Job Search with Financial Information: Theory and Evidence - GSB Faculty ...
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Job Search with Financial Information:
Theory and Evidence
Bong-Geun Choi, Jung Ho Choi, and Sara Malik∗
November 30, 2020
Abstract
This paper examines whether, when, and why job seekers use firms’ financial in-
formation in the job search process. We find first evidence of financial information’s
relevance to job seekers by documenting a substantial increase in job search activity
around earnings announcements in the spirit of Beaver (1968). We also find that fi-
nancial information acquisition by job seekers is positively related to both job postings
and interviews at the firm-county-month level. Spurred by this finding, we develop
a theoretical model of job search paired with firms’ heterogeneous earnings to better
understand job seeker’s information acquisition behavior. Our model predicts that job
seekers trade off the probability of an offer with the value of the employment con-
tract and intensify information acquisition as the number of available positions shrinks
relative to the pool of job seekers. Consistent with these predictions, we find that
firm performance is positively correlated with both job seekers’ search activities and
employers’ posted wages. We also find that the positive association between finan-
cial information acquisition and interviews is stronger when the job market is more
competitive. Overall, these results indicate that, like capital market participants, job
seekers value and use financial reporting.
Keywords: Job Search and Match, Financial Accounting, Firm Heterogeneity.
JEL Classification Numbers: D83, J62, J64, M41, M51.
∗
We thank Maureen McNichols, Kathryn Shaw, and Sorabh Tomar, who helped us with early project
development. We thank Tarik Umar for sharing the geocoding data with us. We thank Judson Caskey, Nick
Bloom, Ron Kasznik, Gwen Yu (discussant), and participants at UCLA, Stanford University Economics
Department Reading Group, and Stanford Accounting Summer Camp. We thank Teamblind for sharing
proprietary job search data with us. We thank Andrew Chamberlain at Glassdoor for supporting our research.
We thank Rishabh Aggarwal, Austin Pennington, and Mohammadhossein Shafinia for their superb research
assistance. Sal Mancuso and the Stanford GSB Data, Analytics, and Research Computing (DARC) team
provided superb hands-on support. Wonhee Lee helped us collect the Glassdoor data. Jung Ho Choi and
Sara Malik thank Stanford Graduate School of Business for research support. Bong-Geun Choi is affiliated
with Fount Inc. Please direct all correspondence to jungho@stanford.edu.
1 Introduction
The importance of financial information to capital providers has been studied extensively in
the academic literature. However, evidence of its importance to job seekers is comparatively
absent. This is especially surprising given the increasingly important role of labor as an
input to production (Zingales, 2000; Benson et al., 2020). Job seekers often have imperfect
information about job vacancies and prospective employers’ characteristics (Autor, 2001). A
natural question is whether job seekers turn to financial information to resolve the imper-
fection. Stated more explicitly, do job seekers use a firm’s financial information in the job
search process?1 If so, when does financial information matter most? What are the primary
economic drivers for why financial information matters to job seekers? Our belief is that the
gap in the literature around these topics is attributable to limited data and an insufficiently
developed theoretical foundation about job seekers’ search behaviors. In this paper, we first
examine job seekers’ search behavior at earnings announcements and during the job search
process. Then, we propose a search theoretic model of the job market with firms’ financial
information. Finally, we test the model’s implications by constructing and exploiting a novel
database that matches job search, job posting, and interview data with financial information
search data.
Anecdotal evidence suggests that financial information is valuable in both the job appli-
cation and interview processes.2 Job seekers are known to care about a wide range of job
characteristics, including wage, monetary compensations, fringe benefits, and work arrange-
ments (Mas and Pallais, 2017). Job postings often do not provide sufficient information
about these factors. For example, wages may not be listed in a job posting and are instead
negotiated after a match is made. Moreover, job seekers also care about firm characteristics
1
Financial information is defined by information provided by financial reporting (e.g., information con-
tained in 10-K or 10-Q).
2
“This research comes in handy at three pivotal times during a job search: first when you’re deciding
what kind of employers you’d like to work for, then when you are ready to apply, and finally when you’re
interviewing and your knowledge of the company is put to the test.” - https://www.indeed.com/career-
advice/finding-a-job/the-complete-guide-to-researching-a-company.
1
that cannot be captured in an employment contract (e.g., career growth and job security)
3
. In these cases, financial information provides rich, germane evidence. Reading financial
reports helps job seekers predict prospective match quality between themselves, jobs, and
employers. It also helps in preparation for interviews and thus improves the likelihood of
being hired.4
Our first analysis is a litmus test for the relevance of financial information to job seekers.
Beaver (1968) examined whether capital market participants took action based on earnings’
perceived information content by measuring abnormal trading volume around earnings an-
nouncements. In the same spirit, we measure abnormal job search volume around earnings
announcements on a popular, online professional network for job seekers. Job search vol-
ume is a proxy for job seekers’ interest in a company. In Figure 1, we find that abnormal
job search volume consistently spikes at earnings announcements. We also find that the
magnitude of job search volume at earnings announcement is increasing in earnings growth
measured year on year. This analysis suggests that, like capital market participants, job
seekers believe earnings announcements contain pertinent information and act accordingly.
We then examine when job seekers engage in financial information search by testing for
search activity in response to both job postings and interviews. We measure job seekers’
financial information search using Internet Protocol (IP) log data from SEC Edgar. We
access large scale job posting data from Burning Glass Technologies (BGT), which collects
job postings from over 40,000 websites. We measure the interview intensity by counting the
number of interviews voluntarily reported by interviewees on the job and recruiting website,
Glassdoor. Our identification strategy exploits the geographical and time variation in job
postings, interviews, and information searches. We measure our variables at the firm-county-
month level. In other words, if, within a county, a company issues a job posting or initiates
3
See Glassdoor Job Seeker Preferences Study (2018).
