression model which tends to explore the mutual impacts between online
job search activities and the unemployed job seekers are summarized as follows.
LOG_SE t= β0 + β1 * UEX t-1 + Error 1
UEX t = β0+ β1 * LOG_SE t-1 + Error 2
UEX = unemployment rate; female unemployment rate, male unemployment rate,
youth unemployment rate, unemployment rate of male high school graduates above 16,
unemployment rate of college graduates above 25
The third regression model which tends to explore the mutual impacts between online job
search activities and the employed users aiming for turnover are summarized as follows.
LOG_SE t= β0 + β1 * TIX t-1 + Error 1
TIX t = β0+ β1 * LOG_SE t-1 + Error 2
TIX = labor turnover rate index: non-farm job quits; non-farm job openings
Then, I developed an auxiliary multiple VAR model to explore how the various changing
conditions in the labor market would affect people’s pattern of online job search activities.
18. Wen 18
CS t: monthly consumer sentiment index
JO t: monthly Non-farm Job Openings measured in thousand
LOG_SE t: monthly search engine job search website engine traffic as defined
previously
UE t: monthly unemployment rate
S&P500 t: monthly closedprice of S&P500
CS t = β0 + β1 *JO t-1 + β2 * LOG_SE t-1 + β3 * UE t-1 + β4 *S&P500 t-1 + Error 1
JO t = β0 + β1 *CS t-1 + β2 * LOG_SE t-1 + β3 * UE t-1 + β4 *S&P500 t-1 + Error 2
LOG_SE t = β0 + β1 *JO t-1 + β2 * CS t-1 + β3 * UE t-1 + β4 *S&P500 t-1 + Error 3
UE t = β0 + β1 *JO t-1 + β2 * LOG_SE t-1 + β3 * CS t-1 + β4 *S&P500 t-1 + Error 4
S&P500 t = β0 + β1 *JO t-1 + β2 * LOG_SE t-1 + β3 * CS t-1 + β4 * UE t-1 + Error 5
The critical assumption governing the auxiliary model is that: the five variables, the CS,
JO, LOG_SE, UE, S&P500 respond simultaneously to each other. In the next section, I would
provide a detailed illustration on the data of this regression.
19. Wen 19
Data
I acquired the log aggregation of the search engine website traffic of the three largest
U.S. job search websites: Monster.com, Indeed.com, Careerbuilder.com, from 12, 2009 to 1,
2016 from Semrush.com. These first-hand data of the user activities serves as my base
measurement of the intensity of the online job search activities. The construction of this variable
as a gauge of online job search activity is also my biggest contribution to the lack of empirical
analysis. The aggregation of the website search engine traffic of these three largest job-search
websites would be sufficient and powerful in the prediction and representation of the general
level of vitality of the online job search activities, because most users of on online job search
services have used at least one of these search websites if they have ever conducted any
searching activities due to the sophistication and leading positions of the online job search
websites in the industry17.
6.6
6.8
7.0
7.2
7.4
7.6
7.8
2009 2010 2011 2012 2013 2014 2015
Log SE
17 This can be verified through the generally recognized rankings fromdifferent institutions.Although different
rankings mightshow their relativepositions differently,their relativeleadingpositions(atleastin the top 10) has
been generally accepted. (Forbes, Quintessential Careers,Robert Half etc.)
20. Wen 20
I collected the monthly data on the unemployment rates including the general
unemployment rate, youth unemployment, unemployment rate of high school graduates above
the age of 16, unemployment rate of college graduate above the age of 25, male unemployment
rate, female unemployment. Also, I collected monthly data on civilian labor force participation
rate including the general civilian labor force participation rate; civilian labor force participation
rate of male; civilian labor force participation rate of female; civilian labor force participation
rate from 16 to 19; civilian labor force participation rate of high school graduates above 25;
civilian labor force participation rate of bachelor degree holder above 25. Furthermore, I
acquired the monthly data on Non-farm Job Quits and Non-farm Job openings. All these data
ranges from 12, 2009 to 1, 2016 from the Bureau of Labor Statistics. In the following space, I
show the graphics of these variables. Total non-farm job openings and quits are indexes of labor
turnover because it is generally believed that the higher the number of job openings or quits, the
higher the rate of recruitment and job separation, which indicates laborers are turning to other
potions at a higher rate.
