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Does Organizational Climate Matter in A Highly Competitive Work Environment?
Wai Fong Chua
The University of Sydney
+61 2 8627 0693
Yu Flora Kuang
The University of Melbourne
+61 3 8344 9806
Ava Wu
The University of Sydney
+61 2 9114 0581
January 2021
Abstract
We show that organizational climate exhibits a significant but limited effect in influencing
employee behavior in the brokerage industry, an industry characterized as being highly
competitive. We find that analysts at brokerage firms with lower-rated organizational climate are
more likely to leave their current firm and switch to brokerage firms with higher-rated
organizational climate. Further, those analysts will deliver better performance after switching firms.
However, these performance improvements become insignificant after the analysts’ initial years
of employment in the new firm. We also show that organizational climate-related analyst turnover
negatively affects the performance of incumbent analysts, even when the leaving analyst is not an
All-Star, while the negative performance effects are resolved and become insignificant in a few
years after the turnover.
Keywords: Organizational climate; Analyst turnover; Analyst forecasts; Brokerage industry;
Brokerage firms
We would like to acknowledge the valuable comments from Mary Barth, Eddy Cardinaels, Simon Fung, Wayne
Guay, Ferdinand Gul, Charles Hsu, Lawrence Huang, Donald Moser, Vic Naiker, Matt Pinnuck, Bo Qin, Steven
Salterio, Wei Shi, Alex Yao, and seminar participants at the 2020 AFAANZ conference, the University of Sydney,
and Deakin University.
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Does Organizational Climate Matter in A Highly Competitive Work Environment?
1. Introduction
Organizational climate refers to employees’ perceptions on ‘how it feels to work around here’
(Churchill, Ford, and Walker 1976; James and Jones 1974). It is a theoretical concept that consists
of a set of perceived organizational attributes that are collectively held by employees (Forhand and
Gilmer 1964; Guin 1973; Hellriegel and Slocum 1974). Behavior economics and management
studies suggest that positive outcomes, such as improved performance and reduced employee
turnover, are expected in firms providing a supportive organizational climate (see Kaplan, Bradley,
Luchman, and Haynes 2009; Payne, Pheysey, and Pugh 1971; Robbins and Judge 2013). What
remains unclear in the literature, however, relates to whether organizational climate affects
employees’ behavior when there is intense competition at work. The high level of competition may
push employees to perform, regardless of workplace climate. Further, given the competition
among employees, firms may find it unnecessary to improve employee experiences at work to
motivate higher productivity. Those factors could indicate that organizational climate becomes
less relevant in the presence of high levels of workplace competition. In this study, we are
interested in understanding whether organizational climate matters in a highly competitive work
environment. Specifically, we focus on the brokerage industry, characterized as highly competitive
(Bradshaw, Ertimur, and O’Brien 2017; Guan, Li, Lu, and Wong 2019), and we explore whether
organizational climate matters in explaining sell-side analyst turnover and their performance.
Brokerage firms, as a type of knowledge-intensive firms (Sveiby and Risling 1986;
Starbuck 1992), operate by relying on analysts’ professional knowledge and expertise (Brown,
Call, Clement, and Sharp 2015; Do and Zhang 2019). As tacit knowledge and expertise is often
not codified in a ready-for-dissemination language (Alvesson 2004; Morris and Empson 1998;
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Vogt 1995), the core inputs in brokerage firms’ operations are privately held by analysts. It is thus
important for the firms to attract and retain their analysts. Further, the ownership, management,
and production functions are typically performed by the same individuals in brokerage firms
(Howard 1991; Malos and Campion 1995). Analysts, with this concentration of functionality, are
often not amenable to command or control hierarchies (Malos and Campion 1995; Pinnington
2011), but particularly sensitive to how they are treated in the workplace (Lamont 1992; Rivera
2012). These arguments would conclude that organizational climate in brokerage firms is
important in influencing analysts’ behavior.
On the other hand, the presence of intense competition analysts are faced with at work may
suggest that organizational climate plays a limited role in affecting their behavior. First, an up-or-
out rule is often implemented in brokerage firms and analysts who fail to be promoted are expected
to leave the firm (Galanter and Palay 1991; Morris and Pinnington 1998). Second, analyst
performance is publicly known, ranked, and clearly individual-based (Bradshaw et al. 2016;
Brown et al. 2015). The promotional pressure, combined with the reputational concern, indicates
that analysts would form ex ante expectations on the toughness of their work environment and will
work hard even under unfavorable organizational conditions. As such, analysts may find
environmental factors in their current firms less relevant.1
We start our examination with a series of interviews. We interviewed several practitioners
in the brokerage industry to learn their views regarding the potential impact of organizational
climate on analyst turnover and performance. Several observations emerge from our interviews.
1 Anecdotes show that controversial employee treatment practices are sometimes observed in brokerage firms,
including those highly influential ones. For example, amid surging employee dissatisfaction, James Gorman, Chief
Executive Officer of Morgan Stanley, states that the company will not adjust its current practice and to employees
who are not satisfied at work, “if you’re really unhappy, just leave”. There seems to be an industry-wide perception:
High frequency of analyst turnover is a ‘norm’ of the profession and promoting work environments to attract and
retain analysts might not be an effective strategy to improve firm performance.
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Firstly, there is consensus among interviewees that intense competition is present not merely inside
individual brokerage firms: The whole brokerage industry is said to be highly competitive and
mobile. Secondly, our interviewees provided mixed views on the importance of organizational
climate in explaining analyst behavior. Some stated that analysts are highly motivated, compete
hard to enter the industry, and strive to thrive during an expected or planned short tenure. They
consider organizational climate a less important factor in affecting analysts’ turnover decisions or
performance in a firm. Others, however, expressed a different view. They believe that lack of
support from the firm causes underperformance of analysts and analysts, especially superior
performers, to leave. The insight we gained from interviews confirmed our initial assessment of
the literature that the association between organizational climate and employee behavior in a
highly competitive work environment represents a question worthy of investigation.
We next conduct an empirical analysis on the effects of organizational climate. To measure
organizational climate in a brokerage firm, we collect information on analysts’ perceptions about
their employers from the Glassdoor2, a crowd-sourcing database that hosts over eight million
anonymous employee self-assessments on their employers (Hales, Moon, Swen, and Song 2018;
Teoh 2018). Specifically, we measure analysts’ perceptions of work-life balance, compensation
and benefits, career opportunities, and senior management support. We adopt a factor analysis to
aggregate the four dimensions and employ a composite construct to quantify analysts’ ratings of
their firm’s organizational climate. Using a sample that consists of 3,143 analyst turnover cases
for the years from 2008 to 2017, we show that brokers with a lower-rated organizational climate
will witness a higher likelihood of analyst turnover, especially among All-Star analysts. Further,
2 Founded in June 2008, the Glassdoor provides a public platform that “collects company reviews...from employees
of large companies and displays them anonymously for all members to see”. Per Scott Dobroski, the Glassdoor
spokesman, the Glassdoor verifies each review of a company and ensures that it comes from real employees “through
technological checks of email addresses and through screening by a content management team”.
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we find that those leaving analysts choose to switch to a broker with a higher-rated organizational
climate.
We next examine the performance consequences of organizational climate-driven analyst
turnover. We show that analysts who move to a brokerage firm with a higher-rated organizational
climate will issue more accurate earnings forecasts with improved timeliness. However, the
beneficial effect of increased organizational climate does not last as the significance of the analysts’
performance improvements in the new firm disappears after the initial years of their employment.
We further examine the influence of organizational climate-driven analyst turnover on their peers’
performance in the leaving firm. We show that incumbent analysts will forecast earnings with
lower accuracy and face a reduced likelihood to become an All-Star after their peers’ turnover. In
addition, we find that the negative effects on incumbent analyst performance become statistically
insignificant within two years after the turnover.
We probe into explanations for the transitory performance effects of organizational climate.
We find that analysts who have moved up from a brokerage firm with lower-rated organizational
climate tend to switch brokerage firms again in a few years, which possibly explains why their
performance becomes insensitive to the current firm’s organizational climate after the first few
years of their tenure in the new firm. We also show that brokerage firms undertake measures, such
as hiring analysts externally and reallocating analysts internally, to cover the industry that used to
be covered by leaving analysts. These measures help mitigate the performance deficiency after
organizational climate-driven analyst turnover and the effectiveness of the measures largely
increases as of the second year after the analyst turnover.
Our study contributes to the literature in several ways. We first contribute to organizational
climate research (Griffin and Moorhead 2014; Mullins 2010). We provide evidence to support the
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effect of organizational climate in a context where employees are arguably less susceptible to
adverse firm-level environmental factors. Our results suggest that a less supportive organizational
environment relates to higher employee turnover, while its effect on employee performance seems
limited and not long-lasing in a highly competitive work environment. We also add to analyst
research. Human capital plays a strategic value in the brokerage industry (Brown et al. 2015; Do
and Zhang 2019; Healy and Palepu 2001). Recently, the industry has witnessed an increasing trend
of analysts, especially the most expert ones, leaving their current firms, from which concern of
analyst ‘brain drain’ arises (Guan et al. 2019). Although prior literature shows that individual
analyst characteristics or external regulatory changes relate to analyst turnover (Bradshaw et al.