4
“Researching potential employers is vital to an effective job search. ... For public companies, you can
get this information from the company as well as access certain financial information, office locations, and
learn how the company is structured. Public companies typically post annual reports and other public
financial documents online.” - https://www.indeed.com/career-advice/finding-a-job/the-complete-guide-to-
researching-a-company.
2an interview, we examine whether Edgar searches for the company’s filings increase in that
region relative to others.
We find that the number of job postings and interviews is positively correlated with
Edgar searches at the firm-county-month level, indicating that job seekers search for financial
information during both the application and interview processes. In a multivariate analysis
on job postings, the elasticity is 0.5475, meaning that a 100% increase in job postings leads
to a 54.75% increase in Edgar searches. The median of annual Edgar searches is 36, meaning
that doubling job postings leads to 19.8 additional Edgar downloads each year. As we control
for county and time variation, the magnitude halves. Our results are effectively unchanged
even after controlling for the influence of firm performance with firm fixed effects. When we
control for home bias, our estimate is 0.0156, indicating that firm-county pair might capture
regional concentration of Edgar searches attributable to both employees and investors. Using
the number of job interviews, we find the same relations qualitatively. When we control for
home bias, our estimate of the elasticity of Edgar searchers to job interviews is 0.0254. These
conservative magnitudes of the elasticity suggest that doubling job postings and interviews
are translated into 0.56 and 0.91 additional downloads per year, respectively. These numbers
are economically significant, especially given that Edgar is only one of many possible means
to acquire financial information. Our robust findings support the argument that job seekers
acquire financial information in the job search process.
To better understand why job seekers use financial information, we present a model of
labor market search. While the relation between financial information acquisition and job
search may appear intuitive upon first glance, it is predicated on complex labor market
frictions. If a worker were always compensated based on his or her marginal productivity,
employers’ financial information would not matter. Likewise, if job seekers could effortlessly
identify all vacancies and submit applications, then job seekers would apply to every job
vacancy (Stigler, 1962). Instead, these search frictions are pervasive and large. Our model is
a hybrid of the two competing approaches to theoretic models with search frictions: directed
3search and random search. The model features firm heterogeneity, i.e., different types of
firms as measured by firm performance. We allow job seekers to choose their optimal search
efforts to secure an offer. Job seekers first direct their search to a group of firms of the
same type and exert their optimal search efforts over that set. Wages are realized afterwards
when the job offers are made, which is the random search component. Compared with the
standard directed search model, in our model, job seekers direct their search to the firm types
rather than to the contracts, which is consistent with the data. Different types of firms have
different distributions for the employment value. Within the same type, the employment
value distribution is matched one to one with the wage distribution. To generate a wage
distribution within the same type firms, we allow on-the-job search within and between
types, which is also realistic.
Our model proposes two major aspects of financial information’s role in the equilibrium
match and the search effort allocation. The first key feature is that job seekers trade off the
probability of receiving a job offer with the value of that offer. In other words, job seekers
understand that if they apply to high performing firms, they are less likely to be hired
by those firms due to a high volume of applications, but conditional on being hired, their
offers are preferred.5 The second feature is that job seeker effort is increasing in competition
in the job market. In other words, if others exert more efforts, a worker should do the
same to maintain the same level of the visibility to employers. Search efforts are costly,
but they can increase the match probability by improving application quality (e.g., a well-
researched cover letter or strong interview performance). Therefore, the model predicts that
intense competition in the job market motivates the job seekers to conduct more financial
information acquisition.
We test the first proposition of the model by evaluating how and why job seekers use
this financial information in their job search process. First, we test whether a firm’s finan-
5
A job seeker applies to firms with better performance with the intention of securing a higher valued job
offer. In the equilibrium, however, a job seeker knows that other job seekers are also likely to apply to those
same firms and competition for the limited number of vacancies will be high. Therefore, the model suggests
a positive correlation between a firm’s performance and the job applications for the firm.
4cial performance is positively correlated with the job search intensity for the firm. Using
proprietary job seeker interest data from Indeed, we find that ROA and Market to Book are
positively correlated with job search intensity. We construct a summary score of each firm’s
financial performance using ROA, Sales Growth, and Market to Book under the assump-
tion that job seekers assess firm financial health use multiple pieces of information (Brown
and Matsa, 2016). We find a positive association between a firm’s summary score and job
search intensity. The coefficient of 0.0255 indicates that an increase in the score by 3 points
increases the number of job application by 7.65 percents. Using the subsample of the BGT
job postings that include posted wages, we find evidence of a positive correlation between a
firm’s financial performance and the posted wage. This result supports the argument that
one reason job seekers apply to high performing firms is the high expected compensation.
We also test the second proposition of the model: job seekers search more intensively
for financial information when the job market is more competitive. We measure job market
competitiveness with the Herfindahl–Hirschman Index where a firm’s market share is defined
by its share of the job postings in the county-month. We segment counties by labor market
concentration. Labor market concentration increases the market power of employers and
decreases the choice set of job seekers, making the job market more competitive assuming the
number of applicants is constant (Azar et al., 2018). We find that labor market concentration
increases the relation between job interviews and Edgar searches. This result supports the
argument that financial information acquisition is increasing in job market competition.
We conduct multiple cross-sectional and robustness tests to ensure that our results sup-
port our hypotheses. We examine which job seeker groups utilize financial information
in the job search process. We find that strengthening the enforcement of non-compete
clauses reduces the relation between job postings and financial information search because the
non-compete clauses limit labor mobility. Financial reports are complex and management-
focused, which implies that the information is likely more accessible and valuable to well-
educated job seekers, job seekers with experience, and job seekers with accounting or finance
5backgrounds. We find that job postings with high education requirements, longer experience
requirements, and in accounting, finance, and manager positions are more strongly corre-
lated with financial information acquisition. Our results are robust to alternative measures
of financial information acquisition: Edgar searches only from residential IPs and Google
searches.6 Finally, we find that the relation between the financial performance variables and
job search intensity is stronger for firms characterized by more Edgar searches, i.e., more fi-
nancial information acquisition. This finding is consistent with job seekers who use financial
information reacting more intensely to financial performance than job seekers who do not
use financial information.
