1. Are They Different? CEOs Made in CEO Factories
Ye Cai, Santa Clara University
Merih Sevilir, Indiana University
Jun Yang, Indiana University
Seminar at IFS, SWUFE
June 11, 2015

2. MOTIVATION Robert Nardelli (Home Depot, Chrysler) James McNerney
(3M, Boeing)
Stephen Bennett
(Intuit, Symantec)
Stanley Gault (Rubbermaid, Goodyear)

3. RESEARCH QUESTIONS
Every one in five CEOs comes from such CEO factories.
How do investors value firms hiring those factory CEOs?
What make factory CEOs (CEO factories) different?
Do firms hiring a factory CEO outperform their peers in the long run?
What actions do factory CEOs take at the new post?
Do factory CEOs receive greater pay?

4. MAIN FINDINGS (1) Factory CEOs
Are associated with higher announcement returns (CARs) at CEO appointment—effect is greater for
Firms with poor performance;
CEOs hired externally; and
CEOs who were key executives at the previous employer;
Slash, fix, and renew the company in the first 3 years;
Deliver better long-run performance (ROA and Tobin’s Q);
Receive greater compensation;
Acquire greater general managerial skills and more connections at the factory.

5. MAIN FINDINGS (2) CEO Factories
Have formal leadership training programs (LDP);
Rotate executives through different sectors, functions, locations, which provide executives with opportunities to develop general managerial skills and acquire business connections;
Have a deep bench of executives at every level and tend to promote internally for the top post (80% vs. 60%);
Do not suffer from losing top talent to other firms.
We believe that CEO factories are where managerial talent acquire their general managerial skills.

6. LITERATURE Effects of CEOs on firm policies
CEO fixed effects (e.g. Bertrand and Schoar, 2003; Graham, Harvey, and Li, 2012);
Innate traits such as overconfidence and risk aversion (e.g. Malmendier and Tate, 2005, 2008; Billett and Qian, 2008; Malmendier, Tate and Yan, 2011; Cronqvist , Makhija, and Yonker, 2012; Graham, Harvey, and Puri, 2013; Kaplan, Klebanov, and Sorensen, 2012);
Personal and professional experiences (e.g. Schoar and Zuo, 2013; Dittmar and Dunchin, 2013);
Increasing importance of general managerial skills (e.g. Murphy and Zabojnik 2004; Custodia, Ferreira, and Matos, 2012; Cremers and Grinstein, 2013);
No idiosyncratic managerial styles, choices of the CEO by the board to fit a firm’s strategy (e.g. Fee, Hadlock, and Pierce, 2013).
This paper focuses on the human capital accumulated at firms highly appraised for their superior abilities in developing corporate leaders, and how such factory experiences affect managerial decisions.

7. Compustat ExecuComp – CEO turnover;
DATA
Compustat ExecuComp – CEO turnover;
BoardEx – data on the incoming CEO’s biographic information including employment history before becoming the CEO;
Factiva – exact CEO appointment dates;
Our final sample consists of 2,335 CEO appointments over the period of 1992–2010:
1,852 unique firms, of which 36 firms are identified as CEO factories (14.3 vs. 1.6 CEOs produced)

8. CEO FACTORIES Company Name Number of CEOs CEO Factory Rank
GENERAL ELECTRIC CO 49 1
INTERNATIONAL BUSINESS MACHINES (IBM) CORP 47 2
PROCTER & GAMBLE CO 28 3
AT&T CORP 21 4
HEWLETT-PACKARD (HP) CO
PEPSICO INC
FORD MOTOR CO 19 7
HONEYWELL INTERNATIONAL INC
MOTOROLA INC 18 9
LUCENT TECHNOLOGIES INC 14 10
GENERAL MOTORS CORP (GM) 13 11
JOHNSON & JOHNSON
XEROX CORP
EXXON CORP
MACY'S INC 12 15
AMERICAN EXPRESS CO 16
INTEL CORP
KRAFT FOODS INC
ROCKWELL AUTOMATION INC
UNITED TECHNOLOGIES CORP (UTC)
BRISTOL-MYERS SQUIBB CO
SEARS ROEBUCK & CO
BAXTER INTERNATIONAL INC 23
DOW CHEMICAL CO
DUPONT(E.I.)DE NEMOURS & CO
INTERNATIONAL PAPER CO
SPRINT CORP
TEXAS INSTRUMENTS INC
ALBERTSONS INC 8 29
CORNING INC
EASTMAN KODAK CO
EMERSON ELECTRIC CO
KROGER CO
ELI LILLY & CO
MERRILL LYNCH & CO INC
SARA LEE CORP