2017; Mikhail, Walther, and Willis 1999; Pizzani 2009), there is limited knowledge on measures
that brokerage firms can employ to deter analyst turnover. Our study suggests that by building a
positive organizational climate brokerage firms will effectively attract and retain analysts. Further,
we highlight that adopting an integrated ‘package’ of practices, including both monetary and non-
monetary aspects, helps brokerage firms in analyst retention, while focusing on a single dimension,
such as compensation, might be insufficient.
Finally, we study the performance effects of organizational climate-driven analyst turnover
in both the leaving firms and the firms that analysts switch to. Our findings on the transitory
performance effects of analyst turnover may reflect a general feature of brokerage firms in being
able to quickly react to analyst turnover, fill expertise gaps, and recover productivity. This, in turn,
also implies that the market for analysts may not be that tight, thus enabling quick replacement of
those who leave. Further, although evidence shows that the affective states of analysts will affect
their performance (Hope, Li, Lin, and Rabier 2020), our findings suggest that there appears to be
a threshold effect for investment in a supportive organizational climate. That is, investment in a
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supportive climate does attract new staff who switch from other firms. However, it appears that
they perform well only for a limited time and they will move somewhere else shortly. The question
of how to continually sustain high levels of analyst performance remains an issue even in firms
rated well in climate terms.
2. Literatures, Interviews, and Research Questions
2.1 Does organizational climate in brokerage firms matter for analysts?
Extant literature conceptualizes organizational climate as aggregates of social variables
within a workplace, which comprises a set of organizational norms, beliefs, and practices that
influence the perceptions and behavior of people working at the organization (Guiso et al. 2015;
Payne, Pheysey, and Pugh 1971; Robbins and Judge 2013). 3 Behavioral economics and
management literature argues that a supportive organizational climate facilitates the development
of employees’ subjective well-being at work and higher employee commitment (Gold et al. 2014;
Griffin and Moorhead 2014; Mullins 2010). It further helps synchronize employees’ perceptions
of organizational beliefs and values, which facilitates information sharing, coordination, and
cultural cohesion within an organizational (Dalal 2005; Griffin and Moorhead 2014; Milgrom and
Roberts 1990). Literature shows that “[employees’] pleasurable or positive emotional state” at
work (Herzberg 1968; Locke and Latham 1990) will foster the development of self-fulfillment
feelings and belongingness in their firm, thereby increasing their productivity (Griffin and
3 While organizational climate is about “experiential descriptions or perceptions of what happens”, organizational
culture is to define “why these things happen” (Ostroff, Kinicki, Muhammad 2012; Schein 2000; Schneider 2000).
Organizational climate is viewed as temporal, subjective, and manipulatable by authority figures (Denison 1996).
Organizational culture is more stable than climate, has strong roots in history, and is resistant to manipulation (Denison
1996; Schein 2010). Further, literature suggests that different research methods are applied in studying organizational
culture and climate. Qualitative research methods are required in studying culture while, in contrast, quantitative
methods should be applied in studying organizational climate (Denison 1996; Schwartz and Davis 1981). Employee
workplace (dis-)satisfaction represents another related concept, with a focus on individual employees’ feelings and
experiences, while organizational climate refers to shared perceptions among employees (Gold, Gronewold, and
Salterio 2014; Kaplan et al. 2009; Tor and Owen 1997).
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Moorhead 2014; Guiso, Sapienza, and Zingales 2015; Pacelli 2019) and their willingness to form
a long-term horizon in their current position (Fredrickson 1998, 2001; Zelenski, Murphy, and
Jenkins 2008).
We extend the investigation to brokerage firms, a type of knowledge-intensive firms whose
employees are subject to intense competition. A flat organizational structure is implemented in
brokerage firms that consists of merely two grades—associates and partners—and associates who
fail to make the promotion to partners are expected to leave the firm (i.e., an up-or-out rule). Thus,
analysts generally do not expect a long tenure in a broker (Bradshaw et al. 2017) and such
expectations will influence their workplace decision making (Baucells and Bellezza 2017; Gollier
and Muermann 2010). Further, although highly professionalized workforces appreciate autonomy
at work and are generally sensitive to HR practices (Lamont 1992; Rivera 2012), studies show that
under-investments in HR—such as deferral compensation and limited support for employee career
development—should be adopted in firms implementing up-or-out promotional systems (Malos
and Campion 1995). The rational is that promotional competition should provide employees who
are still developing their human capital with sufficient incentive to work hard. Thus, analysts with
ex ante expectations on short tenure and limited support at work might be less influenced by firm-
level environmental factors, including organizational climate. Moreover, competition that analysts
are confronted with is reinforced by public observability of individual analyst performance
(Bradshaw et al. 2017). Such reputational concern motivates analysts to perform, even in case of
poor environmental conditions (Bradshaw et al. 2017; Brown et al. 2015). Taken together, it is
unclear whether organizational climate in brokerage firms will affect analyst behavior.
2.3 Results of interviews with analysts
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To gain analysts’ insight on the effects of organizational climate, we interviewed five
analysts and one long-standing operations manager analyst who oversees an analyst team. On
average, they have a general experience of 15.67 years in the brokerage industry. Among the
interviewees, two are females and others are males. They held various positions in their firms,
ranging from associate or junior analysts to senior or partner analysts. The interviews were semi-
structured and each took approximately one hour. We did develop a protocol of questions (see
Appendix A) and analysts were encouraged to speak of their experiences.4
We first asked about the analysts’ general impression of the industry and the firms they
currently (and used to) work at. The interviewees all believe that the profession is extremely
competitive. High employee turnover is a common place for all brokerage firms. One analyst
indicated that forces that contribute to the high mobility may come from analysts themselves. They
said, “Many analysts enter the industry [and a brokerage firm] prepared to move to the buy-side
or the covered firms.” They also commented on brokerage firms’ efforts to retain their analysts. A
senior analyst said, “[Firms] are not concerned [about employees leaving]. They can pay high
salaries to poach analysts from competitors.” An All-Star analyst also said, “I left my previous
firm and the firm immediately recruited my competitor. The performance impact of my turnover
was nil.” Other interviewees, however, gave somehow different opinions. “Our firm would hire
our analysts for five years plus, we want a good tenure out of a good analyst,” a sector analyst said,
“if we poach a ranked analyst at another firm, it’s very rare that they make a contribution that is
significant in the first 12 months, and it’s the second 12 months, we’re probably still behind, if we
have a sort of return on investment mindset, and the third year we’re starting to get it.”
4 This meant that questions were not always asked in the same order and not all questions were explored.
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Next, we asked whether organizational climate was a key factor influencing analyst
performance and turnover decisions. Our interviewees expressed divided views. Some emphasized
that the presence of intense competition urges analysts to remain self-motivated, regardless of
organizational climate. Partly, this was said to be related to the ‘transparent’ performance
management system. Analysts were said to be self-motivated to perform because their performance
was so publicly ranked. “Every month, we have a [performance] ranking. So you know exactly
where you stand, and it tells you when you go up, when you go down, every month,” a senior
analyst said, “Do you get ranked like that every month in any job? Everyone, globally, sees and
knows exactly how you’re ranked. Your performance is so clear.” A lead analyst said, “For me,
the number one [motivation to work] was the ability to continue to provide the best research I
could, no matter where I am.” “If you ask me what makes a successful analyst?” a senior analyst
said, “What do you see are the traits of those really good, successful analysts? I say, it’s self-
motivation and hard work. Other things are not relevant.”
However, other interviewees express a different opinion and believe that firm practices in
providing a supportive organizational climate matters. “Money is important.” a supervisory analyst
said in our interview, “Actually, a range of factors matter.” A senior analyst said, “a combination
of elements will affect my performance. I will leave if I’m not happy with the way things work.
Working long hours could be stressful. People in this profession might get used to it. But still…”
“[I left my previous broker.] The real issue there was that if I stayed, there was no up,” a lead
analyst said, “I’d lost the ability to perform my best because my time was being consumed [with
bureaucratice minutae].” Further, senior management support was also mentioned. A sector
analyst gave an example, “Other analysts are not travelling [due to lack of support by their firms].
Well, I am, supported by the firm and managers. There’s a competitive opportunity. So, if you go
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to a conference where there’s extraordinarily good information, understanding the industry and so
on, at the moment, in my sector, no one else is there. I love my job.”
