What Drives Turnover and Layo¤s at Large Law Firms?
Paul Oyeryand Scott Schaeferz
March 2, 2010
Abstract
We study attrition at 285 large American law …rms in 2008 and 2009. We use information
from publicly announced law …rm layo¤s and lawyer biographies gathered from …rms’web pages
to determine which lawyers are likely to have been laid o¤, which are likely to have left their
…rms voluntarily, and which did not leave their …rms. We show that departures that are not
due to layo¤s are more common for relatively recent law school graduates, for graduates of top
law schools, for female lawyers, and for associates who did not go to the same law school as at
least one partner in his/her o¢ ce. Laid o¤ lawyers are relatively likely to be recent graduates
(especially those from top schools) and are less likely to be bankruptcy, labor, and intellectual
property lawyers. Securities and banking lawyers are more likely to be laid o¤, but this is
primarily because layo¤s were more common at …rms with these lawyers. Our results suggest
that no one main force drives turnover in the labor market for lawyers and that …rm-speci…c
human capital (or at least human capital that is speci…c to large law …rms), specialty-speci…c
human capital, and school-based networks all play some role in sustaining lawyer/…rm matches.
VERY PRELIMINARY.
y
Stanford University Graduate School of Business and NBER. [email protected].
z
David Eccles School of Business and Institute for Public and International A¤airs, University of Utah.
[email protected].
1
Introduction
Di¢ cult economic times have come to the legal industry recently, with the recession leading to a
signi…cant drop o¤ in demand for lawyers at the largest …rms. Layo¤s, pay cuts, and dramatic
drop-o¤s in hiring of newly minted JDs has been widely documented in the general press, the legal
press, and on legal industry websites. While the layo¤s at large law …rms are a relatively recent
and unusual phenomenon, turnover at law …rms has always been high given the up-or-out nature
of these …rms. In this paper, we study turnover at large American law …rms. We analyze the
qualities of lawyers that leave these …rms voluntarily and the qualities of those that …rms laid o¤.
We relate these empirical relationships to models from labor economics and human capital theory
to determine what economic forces drive the turnover and layo¤ processes.
Speci…cally, we start with biographies of over 100,000 lawyers at 285 of the 300 largest American
law …rms, as of the Summer of 2008. We follow these lawyers month-by-month for a year to
determine if they left their …rms. We match data from press accounts of layo¤s at these …rms to
timing of departures (which we determine from the …rms’web sites.) We divide lawyers into three
groups (with some measurement error in terms of who gets assigned to which group) –those that
left their …rms voluntary (or at least not as part of a large layo¤), those that were part of a layo¤
of a group of lawyers, and those that remained with their …rms through the Summer of 2009.
We show that there are a few key variables that are economically and statistically related to
lawyer attrition (either voluntary or by layo¤), but that most lawyer attrition cannot be explained
by the variables we observe. Lawyers that leave voluntarily are more likely to be recent law school
graduates, graduates of top schools, to have relatively few colleagues who graduated in the same
year, and to be women. Also, they are less likely to have gone to the same law school as a partner
in their o¢ ce. Turnover rates do not di¤er much by specialty within …rms, but overall voluntary
turnover rates are lower for securities and banking lawyers, labor lawyers, and lawyers that specialize
in intellectual property.
We also show that layo¤s are much more common for associates than for partners. Among
associates, layo¤s are focused on recent law school graduates (especially those from top schools.)
A few specialties, including bankruptcy, labor, and intellectual property, are relatively immune to
layo¤s. Securities lawyers are more likely to be laid o¤, but only because they work at …rms that
are more prone to layo¤s. Within a given …rm, securities lawyers are no more likely than other
lawyers to be laid o¤. We …nd no evidence that school-based networks or the seniority pro…le of
existing lawyers a¤ect …rms’choices of who to lay o¤.
We argue that these results suggest that …rm-speci…c human capital and/or skills that are
speci…c to large …rms such as the ones in our sample are important at these …rms. Our results
1
also suggest that specialties and school-based networks play a role in the turnover process and that
lawyers from top schools may have greater alternative opportunities that make it hard for …rms to
retain them. We …nd no evidence that …rms employ a pecking order in choosing lawyers strictly by
the prestige of the law schools they attend and our results also do not generally support learning
models from labor economics where …rms gather information about lawyers’ability early in their
tenure. We …nd no evidence that …rms are better informed about lawyers from higher ranked
schools than those at lower ranked schools.
2
Data
We use data from two main sources. First, for a list of layo¤s among major law …rms, we use the
Layo¤ Tracker from the Law Shucks website (http://lawshucks.com/layo¤-tracker/#rawdata as of
February 18, 2010.) This site lists layo¤s by “BigLaw” …rms since January, 2008.1 We focus on
layo¤s between July 1, 2008 and June 30, 2009 because this allows us to look at exactly a one year
period and (as we explain below) our list of lawyers is based on those at a given …rm in July of
2008. From the list provided by Law Shucks, we determined the …rm, the number of lawyers laid
o¤, and, where provided, the o¢ ce(s) a¤ected. We ignore sta¤ layo¤s and layo¤s that did not a¤ect
lawyers in American o¢ ces. We have a total of 141 layo¤s at 100 …rms a¤ecting 3,954 lawyers.
