The Effect of Manager Education on Firm Growth

Francisco Queiro

EDULOG International Conference

May 26th 2017

Education and Aggregate Income Across Countries2010

ALB

ARG

ARM

AUSAUT

BDI

BEL

BEN

BGD

BGR

BHR

BLZ

BOL

BRABRB

BWA

CAF

CANCHE

CHL

CHN

CIV

CMR

COD

COG

COLCRI

CYP

CZE

DEUDNK

DOM

DZA

ECUEGY

ESP

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FIN

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FRA

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GBR

GHAGMB

GRC

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HKG

HND

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KENKGZ

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LUX

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MAR

MDVMEX

MLI

MLT

MMR

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLD

NOR

NPL

NZL

PAK

PAN

PER

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POLPRT

PRY

ROU RUS

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SEN

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TZAUGA

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USA

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ZWE

Slope = 0.287

89

1011

12Lo

g G

DP

per

wor

ker

0 5 10 15Average years of schooling (Age 25+)

1

Education and Individual Earnings in Portugal2009

Slope = 0.10

5.5

66.

57

7.5

Log

indi

vidu

al e

arni

ngs

0 5 10 15Years of schooling

2

Two Views on the Aggregate Effect of Education

1 Aggregate effect ≈ individual effectI Cross-country coefficient biased upward by omitted technology differences

Klenow and Rodriguez-Clare (1997), Hall and Jones (1999)I Holding technology constant, the two effects should be equalI Prevailing view in development accounting (e.g. Caselli, 2005)

2 Aggregate effect > individual effectI Cross-country coefficient reflects human capital externalities, not technology

Lucas (1988)I Technology differences do play key role, but are themselves driven by education

Nelson and Phelps (1966), Romer (1990)

3

Technology and the Education of Managers

”We suggest that [...] production management is a function requiringadaptation to change and that the more educated a manager is, the quickerwill he be to introduce new techniques of production”

Nelson and Phelps (1966)

Macro implication: economic growth increases with the level of humancapital

Benhabib and Spiegel (1994) find support for this prediction across countriesI But subject to similar concerns with omitted variables, reverse causation

Bils and Klenow (2000), Krueger and Lindahl (2000)

4

This Paper

Develop firm-level analog of prediction studied by Benhabib and Spiegel(1994)

I If manager education is a driver of technology, then it increases firm growth

Test it using administrative data on universe of firms and workers in PortugalI Firm growth increases strongly with manager education in cross-sectionI Analyze event studies of management changes → find similar effectI Develop strategy to account for omitted ability → effect remains stable

Explore aggregate implications (preliminary)I Calculate effect on aggregate output of moving from the distribution of

manager education in Portugal to that in the U.S.

5

Outline

1 Data

2 Findings

3 Aggregate Implications

6

Outline

1 Data

2 Findings

3 Aggregate Implications

7

Data: Quadros de Pessoal

Administrative matched employer-employee data on the universe of employerfirms and employees in Portugal, 1995-2009

I Occupational codes that identify managers directlyI No size thresholds and includes the firm’s founding year

Key variablesI Firm growth: annual employment growth rate, winsorized at 99th percentileI Manager education: average years of schooling from no schooling to collegeI Controls: firm age and size, manager age, non-manager education and age

and number of managers

Sample: 1.8 million observations and 359,000 firms

8

Manager Definition

Conceptually: top decision makers

Occupational codes include a hierarchy of managerial titles: ”GeneralManager”, ”Operations Manager”, etc

Take worker(s) with highest managerial title reported; 63% of firm-years and79% of employment

I If no managerial title reported, take firm’s owner(s); 7% of firm-years and 3%of employment

I If no owners reported, assign previous or following year’s managers, if still oralready present; 4% of firm-years and 5% of employment

In total, cover 74% of firm-years and 87% of employment in the data

Findings robust to alternative procedures (e.g. all managerial titles, only stepone)