2.4 Research questions
Based on the insight we gained from the interviews, we formulate several research
questions. Our first research question relates to whether organizational climate affects analysts’
turnover decisions. In line with the literature (Churchill et al. 1976; James and Jones 1974), we
define organizational climate in a brokerage firm as a set of broker-wide shared norms and
practices that will influence analysts’ attitude and behavior at work. Negative perceptions at work
potentially trigger employee turnover (Brief and Weiss 2002; Diener and Ryan 2009) and we
expect that a less supportive organizational climate in a brokerage firm is related to a higher
likelihood of analysts leaving. Further, we also examine which firm the analysts will move to. We
argue that analysts in a less supportive work environment will develop a strong desire to make a
change and move to firms with a more positive climate. Firms that offer a supportive organizational
climate are, therefore, in a pivotal position to attract those employees (McGregor 1960; Ramnath
et al. 2008). We expect that analysts who leave their firm because of a less supportive
organizational climate will switch to a broker with a more supportive organizational climate.
Our next research question is about the performance effects of analyst turnover. Extant
research argues that a positive working environment and pleasant experiences at work significantly
improve employees’ performance (Griffin and Moorhead 2014; Guiso et al. 2015; Jones and
George 1998). A supportive organizational climate potentially stimulates effective collaboration
and interactions among analysts, thereby facilitating the creation of greater output. Thus, we expect
that analysts’ performance will increase after they switch to an improved organizational climate.
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We further examine the performance effects of analyst turnover in the leaving firms. We
do not have a definite expectation on this research question. Analysts who choose to leave might
be those who are least satisfied or productive in the leaving firms. Those employees may create
obstacles for other employees and negatively affect the overall performance of a firm (Hom and
Knicki 2001). After those employees’ turnover, incumbent analysts will be able to deliver better
performance. However, since organizational climate captures aggregate feelings shared among
employees (Churchill et al. 1976; Kaplan et al. 2009; Tor and Owen 1997), the incumbent analysts
may share the negative attitudes of those who choose to leave because of less supportive
organizational climate. Peers’ turnover could demotivate the incumbent analysts further. In this
scenario, one expects a negative association between analyst turnover and the subsequent
performance of analysts in the leaving firms. Therefore, the performance effect of analyst turnover
for a leaving firm remains open.
3. Empirical Methodology
3.1 Sample and data sources
Our sample period covers years from 2008 to 2017.5 Information on analyst forecasts is
from I/B/E/S and employee perceptions at work from Glassdoor. We first identified a sample of
686 unique brokerage firms from I/B/E/S recommendation U.S. file during our investigation
period, and obtained the names of these firms from the I/B/E/S broker translation file. We next
retrieved information on company names from Glassdoor and manually matched this with the
names of brokerage firms from the I/B/E/S file. In the cases of multiple matches, a research
assistant and a co-author went through company descriptions on the Glassdoor webpage of the
5 The year 2008 is the earliest year when data on employee self-assessment of job perceptions becomes available on
the Glassdoor. We begin with the recommendation file, as opposed to the earnings forecasts file, because I/B/E/S
broker translation file can be directly linked to broker identifiers in the recommendation file.
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firm, to ensure the matching is accurate. In the sample with matched broker names we scraped
analysts’ reviews using a Python Script to query the Glassdoor’s application program interface
(Hales et al. 2018). We consider employees whose job titles include ‘analyst’ or ‘research’ are
analysts. We kept reviews filed by analysts and further removed brokerage firms which have fewer
than three employee ratings in one year.
To identify analyst turnover, we retrieved a list of analysts from I/B/E/S who are employed
in our sample brokerage firms. We then link analyst identities to the I/B/E/S detail forecast file to
obtain the earnings forecasts of those analysts. In this way, we obtained a sample of 16,422 analyst-
year observations from 4,960 unique analysts with available Glassdoor data on analysts’
perceptions at work. All-Star rankings of these analysts were manually collected from the archives
of Institutional Investor magazine. 6 We further restrict our sample to cases where financial
information (such as size) of firms covered by analysts is available on COMPUSTAT, and the
actual earnings per share information for these firms is available on I/B/E/S. Our final sample
includes a total of 15,593 analyst-year observations from 4,814 unique analysts. Panel A of Table
1 summarizes our sample selection procedure.
3.2 Empirical measurement
Organizational Climate
Organizational climate is a composite construct (Hellriegel and Slocum 1974). We measure
organizational climate using analysts’ assessments on firm practices in compensation, career
possibilities, work-life balance, and senior management quality. Firm practices in these four
dimensions are argued to shape employees’ perceptions on organizational climate (Dieterly and
6 Consistent with prior literature (Loh and Stulz 2010; Emery and Li 2009; Fang and Yasuda 2011), we recognize an
analyst as an ‘All-Star’ if the analyst was ranked at the 1st place, 2nd place, 3rd place or a runner-up in a given year.
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Schneider 1974; Mullins 2010).7 Furthermore, the perceived importance of these four dimensions
to analysts is also supported by our interviews. On the Glassdoor, employees provide their ratings
on the four dimensions using a five-point scale with one to be lowest rated and five to be highest
rated. For each dimension, we average all ratings from analysts for a brokerage firm in each year
to create a broker-level measure of that dimension for a given broker-year.
We employ principal factor scoring with promax (oblique) rotation to construct an
aggregate measure of organizational climate. This is a useful dimension-reduction tool to
transform a larger set of highly correlated variables into a composite measure that captures the
information in the larger set.8 In our data, only the first factor loaded by the analysis has an
eigenvalue greater than one (i.e. 2.038). 9 We thus keep the first factor and use it to proxy
organizational climate (OC), where a higher value of OC indicates a higher rated and more positive
organizational climate.
Analyst Turnover
We trace the unique analyst-broker code combinations in the I/B/E/S database and
determine whether an analyst completely stops producing forecasts at all, or whether an analyst
moves to a different brokerage firm and issues forecasts there. Following prior studies (Mikhail et
al. 1999; Hong, Kubik, and Solomon 2000), we employ two measures of analyst turnover. The
7 For example, Dieterly and Schneider (1974) assess organizational climate along four dimensions, including
individual autonomy; position structure; reward structure; and consideration, warmth, and support. Our measurement
captures analysts’ perceptions regarding their brokerage firms in similar aspects. 8 For example, the pairwise correlation between employees’ average perception on career opportunities and that on
senior management support (compensation benefits) is 0.7126 (0.6067). 9 We further perform parallel analysis to confirm whether the four dimensions of organizational climate ratings load
on one factor. We compare eigenvalues collected from the factor analysis to eigenvalues from simulated, random data
with similar distributional properties. We follow the literature and consider factors with eigenvalues greater than
components from simulated data with similar distributional properties (Hales et al. 2018). We repeat the parallel
analysis for one hundred times. The eigenvalue obtained from parallel analysis is 0.049. Only the first principal factor
from our factor analysis has an eigenvalue greater than this amount.
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first measure (LEAVEt+1) indicates whether analysts who still produce forecasts or
recommendations in a brokerage firm in year t stop doing so after. It is noticeable that among the
analysts who have left their brokerage firm in year t + 1, some have chosen to leave the brokerage
industry. Our second turnover measure captures turnover cases of analysts who stay in the
brokerage industry, but switch to another brokerage firm on I/B/E/S in year t + 1 (CHGBROt+1).
That is, the analyst issue forecasts or recommendations in both year t and year t + 1, but in different
brokerage firms. In either turnover measure, the benchmark group (i.e., zero cases) includes
analysts who issue forecasts or recommendations in year t and still do so in the same brokerage
firm in year t + 1.
Panel B of Table 1 presents our sample distribution and frequency of analysts’ turnover.
During our investigation window, we identified 3,143 instances of analyst turnover during our
investigation window, among which there are 976 brokerage firm switches. On average, the
percentage of analyst turnover is 20.2%, the percentage of analysts who switch brokerage firms is
8.4%. The turnover rate peaks in 2008, possibly due to the Global Financial Crisis. We thus include
year fixed effects in the analysis. The turnover rates in our sample are similar to those reported in
prior studies (Groysberg, Lee, and Nanda 2008; Hong et al. 2000; Mikhail et al. 1999). Overall, in
line with a general belief of practitioners and academic researchers, we show that analyst turnover
is indeed historically high.