Our paper makes three contributions. First, we provide robust evidence that job seek-
ers, like capital market participants, use financial information. A relatively small number of
papers in the accounting literature study the role of financial information to rank-and-file em-
ployees (Chakravarthy et al., 2014; Dou et al., 2016). For example, Liberty and Zimmerman
(1986) study how wage negotiations with labor unions influence earnings management.7 De-
Haan et al. (2020) study current employees using financial statements for voluntary turnover
decisions (Lester et al., 2020).8 Our paper complements these studies by demonstrating
whether, when, and why prospective employees use financial information in the job search
process. In the spirit of Ball and Brown (1968) and Beaver (1968), we emphasize the per-
6
One important identification challenge is that job postings may induce not only job seekers’ interest but
also other stakeholders’ interest in financial information. We investigate the heterogeneity of job seekers’
financial information search activities mainly to mitigate a concern that the relation between job postings
and Edgar searches captures investors’ search behaviors. Investors extensively use SEC Edgar system to
understand firms’ financial performance especially during earnings announcement periods (Drake et al.,
2015). Furthermore, high income individuals are more likely to invest in the stock market. We find that our
results are strong even during non-earnings announcement periods and in low income areas, supporting our
conjecture that job seekers download firms’ financial reporting information during their job searches.
7
For example, Kedia and Philippon (2009) document the impact of financial misreporting on employee
turnover. Hann et al. (2020) demonstrates the usefulness of aggregate earnings for explaining the labor mar-
ket. Choi et al. (2019) demonstrates that low financial reporting quality increases rank-and-file employees’
wage premiums for high turnover risk, suggesting the usage of financial information in wage determination
(Baik et al., 2019; Bai et al., 2019).
8
While employees are also an important stakeholder of financial information, they are a distinct group
from job seekers. The same financial information may not be equally relevant for both groups. For example,
job seekers may care more about growth whereas current employees are more concerned about pension
obligations.
6ceived information content of financial information that informs job seeker’s behavior.
Zingales (2000) encourages researchers to study the relation between corporate finance
decisions and human capital. Following the call, Berk et al. (2010) establishes the theoretical
foundation of how capital structure decisions influence wage determination via bankruptcy
risk. Agrawal and Matsa (2013) demonstrates that high labor unemployment risk decreases
corporate leverage due to labor market frictions, emphasizing the undiversifiable nature of
human capital. With one notable exception, however, the literature has not explored the
role of financial information in job search. Brown and Matsa (2016) find that, during fi-
nancial crisis, financial professionals apply less often to financial companies that are more
likely to file for bankruptcy due to the separation risk. In addition, those financial com-
panies tend to offer higher wages. Extending Brown and Matsa (2016), we highlight the
importance of positive financial performance for job seekers during earnings announcements
and throughout job search including both the application and the interview processes. Our
model provides a prediction about financial performance (as a signal for the employment
value) and (information) search efforts.
Finally, our model highlights information frictions around firm performance and more
fully represents the realities of the labor market. An information friction about firm char-
acteristics has been overlooked frequently in the literature (Benson et al., 2020). Although
simple directed search is a powerful framework in labor economics, it is predicated on publicly
posted wages, which are rarely available. Mixed results about the relation between posted
wage offers and job applications are puzzling (Marinescu and Wolthoff, 2020). We propose
that financial information may be another allocative mechanism. Likewise, we incorporate
a random component to model job search intensity (an important choice variable for job
seekers) and allow for on-the-job search within and between types.
Section 2 describes the institutional details about job search and financial information
acquisition. We review the literature in the section. Section 3 describes data. Sections 4
and 5 discuss job search around earnings announcements and financial information search in
7the job search process, respectively. Section 6 discusses a theoretical foundation for why job
seekers look for financial information. Sections 7 and 8 test the propositions of the model.
Section 9 concludes.
2 Institutional Setting and Literature Review
2.1 Job Search
In this section, we discuss the information frictions job seekers face during search and match
process in the labor market.9 Identifying these frictions is important to understanding why
job seekers may engage in financial information.
The search and match process starts when employers advertise a job vacancy. Job seekers
face twin information problems: identifying job vacancies and evaluating the prospective jobs
and employers. Workers could hypothetically spend infinite time and energy applying for
jobs but in reality only apply to the subset of prospective jobs and employers they find
most appealing. Job postings typically do not contain sufficient information to make that
determination, so job seekers must engage in information acquisition.10
Historically, job seekers would read help-wanted advertisements in newspapers to identify
vacancies. The development of the internet in the 1990s and 2000s changed the dynamics
of job search substantially. Now, millions of vacancies are summarized in centralized online
job boards. For example, as the leading job board in early 2000s, Monster.Com offered 3.9
million resumes and 430,000 jobs in August 2000 (Autor, 2001). These online job boards are
heavily utilized by job seekers. Current Population Survey (CPS) estimates suggest that by
2011 approximately 75% of job seekers used online job search compared to approximately
25% in 2000. Likewise, the internet made information about employers more accessible
(Gao and Huang, 2020). Firm financial reports are now readily available through company
9
Firms also have their own information frictions when trying to evaluate prospective employees’ produc-
tivity. We omit those for brevity.
10
In fact, most job postings do not include details about compensation.
8websites and the SEC’s Edgar system.
The development of these platforms has not eliminated the information frictions faced
by job seekers. In fact, in some cases they have exacerbated them. Instead of reading a few
newspaper broadsheets’ worth of vacancies, job seekers have access to millions of vacancies.
This has increased the challenge of identifying the subset to which job seekers want to apply.