Civilian Labor Force Participation Rates
30
40
50
60
70
80
2009 2010 2011 2012 2013 2014 2015
CLFPR High School 25
CLFPR 16 TO 19
CLFPR Bachelor 25
CLFPR Female
CLFPR Male
Labor Force Participation Rate
21. Wen 21
Unemployment Rates
0
4
8
12
16
20
2009 2010 2011 2012 2013 2014 2015
F U
Youth Unemployment rate
Unemployment rate
U 25 C
M U
M U 16 H
Index of Labor Turnover
1,000
2,000
3,000
4,000
5,000
6,000
2009 2010 2011 2012 2013 2014 2015
Total Non Farm: Quits
Total Non Farm Job Opening
I get the monthly consumer sentiment index from 12, 2009 to 1, 2016 from
Tradingeconomic.com.
22. Wen 22
50
60
70
80
90
100
2009 2010 2011 2012 2013 2014 2015
Consumer sentiment
I acquired the data on the monthly closing price of S&P 500 from Yahoo Finance.
1,000
1,200
1,400
1,600
1,800
2,000
2,200
2009 2010 2011 2012 2013 2014 2015
S&P 500
In the next section, I present important regression results from my empirical analysis.
23. Wen 23
Quantitative results and interpretation
There exist statistically significant negatives response of the general civilian labor force
participation rate, the civilian labor force participation rate of bachelor degree holder above 25,
the civilian labor force participation rate of high school graduate above 25, the civilian labor
force participation rate of male, and the civilian labor force participation rate of female to online
job search activities.
-.10
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of LABOR_FORCE_PARTICIPATIOto LOG_SE
-.2
-.1
.0
.1
.2
.3
.4
1 2 3 4 5 6 7 8 9 10
Response of CLFPR_BACHELOR_25 to LOG_SE
-.4
-.2
.0
.2
.4
.6
.8
1 2 3 4 5 6 7 8 9 10
Response of CLFPR_HIGH_SCHOOL_25 to LOG_SE
-.10
-.05
.00
.05
.10
.15
.20
.25
1 2 3 4 5 6 7 8 9 10
Response of CLFPR_MALE to LOG_SE
-.10
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of CLFPR_FEMALE to LOG_SE
24. Wen 24
This result is counterintuitive as according to the predictions made previously, online job
search activities are expected to lower the entrance costs for tentative job seekers into the labor
market, and thus increase the labor force participation rates. An explanation for this observed
phenomenon is that the lowering in entrance cost might not be a strong incentive for people to
enter the labor market. On contrary, it is due to this lowered entrance cost that tentative job
seekers become more free and flexible to stay at the gateway of labor market without any
substantial movement and they continue to search and look for opportunities through the online
job search websites. This result in an extension of their searching period, and the more they
search the more they become psychologically hesitant about the worthiness of their entrance
endeavor, and the lower their labor force participation rate.
There exist statistically significant negative responses of the unemployment rate of male
high school graduate above 16, the unemployment rate of college graduate above 25, the
unemployment rate of female to the online job search activities.
-.6
-.4
-.2
.0
.2
.4
.6
.8
1 2 3 4 5 6 7 8 9 10
Response of M_U_16_H to LOG_SE
-.2
-.1
.0
.1
.2
.3
.4
.5
1 2 3 4 5 6 7 8 9 10
Response of U_25_C to LOG_SE
25. Wen 25
-.2
-.1
.0
.1
.2
.3
1 2 3 4 5 6 7 8 9 10
Response of F_U to LOG_SE
There are several interpretation for the regression outcomes.
The labor force of male high school graduates above 16 and the college graduates above 25
are endowed with a averagely higher level of Internet Penetration Rate (including home users,
school users and firm users; most highly comprised of school users, as computer become an
integral part of the standard reservoir of school instruments).
The labor force of male high school graduates above 16 from (2009-2016) is comprised of
the first generation born from the years of 1985- 2001 that are equipped with the internet access
as their daily information exchange platform from their teenage life (which, according to
psychological study, is the most important period during which the enduring life habits are
formed), and are consequently more familiar and habitual users
The labor force of male high school graduates is generally comprised of the population that
are equipped with a relatively lower level of human capital due to their relatively shorter period
of education accomplishment and training. This fact makes the labor force of male high school
graduates above 16 are relatively disadvantaged in the traditional ways of job search due to the
high risk as this population of labor force are often the last to be hired and the first to be fired. As
a consequence of these high cost from uncertainty entailed by the traditional ways of job search,
26. Wen 26
the cost advantage of online job search is more attractive to this population, and are thus more
significantly correlated with their unemployment rate. The same reason could be equally
applicable for the female labor force as they are also facing a generally higher level of searching
costs as a results of their likely rejections to the job offers that do not meet their demand of
flexible time schedule18.