Analyst Performance
We measure analyst performance using mean-adjusted earnings forecast errors and forecast
timeliness (Clement 1999; Call, Chen, and Tong 2009; Green et al. 2014; Bradley, Gokkaya, and
Liu 2017). We define the relative forecast errors PMAFEijt as a scaled difference between an
absolute forecast error (AFEijt) of analyst i for firm j in time t and an average absolute forecast
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error (MAFEjt) made by all analysts who follow firm j at time t.10 This approach allows us to
compare accuracy of earnings forecasts between analysts, even when they follow different firms
in different years. To summarize, the calculation is as followings:
AFEijt = Absolute (Forecast EPSijt – Actual EPSijt)
PMAFEijt = (AFEijt − MAFEjt) / MAFEjt
To measure analysts’ forecast timeliness, we construct the Leader-Follower Ratio (LFR)
(Cooper, Day and Lewis 2001). This ratio captures lead analysts’ superior skills in collecting and
processing information and releasing their earnings forecasts before competing analysts. It is
calculated as the cumulative number of days by which analyst i's forecast of firm j lags the prior
two other analysts’ forecasts divided by the cumulative number of days by which the same forecast
leads the next two forecasts made by other analysts. The higher the LFR, the better the analysts’
performance. In the examination of incumbent analysts’ performance, we focus on their earnings
forecast errors and opportunities to become an All-Star.
Further, following prior literature, we include control variables on analyst characteristics,
broker characteristics, and analysts’ portfolio complexity (Ertimur, Muslu, and Zhang 2011; Do
and Zhang 2019; Guan et al. 2019; Hong et al. 2000; Mikhail et al. 1999). All variable definitions
are provided in Appendix B. Table 2 provides descriptive statistics of the variables. The descriptive
statistics of control variables are in line with those reported in prior studies (Clement 1999; Ertimur
et al. 2011; Hong et al. 2000; Hong and Kubik 2003). An average analyst in our sample has worked
in the profession (EXPERIENCE) for approximately ten years and follows approximately 11 firms
(FIRMFOLLOW) and two to three industries (INDFOLLOW). Further, on average a brokerage
10 We identify all annual earnings forecasts issued by an analyst during the first 11 months of a fiscal year and with a
minimum forecast horizon of 30 days (Clement 1999). We retain the most recent annual earnings forecast of analyst
i issued for firm j in time t.
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firm employs nearly 86 analysts (BROKERSIZE). There is approximately 8.8% of analysts in our
sample who are ranked as All-Stars (ALLSTAR).
4. Discussion of Empirical Results
4.1 Empirical Results on Analyst Turnover Likelihood
4.1.1 Organizational climate and analyst turnover
Table 3 reports our empirical results on the effect of organizational climate on analyst
turnover, with broker- and year-fixed effects included. In Column (1) of Panel A, LEAVE is used
as the dependent variable which indicates whether an analyst leaves their current firm, and
CHGBRO, an indicator for an analyst switching to another brokerage firm, is the dependent
variable in Column (2). Marginal effects of the estimates are reported in Columns (2) and (4),
respectively. The coefficients of OC are significantly positive (p-value < 0.05) in both Columns
(1) and (3), which suggests that analysts have a higher likelihood of leaving their current position,
either to quit the profession or to join another brokerage firm, when the organizational climate in
their current firm has a lower overall rating. In terms of economic significance, for an average
analyst in our sample, a one standard deviation decrease in the rating of organizational climate will
enhance an analyst’s propensity of departure (switching brokerage firms) by about 3.74%
(15.22%).11
We further examine whether organizational climate in an analyst’s current firm affects their
decision regarding which brokerage firm to join next. We follow the broad logic of Mikhail et al.
(1999) and compare the organizational climate of an analyst’s previous and subsequent brokerage
firms in the cases of analyst turnover. We rank OC of all brokerage firms into quintiles for each
11 In any given year, an analyst has a 20.2% (8.4%) chance of turnover (joining another brokerage firm). With a one
standard deviation decrease in OC, an average analyst in our sample will increase her likelihood of leaving (joining
another broker) by [0.0098*0.770/0.202 =3.74%] ([0.0166*0.770/0.084] = 15.22%).
17
year (i.e., one to be the lowest and five to be highest) and create an indicator variable
CHGBRO_UP that equals one if the analyst moves to a brokerage firm in a higher OC quintile in
year t + 1, and zero otherwise. Using a sample of analysts who switch brokerage firms (i.e., 976
observations), we regress CHGBRO_UP on OC * (-1) and control variables. Panel B Table 3
reports our findings. The significant coefficients on OC * (-1) in the two columns (p-value < 0.01)
suggest that analysts are more likely to move to firms with a higher OC when OC in their current
firm is lower. In sum, our findings suggest that by providing a supportive organizational climate
brokerage firms can effectively retain their employees; Further, analysts in lower-rated
organizational climate will leave for a brokerage firm that offers more supportive organizational
climate, which indicates that organizational climate is an important factor that analysts will assess
on in making turnover decisions.
4.1.2 Effects of individual dimensions in organizational climate
Analysts highlighted in our interviews that individual dimensions of workplace
environment are important. We next examine the impact of individual dimensions, including work-
life balance, compensation and benefits, career opportunities and senior management support,
respectively, on analyst turnover.12 Columns (1) to (4) in Panel A of Table 4 present our findings
on the four individual dimensions, respectively, where CHGBRO is used as the dependent variable.
The results are broadly consistent with what we learnt from interviews. That is, demand for a pay
rise explains why analysts move to another firm; Being unhappy with senior managers also triggers
analyst turnover. Further, limited promotion or career advancement opportunities in the current
firm motivate analysts to explore job opportunities elsewhere. However, as shown in Column (1),
12 Untabulated descriptive statistics show that all the four individual factors have a similar value on the means, i.e.,
approximately 3.20.
18
analysts’ perceptions on work-life balance do not exhibit any significant power in explaining their
propensity to leave, which possibly reflects an observation that the profession of sell-side analysts
is subject to low work-life balance, resulting in lack of variations in this respect across firms
(Bradshaw et al. 2017).
4.1.3 All-Star analyst turnover
We next explore plausible variations among analysts and investigate whether the
relationship between organizational climate and turnover is different in an elite group compared
to an average analyst group. We define the elite group as analysts who have ever been awarded
with an All-Star status, i.e., Ever-All-Star (Emery and Li 2009). The average group consists of all
the remaining analysts, i.e., Never-All-Star. We perform the analysis in these two subsamples,
respectively, and our results are reported in Panel B of Table 4.13 We find that the coefficients on
OC are significantly negative across all columns for both subsamples, suggesting that analysts in
general make turnover decisions considering the quality of work climate. We also compare the
coefficient differences across the two subsamples. Specifically, we perform Chi-square tests in
coefficient comparisons. Statistics of the tests show that the coefficient of OC is more significant
in the Ever-All-Star subsample than in the Never-All-Star subsample (p-value < 0.01). Therefore,
elite analysts are more sensitive to organizational climate, possibly reflecting their greater pool of
alternative job opportunities elsewhere (Guan et al. 2019), and have a higher tendency to leave
when working environment is less supportive.
4.2 Empirical Results on Performance Subsequence of Analyst Turnover
4.2.1 Analysts’ forecast performance after switch
13 The number of observations decreases as some observations are dropped out automatically in the analysis.
19
We next examine analysts’ forecasting performance after switching to a broker with a
higher-rated organizational climate. We construct a dummy variable (SWITCH) indicating an
analyst moving to a brokerage firm with higher OC than their current firm. We consider analysts
who do not switch firms during the cases with a zero value on SWITCH. We regress the two
performance variables, namely relative forecast errors (PMAFE) and mean-adjusted forecast
timeliness (LFR), respectively, on SWITCH. We perform the analysis for years t + 1, t + 2, and t +
3, respectively, where t is the year when analyst turnover occurs. We include a set of control
variables that potentially affect analyst performance (Mikhail et al.1997; Clement 1999; Call et al.
2009).14 In addition, analyst and year fixed effects are included to control for secular trends or
time-invariant analyst characteristics.15 Further, standard errors are estimated by double clustering
at the analyst and firm levels.
Table 5 reports our results. In Panel A where analyst performance is measured by PMAFE,
a significantly negative sign on SWITCH emerges in year t + 1 (p-value < 0.01), the first year after
an analyst moves to a firm with higher OC, while it becomes insignificant afterwards. The findings
thus indicate a transitory effect of organizational climate in improving analysts’ forecasting
accuracy. Panel B presents a similar performance effect when analyst forecasting performance is
measured by LFR. Altogether, our findings suggest that analysts show immediate performance
improvements after switching to a broker with a more supportive organizational climate while the
performance effects become insignificant after their initial years of employment in the new firm.
We next explore explanations for the transitory performance effects of organizational
climate. We argue that analysts who are considering moving to another firm may find
14 Further, we measure the control variables in t + 1, t + 2, and t + 3, respectively, corresponding to the measurement
of SWITCH. 15 We obtain consistent results when controlling for broker fixed effects instead of analyst fixed effects.
20
organizational climate in the current firm less relevant to their performance. We examine whether
analysts who switch from a lower-rated organizational climate are planning to move again soon
after joining the new firm. We find that more than 50% of those analysts will leave before year t
+ 3, the third year of their tenure in the new firm, while merely 25% of them will stay in the new
firm till after year t + 4.16 Analysts’ intention of switching firms explains why the performance
effects of organizational climate seem transitory and become insignificant in a few years after they
join a firm with higher-rated organizational climate.