Many of these layo¤s were in both U.S. and foreign o¢ ces, so the number of American lawyers
a¤ected was actually less than this total. Layo¤ size varies from two (Haynes and Boone laid o¤
two lawyers and three sta¤ in its New York o¢ ce in March, 2009) to 200 (White and Case laid o¤
200 lawyers and 200 sta¤ worldwide in March, 2009 –see Feuer (2009)).
The Layo¤ Tracker is, by its creators’ own admission, incomplete and subjective as to which
…rms get included. This introduces measurement error and probably an overall undercount of
layo¤s in our data. More problematically, it also introduces bias in that Layo¤ Tracker is surely
more likely to document a layo¤ at one of the largest …rms in our sample than at one of the smaller
ones. To assess the importance of this issue, we reran all the analysis in the paper using only …rms
with at least four hundred lawyers (roughly the largest 100 …rms.) This had no material e¤ect on
any of our conclusions.
Our second primary data source is the biographies of lawyers at large law …rms, which we
downloaded from …rms’ web pages in the …rst two weeks of July 2008. We started with a list of
1
See http://lawshucks.com/layo¤-tracker/#methodology for details on how they assemble the layo¤ data. The
most relevant detail there is that, “We don’t exactly follow the AmLaw 100 or Vault rankings or anything else
scienti…c. It’s purely our subjective assessment of what constitutes ‘BigLaw.’”
2
the 300 largest US law …rms, according to www.lawperiscope.com in August 2007. This includes
all …rms on the American Lawyer 200 and Vault 100 lists, as well as many others. We were able
to gather usable information from 285 …rms and a total of 104,639 lawyers that held the rank of
partner, associate, or “of counsel” and for whom we know the …rm they work for, the law school
they attended, and the city where they practice. Further details on the lawyer dataset (that is, our
list of lawyers as of July 2008) can be found in Oyer and Schaefer (2010). We then revisited each
lawyer’s webpage for the next twelve months to see if the lawyer was still listed as working at that
…rm.
We generated “laid o¤” and “left, but not laid o¤” dummy variables based on whether the
person had left the …rm by the end of June 2009. Speci…cally, we classi…ed a person as being “laid
o¤” if he was listed on his …rm’s web page the month before the …rm announced a layo¤ but was
no longer listed on the web page two months after the layo¤ (allowing time for the …rm to update
its web page.) If the Layo¤ Tracker provided the speci…c o¢ ces that were a¤ected by a layo¤, we
did not count people as laid o¤ if they left other o¢ ces of the …rm during the layo¤ window. If the
Layo¤ Tracker did not list speci…c o¢ ces, then we consider any lawyer that left during the layo¤
window to have been laid o¤. We de…ne a person as “left, but not laid o¤”if he/she was no longer
on his …rm’s web page at the end of June 2009 and we did not determine that he/she was laid o¤.
Clearly, our assignment of individuals to the “laid o¤” category is imperfect. As we already
noted, there are layo¤s by …rms in our sample that Layo¤ Tracker did not report. On the other
hand, there are surely some voluntary departures of lawyers during some …rms’layo¤ windows that
we mischaracterize as layo¤s. Our methods identify 3,103 lawyers as laid o¤. This is fewer than
the Layo¤ Tracker identi…es at our …rms, but their list includes some lawyers in foreign o¢ ces that
are not in our sample. If we limit the layo¤ window to lawyers that are no longer on their …rms’
websites one month after the layo¤ (rather than two), our laid o¤ sample would drop to 2,323.
Rerunning our analysis with this change leads to no material e¤ect on the interpretation of our
results.
Figures 1 and 2 provide graphical evidence that, while our measures of layo¤s are unlikely to
be perfect, we are capturing layo¤s to at least some degree. Figure 1 shows the attrition of lawyers
from White and Case over the year we study. The graph shows sharp drops in the survival rate of
lawyers at the …rm surrounding their two large and public layo¤s. The graph shows large numbers
of associates departing at the times of the layo¤s and a smaller partner exodus (a trend that we will
see for the sample as a whole, as well.) Figure 2 focuses on associates at three …rms –White and
Case, Morrison and Foerster (which had one sizeable layo¤), and Cleary Gottlieb (which had no
reported layo¤s.) The graph shows signi…cant attrition around the times of the announced layo¤s
3
Figure 1: Attorney Attrition at White and Case
at the two …rms and a smooth attrition process at the …rm that did not announce a layo¤. We
conclude that our layo¤ measure, while imperfect, is informative.
We use other data sources to measure the prestige of law schools and law …rms. We use the
US News and World Report 2006 rankings of law schools and the Vault 100 rankings of law …rm
prestige.
Figures 3 and 4 show the geography of the associates in our sample. Figure 3 shows the location
of all the associates as of July 2008. In the …gure, each square is proportional to the number of
associates working in that city. The …gure shows that, while a large share of the sample works
in major cities such as New York, Washington, and Atlanta, there are also many lawyers spread
out in cities throughout the country. Figure 4 shows the locations of associates that we identify
as laid o¤. Note that this sample is much more concentrated in major cities than the sample as a
whole, which probably re‡ects a mix of the selection issue (bigger …rms in bigger cities are more
likely to have their layo¤s noticed by Layo¤ Tracker) and the fact that lawyers doing securitization
and other work with signi…cant demand reductions are concentrated in major cities (and especially
…nancial centers.)