The sensitivity of the unemployment rate of the college graduates above 25 to the online job
search activities is most likely the results of both their generally higher ability that comes with
their educational experience to make accurate comprehension and assessment about the job-
related information provided by the online job search websites as well as their relatively high
reservoir of human capital which is potentially favored by employers.
There exit no statistically significant response of the general unemployment rate, the youth
unemployment rate, the male unemployment rate to online job search activities.
-.12
-.08
-.04
.00
.04
.08
.12
.16
.20
1 2 3 4 5 6 7 8 9 10
Response of UNEMPLOYMENT_RATE to LOG_SE
-.4
-.2
.0
.2
.4
.6
1 2 3 4 5 6 7 8 9 10
Response of YOUTH_UNEMPLOYMENT_RATE to LOG_SE
18 Female workforce often assumeadditional family responsibilitiessuch as childcare,cooking,housework in their
lives.This means that an important consideration for their career choiceis whether the job offer contains a
relatively flexibletimeschedule.
27. Wen 27
-.2
-.1
.0
.1
.2
.3
1 2 3 4 5 6 7 8 9 10
Response of M_U to LOG_SE
A critical qualitative observation at this point is that, for various populations of unemployed
job seekers, it is precisely those populations whose personal profiles match the efficiency
requirement of online job search that benefit most from this job method which is reflected in the
negative responses of their unemployment rates19. The male high school graduates often have
low level of professional qualification and professional relationship. They are exposed at early
age to the internet and are also served with the computer devices at their educational facilities
which means that it is likely the case that their familiarity with these techniques renders them
high level of searching technicality. Also, due to their low-end positions in the production
process, they weigh the benefit of cost saving and risk spreading effects associated with online
job search more than its screening inefficiency. These characteristics make their personal profiles
match the efficiency requirements of online job search and thus give them a better impact
outcome.
The same logic is equally applicable to the female and the college graduates above 25,
however, it does not fit them as perfectly as for the male high school graduates. College
graduates mainly benefit from online job search because of their high level of searching
technicality, which is demonstrated by both their online searching techniques and their
19 As specified in the firstqualitativediscussion.
28. Wen 28
intellectual ability. In a likely way, the female unemployed job seekers benefit from online job
search from their capitalization on the cost saving and risk spreading effects. The reason for this
observation is that: as the female workers are socially burdened with more family
responsibilities, it is often the case that they demand relatively flexible working schedules, which
makes the probability of failure for their job search endeavors relatively higher than the others
due to this extra condition. Thus, the empirical results for the unemployed job seekers almost
comply with my qualitative predictions from the perspective of the fitness of personal profiles of
job seekers for the efficiency of their chosen job search method.
There exist no statistically significant response of total non-farm openings and total non-farm
quits to online job search activities.
-80
-40
0
40
80
120
160
200
240
1 2 3 4 5 6 7 8 9 10
Response of TOTAL_NON_FARM_JOB_OPENI to LOG_SE
-40
-20
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10
Response of TOTAL_NON_FARM__QUITS toLOG_SE
These regression results indicate that the online job search system did not provide the
employed users with an effective tool to facilitate their labor turnovers. The reason behind this
observation could be the failure of the online job search system to satisfy any of the three
criterions for its well-functioning according to previous statements.
For the auxiliary multiple VAR model, I only incorporate the regression results in the
appendix as it did not produce significant information for this analysis.
29. Wen 29
Conclusion
The sensitivity of labor force unemployment rate to online job search activities is diverse in
respective to their different demographic characteristics and their different relative positions in
the labor market. And the observed regression results for the unemployed job seekers comply
with the qualitative predictions from the perspective of the fitness of the users’ personal profile
with the requirements of the chosen job search method. The ability of the online job search
system to improve the labor market outcomes for the tentative job searcher out of labor force and
the employed users aiming for turnover is not as strong as predicted by previous scholars. Also,
in order to make substantial improvement for the online job search system, there are several
caveats for the providers of online job search services. Firstly, online job search systems have
been proved to be most useful for the employed job seekers, which indicates that the provider
and designers of these systems should primarily focus on their services for the unemployed job
seekers. Secondly, the online job search system would produce more conductive results for the
general public if the providers of these services would be able to customize its service for
different populations in accordance with their different personal profiles. Thirdly, the employers
taking online job search into their recruitment consideration should try to foster more
standardized procedures of job application and verification to increase their screening ability and
boost their efficiency of identification and classification of the online applicants with
heterogeneous backgrounds.