4.2.2 Impact on incumbent analysts’ performance
We next investigate whether organizational-climate driven analyst turnover will influence
the performance of incumbent analysts in leaving firms. Specifically, we examine incumbent
analyst performance in forecasting and opportunities to become an All-Star during a three-year
window after their peers leave for a higher-rated organizational climate. Following Do and Zhang
(2019), we focus on a sample of brokerage firms that experience at least one instance of analyst
turnover. We further constrain that the analyst moves to a broker in a higher OC quintile. We
construct a variable, DEPART, which is a dummy variable that equals one if an incumbent analyst
i in the leaving firm follows the same industries as the leaving analyst(s) in year t. The variable
DEPART is zero in the cases of remaining incumbent analysts who work at the same brokerage
but cover other industries in year t.
Our empirical results are reported in Table 6. In Panel A, the significantly positive sign of
DEPART for year t + 1 (p-value < 0.05) suggests that analysts who follow the same industries as
their leaving peers generate larger forecast errors in the first year after the turnover. Further,
DEPART is significantly negative in Panel B (p-value < 0.10), which indicates a reduced likelihood
16 In comparison, in our sample average tenure of analysts in a brokerage firm is 6 years.
21
of those analysts to become an All-Star in that year. Altogether, the results suggest that the
departures of analysts who switch to a broker with higher OC have a detrimental performance
effect to incumbent analysts who follow the same industry.17 We further compare individual
characteristics of departing analysts with their incumbent peers who follow the same industries.
Panel A in Table 6 reports our findings. We show that departing analysts have an average
experience of 16.5 years, and 19.3% of them are All-Star analysts. In comparison, their incumbent
peers who follow the same industries have an average experience of 14.4 years and only 7.94% of
them are All-Stars. The differences are also statistically significant (p-value < 0.01), suggesting
that the departing analysts are more talented and experienced than the rest in the cohort. In line
with our prior findings, the findings show that high-quality analysts appear to be most sensitive to
organizational climate and become first squeezed out in the cases of a poor working environment.
Further, the findings also indicate that analyst turnover represents a matching process where by
switching firms, analysts intend to join brokerage firms that better match with their capability and
potential. Organizational climate is a significant factor analysts refer to in judging whether a firm
is a potential match to their capability and experiences.
We further explore how long the effect of losing those analysts will last for. We replicate
the analysis on peer analysts’ performance for more years, including t + 2 and t + 3, respectively.
As shown in Panels A and B of Table 6, the significance on DEPART disappears after year t + 1,
which suggests that analyst turnover affects incumbent peers’ performance for a limited time and,
on average, it appears the required mitigation period takes no longer than one year (or two years
including the turnover year). What are the plausible measures brokerage firms undertake to resolve
the undesirable performance effects after organizational climate-driven analyst turnover? In our
17 Untabulated statistics also show that there is a loss of industry coverage in the brokerage firm in year t + 1.
22
interviews, analysts underscored that external hiring can effectively mitigate the performance
deficiency due to analyst turnover. Apart from that, firms may also reallocate analysts internally
to cover the leaving analysts’ tasks. We find that both methods, external hiring and internal
reallocating, are applied in brokerage firms after analysts leave for a higher-rated organizational
climate—in comparison hiring externally is more prevalent, i.e., about three times more frequently
observed, than internal reallocating. Comparing the experiences of those analysts, we find that
analysts who are internally reallocated generally have more experiences than the external hires.
However, neither external hires nor internally reallocated analysts show greater experiences or
expertise compared to the analysts who have left for a better organizational climate. Therefore, our
findings suggest that knowledge depletion indeed occurs at organizational climate-driven analyst
turnover, while firm practices in acquiring and reallocating resources work effectively to resolve
the undesirable performance effects shortly after the turnover.
5. Sensitive Analysis and Robustness Tests
5.1 Endogeneity
We are aware of possible endogeneity in our analyst turnover analysis. Specially, we face
sample selection issues as the Glassdoor survey ratings are given voluntarily. Analysts who choose
to report their ratings for their employers to Glassdoor might try to damage or improve a firm’s
image in a biased way. Further, an omitted-variable concern might be relevant if there are
brokerage firm-specific or time-varying omitted variables that drive both the ratings and analysts’
decision to leave. In this section, we employ several techniques to handle these endogeneity issues.
We first use a two-stage Heckman correction method to mitigate potential self-selection
bias (Heckman 1979). At the first stage, we employ a probit regression to predict the likelihood of
a broker-year to have at least one analyst rating. We use the average analyst-employee ratio for
23
brokerage firms within the same decile of firm size as an instrumental variable (Huang, Masli,
Meschke, and Guthrie 2017). In the regression, we control for brokerage firm size, brokerage age,
the number of firms and industries covered by a brokerage firm, and the average mean-adjusted
forecast errors of the analysts employed by the brokerage firm. At the second stage, we include an
inverse Mills ratio, computed based on the first-stage probit regression results, as an additional
independent variable to correct for self-selection in analysts filing reviews. We find that after the
correction the coefficient on OC remains negative and significant.
We also conduct a difference-in-difference (DID) and a 2SLS regression to further address
the endogeneity issues. We employ CEO turnover as a quasi-natural experiment that imposes a
negative shock to brokerage firm’s organization culture (Graham et al. 2016; Litwin and Stringer
1968). We manually collect the CEO turnover data from various sources including brokerage
websites, S&P Capital IQ, and LinkedIn. In our sample period, we identify 13 brokerage firms that
have experienced CEO turnover, which affects 625 analyst-year observations. Using the DID
approach, we define treatment firms as those that experienced a CEO change and the remaining
brokerage firms are control firms. We adopt a generalized DID regression approach and include
an indicator for the treatment firms, interacting with an indicator for years after CEO turnover
(Roberts and Whited 2013). We find a significantly positive sign on the interaction term, which
indicates that analysts are more likely to leave a brokerage firm when the firm experiences a
negative shock to organizational climate. In the 2SLS regression, we use the interaction term in
the DID as an instrumental variable, to extract the exogenous component of organizational climate.
Our prior findings remain consistent. Appendix C exhibits the results of our endogeneity analyses.
In summary, our prior results on the association between organizational climate and analyst
turnover are robust after we address endogeneity concerns.
24
5.2 Other Robustness Tests
We perform several additional tests to check the robustness of our findings. First, we
conduct falsification tests to check the effects of analyst turnover on incumbent analysts’
performance. We create a set of placebo events. Specifically, we lag one year for analyst turnover
and assume that those events take place in year t – 1, rather than year t. We replicate our analysis
of incumbent analysts’ forecast errors and likelihood of becoming All-Star using these placebo
turnover events. The insignificant coefficients on DEPART in the tests suggest that it is OC-driven
departure of analysts rather than correlated variables existing before the departure that influences
incumbents’ performance.
Next, we examine analysts’ performance in year t – 1 as benchmark to evaluate their
performance in years t + 1, t + 2, and t + 3, respectively, after switching to a brokerage firm with
higher OC. We also remove years with significant events, such as the Global Financial Crisis.
Finally, we vary our methods in measuring organizational climate. Instead of using the average
ratings for a given broker-year, we use median ratings of analysts in each broker-year to construct
the factor scores. We also check whether our results are robust when OC is measured by a total
score with the four dimensions equally weighted, rather than a principal factor score. In all cases,
we obtain consistent results.
6. Conclusions
Prior literature suggests that organizational climate has a major impact on employee
behavior (Mullins 2010; Robbins and Judge 2013). We examine whether the effect of
organizational climate applies in the brokerage industry, an industry characterized as highly
competitive and subject to extremely high employee turnover (Bradshaw et al. 2017). Our results
from both interviews and empirical analyses show that organizational climate does explain
25
analysts’ turnover and their performance. We find that analysts, especially expert performers, are
more likely to leave in the cases of a less supportive environment provided by their firm. Further,
those analysts choose to join a brokerage firm with a higher-rated organizational climate. We also
follow up on how analysts perform in their new firms. We find that after switching to a firm with
a higher-rated organizational climate, analysts will deliver improved performance in forecasting.
We further examine whether analyst turnover affects the performance of incumbent analysts in the
leaving firm. We find that analyst turnover that is driven by organizational climate will negatively
affect the outputs of incumbent analysts.
However, the performance effects of organizational climate seem to be transitory. The
improvements on analyst performance after they move up to a more supportive organizational
climate become statistically insignificant after their initial years in the new firm. We show that
those analysts tend to have short tenure in the new firm and plan to move again soon after joining
the new firm. We also find that the detrimental effects of analyst turnover on incumbent analyst
performance are not persistent. We show that brokerage firms that lose analysts due to
organizational climate manage to mitigate the undesirable performance effects by hiring externally
and reallocating resources internally. Taken together, our findings suggest that workplace
competition plays a significant role in explaining the variations in the effects of organizational
climate.