Table 1 provides some basic statistics for all lawyers in our sample, then similar statistics
by layo¤ and attrition status for all lawyers, and then separate details for associates only. The
average year of graduation for the sample as a whole indicates that a typical lawyer in our sample
4
Figure 2: Associate Attrition
has …fteen years of experience. Average experience is much lower for those who leave their …rm
between Summer 2008 and Summer 2009 (twelve years of experience) and those who are laid if
in that window (nine years.) About a third of the total sample is female, though it is closer to a
half for associates. Female turnover is higher, which is partially due to the fact that the women in
the sample tend to be younger. About a quarter of the sample are securities or banking lawyers
and the layo¤ rate is higher for this group.2 Litigators make up approximately 40% of the sample
and they appear to have been laid o¤ at a lower rate. A little over half the sample works at a
…rm ranked by Vault as one of the 100 most prestigious …rms. These …rms are over-represented in
the layo¤ sample, which is likely to be at least partially due to the selection issues we mentioned
above. Finally, about a quarter of the sample went to a Top 10 law school, but that fraction does
not appear to vary in any meaningful way between ranks (that is, associates relative to the sample
as a whole) or between the laid o¤, turnover, and entire samples.
Figures 5 and 6 further highlight the potential importance of the relationship between seniority
and both turnover and being laid o¤. Figure 5 shows kernel densities of the year of law school
graduation for all lawyers in our sample, for those that leave their …rms between July 2008 and the
2
We determined the specialties of lawyers in our sample by matching keywords in lawyers’ descriptions of their
specialty to twenty-seven law specialties listed by National Association for Law Placement (2000). Throughout the
paper, we combine the Securities and Banking categories into one group that we refer to simply as Securities.
5
Panel A: All Lawyers
Female
Law School Graduation
Partners
Securities/Banking
Litigator
Vault-Ranked Firm
Top 10 Law School
N
All
0.306
1992.9
(12.10)
0.457
0.252
0.437
0.542
0.264
104,639
Left (not Laid O¤)
0.368
1996.0
(11.39)
0.275
0.217
0.418
0.551
0.262
14,786
Laid O¤
0.377
1998.9
(9.62)
0.157
0.295
0.330
0.792
0.277
3,117
All
0.427
2002.3
(5.87)
0.195
0.428
0.614
0.244
48,467
Left (not Laid O¤)
0.446
2002.3
(5.32)
0.191
0.416
0.607
0.262
9,454
Laid O¤
0.416
2002.9
(4.55)
0.281
0.314
0.821
0.263
2,347
Panel B: Associates
Female
Law School Graduation
Secuirites/Banking
Litigator
Vault-Ranked Firm
Top 10 Law School
N
Table 1: Summary Statistics
6
Total Sample of Associates
Figure 3: Lawyer Locations
end of June 2009, and those that are laid o¤ in that window. First note the dramatic increase in
the number of lawyers as the graph moves closer to the present.3 The graph shows that these …rms
are leveraging the skills of small cohorts of senior attorneys over larger groups of recent graduates.
The graph also makes it clear that, in order to make this hierarchy feasible in steady state, the
attrition rate is much higher among recent graduates than it is among senior lawyers and that
layo¤s are focused on the most recent graduates of all.
Figure 6 shows similar densities, but is limited to associates in the sample. Non-layo¤ turnover
is actually quite constant with regards to seniority for this group in that the seniority pro…les of
associates as a whole and those that leave their …rms are nearly identical. But the laid o¤ associates
group is noticeably less senior than associates as a whole.
Overall, Table 1 and Figures 5 and 6 suggest turnover and layo¤s are related to seniority,
to specialty, and to rank. In the next section, we use concepts from labor economics to derive
hypotheses of why these relationships may exist. In later sections, we more carefully analyze the
data and test the hypotheses.
3
The fact that the densities peak and then drop o¤ for the last two classes of law school graduates re‡ects delayed
starts due to clerkships and, to a lesser extent, that some …rms are slow in adding new lawyers to their websites.
7
Laid-Off Associate Office Locations
Figure 4: Laid O¤ Lawyer Locations
3
Conceptual Framework
In this section, we apply concepts from labor economics and human capital theory to develop
testable hypotheses regarding lawyer attrition and layo¤s. We consider how human capital development of lawyers, optimal hierarchical structure of law …rms, di¤erences in demand for various
specialties of lawyers, and the networks lawyers develop in school will a¤ect lawyers’propensity to
leave their …rms and to be laid o¤ by their …rms. In later sections, we will test these hypotheses
using data from several hundred large U.S. law …rms.
Human Capital Theory –If lawyers acquire general human capital on the job, there is no reason
to expect any relationship between turnover (voluntary or forced) and time spent as a lawyer.