9

Managers by Education Level

10

College-Educated Managers by Field

11

Summary Statistics

Mean SD P10 P50 P90Firm Growth (p.p.) 1.37 31.27 -33.33 0.00 33.33Number of Workers 13.82 84.58 2.00 5.00 22.00Number of Managers 1.52 1.67 1.00 1.00 2.00Manager Education 8.69 4.33 4.00 9.00 17.00Non-Manager Education 7.56 3.03 4.00 6.96 12.00Manager Age 44.92 10.32 32.00 44.50 59.00Non-Manager Age 35.74 8.50 25.25 35.00 46.94Firm Age 12.39 12.35 2.00 9.00 27.00

12

Outline

1 Data

2 Findings

3 Aggregate Implications

13

Firm Size by Manager Education, 2009 Cross-Section

48

1632

64N

umbe

r of W

orke

rs

0 10 20 30 40 50 60 70Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Manager Education

14

Firm Revenue by Manager Education, 2009 Cross-Section

150

1500

1500

0R

even

ue (E

UR

thou

sand

)

0 10 20 30 40 50 60 70Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Manager Education

15

Value Added by Manager Education, 2009 Cross-Section

4040

040

00V

alue

Add

ed (E

UR

thou

sand

)

0 10 20 30 40 50 60 70Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Manager Education

16

Firm Size by Non-Manager Education, 2009 Cross-Section

48

1632

64N

umbe

r of W

orke

rs

0 10 20 30 40 50 60 70Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Non-Manager Education

17

Firm Size by Manager Education, 1995 Cohort Survivors

510

15N

umbe

r of W

orke

rs

0 2 4 6 8 10 12 14Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Manager Education at Entry

18

Fraction of Survivors by Manager Education, 1995 Cohort

0.2

5.5

.75

1Fr

actio

n of

Sur

vivo

rs

0 2 4 6 8 10 12 14Firm Age

[0,6) [6,9) [9,12)[12,15) 15+

Manager Education at Entry

19

Firm Size Distribution, Firms Aged 30+ in 2009Cross-Section

0.1

.2.3

.4D

ensi

ty

1 4 16 64 256 1024 4096Firm Size

[0,6) 15+Manager Education

20

Event Studies of Management Changes

1 Firms that switch to college educated managers vs firms that switch tomanagers with high school or less

I Assumption: parallel growth trendsI Examine graphical evidence on growth pre-trendsI Restrict to owner-managed firms (likely to be family successions)I Placebo tests with changes in other occupations

2 Firms that lose college educated managers vs firms that lose managers withhigh school or less

I Focus on losses where managers leave the sample permanently before age 55I Likely to have left the labor force for exogenous reasons, e.g. death or disabilityI Sanity check: over half of losses can be accounted for by expected deaths alone

21

Design 1: Effect on Firm Growth

∆ growth = 4.91 (1.63)

∆ growth / ∆ education = 0.64

-20

24

Firm

Gro

wth

(%)

-3 -2 -1 0 1 2Time Relative to Entry of New Manager(s)

College High School or Less

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Design 1: Owner-Managed Before and After Change

∆ growth = 7.57 (2.94)

∆ education = 7.94 (0.16)

∆ growth / ∆ education = 0.95

-20

24

6Fi

rm G

row

th (%

)

-3 -2 -1 0 1 2Time Relative to Entry of New Manager(s)

College High School or Less

23

Design 1: Changes in Other Occupations

∆ growth = -0.03 (1.73)∆ education = 6.51 (0.14)

-20

24

6Fi

rm G

row

th (%

)

-3 -2 -1 0 1 2Time Relative to Entry of New Professional(s)

College High School or Less

Professionals

∆ growth = 0.38 (0.74)∆ education = 4.86 (0.06)

-20

24

6Fi

rm G

row

th (%

)

-3 -2 -1 0 1 2Time Relative to Entry of New Office Worker(s)