26
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Appendix A
Interview Questions
1. General questions:
What is your role in the firm?
How long have you been here?
Gender
2. General questions to investigate areas of organizational climate
Do you think the general climate of the workplace influences a person’s decision to stay
or leave your current firm? Why or why not?
Do you have family or career responsibilities?
How do you manage these responsibilities as well as full-time employment?
Would you like to comment generally on remuneration structures within the industry?
Would you like to comment generally on remuneration structure within your firm?
What future carer opportunities are of interest to you?
How does the firm support these career intentions and aspirations?
How often do you have a performance review with your manager?
How do you think senior management play a role in improving or influencing your
performance?
3. The impact of turnover
In your experience, how long do analysts stay in this industry??
Is it common for analysts to move between brokerage firms or do they stay in the same
firm for a while?
Do you think a colleague’s departure affects the motivation of others to perform and your
productivity?
31
Appendix B
Variable Definitions
Variable Definition
OC A principal factor score for a brokerage firm in year t, loaded from the average
rating of a brokerage firm by its analysts across four individual dimensions that
include: (1) average rating of a brokerage firm’s work-life balance by its analysts
in year t; (2) average rating of a brokerage firm’s compensation and benefits by
its analysts in year t; (3) average rating of a brokerage firm’s career opportunities
by its analysts in year t; (4) average rating of a brokerage firm’s senior
management by its analysts in year t.
WORK-LIFE
BALANCE
average rating of a brokerage firm’s work-life balance by its analysts in year t.
COMPENSATION
AND BENEFITS
average rating of a brokerage firm’s compensation and benefits by its analysts in
year t.
CAREER
OPPORTUNITIES
average rating of a brokerage firm’s career opportunities by its analysts in year t.
SENIOR
MANAGEMENT
average rating of a brokerage firm’s senior management by its analysts in year t.
LEAVE an indicator variable that equals one if analysts who still produce forecasts in a
brokerage firm in year t leave the firm in year t + 1, and zero otherwise.
CHGBRO an indicator variable that equals one if analysts who still produce forecasts in a
brokerage firm in year t switch to another brokerage firm on I/B/E/S in year t +
1, and zero otherwise.
CHGBRO_UP an indicator variable that equals one if analysts who still produce forecasts in a
brokerage firm in year t moves upwards to a brokerage firm in a higher OC
quintile in year t + 1, and zero for analysts who switch brokers without moving
upwards to a brokerage firm in a higher OC quintile in year t + 1.
TOPACCU an indicator variable that equals one if an analyst’s relative annual earnings
forecast accuracy score is in the top 10% of all analysts and zero otherwise, where
the relative accuracy score is calculated as per Hong and Kubik (2003).
BOTACCU an indicator variable that equals one if an analyst’s relative annual earnings
forecast accuracy score is in the bottom 10% of all analysts and zero otherwise,
where the relative accuracy score is calculated as per Hong and Kubik (2003).
EXPERIENCE number of years since an analyst first issued an earnings forecast (for any firm)
recorded in I/B/E/S.
BROKERSIZE number of analysts employed by a brokerage firm in year t
FIRMFOLLOW number of firms followed by analyst i in year t.
32
Variable Definition
INDFOLLOW number of two-digit SICs followed by analyst i in year t.
AVGSIZE average size of all the firms covered by analyst i in year t, where size of the firm
is calculated by natural logarithm of total assets.
RELOPTM an indicator variable that equals one if analyst i’s forecast for firm j is greater than
the consensus forecast for firm j (i.e. an average forecast issued by other analysts
excluding analyst i), and zero otherwise.
ALLSTAR an indicator variable that equals one if an analyst is named to Institutional
Investor’s All-Star team in year t, and zero otherwise.
SWITCH an indicator variable that equals one if an analyst who switches brokerage firms,
moves up to a brokerage firm in a higher OC quintile than the current firm, and
zero for analysts who do not switch firms during the year.
DEPART an indicator variable that equals one if analyst i works in the same brokerage firm
and follows the same industries as the leaving peer(s) in year t, and zero for
remaining incumbent analysts who work at the same brokerage but cover other
industries in year t.
GEXP total number of years that analyst i appeared in I/B/E/S minus the average tenure
of analysts following firm j at year t.
FEXP number of years through year t for which analyst i supplied at least one earnings
forecast for firm j minus the average number of years for analysts following firm
j had supplied earnings forecasts through year t.
TOP10 an indicator variable coded one if an analyst works at a top-decile brokerage house
minus a mean value of top decile brokerage firm indicators for analysts following
firm j at year t.
NFIRM number of firms followed by analyst i for firm j at year t minus an average number
of firms followed by analysts following firm j at year t.
NIND number of two-digit SICs followed by analyst i at year t minus an average number
of two-digit SICs followed by analysts following firm j at year t.
DAYS age of analyst i forecast minus an average age of forecasts issued by analysts
following firm j at year t.
FREQUENCY number of forecasts analyst i announced for firm j in year t minus an average
number of forecasts announced by all analysts following firm j at year t.
PMAFE proportional mean absolute forecast error calculated as difference between an
absolute forecast error for analyst i on firm j and a mean absolute forecast error
for firm j at year t scaled by the mean absolute forecast error for firm j at year t.
LAGPMAFE proportional mean absolute forecast error calculated as difference between an
absolute forecast error for analyst i on firm j and an mean absolute forecast error
33
Variable Definition
for firm j at year t − 1 scaled by the mean absolute forecast error for firm j at year
t − 1.
LFR the cumulative number of days by which analyst i's forecast of firm j lags the prior
two other analysts’ forecasts divided by the cumulative number of days by which
the same forecast leads the next two forecasts made by other analysts
ANALYST
analyst – employee ratio, calculated as the number of reviews submitted by
analysts divided by the number of reviews submitted by total employees within a
brokerage firm k in year t.
ANALYST_BRO the average analyst-employee ratio (ANALYST) for brokerage firms within the
same decile of firm size
BROKER_AGE the number of years a brokerage firm exists in I/B/E/S
BROKER_NFIRM the number of firms followed by a brokerage firm k in year t
BROKER_NIND the number of industries followed by a brokerage firm k in year t
BROKER_PMAFE
the average mean-adjusted forecast errors of the analysts employed by the
brokerage firm k in year t
INVMILLS the inverse Mills ratio
TREAT × POST
The interaction term between TREAT and POST, where TREAT equals one for the
treatment brokerage firms that experience CEO change and equals zero otherwise.
POST equals one for years after the CEO change event and equals zero otherwise.
34
Appendix C
Tests to Address Endogeneity
This table reports results of tests to address endogeneity. All standard errors are clustered at the analyst
levels. The p-values are in parentheses. ∗ indicates significance at the 10% level; ∗∗ significance at 5%; ∗∗∗
significance at 1%. Variable definitions are provided in the Appendix B.
Panel A: First stage Heckman regression
VARIABLES DV = whether a brokerage house to have at least one analyst rating
ANALYST_BRO 5.7131***
(0.000)
BROKERSIZE 0.0045***
(0.000)
BROKER_AGE -0.0129***
(0.000)
BROKER_NFIRM 0.0687***
(0.000)
BROKER_NIND -0.1172***
(0.000)
BROKER_PMAFE -0.1105***
(0.000)
Year fixed effects yes
N 50,137
Pseudo R2 0.1289
Panel B: First stage 2SLS regression
VARIABLES DV = OC
(1) (2)
TREAT × POST -0.3044*** -0.3258***
(0.000) (0.000)
ANALYST 0.5993*
(0.067)
TOPACCU 0.0217 0.0215
(0.244) (0.248)
BOTACCU -0.0106 -0.0102
(0.576) (0.591)
GEXP 0.0001 0.0001
(0.862) (0.863)
BSIZE 0.0007** 0.0008**
(0.025) (0.017)
NOFIRM 0.0010 0.0009
(0.258) (0.264)
NOIND -0.0048 -0.0048
(0.129) (0.129)
35
VARIABLES DV = OC
(1) (2)
AVGSIZE 0.3520*** 0.3515***
(0.000) (0.000)
RELOPTM -0.0001 -0.0001
(0.627) (0.612)
ALLSTAR 0.0291 0.0302
(0.158) (0.143)
Broker fixed effects yes yes
Year fixed effects yes yes
N 14,968 14,968
R-squared 0.442 0.443
Panel C: Tests to address endogeneity issues in analyst turnover
DV = LEAVE
Heckman DID 2SLS
(1) (2) (3)
OC -0.0710* -0.0871*
(0.060) (0.083)
TREAT × POST 0.1935*
(0.080) TOPACCU -0.2189*** -0.2128*** -0.0172
(0.004) (0.006) (0.119)
BOTACCU 1.2856*** 1.2788*** 0.2890***
(0.000) (0.000) (0.000)
EXPERIENCE 0.0175*** 0.0190*** 0.0020***
(0.000) (0.000) (0.000)
BROKERSIZE 0.0012 -0.0003 0.0001
(0.406) (0.808) (0.688)
FIRMFOLLOW -0.0871*** -0.0874*** -0.0088***
(0.000) (0.000) (0.000)
INDFOLLOW -0.0357** -0.0329* -0.0035*
(0.037) (0.057) (0.061)
AVGSIZE 0.0715 0.0435 0.0380
(0.519) (0.691) (0.108)
RELOPTM 0.0023*** 0.0024*** 0.0004***
(0.002) (0.001) (0.000)
ALLSTAR -0.5980*** -0.6056*** -0.0431***
(0.000) (0.000) (0.000)
INVMILLS 0.6827**
(0.021) Broker fixed effects yes yes yes
Year fixed effects yes yes
Hansen p-value
36
DV = LEAVE
Heckman DID 2SLS
(1) (2) (3)
Cragg–Donald Wald F stats 138.379
Wald chi2 1859.85 N 15,593 14,968 14,968
Pseudo R2 0.1753 0.1764 0.171
37
Table 1
Sample Selection and Sample Distribution
This table reports the sample selection in Panel A and sample distribution and analyst turnover by years in
Panel B.