However, if a lawyer acquires any …rm-speci…c human capital on the job, then we would expect
turnover probabilities to decrease with the time spent on the job. While the legal services industry
is likely to be an area where human capital is primarily general, prior work has shown evidence of
the importance of speci…c human capital in similar industries (see Groysberg, Nanda and Nohria
(2004)). If this is the case in the legal profession as well, then we would expect turnover and layo¤
probabilities both to be decreasing in the time a lawyer has been at a given …rm. Unfortunately, we
only observe time since the lawyer received his law degree. Though this should be highly correlated
to the time spent at the current …rm, there are some …rm-to-…rm transitions that muddy this
relationship. If human capital is speci…c to large law …rms such as the ones in our sample, rather
8
.1
.08
Density
.04
.06
.02
0
1970
1980
1990
2000
Year Graduated Law School
Whole Sample
Laid Off
2010
Left, not Laid Off
0
.05
Density
.1
.15
Figure 5: Kernel Density of Law School Graduation Year –Entire Sample
1995
2000
2005
Year Graduated Law School
All Associates
Laid Off
2010
Left, not Laid Off
Figure 6: Kernel Density of Law School Graduation Year –Associates
9
than to an individual …rm, we would expect to see turnover and layo¤ probabilities decrease with
time since graduation. So if we …nd a negative empirical relationship between years as a lawyer and
turnover (or layo¤s), this …nding would be consistent with lawyers developing …rm-speci…c human
capital, large-law-…rm-speci…c human capital, or both.
Optimal Hierarchies –The law …rms in our sample are all organized as hierarchies. Associates
are subject to an up-or-out decision which leads to some becoming partners and others leaving
the …rms (though there has been an increase in the use of long-term non-partner relationships, as
well.)4 The degree of leverage (that is, the ratio of associates to partners) varies considerably within
our sample. For example, Cravath, Swine & Moore has over three associates for each partner while
Foley & Lardner has more partners than it has associates. There is also considerable variation
in the hierarchy of associates across …rms, with some …rms employing a large number of recent
graduates while others rely more on associates with more legal experience. Using the same …rms to
illustrate this point, as of July 2008, 40% of the associates at Foley & Lardner had received their
law degrees in 2005 or later while 27% of Cravath associates graduated in that time frame. While
individual …rms choose di¤erent hierarchical structures, we start from the assumption that the rank
and years-since-graduation distributions that we see are optimal for each …rm. Then, all else equal,
we would expect these …rms to lay o¤ equally across the rank and seniority distributions. That
is, if optimizing the hierarchical structure is the primary organizational consideration of a law …rm
and if the recent drop in demand for legal services did not alter the optimal hierarchical structure
of …rms, we would not expect to …nd any relationship between rank or seniority and probability
of being laid o¤. We would, on the other hand, expect overall attrition to be related to a given
…rm’s hierarchy. That is, …rms that employ a relatively large share of recent graduates need to
have higher turnover of recent graduates in order to maintain their structure.
Specialties –The recent negative shock to the demand for legal services was not evenly spread
across the legal industry. Lawyers that did work related to securitization, private equity deals,
and other securities work saw a bigger drop in demand than, for example, labor lawyers and
intellectual property lawyers. At the opposite extreme, bankruptcy lawyers are widely thought
to have bene…ted from the recession.5 If lawyers can change their specialty at minimal cost, the
downturn would not have had di¤erential impact on lawyers of di¤erent specialties. But there is
considerable specialization among lawyers and, as a result, we would expect layo¤s to be more
4
See Garicano and Hubbard (2007) for a theoretical and empirical analysis of hierarchical structure of US law
…rms and Galanter and Henderson (2008) for evidence on the growth of permanent “o¤-track” attorneys at large
…rms.
5
Demand for labor lawyers could also be relatively resilient to the recession, given that wrongful termination suits
increase during slow economic periods (see Donohue and Siegelman (1993).)
10
focused on securities lawyers than other types of lawyers and we would expect bankruptcy lawyers
to be less likely to have been laid o¤. We have no prior expectations about the overall attrition
rates by specialty, so we do not have a hypothesis for how attrition that is not layo¤ related will
vary across specialties.
Social Networks - Whether for unproductive reasons such as an “old-boy network” or for more
e¢ cient reasons such as employees that know one another better work more productively with one
another, we would expect that workers that are more attached to other employees of the …rm will
be less likely to leave a …rm (either by choice or by layo¤.)6 Ideally, we would measure social
connections and networks by the degree to which lawyers socialize. But the best proxy we have for
connections among lawyers at a given …rm is whether they went to the same schools (both for law
school and undergraduate.) If university-based social networks are important, we would expect to
see lower attrition and fewer layo¤s of lawyers whose alma maters are better represented among
the partners of their …rms.
Employer Learning – Labor economists have shown the importance of employers learning the
ability of employees over time (see, for example, Farber and Gibbons (1996)). When law …rms hire
new lawyers, both the …rm and employee have only a noisy estimate of how productive the lawyer
will be. This signal is likely to be less noisy if the lawyer is more experienced (that is, when hiring
an associate with several years of experience elsewhere). It is also possible that the signal is less
noisy when the lawyer comes from a highly ranked law school.7 Graduates of top schools have
been vetted by the admissions process and made it through the demanding curriculum. Given the
fact that graduates of top schools are highly represented in our sample, most …rms have plenty of
graduates from top schools that can assess the quali…cations of aspiring lawyers at these schools.