More importantly, the platforms provide an opportunity to trace previously unobservable
jobseeker behavior during the job search process.11
After applying to vacancies advertised by employees, job seekers may be contacted by
firms for an interview. Firms use interviews to acquire more information about prospective
employees and for screening (Barach and Horton, 2021). Job seekers can use interviews to
highlight their fit for the vacancy and evaluate the company (Judge et al., 2000). Many job
seekers spend time preparing for their interviews by researching the prospective employer
and networking with current employees. They may also engage in financial information
acquisition. If the company decides the job seeker is a good match, it will extend an offer
to the job seeker. Wage negotiation may follow. Once the offer is accepted, the job search
process is complete.
2.2 Literature Review
A recent literature uses the development of technologies and intermediaries to expand our
understanding of the labor market. Kuhn and Mansour (2014) document that unemployed
workers who use the internet during their job search have shorter joblessness durations.
Baker and Fradkin (2017) find that an increase in unemployment benefits is associated
with a decrease in job search activity in the Google search box, likely because it reduces
the incentive to find a new job. A few recent papers study the importance of employers’
11
Internet platforms have also firms’ information acquisition costs about prospective employees. Informa-
tion about workers’ characteristics can be divided into two categories: low and high ”bandwidth” variables
(Autor, 2001). Low bandwidth data are objectively verifiable information such as education and experience.
In recent years, multiple intermediaries including LinkedIn provide this verifiable information about potential
workers at a lower cost by publicly posting resumes of its members.
9reliance on new institutions. Benson et al. (2020) finds that firms’ reputation about payment
history has an impact on freelancers’ application behaviors in the gig economy. Using a field
experiment and the crash of the review board as a research setting, the authors find that
firms’ reliable payment history allows them to get more applications at a lower wage on
Amazon Mechanical Turk.
The implementation of the Edgar system also provides a new opportunity to study fi-
nancial information acquisition (Drake et al., 2015). For example, Bernard et al. (2020)
demonstrate that competing companies search for other companies’ financial reports. This
bilateral search behavior is associated with the similarity in investment decisions. Drake et al.
(2017) find that Edgar searches vary across counties. High search volumes are associated
with income and education levels.
3 Data
In this section, we describe the data sets used in our analysis: job posting data, job in-
terview data, financial information acquisition data, and job search intensity data. These
data sets have many strengths, chiefly among them is broad and deep coverage. This is a
significant departure from prior literature’s reliance on surveys. In addition to these data
sets, we also use the well-known merged quarterly Compustat-CRSP data set to evaluate
firm performance.12
3.1 Job Posting Data and Job Interview Data
We use large scale data from Burning Glass Technologies (“BGT”), an analytics software
company, to identify job vacancies. Every day, BGT scans more than 40,000 sources, in-
cluding job boards and corporate websites, to create a catalogue of new job postings. The
company then uses its natural-language processing software to identify the job title, occu-
12
We winsorize the Compustat-CRSP variables at the 1st and 99th percentile of all Compustat-CRSP
firms to minimize the influence of outliers.
10pation, employer, and skills for each vacancy. Each observation in the BGT data represents
a job posting. In total, BGT provides 52 standardized fields for each observation. These
standardized fields allow for improved comparability of job postings across region and time.
The BGT data set spans 2007 and 2010 to present and covers all 50 states. As part of our
data processing, we aggregate the data to the firm-county-month level and count the number
of job postings. BGT classifies job listings into 24 occupation family codes. For each firm-
county-month tuple, we also count the number of job postings in each distinct occupation
code. Our Appendix indicates that our sample firms post vacancies across various occupation
families, including sales, management, and health care.13
We use proprietary data from Glassdoor, an online job and recruiting website, to collect
a record of job interviews. Each record is interviewee-specific and includes the interview
date, firm name, interview location (city and state), an indicator for whether the interviewee
received an offer, and an indicator whether the interviewee accepted or declined an offer.
These records are self-reported by the interviewees and are not corroborated by the individual
firms.
3.2 Financial Information Search Data
We use the SEC’s publicly available server log files to identify financial information acqui-
sition. The server log files measure traffic for Edgar filings through SEC.gov. Each record
contains the accessor’s semi-anonymized IP address, the date of access, the firm CIK num-
ber, and the searched document. We geo-locate each user by using the MaxMind data and
are therefore able to include each accessors’ city and state for each record. The data are
aggregated to firm-county-month level, summing the number of searches. We exclude CIK-
IP-date tuples with a large number of searches to eliminate records conducted by bots and
crawlers (Ryans, 2018).
13
Prior literature in economics has compared the BGT data to other commonly used data sources on job
vacancies (Hershbein and Kahn, 2018). Relative to the survey data collected by the U.S. government, the
BGT data represent the near universe of job postings. The data set generally over-represents technical fields,
which are more likely to be posted on websites, but the over-representation does not change over time.
11We acknowledge that job seekers may access firms’ financial information through different
conduits, including companies’ homepages, search engines, and Yahoo finance. We find that
some firms link to the SEC Edgar filings on their websites. Thus, job seekers are likely to
acquire firms’ financial information from the Edgar system without explicitly noticing it.
One salient example is Alphabet, Inc.’s investor page. When one clicks on the link to a
10-K, it directs the viewer to the filing hosted on the SEC’s website. Additionally, when
users search for firms’ annual reports on Google, financial reports in the Edgar system are
frequently ranked in the top five to ten Google search results.
To directly tackle this alternative source of financial information, we supplement the
SEC’s publicly available server log files with Google Trends data. Google Trends data
measure the number of searches in the Google search bar across different regions and across
time.14 We collect Google Trends data from 2011 to 2016 for firms in the S&P500 as of 2019.
Google search is a popular way to look for companies’ financial information, but the Trends
data may also represent search for purposes other than employment.
3.3 Job Search Intensity Data
We collect proprietary data from Indeed, an online job and recruiting website, to measure job
search intensity. For a large subset of the firms on its website, Indeed creates two separate
daily indices: one for job postings intensity and one for job search intensity. The job search
intensity from Indeed is measured in the same way as Google Trends data. Indeed tracts
users search activities on the search bar on its website. For example, if more users search
for Amazon within Indeed, Indeed data indicate higher job search intensity for Amazon.