9. SUMMARY STATISTICS
Factory CEOs
Non-factory CEOs
Difference
Mean
Median
Firm Characteristics
Market cap ($mil)
12,499
2,544
5,018
1,346
7,481
***
1,198
Tobin's Q
2.126
1.641
1.887
1.440
0.239
0.201
Leverage
0.236
0.216
0.223
0.207
0.014
0.008
ROA
0.025
0.038
0.032
0.041
-0.007
-0.002
BHAR
-0.035
-0.067
-0.019
-0.032
-0.016
CEO Characteristics
External CEO
0.535
1.000
0.366
0.000
0.169
KeyExec at last job
0.512
0.555
-0.044 *
Male
0.960
0.976
CEO age
51.565
52.000
51.593
-0.029
MBA
0.420
0.336
0.085
Elite School
0.282
0.228
0.054 **
For factory CEOs, the mean (median) # of years working at a factory firm is 13 (11) years,
and the mean (median) #of years after leaving a factory firm is 7.6 (5.1) years.

10. SUMMARY STATISTICS Fraction of factory CEOs by FF 12 industry
Highest 3 industries:
Telecom (35.7%), business equipment (31.7%), and chemical products (30.4%);
Lowest 3 industries:
Utilities (4.8%), Finance (6.6%), and Energy (10.3%).

11. FACTORY VS. NON-FACTORY CEOS
CAR around the CEO appointment: [-1,+1] days
Full sample
Factory CEOs
Non-factory CEOs
Difference
# of obs.
2,335
471
1,864
Mean
0.549
***
1.366
0.342 **
1.024
Median
0.208
0.445
0.147 *
0.298
Note: (1) use the CRSP value-weighted return as the market return; (2) estimate the market model parameters over the 200 trading days ending one month before the CEO appointment date.

12. LOW AND HIGH BHAR FIRMS Full sample Factory CEOs Non-factory CEOs
Full sample
Factory CEOs
Non-factory CEOs
Difference 
Low BHAR
# of obs.
1,167
252
915
Mean
1.168
***
2.454
0.814
1.640
Median
0.582
1.280
0.342
0.937
High BHAR
1,168
219
949
-0.070
0.115
-0.112
0.227
-0.002
-0.057
0.013
BHAR: Buy-and-hold abnormal return during the six-month period ending one month before the CEO appointment date with CRSP value-weighted return as the market index.

13. EXTERNAL AND INTERNAL CEOs
Full sample
Factory CEOs
Non-factory CEOs
Difference
External CEOs
# of obs.
935
252
683
Mean
1.314
***
2.334
0.938
1.395
Median
0.647
1.030
0.484
0.546 **
Internal CEOs
1,400
219
1,181
0.038
0.253
-0.002
0.255
0.009
0.032
0.004
0.028

14. KEY EXEC AND NON-KEY EXEC AT PREVIOUS FIRMS
Full sample
Factory CEOs
Non-factory CEOs
Difference 
KeyExec
# of obs.
1,276
241
1,035
Mean
0.503
***
1.750
0.212
1.538
Median
0.142 *
0.353
0.072
0.281 **
Non-keyExec
1,059
230
829
0.605
0.964
0.505
0.459
0.266
0.607
0.222
0.385

15. CAR REGRESSIONS: CAR of [-1, +1] days

16. Factory CEO * External CEO 1.044* (0.093)
(1)
(2)
(3)
Factory CEO (0,1)
0.776**
0.270
0.049
(0.024)
(0.554)
(0.919)
Factory CEO * BHAR
-3.007***
(0.002)
Factory CEO * External CEO
1.044*
(0.093)
Factory CEO * KeyExec at last job
1.535**
(0.018)
Firm size
-0.056
-0.009
-0.054
(0.539)
(0.920)
(0.555)
Tobin's Q
0.066
0.009
0.070
(0.570)
(0.932)
(0.542)
Leverage
0.599
0.742
0.588
(0.460)
(0.334)
(0.469)
ROA
-0.265
-0.549
-0.196
(0.844)
(0.665)
(0.884)
BHAR
-1.410***
-2.090***
-2.080***
(0.005)
(0.000)
External CEO
1.266***
1.022***
1.331***
(0.001)
KeyExec at last job
0.505*
0.549**
0.225
(0.074)
(0.042)
(0.466)
Male
-0.116
0.261
0.000
(0.887)
(0.730)
(1.000)
Log(CEO age)
-0.366
-0.666
-0.318
(0.716)
(0.490)
(0.753)
MBA
-0.499*
-0.399
-0.484*
(0.076)
(0.134)
(0.085)
Elite School
0.427
0.493*
0.415
(0.174)
(0.099)
(0.187)
Constant
1.351
1.914
1.142
(0.737)
(0.618)
(0.777)
Observations
2,335
Adjusted R-squared
0.013
0.024
0.011
Fixed Effect
Year-Ind

17. We obtain similar results on CAR comparisons:
ROBUSTNESS (1)
We obtain similar results on CAR comparisons:
Using a window of [-2,+2] days;
Using 3- or 4-factor models;
Using rolling windows of 4-6 years to define CEO factories;
Matching on
Hiring firm characteristics (PSM based on SIC2, size, Q, and ROA);
Factory firm characteristics (# of segments, size, Q, and ROA).

