What Drives Turnover and Layo s at Large Law Firms?
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- Jeffrey Summers
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1 What Drives Turnover and Layo s at Large Law Firms? Paul Oyer y and Scott Schaefer z March 2, 2010 Abstract We study attrition at 285 large American law rms in 2008 and 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. pauloyer@stanford.edu. z David Eccles School of Business and Institute for Public and International A airs, University of Utah. scott.schaefer@utah.edu.
2 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 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 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
3 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 ( -tracker/#rawdata as of February 18, 2010.) This site lists layo s by BigLaw rms since January, 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 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 We started with a list of 1 See -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
4 the 300 largest US law rms, according to in August 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 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
5 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 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
6 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
7 Panel A: All Lawyers All Left (not Laid O ) Laid O Female Law School Graduation (12.10) (11.39) (9.62) Partners Securities/Banking Litigator Vault-Ranked Firm Top 10 Law School N 104,639 14,786 3,117 Panel B: Associates All Left (not Laid O ) Laid O Female Law School Graduation (5.87) (5.32) (4.55) Secuirites/Banking Litigator Vault-Ranked Firm Top 10 Law School N 48,467 9,454 2,347 Table 1: Summary Statistics 6
8 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
9 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
10 Density Year Graduated Law School Whole Sample Laid Off Left, not Laid Off Figure 5: Kernel Density of Law School Graduation Year Entire Sample Density Year Graduated Law School All Associates Laid Off Left, not Laid Off Figure 6: Kernel Density of Law School Graduation Year Associates 9
11 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
12 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
13 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
14 Driver of Lawyer Predicted Relationship to: Turnover/Layo s Factor Empirical Proxy Turnover Layo s Human Capital (Firm- Seniority Year of Law School + + or Law-Speci c) Graduation Optimal Hierarchy Hierarchical Rank and Higher for 0 Position Graduation Year big groups Specialty-Speci c Specialty Securities?? + Human Capital Bankruptcy?? - Social Networks Connections Same School - - as Partner Learning Employee Risk School Quality * + 0 Recent Graduate Assortive Matching Lawyer Lower Ranked?? + Ability Law School Assortive Matching Ability Low Rank School *?? + and Learning and Risk Recent Graduate 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 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 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
15 (1) (2) (3) (4) Law Graduation Year (*100) (0.0433) (0.0460) (0.0468) (0.0468) Top Ten Law School (0.0047) (0.0060) (0.0062) (0.0062) Top Ten * Recent (0.0088) (0.0086) (0.0086) Other Top 20 School (0.0051) (0.0051) (0.0052) (0.0053) Partner from Same Law School (0.0045) (0.0044) (0.0044) (0.0045) Cohort Size (0.0444) (0.0474) (0.0524) (0.0523) Female (0.0039) (0.0039) (0.0038) (0.0038) Securities (0.0050) (0.0050) (0.0053) (0.0053) Bankruptcy (0.0114) (0.0114) (0.0111) (0.0111) Labor (0.0072) (0.0072) (0.0074) (0.0074) IP (0.0060) (0.0060) (0.0063) (0.0063) Controls Firm no no yes yes City no no no yes Pseudo R-square N 41,650 41,650 40,138 40,100 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
16 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 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 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
17 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 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
18 (1) (2) (3) (4) Law Graduation Year (*100) (0.0289) (0.0295) (0.0529) (0.0499) Top Ten Law School (0.0025) (0.0034) (0.0057) (0.0058) Top Ten * Recent (0.0046) (0.0078) (0.0078) Other Top 20 School (0.0026) (0.0026) (0.0044) (0.0046) Partner from Same Law School (0.0024) (0.0024) (0.0040) (0.0041) Cohort Size (0.0245) (0.0261) (0.0540) (0.0543) Female (0.0021) (0.0021) (0.0034) (0.0035) Securities (0.0023) (0.0023) (0.0044) (0.0044) Bankruptcy (0.0061) (0.0061) (0.0102) (0.0103) Labor (0.0040) (0.0040) (0.0072) (0.0073) IP (0.0034) (0.0034) (0.0060) (0.0061) Controls Firm no no yes yes City no no no yes Pseudo R-square N 41,650 41,650 21,252 21,093 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
19 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
20 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 19
21 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
22 References Donohue, John J. and Peter Siegelman, Law and Macroeconomics: Employment Discrimination Litigation over the Business Cycle, Southern California Law Review, 1993, 66, Farber, Henry S. and Robert Gibbons, Learning and Wage Dynamics, Quarterly Journal of Economics, 1996, 111, Feuer, Alan, A Study in Why Major Law Firms Are Shrinking, New York Times, 2009, p. B1. June 5. Galanter, Marc S. and William D. Henderson, The Elastic Tournament: A Second Transformation of the Big Law Firm, Stanford Law Review, 2008, 60, Garicano, Luis and Thomas N. Hubbard, Managerial Leverage is Limited by the Extent of the Market: Hierarchies, Specialization, and the Utilization of Lawyers Human Capital, Journal of Law and Economics, 2007, 50, Greenwald, Bruce C., Adverse Selection in the Labor Market, Review of Economic Studies, 1986, 53, Groysberg, Boris, Ashish Nanda, and Nitin Nohria, The Risky Business of Hiring Stars, Harvard Business Review, 2004, 82, Lazear, Edward P., Hiring Risky Workers, in Isao Ohashi and Toshiaki Tachibanaki, eds., Internal Labour Markets, Incentives, and Employment, New York: St. Martins Press, Mystal, Elie, Partner De-Equitization at Paul Hastings, abovethelaw.com, National Association for Law Placement, The O cial Guide to Legal Specialties, Chicago: The BarBri Group, Oyer, Paul and Scott Schaefer, The Returns to Attending a Prestigious Law School, Stanford University and University of Utah. and, Firm/Employee Matching: An Industry Study of American Lawyers, Stanford University and University of Utah. Parkin, Rachel, Legal Careers and School Concentrations, Harvard University. Rosen, Sherwin, Authority, Control and the Distribution of Earnings, Bell Journal of Economics, 1982, 13, Spurr, Stephen J. and Glenn T. Sueyoshi, Turnover and Promotion of Lawyers: An Inquiry into Gender Di erences, Journal of Human Resources, 1994, 29,
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