Consequently, these indices are relative measures. We aggregate the data to the firm-quarter
level and take the average of the indices. One advantage of this data set is that it tracks
behaviors of individuals who are very likely to be job seekers.
14
Google Trends data are randomly sampled statistics. The measure is noisy, which may lead to slightly
different search volumes when downloading the same series. We believe this noise is uncorrelated with our
test and will not introduce bias. It may, however, reduce our statistical power.
12We also obtain proprietary data from Teamblind, an online professional social network
with job boards and anonymous forums, as a second measure job search intensity. The data
covers a smaller number of firms but is high-frequency and captures anonymized individuals’
job search queries. The sample period is from September 2019 to September 2020. For
each company-day, we count the number of unique (anonymized) users who search for the
company’s name.
4 Job Search and Earnings Announcements
4.1 Research Design
To provide initial evidence of firm financial information’s relevance to job seekers, we measure
firm-specific abnormal job search volume at quarterly earnings announcements. This analysis
is in the spirit of Beaver (1968) and Landsman and Maydew (2002). We use earnings
announcement dates from IBES and daily search volume from Teamblind. Abnormal job
search volume is measured using a z-statistic; we compute firm-specific means and standard
deviations of search volume over all days exclusive of the +/- 10 day range around each
earnings announcement.
4.2 Empirical Results
In Figure 1, we plot abnormal job search volume from -20 days to 20 days around earn-
ings announcement dates.15 We find that abnormal job search volume spikes at earnings
announcements. The daily fluctuations around earnings announcements are minimal. This
figure is consistent with job seekers using earnings announcements to learn about potential
employer’s financial performance and then directing their search activity to said employ-
ers. The magnitude of the job market reaction in Figure 1, 0.4, is strikingly similar to the
15
The number of unique companies in Figure 1 is about 50. Due to confidentiality issues, we cannot
provide the exact number.
13magnitude of the capital market reaction (also 0.4), in Figure 1 in Landsman and Maydew
(2002).
One alternative explanation for our figure is that job seeker interest increases at earnings
announcements because firms are featured in the news. To clarify the relative strengths of
these explanations, we compute year-on-year earnings growth and group earnings announce-
ments into above median earnings growth and below median earnings growth announcements.
Our contention is that both high earnings growth and low earnings growth firms are likely
be to be featured in the news. We then reproduce our plot separately for each group in
Figure 1 Panels B and C. We make two observations. First, the spike in abnormal job search
volume at earnings is larger in the above median earnings growth group compared to the
below median group. This result is comparable to Beaver et al. (2018)’s finding that the
capital market reaction to profitable firms is substantially larger than that for loss firms.
This analysis is distinct from Brown and Matsa (2016) because our study focuses on the in-
formation content of financial reports and the importance of positive financial performance.
Second, search volume is generally higher after earnings announcements in the above median
earnings group compared to the below median group. This suggests job seekers react to earn-
ings announcements’ information content even after earnings announcements. Collectively,
our results indicate that, like capital market participants, job seekers act on the perceived
information content of earnings announcements.
5 Job Postings, Interviews, and Financial Information
5.1 Research Design
We estimate the relation between job postings (interviews) and financial information acqui-
sition using the data set created by merging Edgar log data with BGT job postings and
14Glassdoor job interviews data. The specification is:
EdgarSearchesjst = β · JobP ostings(Interviews)jst + γ · Xjst + αj + µs + τt + jst (1)
where j indexes company, s indexes county, and t indexes month. Our first variable of interest
is JobP ostingsjst , which is the log of one plus the number of job postings in a firm-county-
month tuple. Our second variable of interest is Interviewsjst , which is the log of one plus
the number of job interviews in a firm-county-month tuple. We measure EdgarSearchesjst
as the log of one plus the number of Edgar Searches, also in a firm-county-month tuple.
Note that in order to avoid dropping observations without Edgar searches, job postings, or
interviews, we add one to the raw value before taking the log. Our control variables include
ROA, the sum of the last four quarters of operating income after depreciation divided by
total assets lagged five quarters; Sales Growth, the previous quarter’s sales minus sales lagged
five quarters, divided by sales lagged five quarters; Market to Book, the previous quarter’s
market cap divided by previous quarter’s common equity; Size, the log of the previous
quarter’s total assets; Leverage, the previous quarter’s current and long term debt divided
by previous quarter’s total assets; and Labor Productivity, the sum of the previous four
quarters’ sales divided by the most recent employee count, subsequently logged. In addition
to the fixed effects indicated in Equation 1, we also run this specification with varied, more
dense fixed effects. Standard errors are clustered at the firm level.
5.2 Job Postings and Financial Information
Table 1 Panel A summarizes descriptive statistics. The number of firm-county-month obser-
vations is 0.4 million. After matching firms in the BGT job posting data set with firms in
the Edgar financial information download data set, we identify approximately 2,400 unique
common firms. The median number of non-logged Edgar searches at the firm-county-month
level is 3. The 75th percentile is 14. The median number of non-logged job postings at
15the firm-county-month level is 2 because we exclude a firm-county-month pair without job
postings.16 We focus on three performance measures in the paper: ROA, Sales Growth, and
Market to Book. The median of ROA is 9.8%. The median of total assets is $8,752M.
Figure 2 is a set of heat maps of job postings, interviews, and Edgar searches for the
firm Packaging Corporation of America (“PCA”). PCA is one of the largest producers of
containerboard and packaging material in the U.S. It is headquartered in Illinois and has
establishments across the country, including in California, Texas, and Maryland. The figure
shows that PCA issues job postings in the states in which its plant, mills, and customer
service centers are located and those states are characterized by more Edgar searches for
PCA’s financial information. PCA also conducts interviews in the states where it has a
presence, including Illinois and Texas. Figure 3 illustrates the time-series variation in job
postings and Edgar searches for PCA. The figure indicates that the number of job postings
changes over time. We exploit both cross-sectional and time-series variation in job postings
and interviews to study the relation between job postings and Edgar searches.