Panel A: Sample selection procedure
Sample selection criteria Number of analyst-year Number of analysts
Analyst-year with earnings forecast errors
for 2008-2017 39,242 9,732
Keep: brokerage firms with at least three
analysts’ ratings on Glassdoor over the
sample 16,422 4,960
Keep: analysts’ characteristic variables and
covered firm characteristics 15,593 4,814
Final Sample 15,593 4,814
Panel B: Sample distribution and analyst turnover
Analysts leave brokers in year t + 1 Analysts switch brokers in year t + 1
Number of
analysts
Number of
turnover
Percent
turnover
Number of
analysts
Number of
switch
Percent
switch
2008 1,303 472 0.362 895 142 0.159
2009 1,139 261 0.229 891 90 0.101
2010 1,622 233 0.144 1,345 118 0.088
2011 1,775 326 0.184 1,425 116 0.081
2012 1,613 335 0.208 1,233 80 0.065
2013 1,807 345 0.191 1,333 127 0.095
2014 2,025 359 0.177 1,487 144 0.097
2015 2,198 414 0.188 1,545 90 0.058
2016 2,111 398 0.189 1,437 69 0.048
Total 15,593 3,143 0.202 11,591 976 0.084
38
Table 2
Descriptive Statistics
This table reports the summary statistics. The sample consists of 15,593 analyst-year observations from
2008 to 2017. All continuous variables are winsorized at the 1st and 99th percentiles. Detailed descriptions
of variables are provided in the Appendix B.
VARIABLE N MEAN SD P25 P50 P75
LEAVE 15,593 0.202 0.401 0.000 0.000 0.000
CHGBRO 11,591 0.084 0.278 0.000 0.000 0.000
OC 15,593 0.016 0.819 -0.320 0.077 0.423
TOPACCU 15,593 0.094 0.291 0.000 0.000 0.000
BOTACCU 15,593 0.090 0.287 0.000 0.000 0.000
EXPERIENCE 15,593 9.850 9.117 3.000 6.000 15.000
BROKERSIZE 15,593 85.825 47.239 42.000 92.000 118.000
FIRMFOLLOW 15,593 10.693 8.739 3.000 9.000 17.000
INDFOLLOW 15,593 2.584 2.030 1.000 2.000 3.000
AVGSIZE 15,593 8.426 0.784 8.083 8.585 8.745
AVGSIZE (raw) 15,593 30881.4 147266.8 1162.443 4135.837 14863.64
RELOPTM 15,593 51.304 28.673 33.333 50.000 66.667
ALLSTAR 15,593 0.088 0.283 0.000 0.000 0.000
39
Table 3
Organizational Climate and Analyst Turnover
This table Panel A reports the results of the relation between organizational climate and turnover, where
turnover is measured by indicator variables capturing whether analysts leave the firms (LEAVE) or switch
to another brokerage house on I/B/E/S (CHGBRO). Panel B reports the results on analysts’ likelihood to
join a brokerage firm with higher rated organizational climate than their current brokerage firm
(CHGBRO_UP). All standard errors are clustered at the analyst levels. The p-values are in parentheses. ∗
indicates significance at the 10% level; ∗∗ significance at 5%; ∗∗∗ significance at 1%. Variable definitions
are provided in the Appendix B.
Panel A: Likelihood of analyst turnover
VARIABLES
DV = LEAVE DV = CHGBRO
Regression
coefficients
Marginal
effects
Regression
coefficients
Marginal
effects
(1) (2) (3) (4)
OC -0.0752** -0.0098** -0.2498*** -0.0166***
(0.046) (0.046) (0.000) (0.000)
TOPACCU -0.2208*** -0.0287*** -0.1205 -0.0080
(0.003) (0.003) (0.386) (0.385)
BOTACCU 1.2883*** 0.1675*** 0.4115*** 0.0274***
(0.000) (0.000) (0.001) (0.001)
EXPERIENCE 0.0175*** 0.0023*** 0.0227*** 0.0015***
(0.000) (0.000) (0.000) (0.000)
BROKERSIZE -0.0002 -0.0000 0.0023 0.0002
(0.869) (0.869) (0.364) (0.363)
FIRMFOLLOW -0.0873*** -0.0113*** 0.0005 0.0000
(0.000) (0.000) (0.930) (0.930)
INDFOLLOW -0.0352** -0.0046** -0.0629*** -0.0042***
(0.040) (0.040) (0.009) (0.009)
AVGSIZE 0.0303 0.0039 0.0323 0.0021
(0.781) (0.781) (0.286) (0.286)
RELOPTM 0.0022*** 0.0003*** 0.0001 0.0000
(0.002) (0.002) (0.925) (0.925)
ALLSTAR -0.5990*** -0.0779*** -0.0706 -0.0047
(0.000) (0.000) (0.648) (0.648)
Broker fixed effects yes yes yes yes
Year fixed effects yes yes yes yes
N 15,593 15,593 11,591 11,591
Pseudo R2 0.175 0.158
40
Panel B: Likelihood of analyst moving up
VARIABLES
DV = CHGBRO_UP
Regression coefficients Marginal effects
(1) (2)
OC * (-1) 3.1188*** 0.4068***
(0.000) (0.000)
TOPACCU 0.0814 0.0106
(0.812) (0.812)
BOTACCU -0.4854 -0.0633
(0.213) (0.214)
EXPERIENCE 0.0061 0.0008
(0.659) (0.658)
BROKERSIZE -0.0069* -0.0009*
(0.084) (0.082)
FIRMFOLLOW 0.0074 0.0010
(0.662) (0.662)
INDFOLLOW -0.0932 -0.0122
(0.150) (0.149)
AVGSIZE 0.0658 0.0086
(0.381) (0.383)
RELOPTM 0.0025 0.0003
(0.537) (0.538)
ALLSTAR 0.6631** 0.0865**
(0.026) (0.024)
Broker fixed effects yes yes
Year fixed effects yes yes
N 711 711
Pseudo R2 0.3908
41
Table 4
Individual Dimensions of Organizational Climate and Analyst Turnover
This table reports additional results. Panel A report the results of the relation between the individual
dimensions of organizational climate and turnover, where turnover is measured by an indicator variable
capturing whether analysts switch to another broker on I/B/E/S (CHGBRO) in year t + 1. Individual
dimensions of organizational climate include analysts’ ratings on work-life balance (Column 1),
compensation and benefits (Column 2), career opportunities (Column 3), senior management (Column 4).
Panel B present the results with a comparison of Ever-All-Star versus Never-All-Star analysts groups. Panel
C presents the results on the likelihood of analysts switching to a brokerage house with higher OC in year
t + 1 (CHGBRO_UP). All standard errors are clustered at the analyst levels. The p-values are in parentheses.
∗ indicates significance at the 10% level; ∗∗ significance at 5%; ∗∗∗ significance at 1%. Variable definitions
are provided in the Appendix B.