30. Wen 30
References
1. The boundaryless career: Anew employment principle for a new organizational era, DM
Rousseau, 2001
2. Labor Economics, George, J. Borjas, 2001
3. Job Search on the Internet, E-Recruitment, and Labor Market Outcomes, Farrukh
Suvankulov, 2007
4. Private discrimination and social intervention in competitive labor market, SJ Lundberg,
R, Sartz, 1983
5. The Internet, Job Search, and Worker Mobility, Betsey Stevenson. 2003 11
6. Job Search Methods, Internet Versus Traditional, Peter Kohn and Mikai Skuterud, 2011
7. Technical Statistics Report, PewResearch Center, Aaron Smith, 2015,11
8. Online Labor Markets, John Horton, Harvard, 2009
9. The Utility of Skills in Online Labor Markets, Mario kokkodis, Panagiotis G.Ipeirotis,
2007
10. Digitization of the Contract Labor Market: A research Agenda , Ajay Agrawal, John
Horton, Nico Lacetera, Elizabeth Lyons, September 2013
11. Labor Dynamics in a Mobile Micro-Task Market, Mohamed Musthag, Deepak Genesan,
2012
12. Semrush.com, Web Traffic Statistics Summary for Monster.com, Careerbuilder.com,
Indeed.com
13. Bureau of Labor Statistics: Unemployment rates, Civilian Labor Force Participation rates,
Total Non-Farm Job Openings, Total Non-Farm Job Quits
14. Is Internet Job Search Still Ineffective? Peter Kuhn, Hani Mansour, 2011, 9
15. The Internet, Job Search and Worker Mobility, Betsey 2003
16. Investopedia: Job Search Methods, definitions and summarizations on their
characteristics
31. Wen 31
Appendix A: auxiliary results
The first set of empirical analysis outcomes is focused on the impact of online job search
for the tentative job seekers out of labor force. The examined variable is the civilian labor force
participation rate for different demographic groups.
There is no statistically significant response of civilian labor force participation rate of
laborer from 16 to 19 to online job search activities.
-.2
.0
.2
.4
.6
.8
1 2 3 4 5 6 7 8 9 10
Response of CLFPR_16_TO_19 to LOG_SE
There exist statistically significant negative responses of online job search activities to
the general civilian labor force participation rate, the civilian labor force participation rate of
bachelor degree holder above 25, the civilian labor force participation rate of male, the civilian
labor force participation rate of female.
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of LOG_SEto LABOR_FORCE_PARTICIPATIO
-.06
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to CLFPR_BACHELOR_25
32. Wen 32
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to CLFPR_MALE
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to CLFPR_FEMALE
These observations are reasonable as more tentative job seekers find their way into the
labor market, the less their necessity of using the online job search for its informational
convenience, and the less the online job search activities.
There is no statistically significant response of online job search activities to the civilian
labor force participation rate of high school graduates above 25, the civilian labor force
participation rate from 16 to 19,
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to CLFPR_HIGH_SCHOOL_25
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to CLFPR_16_TO_19
The second set of empirical analysis outcomes is focused on the impact of online job
search for the unemployed job seekers. The examined variable is the employment rate for
different demographic groups
33. Wen 33
There exist statistically significant negative response of online job search activities to the
general unemployment rate, the female unemployment rate, the youth unemployment rate, and
the male unemployment rate.
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to UNEMPLOYMENT_RATE
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to F_U
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to YOUTH_UNEMPLOYMENT_RATE
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to M_U
A qualitative conclusion to be drawn from these results is that: most of the population are
still driven away to other ways of job search when the unemployment goes high, this fact
indicates both the general inefficiency of the online job search system and the public’s
preference back to the traditional methods in dire economic circumstance. An explanation for
this phenomenon still attributes the underlying reason to the high level of requirement for
screening ability of the employers due to the large volume of heterogeneous applicants with
limited amount of information qualification which results in a lowered job matching rate for the
online job seekers. This would cause especially serious problem of adverse selection in the
34. Wen 34
period during which the unemployment rate is not optimistic and escalate the downturn in the
job-matching rates for the online job seekers.
There exist no statistically significant response of online job search activities to the
unemployment rate of college graduates above 25, and the unemployment rate of male high
school graduates above 16.
-.06
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to U_25_C
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to M_U_16_H
The third set of empirical analysis outcomes is focused on the impact of online job search for
the employed users aiming for turnover. The examined variable is index of labor turnover, the
total non-farm job openings and the total non-farm job quits.
There exist no statistically significant response of online job search activities to total non-
farm job openings and total non-farm job quits.
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SE to TOTAL_NON_FARM_JOB_OPENI
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LOG_SEto TOTAL_NON_FARM__QUITS