In Table 1 Panel B, we find that Edgar searches are positively correlated with job post-
ings. The correlation is 0.2896, which is the highest relative to the other financial variables
in our table. Job postings are also positively and highly correlated with size and profitabil-
ity (ROA). Overall, a univariate analysis supports our hypothesis that job seekers gather
financial information about potential employers.
Table 2 Panel A echoes our univariate analysis. We start with controls but without fixed
effects to ensure that both across and within firm and across and within county variation
support our arguments. The elasticity is 0.5475, meaning that a 100% increase in job post-
ings leads to a 54.75% increase in Edgar searches. To mitigate concerns over macroeconomic
and geographic factors, we use time and county fixed effects. For example, economic recov-
ery might increase both Edgar searches and job postings. In addition, counties may have
16
In Online Appendix Table A.2, we re-estimate this equation on a data set that includes firm-county-
month pair without job postings if the firm-county pair has at least one job posting over the entire sample
period.
16a large number of both investors and employees. We also use firm fixed effects because
large firms have more investors and employees. These fixed effects reduce the coefficient on
JobP ostingsjst but the coefficient is still statistically and economically significant. Finally,
excluding observations in a firm’s headquarters county does not change the main implication
of our paper. It is worth mentioning that the role of the financial performance variables in
Table 2 is to serve as controls. Prior papers indicate that financial performance is associ-
ated with investors’ attention. Later, we use firms’ financial performance as an independent
variable to study the relation between job search intensity and financial performance.
Table 2 Panel B shows that our results are robust to alternative fixed effects. To address
alternative explanations, we introduce the interacted fixed effects. We first use firm-month
fixed effects. Firms with high sales growth might drive job postings and investors attentions
at the same time. In this specification, we capture the association between Edgar searches
and job postings within a firm-month pair but across different counties to mitigate concern
that firm performance is driving our results. We then use county-firm and county-month
fixed effects additionally. Investors are more likely to invest in firms near them. Firms are
also more likely to hire local job seekers. The county-firm interacted fixed effect captures
this home bias. In Column (3), the coefficient of JobP ostings is 0.0156, indicating that
firm-county pair might capture regional concentration of Edgar searches due to employees
as well as investors. Across the different specifications, we continually find the positive and
significant relation between job postings and Edgar searches, supporting the argument that
job seekers acquire financial information during job applications.
5.3 Job Interviews and Financial Information
Table 3 Panel A echoes our findings with job postings. We start with controls but without
fixed effects to ensure that both across and within firm and across and within county variation
support our arguments. The elasticity is 1.7935, meaning that a 100% increase in job postings
leads to a 179.35% increase in Edgar searches. As the same in Table 2, we use time and county
17fixed effects and exclude observations in a firm’s headquarters county. These specifications
reduce the coefficient but the coefficient is still statistically and economically significant.
Table 3 Panel B shows that our results are robust to alternative fixed effects. Especially, in
Column (3), the coefficient of Interviews is 0.0254 even after controlling for both firm-month
and county-firm fixed effects.
The economic magnitude of the coefficients in Column (3) of Table 2 and 3 is still mean-
ingful. The median of the number of non-logged Edgar searches is 3. The number is not large
because we measure Edgar searches at the firm-county-month level and we exclude Edgar
searches by bots and crawlers. Doubling job postings and interviews translate into additional
0.0468 and 0.0762 Edgar searches.17 Annually, doubling job postings and interviews within
a firm-county increases Edgar searches from 36 to 36.56 and from 36 to 36.91, respectively.
Acknowledging that job seekers are one of multiple user groups of Edgar searches and Edgar
search is only one of multiple methods to search for financial information by job seekers, one
additional Edgar search is economically significant. In addition, we separately measure job
seekers’ financial information search in the application process and the interview process.
Finally, the coefficient captures the average effect. We study the heterogeneous effect of job
postings on financial information searches in Table 8.
6 Theoretical Foundation
We present a model of labor market search where unemployed workers can direct their search
on a firm type (e.g., productivity or performance) but not on a specific wage contract. Wage
offers are randomly drawn from the firm type specific wage distribution. Moreover, we allow
on the job search at exogenous match rates as in Burdett and Mortensen (1998). This
feature allows us to have non-degenerate wage distributions between homogeneous workers
and homogeneous firms. Also, motivated by our empirical results on job seekers’ financial
17
Doubling job postings and interviews is consistent with the standard deviation of EdgarSearches in
Table 1 and twice the standard deviation of Interviews only with the sample with non-zero interviews.
18information acquisition, we model optimal choice of search efforts in each submarket. Search
efforts are costly, but they can increase the effectiveness of search, thus increasing the match
probability.
In the steady state, our model suggests that more productive firms tend to offer higher
value contracts and attract more job applications. Consequently, more productive firms are
more competitive to match. From the perspectives of a job seeker, the merit of a higher value
contract is offset by a lower chance of receiving an offer. Moreover, more intense competition
to match with a productive firm leads to more intense job search efforts (including financial
information acquisition).
The model implies a positive relation between the firm’s productivity type (or perfor-
mance) and the value of employment, a positive relation between the firm’s productivity
type and the job search efforts, and a positive relation between the firm’s productivity type
and the number of job applications.
6.1 Model Environment
We study the steady state implications of a continuous time economy. There exits a unit
measure of continuum of workers. Workers are identical and are infinitely lived. The economy
is populated with firms, potentially of a larger mass than one. However, each firm can hire
only one worker, and the measure of participating firms in the labor market is endogenously
determined by the free entry condition. We assume that firms and workers are risk neutral,
and they discount the value of future time at an instantaneous rate r > 0.