18. ROBUSTNESS (2)
Panel A: Various rolling windows in constructing CEO factories
(1)
(2)
(3)
(4)
(5)
(6)
Factory CEO - rolling 4YR
1.054***
0.929**
(0.002)
(0.013)
Factory CEO - rolling 5YR
0.893**
0.771**
(0.012)
(0.049)
Factory CEO - rolling 6YR
0.814**
0.697*
(0.027)
(0.082)
Controls No
T5 Col3
Observations
2,128
2,024
1,910
Adjusted R-squared
0.004
0.017
0.003
0.016
0.002
0.020
Fixed Effect
Year-Ind

19. ROBUSTNESS (3) Factory CEO Matched non-factory CEO Difference
Panel B: CAR comparison by matching hiring firm
Factory CEO
Matched non-factory CEO
Difference
Match on SIC2 and size
Mean
1.440
***
0.372
1.069
Median
0.522
0.224
0.316
Match on SIC2, size, Q, and ROA
1.767
-0.097
1.864
0.665
-0.465
1.483
Panel C: CAR comparisons by matching on factory firm characteristics
Factory CEO
Matched non-factory CEO
Difference
Match on # of segments and size
Mean
1.570
***
1.103
0.467
Median
0.620
0.278
Match on # of segments, size, Q, and ROA
1.889
1.085
0.804
0.716
0.501
0.353

20. ROBUSTNESS (4)
Robust to using different definitions of CEO factories (top 20, 30, 40, 50);
Factory firm effect vs. large firm effect (largest 20, 30, 40, 50 firms);
Factory firm effect vs. multiple-segment effect (top 20, 30, 40, and 50 firms with the largest number of segments);
Factory firm effect vs. best performing firm effect (best performing 20, 30, 40, and 50 firms).

21. ROBUSTNESS (5) Panel E: Various definitions of CEO factories (1) (2)
(1)
(2)
(3)
(4)
Factory CEO – top 20
0.903**
(0.017)
Factory CEO – top 30 (Table 5 Col 3)
0.861**
(0.012)
Factory CEO – top 40
0.944***
(0.006)
Factory CEO – top 50
0.799**
(0.014)
Controls
Same as Table 5 Col 3
Observations
2,335
Adjusted R-squared
0.009
0.010
Fixed Effect
Year-Ind

22. ROBUSTNESS (6) Panel F: Large firm effect. vs. factory firm effect (1)
(1)
(2)
(3)
(4)
Largest 20 firms CEO
0.556
(0.218)
Largest 30 firms CEO
0.328
(0.430)
Largest 40 firms CEO
0.301
(0.444)
Largest 50 firms CEO
0.455
(0.222)
Controls
Same as Table 5 Col 3
Observations
2,335
Adjusted R-squared
0.007
Fixed Effect
Year-Ind

23. ROBUSTNESS (7)
Panel G: multi-segment firm effect. v.s. factory firm effect
(1)
(2)
(3)
(4)
Top 20 Multi-segment firms CEO
0.485
(0.389)
Top 30 Multi-segment firms CEO
0.648
(0.143)
Top 40 Multi-segment firms CEO
Top 50 Multi-segment firms CEO
0.504
(0.193)
Controls
Same as Table 5 Col 3
Observations
2,335
Adjusted R-squared
0.007
Fixed Effect
Year-Ind

24. WHAT DO FACTORY FIRMS DO? (1)
CEO factories tend to
Have a formal leadership training program (LDP)— build connections and learn about best practice;
Rotate executives through different sectors, functions, locations—develop general managerial skills;
Have a deep bench of executive at each level and tend to promote internally (80% vs 60% internal promotions for the CEO post); and
Do not suffer in CARs from losing top talent to other firms.
CEOs who have spent longer time at a factory firm and CEOs who joined the new firm shortly after departing from a factory are associated with greater CARs.

