Determinants of Efficiency of Law Firms Presenter: EunYoung Whang Coauthors: Rajiv Banker, Marina...
-
Upload
fay-dickerson -
Category
Documents
-
view
212 -
download
0
Transcript of Determinants of Efficiency of Law Firms Presenter: EunYoung Whang Coauthors: Rajiv Banker, Marina...
Determinants of Efficiency of Law Firms
Presenter: EunYoung Whang
Coauthors: Rajiv Banker, Marina Angel
Temple University
July 11, 2009
Organizational Changes
• During last three decades, law firms have changed dramatically
• Total number of lawyer have more than tripled in 2008 compared to 1984 (AmLaw 100)
• Transition from “boutique” law firms to “megafirms” that provides specialized and full-line legal services globally
• Expansion of partnership structure from one-tier to multi-tier partnership
Megafirms• Some law firms have grown• To provide full line of legal service in different
specializations • To have more resources to attract big corpo-
rate and institutional clients• To expand geographical reach, especially in
international market
Multi-Tier Partnerships • Adopted to retain and motivate talented lawyers
with more promotion opportunities• One-tier partnership law firms have only equity
partners• Two-tier partnership law firms have both equity
and non-equity partners• Only 20% of AmLaw 100 firms have one-tier part-
nership in 2008, down from 55% in 1994• Number of non-equity partners is increasing at a
faster rate than equity partners and may have di-luted the “rain-making” intensity of senior partners
Lawyer Leverage
• Unlike manufacturing firms, law firms assets are human capital resources; equity part-ners, non-equity partners, and lawyers
• More lawyers per partners leverages the tal-ent and ability of partners
• Very high span-of-control may affect ability to manage effectively
Apparent Contradictions?
• Baker & McKenzie ranked first by the number of lawyers but ranked 98 out of 100 firms in terms of revenue per lawyer(RPL)(AmLaw 100, 2008)
• Wachtell, Lipton, Rosen & Katz ranked last in terms of the number of lawyers but ranked first in terms of RPL (AmLaw 100, 2008)
Hypotheses
• Hypothesis 1: The size of law firms is posi-tively related to productivity
• Hypothesis 2: The proportion of non-equity partners has a negative relationship with productivity
• Hypothesis 3: Higher leverage (lawyers : partners ratios) is associated with higher productivity
Two-Stage DEA Analysis• Banker & Natarajan (2008) prove the estima-
tor is consistent• 1st stage: Estimate efficiency score with DEA • 2nd stage: Regress efficiency score on contex-
tual variables• Recent Monte Carlo evidence shows this
simple approach outperforms the bootstrap approach
Data
• AmLaw 100 dataset provides for human capi-tal resource and financial data
• Sample period: 2000~2007• Sample size: 648 firm-year observations
(81 law firms * 8 years)
Human Resources Inputs• Equity partners
- highest job title (most highly compensated)
- have ownership (share profit & loss)
- rainmaker and most productive personnel • Non-equity partners
- intermediate step to become equity partner
- paid fixed salary (do not share profit & loss)• Lawyers
- do most of legal work in law firm
- paid fixed salary (do not share profit & loss)
Output Variable
• Gross revenue: fee income generated from legal work
• Deflated by Consumer Price Index(CPI)
DEA Estimation Models• Technical Efficiency:
Banker, Charnes and Cooper (BCC) (1984)
• Aggregated Efficiency: Charnes, Cooper and Rhodes(CCR)(1978, 1981)
• Scale Efficiency
= CCR Efficiency/BCC Efficiency• Estimated (1) year-by-year, (2) pooled
Efficiency Trends from Pooled Estimation
2000 2001 2002 2003 2004 2005 2006 20070
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Cross section efficiency(BCC)Pooled efficiency(BCC)Cross section efficiency(CCR)Pooled efficiency(CCR)
Test of Returns to Scale
Null Hypothe-ses
Alternative Hypotheses
Test Result
CRS VRS Rejected
NDRS VRS Supported
NIRS VRS Rejected
CRS NDRS Rejected
CRS NIRS Supported
Returns to Scale Inference
CRS
IRS
X0
Y
Second Stage ModelLog(θ) = f{Size, %Non-equity partners,
Leverage, Control Variables}• Control variables:
- Regional firm
- International firm
- Geographic regions
- Post-merger
- Year trend(for pooled analysis)
Variable MeasurementSize (+) = # of lawyers+# of partners
%non-eq-uity part-ners
= # of non-equity partners/# of equity partners
Leverage = # of lawyers/# of equity partners
Regional =1 have no more than 45% of firm’s attorneys were located in any one region of the country, =0 o/w
Interna-tional
=1 more than 40% of lawyers located outside US, =0 o/w
Post-merge =1 for post merger years
Northeast =1 if headquarter location is in New York, Philadelphia, Pittsburg, or Boston
West =1 if headquarter location is in San Francisco, Los Angeles, Seattle or Palo Alto
Midwest =1 if headquarter location is in Chicago, St. Louis, Milwaukee, Kansas City, Cleveland, or Minneapolis
Dealing with Panel Data
• Fama-McBeth Regressions: year-by-year cross-sectional model
• Prais-Winsten Regression: pooled efficiency
Fama-MacBeth Regression Results
Hypo.Sign
BCC CCR
Intercept -0.587*** -0.772***
Size + 0.0001*** -0.000006
%non-equity partner
_ -0.131*** -0.119***
Leverage + 0.078*** 0.118***
Prais-Winsten Regression Results
Hypo. Sign
BCC CCR
Intercept -0.674*** -0.898***
Size + 0.0001*** -0.000006
%non-equity partner
_ -0.132*** -0.118***
Leverage + 0.083*** 0.123***
Robustness Check• One stage parametric production func-
tions with contextual variables
- Translog function
- Cobb-Douglas function• Same control variables as in the DEA
models
Parametric Function Results
Predicted Sign
Translog Model
Cobb-Dou-glas Model
Size + -0.0003 -0.0003**
%non-eq-uity part-ner
_ 0.004 -0.012
Leverage + 0.041 0.041
Conclusion• Organizational changes have resulted in pro-
ductivity improvement in law firms during 2000 – 2007
• Technical efficiency (BCC) increases with
- the size of law firm- smaller proportion of non-equity partners- the degree of leverage