Hiring from lower ranked law schools may be seen as hiring more “risky”workers, where the learning
process will be more important.8 If graduates of lower-ranked schools are, in fact, riskier, we would
expect attrition of recent graduates of lower-ranked schools to be high relative to attrition of recent
graduates of higher-ranked schools. Note that this line of reasoning does not imply that graduates
of lower-ranked schools will have higher levels of attrition overall (for example, they may have
fewer job opportunities elsewhere), but learning/risk considerations mean that attrition rates of
graduates of lower-ranked law schools will decline more sharply with seniority than attrition rates
6
For evidence on the importance of school-based networks in lawyer hiring, see Oyer and Schaefer (2010), and for
evidence on the importance of these networks for promotion at law …rms, see Parkin (2006).
7
See Oyer and Schaefer (2009) for evidence that the types of work and compensation of lawyers is very strongly
in‡uenced by the prestige of the law schools they attend.
8
See Lazear (1998) for a formal model of the implications of hiring risky workers.
11
of graduates of more prestigious schools.
Assortive Matching Top Lawyers – Suppose that, at least to a …rst approximation, the most
talented lawyers go to the best law school, the next most talented go to the next law school, and
so on. In addition, suppose that, all else equal, every …rm would like to hire the most talented
lawyers it can get and, as a result, every …rm hires from the highest ranked schools it can recruit
from.9 In this case, a typical …rm will hire from less prestigious schools (and will hire less talented
lawyers) when demand for lawyers is strong and will focus on top schools when demand for lawyers is
weaker. If this is true, then we would expect that, when the recession lowered the demand for lawyer
services, layo¤s would be more prevalent for lawyers from lower-ranked schools. Combining this
assortive matching idea with the learning notion discussed above, we might expect that, conditional
on surviving at the …rm for a number of years, law school quality would no longer be related to
expected productivity. This leads us to hypothesize that lawyers from lower-ranked law schools are
more likely to be laid o¤ than lawyers from higher-ranked schools, but that this relationship will
be less strong for more senior lawyers.
We summarize the implications of these potential in‡uences on lawyer attrition and layo¤s in
Table 2.
4
Empirical Results
4.1
Attrition
We now look at which lawyers leave their …rms between the summers of 2008 and 2009 without, to
the best of our knowledge, being laid o¤. Note, however, that attrition that is not due to layo¤ is
not necessarily voluntary. Given the up-or-out nature of these …rms, lawyers are regularly denied
promotion and/or encouraged to look for alternative employment at the …rms in our sample. At this
point, we focus on associates because they are much more likely than partners to leave their …rms
and to be laid o¤. Despite anecdotal evidence that partners were a¤ected by layo¤s through the
“de-equitization” process (see, for example, Feuer (2009) and Mystal (2009)), we …nd no evidence
of large direct e¤ects on partners. Even at …rms with the largest layo¤s, partner attrition rates
have been at standard levels throughout the process.
Of the 41,650 associates for whom we have full information on what law school they went to and
when they graduated, 19.2% left without being laid o¤. This rate is slightly higher in New York
(21.5%) and for graduates of Top 10 law schools (20.8%), but this probability does not otherwise
9
That is, assume the legal services market is an assignment model as in Rosen (1982).
12
Driver of Lawyer
Turnover/Layo¤s
Human Capital (Firmor Law-Speci…c)
Predicted Relationship to:
Turnover
Layo¤s
+
+
Factor
Seniority
Empirical Proxy
Year of Law School
Graduation
Optimal Hierarchy
Hierarchical
Position
Rank and
Graduation Year
Higher for
big groups
0
Specialty-Speci…c
Human Capital
Specialty
Securities
Bankruptcy
??
??
+
-
Connections
Same School
as Partner
-
-
Employee Risk
School Quality *
Recent Graduate
+
0
Assortive Matching
Lawyer
Ability
Lower Ranked
Law School
??
+
Assortive Matching
and Learning
Ability
and Risk
Low Rank School *
Recent Graduate
??
+
Social Networks
Learning
Table 2: Summary of Empirical Predictions
di¤er by the characteristics listed in Table 1.
Table 3 explores the determinants of attrition more formally, using a logit analysis. Each column
reports the results of a logit regression where the dependent variable is our “left, but not laid o¤”
indicator variable and the sample is all associates in our sample as of July of 2008. All coe¢ cients
in this table (and in Table 4) are the marginal e¤ect on attrition probability of a one unit change
in the explanatory variable. For example, the 0.0297 coe¢ cient on Top Ten Law School in column
1 indicates that, all else equal, the probability of a Top Ten graduate leaving his/her …rm is 2.97
percentage points higher than graduates of other schools. As we move to the right of the table,
we add sets of indicator variables for …rms and cities so that, in column 4, the coe¢ cients should
approximate the di¤erences in turnover probability for associates in the same o¢ ce of the same
…rm who di¤er in a given explanatory variable.