Firms are heterogeneous in terms of their own performance yi ∈ {yH , yL } where i ∈
{H, L} is the productivity index of a firm so that yH ≥ yL . We denote the measure of
type i firms recruiting in the labor market (i.e., vacant for a job) as mi and the measure of
workers employed by a type i firm as ni . Once a type i firm is filled for a job with a worker,
the firm-worker pair produces output yi . Each pair faces an exogenous separation at rate
δi ∈ {δH , δL }, with δH ≥ δL . We denote the measure of unemployed workers seeking a job at
19a type i firm as ui . Employed workers receive different wages w, and unemployed workers
receive unemployment benefits b, with 0 ≤ b < yL ≤ yH , while firms with a vacancy should
bear job posting cost k to recruit. All unemployed workers participate in the labor market
(i.e., either employed or unemployed), so nH + nL + uH + uL = 1.
We make a few key assumptions on the search and match process as follows. First, the
market is segmented due to directed search as in Moen (1997) and Acemoglu and Shimer
(1999). However, in our model, the segmentation and the directed search are based on a
firm’s type instead of a contract. Knowing the equilibrium wage offer value distribution, the
job separation rate, the future on the job search prospects, and the competitiveness of the
job market involved with each type of the firms, workers direct their search over the firm
type. Therefore, the submarket for the segmentation is defined by the type of the firms.
Once workers choose which type of the firms to direct in their search, they should compete
with other workers pursuing a job in the same submarket.
Second, we allow on the job search as in Burdett and Mortensen (1998) and Hoffmann
and Shi (2016). Knowing the matching chance and the offer value distribution in each
submarket, firms with job vacancies offer optimal wages to maximize their profits. A higher
valued offer is more likely to be accepted and more likely to retain a worker. The optimal
value offer decisions made by the firms form a nondegenerate wage offer distribution, which
can be also interpreted as a mixed strategy. If a firm of type i posts a vacancy, then it
should compete with the same type vacant firms to attract job applications by offering a
different wage. In each submarket, unemployed workers have a queue to be matched with a
firm. The key departure from Burdett and Mortensen (1998) or Hoffmann and Shi (2016) is
that an unemployed worker who directs his search to type i firms receives a wage offer at an
endogenously determined arrival rate which will be explained later. Moreover, an employed
worker at an i type firm receives an offer at an exogenous arrival rate λ from the same type
firms as the current employer and at an exogenous arrival rate σ from the other type of the
firms.
20Third, we consider costly search efforts by the workers as in Decreuse and Zylberberg
(2011). However, in our model, directed search and the search effort allocation are about the
firm’s type and conducted before the workers receive a wage offer. Moreover, once the workers
direct their search to a type of the firms, they cannot further direct their searches on a specific
contract. We denote the function that maps each submarket to search effort by s : {H, L} →
R+ , where si ∈ {sH , sL } is the search efforts level exerted by other unemployed workers in
each submarket i. In a symmetric equilibrium, job search efforts s = si are involved with
the search cost function c (·) as well as the search effectiveness function x (·). Both of them
are increasing functions of s. Marginal cost of search increases in the search efforts, and
marginal effectiveness of search decreases in s. We assume that the matching technology of
the economy M (·, ·) is an increasing CRS function of its arguments. Then the effectiveness
of search of an unemployed worker with search intensity s at submarket i is expressed by
M [x(s)ui ,mi ]
an endogenous rate of αi (s; si ) := x(si )ui
, given the measures of the vacancies and
unemployed job seekers as well as other job seekers’ search intensity at submarket i , si .
Assumption 1.
• The search effort cost function c : [0, +∞) → [0, +∞) is strictly increasing, strictly
convex, and twice differentiable.
• The search effort cost function satisfies c (0) = 0, and c0 (+∞) = +∞.
• The search effort effectiveness function x : [0, +∞) → [0, +∞) is strictly increas-
ing, strictly concave, and twice differentiable.
• The search effort effectiveness function satisfies x (0) = 0 and x0 (0) = +∞, and
x0 (+∞) = 0.
• An unemployed worker pursuing a job at a type i firm may meet a type i vacancy
at an endogenous arrival rate αi (s; si ), which increases in the worker’s own search
efforts s given si , ui and mi .
21• An employed worker at a type i firm may meet the same type firms with a vacancy
at an exogenous arrival rate λi .
• An employed worker at a type i firm may meet the other type firms with a vacancy
at an exogenous arrival rate σi .
From Assumption 1, we know that a type i firm with a vacancy may meet an unemployed
M [x(si )ui ,mi ]
worker pursuing a job at a type i firm at an endogenous arrival rate mi
. An employed
worker receives an offer from a same type employer at an exogenous arrival rate λi , and an
employed worker at the other type of a firm at an exogenous arrival rate σi . A type i firm
with a filled job may separate from the worker with an exogenous probability δi , in which
case the workers become unemployed and the firms lose their workers for an unmodeled
reason. In addition, a type i firm with a filled job may lose its worker when the employee
was matched by a vacant firm offering a higher value with a higher wage.
6.2 Worker
Wage w is the source of utility value for the workers. We denote the life time utility value
of being an unemployed worker by Vu and the utility value of unemployment seeking a job
at a type i firm by Vu,i , respectively. Workers may receive only one offer at a time. The
utility value of employment at a type i firm with wage w by Ve,i (w), respectively. We denote
the support of the life time utility value distribution for employed workers at type i firms as
Fi as suppi ≡ V i , V i . We also denote the supports of wage that corresponds to suppi as
b
[wi , wi ]. Then we know that V i ≥ Vu and Vu ≥ r
because any wage should provide better
utility value than unemployment, and employed workers hope to receive wage offers better
than the unemployment benefit.
Assumption 2.
• A type i firm which has been idling with a vacancy strategically offer a wage of
value V ∼ Fi (V ) to attract a worker to match where Fi (V ) is an endogenous
22mixed strategy equilibrium value of the offer by a type i firm.
• Employment value for type i firms with a filled job form a cumulative distribution
function Gi (V ).