Panel A: Individual dimensions of organizational climate
DV = CHGBRO
Work-life
balance
Compensation
and benefits
Career
opportunities
Senior
management
(1) (2) (3) (4)
OC -0.0667 -0.3302*** -0.1411** -0.3205***
(0.336) (0.000) (0.031) (0.000)
TOPACCU -0.1237 -0.1174 -0.1275 -0.1130
(0.371) (0.400) (0.357) (0.415)
BOTACCU 0.4098*** 0.4145*** 0.4096*** 0.4069***
(0.001) (0.001) (0.001) (0.002)
EXPERIENCE 0.0227*** 0.0224*** 0.0227*** 0.0228***
(0.000) (0.000) (0.000) (0.000)
BROKERSIZE 0.0017 0.0025 0.0020 0.0020
(0.513) (0.336) (0.442) (0.432)
FIRMFOLLOW 0.0008 0.0006 0.0007 0.0004
(0.899) (0.916) (0.915) (0.951)
INDFOLLOW -0.0620*** -0.0622*** -0.0628*** -0.0625***
(0.009) (0.009) (0.009) (0.009)
AVGSIZE 0.0296 0.0325 0.0308 0.0316
(0.326) (0.281) (0.308) (0.296)
RELOPTM 0.0001 0.0001 0.0001 0.0001
(0.944) (0.931) (0.933) (0.958)
ALLSTAR -0.0845 -0.0667 -0.0780 -0.0739
(0.582) (0.667) (0.613) (0.632)
Broker fixed effects yes yes yes yes
Year fixed effects yes yes yes yes
N 11,591 11,591 11,591 11,591
Pseudo R2 0.1547 0.1584 0.1553 0.1588
42
Panel B: Ever-All-Star vs. Never-All-Star analysts
VARIABLES
DV = LEAVE DV = CHGBRO
Ever-All-Star Never-All-Star Ever-All-Star Never-All-Star
(1) (2) (3) (4)
OC -0.1528* -0.0652* -0.3040*** -0.2845***
(0.085) (0.095) (0.001) (0.000)
TOPACCU -0.6981 -0.2322*** -0.6866 0.0249
(0.121) (0.003) (0.147) (0.863)
BOTACCU 2.6794*** 1.2066*** 0.4586 0.4731***
(0.000) (0.000) (0.186) (0.001)
EXPERIENCE 0.0327*** 0.0147*** 0.0191** 0.0251***
(0.000) (0.000) (0.014) (0.000)
BROKERSIZE -0.0023 -0.0015 -0.0091*** 0.0039
(0.162) (0.316) (0.000) (0.207)
FIRMFOLLOW -0.0658*** -0.0925*** -0.0263** 0.0095
(0.000) (0.000) (0.029) (0.191)
INDFOLLOW -0.0216 -0.0421** -0.0006 -0.0811**
(0.501) (0.033) (0.988) (0.011)
AVGSIZE -0.1056 0.0849 -0.0097 0.0093
(0.367) (0.449) (0.878) (0.782)
RELOPTM 0.0027 0.0024*** -0.0096** 0.0016
(0.392) (0.001) (0.001) (0.330)
Broker fixed effects yes yes yes yes
Year fixed effects yes yes yes yes
Chi-square statistics
for coefficient
difference on OC 155.37*** 50.86***
N 2,877 12,716 2,643 8,787
Pseudo R2 0.1624 0.1575 0.0913 0.1545
43
Table 5
Analyst Performance After Moving Up
This table reports the results of analysts’ forecasting performance in year t + 1 (Column 1), t + 2 (Column
2) and t + 3 (Column 3) after they switch to a broker with higher OC. Results are presented in Panel A
(Panel B) when analysts’ forecasting performance is measured by forecast errors (forecast timeliness). All
standard errors are double clustered at the analyst and year levels. The p-values are in parentheses. ∗
indicates significance at the 10% level; ∗∗ significance at 5%; ∗∗∗ significance at 1%. Variable definitions
are provided in the Appendix B.
Panel A: Forecast errors
VARIABLES
t + 1 t + 2 t + 3
(1) (2) (3)
SWITCH -0.0641*** -0.0254 0.0413
(0.010) (0.329) (0.122)
GEXP -0.0044*** -0.0052*** -0.0042**
(0.001) (0.000) (0.013)
FEXP -0.0010 -0.0006 -0.0001
(0.156) (0.469) (0.918)
TOP10 0.0054 0.0211 0.0252
(0.729) (0.284) (0.269)
NOFIRM -0.0006 -0.0002 0.0001
(0.545) (0.860) (0.953)
NOIND -0.0046 -0.0065* -0.0074*
(0.187) (0.097) (0.099)
DAYS 0.0043*** 0.0043*** 0.0041***
(0.000) (0.000) (0.000)
FREQUENCY -0.0134*** -0.0129*** -0.0119***
(0.000) (0.000) (0.000)
LAGPMAFE 0.1008*** 0.0964*** 0.0963***
(0.000) (0.000) (0.000)
Analyst fixed effects yes yes yes
Year fixed effects yes yes yes
N 90,159 69,223 51,619
R-squared 0.242 0.240 0.230
44
Panel B: Forecast Timeliness
t + 1 t + 2 t + 3
VARIABLES (1) (2) (3)
SWITCH 3.7983* 0.2518 -0.7959
(0.067) (0.889) (0.720)
GEXP 0.0117 0.0188 -0.0661
(0.932) (0.904) (0.703)
FEXP -0.0125 0.0303 0.0565
(0.849) (0.674) (0.445)
TOP10 3.8983*** 4.0396*** 2.5452
(0.000) (0.001) (0.129)
NOFIRM 0.0522 0.0775 0.0430
(0.517) (0.426) (0.695)
NOIND -1.1897*** -1.4778*** -1.1479**
(0.001) (0.000) (0.014)
DAYS -0.0554*** -0.0609*** -0.0605***
(0.000) (0.000) (0.000)
LAGPMAFE 0.0212 0.0314 0.0061
(0.714) (0.615) (0.928)
Analyst fixed effects yes yes yes
Year fixed effects yes yes yes
N 69,544 52,490 39,059
R-squared 0.130 0.129 0.132
45
Table 6
Incumbent Analyst Performance
This table reports the results of the impacts of organizational climate driven analyst turnover on the
performance of incumbent analysts who cover the same industry, where incumbent analysts’ performance
is measured by forecast errors (Panel A) and their chance to become an All-Star in years after their peers’
turnover (Panel B). We examine the incumbent analysts’ performance in year t + 1, t + 2 and t + 3,
respectively, after the analyst departure. All standard errors are double clustered at the analyst and year
levels. The p-values are in parentheses. ∗ indicates significance at the 10% level; ∗∗ significance at 5%; ∗∗∗
significance at 1%. Variable definitions are provided in the Appendix B.
Panel A: Forecast errors
VARIABLES
t + 1 t + 2 t + 3
(1) (2) (3)
DEPART 0.0237** -0.0029 -0.0167
(0.035) (0.811) (0.199)
GEXP -0.0010 -0.0024 -0.0001
(0.476) (0.111) (0.969)
FEXP -0.0009 -0.0006 -0.0010
(0.315) (0.499) (0.320)
TOP10 0.0510*** 0.0444** 0.0276
(0.005) (0.041) (0.175)
NOFIRM 0.0006 0.0000 -0.0009
(0.587) (0.982) (0.567)
NOIND -0.0015 -0.0039 -0.0049
(0.699) (0.388) (0.306)
DAYS 0.0044*** 0.0042*** 0.0042***
(0.000) (0.000) (0.000)
FREQUENCY -0.0107*** -0.0129*** -0.0127***
(0.000) (0.000) (0.003)
LAGPMAFE 0.1062*** 0.1230*** 0.1226***
(0.000) (0.000) (0.000)
Analyst fixed effects yes yes yes
Year fixed effects yes yes yes
N 64,301 57,168 48,486
R-squared 0.263 0.253 0.266
Average general experience of departing analysts 16.5 (p-value < 0.01)
Average general experience of other analysts 14.4
Percentage of departing analysts who are all-stars 19.3% (p-value < 0.01)
Percentage of other analysts who are all-stars 7.94%
46
Panel B: Likelihood to become an All-Star
Dependent Variable
= ALLSTAR
t + 1 t + 2 t + 3
(1) (2) (3)
DEPART -0.2495* -0.1213 0.0512
(0.074) (0.374) (0.732)
TOPACCU -1.0122*** -1.0408*** -0.6901***
(0.000) (0.000) (0.009)
BOTACCU -0.5971* -0.5502 -0.9061**
(0.074) (0.112) (0.035)
GEXP 0.0699*** 0.0706*** 0.0683***
(0.000) (0.000) (0.000)
BSIZE -0.0002 -0.0014 -0.0019
(0.928) (0.650) (0.537)
NOFIRM 0.0809*** 0.0824*** 0.0824***
(0.000) (0.000) (0.000)
NOIND 0.1368*** 0.1355*** 0.1490***
(0.002) (0.002) (0.002)
AVGSIZE 0.2584*** 0.2714*** 0.2968***
(0.000) (0.000) (0.000)
RELOPTM -0.0016 0.0002 -0.0007
(0.565) (0.948) (0.835)
Broker fixed effects yes yes yes
Year fixed effects yes yes yes
N 4,485 3,965 3,248
Pseudo R2 0.3168 0.3118 0.3008