13
Law Graduation Year (*100)
Top Ten Law School
(1)
0.1701
(0.0433)
0.0297
(0.0047)
0.0122
(0.0051)
-0.0299
(0.0045)
-0.1998
(0.0444)
0.0156
(0.0039)
-0.0093
(0.0050)
-0.0236
(0.0114)
-0.0358
(0.0072)
-0.0153
(0.0060)
(2)
0.2085
(0.0460)
0.0199
(0.0060)
0.0239
(0.0088)
0.0127
(0.0051)
-0.0294
(0.0044)
-0.1586
(0.0474)
0.0155
(0.0039)
-0.0096
(0.0050)
-0.0245
(0.0114)
-0.0365
(0.0072)
-0.0154
(0.0060)
(3)
0.2019
(0.0468)
0.0181
(0.0062)
0.0227
(0.0086)
0.0099
(0.0052)
-0.0314
(0.0044)
-0.1649
(0.0524)
0.0159
(0.0038)
0.0027
(0.0053)
-0.0171
(0.0111)
-0.0107
(0.0074)
-0.0023
(0.0063)
(4)
0.2036
(0.0468)
0.0168
(0.0062)
0.0230
(0.0086)
0.0071
(0.0053)
-0.0289
(0.0045)
-0.1669
(0.0523)
0.0158
(0.0038)
0.0020
(0.0053)
-0.0143
(0.0111)
-0.0094
(0.0074)
-0.0034
(0.0063)
no
no
no
no
yes
no
yes
yes
0.0038
41,650
0.0042
41,650
0.0590
40,138
0.0628
40,100
Top Ten * Recent
Other Top 20 School
Partner from Same Law School
Cohort Size
Female
Securities
Bankruptcy
Labor
IP
Controls
Firm
City
Pseudo R-square
N
Table 3: Attrition Logits – Dependent Variable = 1 if the lawyer leaves his/her …rm and is not
laid o¤. Sample is all associates in o¢ ces with at least ten partners. Sample size changes when
…xed e¤ects are added because …rms and/or cities with no turnover drop out. “Partner from Same
Law School” is an indicator variable that equals one if at least one partner in the associate’s o¢ ce
went to the same law school as the associate. “Cohort Size” is the fraction of the …rm’s associates
that graduated in the same year as the lawyer. “Recent” is an indicator variable that equals one
for lawyers that graduated law school in 2005 or later. Each regression includes the direct e¤ect
of this variable, though we do not report it in the table. Securities, Bankruptcy, Labor, and IP
(Intellectual Property) are indicator variables that equal one if the lawyer’s web page indicates
he/she does that type of law. A given lawyer can do multiple specialties. The excluded category
for specialties is all lawyers that do not …t into at least one of the specialties in the table, including
Corporate, Trusts and Estates, Environmental, and other specialties. Coe¢ cients are marginal
e¤ects of a one unit change in the explanatory variable. Standard errors are in parentheses.
14
The …rst row of the table shows that time since graduation is signi…cantly related to turnover,
but that the e¤ect is not large.10 An associate who has been out of law school for six years is only
one percentage point less likely to leave his/her …rm than an associate that graduated one year
ago. This indicates that …rm-speci…c or “Biglaw”-speci…c human capital is likely to be present in
this labor market, but it is not the dominant driver of turnover.
Graduates of highly ranked law schools, and especially Top 10 schools, are more likely to turn
over. This is especially true for recent graduates (those who …nished law school in 2005 or later.)
Recent graduates of top schools are more than two percentage points more likely to turn over than
other recent graduates or other graduates of Top 10 schools. This may indicate that, at least
among lawyers with limited experience, graduating from a top school provides more exposure and
opportunities.
We created three variables to capture the social connections between associates and partners, all
based on education. The variable we use in the tables is a dummy variable that equals one if there
is at least one partner in a given associate’s o¢ ce that went to the same law school as the associate.
We also created variables for the share of partners in the o¢ ce that went to the associate’s law school
and an indicator for whether there is a partner in the o¢ ce that went to the same undergraduate
school as the associate. Using these measures led to similar, though slightly weaker, results. The
table shows that, no matter what controls we include, the probability of turnover is about three
percentage points lower (that is, turnover is lowered by about one-seventh) if the associate went
to the same law school as at least one partner in the o¢ ce. This is a large e¤ect and is consistent
with important school-based networks a¤ecting retention (in addition to a¤ecting hiring, as shown
in Oyer and Schaefer (2010), and a¤ecting promotion, as shown in Parkin (2006).)
The coe¢ cient on cohort size can be interpreted as follows. Suppose that, as of the beginning
of Summer 2008, 20% of a …rm’s associates graduated from law school in 2004 and 10% graduated
in 2003. Then, after adjusting for the fact that we would expect slightly higher turnover for the
class of 2004 lawyers (due to the “Law Graduation Year”and “Recent Graduate”e¤ects), we would
expect that turnover of the 2004 cohort would be 1.5 to two percentage points higher than for the
2003 cohort. This means that large cohorts get even larger (on a relative basis) over time, so …rms
would have to take steps to keep the hierarchical structure constant over time.