• Fi and Gi are differentiable in the interior of their supports.
• V H ≥ V L but no restriction on the comparison between V H and V L .
The value function for unemployed workers is rVu = max {rVu,H , rVu,L } so that
b + max αH (s˜H ; sH ) 0∞ max {0, z − Vu,H } dFH (z) − c (s˜H ) ,
R
s˜H
rVu = max , (T 1)
b + max αL (s˜L ; sL ) ∞ max {0, z − Vu,L } dFL (z) − c (s˜L )
R
s˜L 0
for given {sH , sL }. The value function for employed workers at type i ∈ {h, l} firm with
wage w ∈ R+ is
w + δi [Vu − Ve,i (w)]
R∞
rVe,i (w) = +λi max {0, z − Ve,i (w)} dFi (z) . (T 2)
0
R∞
+σi
0
max {0, z − Ve,i (w)} dF−i (z)
6.3 Firm
A firm with a vacancy seeking a worker should pay instantaneous vacancy cost k ≥ 0.
Acknowledging the distribution of offer value V from type and the employment value dis-
tribution of employed workers, Gi , on suppi ≡ V i , V i , firms can form a rational expec-
tation about the offer acceptance rate for an offer of value V ∈ suppi from a type i firm:
hi (V ) ≡ αi xi ui + λi ni Gi (V ) + σ−i n−i G−i (V ) for any V ≥ Vu . For a type i firm offering
hi (V )
V ∈ suppi , the vacancy filling rate is mi
: αimxiiui from an unemployed worker, λi ni
mi
Gi (V )
σ−i n−i
from an employed worker at a same type firm, mi
G−i (V ): from an employed worker at
a different type firm. Then it must be that h0i (V ) > 0 for all V ∈ suppi . The instantaneous
profit of a type i operating firm offering wage wi (V ) of value V is yi − wi (V ), the effective
23discount rate of a type i operating firm offering value V is r + qi (V ) where qi (V ) is the
effective separation rate from a job from type i firm with value V . Then the present value
yi −wi (V )
of the profit stream for a type i operating firm offering V to its employee is r+qi (V )
. Using
this, the expected flow profit of job posting for a type i vacant firm offering V and paying
posting cost k is,
yi − wi (V ) hi (V )
π̂i (V ) = −k (T 3)
r + qi (V ) mi
where for V ∈ V H , V H ,
λH [1 − FH (V )] + σH [1 − FL (V )] + δH
if V ∈ V H , V L
qH (V ) =
λH [1 − FH (V )] + δH
if V ∈ V L , V H
and for V ∈ V L , V L ,
λL [1 − FL (V )] + σL [2 − FH (V )] + δL
if V ∈ [V L , V H )
qL (V ) = .
λL [1 − FL (V )] + σL [1 − FH (V )] + δL
if V ∈ V H , V L
We denote the maximized expected profit of job posting for a type i vacant firm offer-
ing any V ∈ suppi as πi , i.e., π̂i (V ) = πi for i ∈ {H, L} and V ∈ suppi . Note that the free
entry condition by offering V requires that πi = 0 for i ∈ {H, L}.
6.4 Symmetric Steady State Equilibrium
Steady state requires outf low = inf low for each V = Vi (w) ∈ V L , V H in the CDF’s, Fi
18
and Gi , for i ∈ {H, L}. We define the symmetric steady state equilibrium as follows .
Definition 1. {ui , si , ni , Gi (V ) , mi , Fi (V ) , πi , Vu,i , Wi (V )}i∈{H,L},V ∈[V ]
i ,V i
18
The derivation of the symmetric steady state equilibrium condition is shown in the Online Appendix.
241. Value function of unemployed workers
rVu = max {rVu,H , rVu,L }
where
" #
Z VH
rVu,H = b + αH (sH ; sH ) V H − Vu,H + {1 − FH (x)} dx − c (SH )
VH
Z VL
rVu,L = b + αL (sL ; sL ) {1 − FL (x)} dx − c (sL )
VL
µ(xi (s)ui ,mi )
with αi (s; si ) = xi (si )ui
and the assumptions
(a) Given other unemployed workers’ search efforts si for i ∈ {H, L}, the optimal
search intensity of an unemployed worker is si for i ∈ {H, L}
(b) V L = Vu and V H ≥ V L .
2. Workers are indifferent to submarkets:
Vu,H = Vu,L = Vu .
3. Value function of employed workers at H type firm offering wage w ∈ [wH , wH ]
(a) If VH (w) ∈ V H , V L :
Z VH
rVH (w) = w + λH {1 − FH (x)} dx
VH (w)
Z VL
+ σH {1 − FL (x)} dx + δH (Vu − VH (w))
VH (w)
25
(b) If VH (w) ∈ V L , V H :
Z VH
rVH (w) = w + λH {1 − FH (x)} dx + δH (Vu − VH (w))
VH (w)
4. Value function of employed workers atL type firm offering wage w ∈ [wL , wL ]
(a) If VL (w) ∈ [V L , V H ):
Z VL
rVL (w) = w + λL {1 − FL (x)} dx
VL (w)
" #
Z VH
+ σL V H − VL (w) + {1 − FH (x)} dx + δL (Vu − VL (w))
VL (w)
(b) If VL (w) ∈ V H , V L :
Z VL
rVL (w) = w + λL {1 − FL (x)} dx
VL (w)
Z VH
+ σL {1 − FH (x)} dx + δL (Vu − VL (w))
VL (w)
5. Equal profit condition within each type19 :
yi − wi (V ) hi (V )
π̂i (V ) = − k = πi
r + qi (V ) mi
for i ∈ {H, L} and V ∈ suppi
6. Free entry for each type of firms: equal profit condition between firm types20 :
πi = 0
for i ∈ {H, L}.
19
This condition is used to find the offer and employed workers’ value distributions, for any given (mH , mL ).
20
This condition is used to solve mH and mL .
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