25. Panel A: Factory firms and LDP
WHAT DO FACTORY FIRMS DO? (2)
Panel A: Factory firms and LDP
Variable
All firms that have produced sample CEOs
Factory
firms
Non-factory
Difference
(N=1,852)
(N=36)
(N=1,816)
LDP1
5.89%
52.78%
4.96%
47.82%
***
LDP2
9.99%
77.78%
8.65%
69.13%
Panel B: GAI before joining the factory firm and at the CEO appointment
Variable
Full sample
Factory CEO
Non-factory CEO
Difference
Before joining factory
GAI
0.118
0.113
0.119
-0.005
# of positions
0.137
0.151
0.134
0.017
# of firms
0.110
0.098
0.114
-0.016
# of industries
Pre-CEO flag
0.006
0.008
0.005
0.003
Conglomerate
0.072
0.068
-0.004
At the CEO appointment
2.015
2.815
1.812
1.002
***
3.858
5.119
3.539
1.580
1.338
2.042
1.160
0.882
1.287
1.955
1.117
0.838
0.179
0.234
0.165
0.069
0.827
0.983
0.788
0.195

26. Panel A. Number of years spent at the factory firm
WHAT DO FACTORY FIRMS DO? (3)
Panel A. Number of years spent at the factory firm
Factory CEOs
Years spent
at factory
>= median (11)
< median
Difference
# of obs.
471
235
236
Mean
1.366
***
1.857
0.877 **
0.981 *
Median
0.445
0.620
0.348
0.272
Panel B. Number of years after leaving the factory firm
Factory CEOs
Years after leaving factory
< median (5)
>= median
Difference
# of obs.
471
234
237
Mean
1.366
***
1.903
0.836 **
1.068 *
Median
0.445
1.016
0.032
0.984

27. WHAT DO FACTORY CEOS DO IN THE FIRST THREE YEARS
Stop the bleeding (slash): terminate underperforming large segments;
Underperforming: segment performance below sample median;
Do better at things already good at (fix): improve performance ranks of remaining segments;
OPS/SALES;
Within 2-digit SIC industry;
1 year prior to CEO appointment to 3 years after the appointment;
Invest into growth (renew): increase R&D investments.

28. SEGMENT TERMINATION (SLASH)
VARIABLES
(1)
(2)
(3)
(4)
Factory CEO
0.069***
0.024
0.066**
0.074
(0.001)
(0.458)
(0.016)
(0.113)
Factory CEO * Large segment
0.074*
-0.015
(0.064)
(0.799)
Factory CEO * Underperforming segment
0.005
-0.088
(0.884)
(0.141)
Factory CEO * Large segment * Underperforming segment
0.173**
(0.026)
Large segment * Underperforming segment
-0.038
(0.305)
Large segment
-0.054***
-0.071***
-0.052*
(0.002)
(0.000)
(0.056)
Underperforming segment
0.077***
0.078***
0.076***
0.091***
Segment age
-0.004***
(0.003)
(0.004)
Constant
0.456***
0.463***
0.457***
0.455***
Observations
4,307
Adjusted R-squared
0.111
0.112
0.113
Fixed Effect
Year-Ind

29. REMAINING SEGMENT PERFORMANCE (FIX)
(1)
(2)
Factory CEO
0.036**
0.028**
(0.019)
(0.038)
Segment age
-0.001*
(0.087)
Segment size
0.000
(0.988)
Segment performance_pre
0.514***
(0.000)
Constant
0.587***
0.282***
Observations
2,162
Adjusted R-squared
0.119
0.359
Fixed Effect
Year-Ind

30. R&D INVESTMENTS (RENEW)
(1)
(2)
Factory CEO (0,1)
0.006***
0.002*
(0.006)
(0.053)
R&D_pre
0.837***
(0.000)
Log(sale)
-0.000
(0.321)
Tobin's Q
0.000
(0.341)
Leverage
(0.948)
ROA
-0.021***
BHAR
-0.002
(0.123)
Sales growth
0.003
(0.214)
Cash/assets
0.023***
Constant
0.027***
(0.208)
Observations
2,299
Adjusted R-squared
0.427
0.839
Fixed Effect
Year-Ind

31. LONG TERM PERFORMANCE AND CEO COMPENSATION
Given the positive announcement return and the three actions taken by factory CEOs, we expect to and find stronger subsequent performance (ROA and Tobin’s Q) in the first three years after the CEO appointment;
CEOs whose general managerial skills are stronger should receive greater compensation (Custodia et al. 2013), and we find supportive evidence.

32. LONG-RUN PERFORMANCE (1) (2) (3) (4) ROA Tobin's Q Factory CEO (0,1)
(1)
(2)
(3)
(4)
ROA
Tobin's Q
Factory CEO (0,1)
0.017**
0.014**
0.107**
0.072*
(0.012)
(0.018)
(0.030)
(0.061)
Controls No
Yes
Year-Industry interactive Fixed Effect
Observations
2,096
2,122
Adjusted R-squared
0.047
0.259
0.130
0.499

33. CONCLUDING REMARKS
How to market the paper? Your comments are GREATLY welcomed!