Not surprisingly, we …nd that women turn over at a higher rate – being female increased the
10
So that the coe¢ cients and standard errors for graduation year will have more signi…cant digits, we multiply
both by 100 in the tables. This means that the coe¢ cients can be interpreted directly as percentages. The 0.1701
coe¢ cient on graduation year in column 1 of Table 3 means that, all else equal, the probability that a 2005 law school
graduate leaves his/her …rm is 0.17 percentage points higher than a 2004 graduate.
15
turnover rate by about 7.5% (or 1.5 percentage points).11 After controlling for …rm and city, we
…nd little evidence of di¤erences in turnover rates for Securities or Bankruptcy lawyers compared
to other lawyers. Though we are not as interested in Labor or IP lawyers for testing hypotheses,
we look at their turnover for comparison and …nd that that they too do not exhibit signi…cantly
di¤erent turnover levels. The rate of turnover does not appear to di¤er markedly by specialty, at
least not within …rms and within cities.
4.2
Layo¤s
We now turn to the determinants of layo¤s. Just under …ve percent (4.8%) of our sample of
associates was laid o¤ in the time from July of 2008 through June of 2009. The probability was
noticeably higher among those who graduated in 2005 or later (5.6%), practiced securities law
(6.7%), worked in New York (6.7%), and, especially, securities lawyers who worked in New York
(10.3%). Given that …rms vary signi…cantly in their specialties and their presence in large markets
like New York, adding …rm and city e¤ects may have a more dramatic e¤ect on the layo¤ analysis
than in the attrition analysis.
Table 4 presents the results of logits similar to those in Table 3 except that the dependent
variable now equals one if we identify the lawyer as having been laid o¤ during our layo¤ window.
As with attrition, recent law school graduates are more likely to be laid o¤, but the e¤ect is much
larger for layo¤s. The coe¢ cients on “Law Graduation Year” are similar in Tables 3 and 4, but
the underlying probability of layo¤ is about one fourth as large. So the e¤ect of graduating one
year later is about four times as large, proportionally, on being laid o¤ as it is on other attrition.
This suggests that …rms view more experienced lawyers as more pro…table than less experienced
lawyers, which may mean the …rm captures a disproportionate share of any speci…c human capital.
11
Spurr and Sueyoshi (1994) detail the level and trends in lawyer turnover by gender.
16
Law Graduation Year (*100)
Top Ten Law School
(1)
0.1566
(0.0289)
0.0044
(0.0025)
0.0067
(0.0026)
-0.0018
(0.0024)
-0.0531
(0.0245)
-0.0004
(0.0021)
0.0145
(0.0023)
-0.0097
(0.0061)
-0.0120
(0.0040)
-0.0073
(0.0034)
(2)
0.1250
(0.0295)
-0.0002
(0.0034)
0.0094
(0.0046)
0.0068
(0.0026)
-0.0019
(0.0024)
-0.0825
(0.0261)
-0.0004
(0.0021)
0.0150
(0.0023)
-0.0093
(0.0061)
-0.0119
(0.0040)
-0.0071
(0.0034)
(3)
0.1522
(0.0529)
-0.0094
(0.0057)
0.0202
(0.0078)
0.0022
(0.0044)
0.0031
(0.0040)
-0.0259
(0.0540)
-0.0024
(0.0034)
0.0024
(0.0044)
-0.0175
(0.0102)
-0.0166
(0.0072)
-0.0184
(0.0060)
(4)
0.1476
(0.0499)
-0.0094
(0.0058)
0.0200
(0.0078)
0.0036
(0.0046)
0.0010
(0.0041)
-0.0259
(0.0543)
-0.0028
(0.0035)
0.0014
(0.0044)
-0.0202
(0.0103)
-0.0167
(0.0073)
-0.0159
(0.0061)
no
no
no
no
yes
no
yes
yes
0.0126
41,650
0.0134
41,650
0.0619
21,252
0.0675
21,093
Top Ten * Recent
Other Top 20 School
Partner from Same Law School
Cohort Size
Female
Securities
Bankruptcy
Labor
IP
Controls
Firm
City
Pseudo R-square
N
Table 4: Layo¤ Logits –Dependent Variable = 1 if the lawyer leaves is laid o¤. See previous table
for details on sample and explanatory variables. Coe¢ cients are marginal e¤ects of a one unit
change in the explanatory variable. Standard errors are in parentheses.
17
Once controls for …rm and city are introduced, there is no relationship between the quality of
law school and layo¤ probabilities, which contradicts the basic version of the assortive matching
model we described. However, there is a large (both economically and statistically) e¤ect from the
interaction of recent graduation and graduating from a Top 10 school. This means that, relative
to other new graduates and other graduates of Top 10 schools, new graduates of Top 10 school are
more likely to be laid o¤. This does not …t any of our hypotheses. We can only speculate as to
what causes this relationship –perhaps …rms realize that recent Top 10 graduates are more likely
to leave (as we saw above) and think less is to be lost by laying them o¤.
We …nd no relationship between associate/partner school ties and layo¤ probability. It appears
that partners do not (or are not able to) favor graduates of the schools they went to when determining who to lay o¤ and that social networks (at least those related to schools) do not a¤ect the
layo¤ process.
We also …nd no support for the optimal hierarchy hypothesis as it relates to layo¤s. Cohort
size is not related to layo¤ probability once we control for …rms and cities. Genders are also not
di¤erentially a¤ected by layo¤s, which is what we would expect given the potential for legal action
if layo¤s focused on an identi…able group such as women.
Finally, we …nd that specialties are related to layo¤s. However, at least in the case of securities
lawyers, the e¤ect seems to be more on which …rms execute layo¤s than on which lawyers at a
given …rm get laid o¤. In columns 1 and 2, when we do not include …rm or city e¤ects, we …nd
that securities lawyers are much more likely to get laid o¤ than other lawyers. But this e¤ect is
substantially smaller once we control for …rm e¤ects, indicating that part of this relationship is due
to the fact that …rms with securities lawyers are more likely to have a layo¤. The e¤ect is weakened
further by city e¤ects in column 4, because there are more securities lawyers and more layo¤s in
New York and other …nancial centers. Bankruptcy lawyers, on the other hand, are much less likely
to be laid o¤ (about 40% less likely) than other lawyers even controlling for …rm and location. These
results suggest that, conditional on executing a layo¤, …rms do not target their securities lawyers
but they do protect their bankruptcy lawyers. Note, however, that …rms also appear to protect
Labor and Intellectual Property lawyers. So it may be the case that our de…nition of securities is
simply too broad a category and …rms are targeting securities lawyers by not laying o¤ lawyers
that do work clearly unrelated to …nancial services.
4.3
Interpretation
We now compare the results in Tables 3 and 4 to the predictions summarized in Table 2, so that
we can see which theories are consistent with the data on lawyer attrition and layo¤s. The human
18
capital theory is well supported by the data in that seniority is signi…cantly linked to turnover.
However, the fact that seniority is not strongly related to attrition in economic terms means that
this is far from an exclusive factor driving the labor market for young lawyers. The optimal
hierarchy idea seems to be rejected by the data. There is no evidence that …rms try to keep their
seniority pro…les constant over time and that turnover is highest among the biggest cohorts in a
given …rm. Also, the fact that …rms are concentrating layo¤s on young lawyers and, as other sources
have shown, are deferring the start dates of new attorneys means they are letting their …rms slowly
become more weighted towards senior attorneys during the current slow economy.12
The evidence on specialty-speci…c human capital and social networks is mixed. There is some
evidence that bankruptcy law and specialties that are not focused on securities were not as a¤ected
by layo¤s. It is also clear that layo¤s were more likely at …rms that do more securities and banking
law, so these …rms were not able to quickly retool to serve other types of clients. But the within…rm propensity to lay o¤ lawyers based on specialty is not all that great, suggesting …rms may be
either hoarding or reallocating lawyers who work in specialties where demand has been especially
hard hit (such as securities.)
The attrition patterns in Table 3 are consistent with social networks based on law schools (and
are also consistent with the evidence in Oyer and Schaefer (2010)). Associates with education-based
connections to partners in their o¢ ce are less likely to leave their …rm. We did not …nd any evidence
that school-based networks help connected associates to keep their jobs when their …rms resort to
layo¤s, however.
The learning and assortive matching models both …nd very little support in the data because we
found that recent graduates of top law schools are relatively likely to leave their …rm and to be laid
o¤, even controlling for the quality of their law school and their data of graduation. That is, the
turnover and layo¤ probabilities decline more sharply with experience for Top 10 graduates than
other lawyers, which runs counter to the implications of employer learning and assortive matching,
respectively. We can provide the following (admittedly ex post) rationalization of the attrition
result with the learning model –recent graduates of lower ranked schools are “risky”to all …rms so
graduates of these schools have few outside opportunities to consider until the market has learned
about them. This process may be particularly slow if learning is asymmetric (that is, the employer
learns a worker’s ability more quickly than the market does as in Greenwald (1986)). In any case,
we have very little reason to think that …rms are better informed about the new attorneys they
hire from top schools than they are about those they hire from lower-ranked schools, though the
12
For discussions of many …rms’ decisions to defer the start dates of new attorneys, see articles on
www.abovethelaw.com.
19
evidence is consistent with the rest of the market (that is, any …rm other than the one that hires
a given lawyer) having less information about these lawyers. Overall, it appears that …rms are
reasonably e¤ective at picking out appropriate lawyers from various law school tiers in the initial
hiring process.
5
Conclusions
Large law …rms are transient institutions, with many of the people that work there leaving within
a few years of arriving. Turnover at large law …rms has been even more dramatic recently, due
to layo¤s brought on by a signi…cant drop in demand for legal services. In this paper, we showed
that the rate of both voluntary and involuntary turnover declines sharply with experience. We also
showed that voluntary turnover is related to school-based lawyer networks, gender, and law school
prestige. Layo¤s are more common in some specialties than in others.
These turnover patterns suggest that …rm-speci…c or big-law-…rm-speci…c human capital is
important at these …rms but that it is far from the only force driving attrition. School-based
networks and demand for lawyers with certain specialties also contribute to attrition patterns, but
a great deal of turnover cannot be explained by factors we can measure. Also, we found no evidence
to suggest that …rms hire from a pecking order of law schools, that they actively try to keep their
hierarchies optimized in terms of the seniority distribution, or that …rms learning about employees’
ability over time is critical in this labor market.
20
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21