High School or More 9th Grade or Less

Office Workers

∆ growth = -0.13 (1.30)∆ education = 4.71 (0.10)

-20

24

6Fi

rm G

row

th (%

)

-3 -2 -1 0 1 2Time Relative to Entry of New Service Worker(s)

High School or More 9th Grade or Less

Service Workers

∆ growth = -0.27 (0.84)∆ education = 5.00 (0.07)

-20

24

6Fi

rm G

row

th (%

)

-3 -2 -1 0 1 2Time Relative to Entry of New Blue-Collar Worker(s)

9th Grade or More 6th Grade or Less

Blue-Collar Workers

24

Design 2: Effect on Firm Growth

∆ growth = -1.66 (0.54)

∆ growth / ∆ education = 1.68

-1.5

-1-.5

0.5

Ave

rage

Firm

Gro

wth

-3 -2 -1 0 1 2Time Relative to Loss of Manager

College High School or Less

25

Full Sample Extension

∆gjt = α0 + α1∆sjt + θXjt + δt + εjt

1-year ∆ 3-year ∆ By level Owner Ed. Revenue(1) (2) (3) (4) (5)

Manager Education 0.409∗∗∗ 0.387∗∗∗ 0.498∗∗∗ 0.664∗∗∗

(0.120) (0.099) (0.171) (0.247)Owner Education -0.044

(0.080)Relative to [0,6):

[6,9) 2.199∗

(1.170)[9,12) 2.624∗∗

(1.291)[12,15) 2.733∗∗

(1.365)15+ 6.466∗∗∗

(1.558)Observations 659457 659457 659457 480515 489542Number of Firms 206215 206215 206215 166058 177900

26

Effect by Field of College DegreeRelative to not having a college degree:

27

Can Estimates Account for Cross-Sectional Differences?

Relative firm size, college vs primary-school manager

Firm Age Bin Simulation Data Sim/Data1 to 5 1.22 1.26 976 to 10 1.48 1.59 9311 to 15 1.80 1.81 9916 to 20 2.20 2.57 8621 to 25 2.63 2.72 9726 to 30 3.24 2.88 11331 to 35 3.88 4.00 9736 to 40 4.74 5.76 8241 to 45 5.71 7.89 7246 to 50 7.04 7.25 97

28

Income in Other Occupations as Proxy for Ability

Suppose firm growth is given by

gjt = α0 + α1sjt + α2ajt + εjt (1)

Results could be biased by omitted ability ajt

Assume the manager’s outside option working as a non-manager is given by

ln wjt = β0 + β1β1sjt + β2ajt (2)

Then solving (2) for ajt and plugging into (1)

gjt = α0 − α2β0β2

+ (α1 − α2β2β1α2β2β1α2β2β1α2β2β1)sjt +

α2β2

α2β2

ln wjt + εjt (3)

29

Income in Other Occupations as Proxy for Ability

AssumptionsI Ability measured by a scalar ajtI α1

α26= β1

β2, i.e. relative importance of schooling and ability differs in

management and other occupations

Implement using a sample of managers observed in other occupations beforebecoming managers

I Use last observed income as non-manager, residualized on year and experiencedummies (same results using average)

Also examine robustness to other measures of ability and experienceI AgeI Management and industry experienceI Number of prior occupations (Lazear, 2005)I Tenure at firm

30

Education vs Other Manager Characteristics

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Education 0.51∗∗∗ 0.45∗∗∗ 0.46∗∗∗ 0.51∗∗∗ 0.53∗∗∗ 0.49∗∗∗ 0.50∗∗∗ 0.47∗∗∗ 0.39∗∗∗

(0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04)Log Outside Option 1.07∗∗∗ 1.30∗∗∗

(0.22) (0.22)

Bias-corrected Education 0.53∗∗∗ 0.49∗∗∗

(Return to Education=7%) (0.04) (0.04)Age -0.08∗∗∗ -0.09∗∗∗ -0.11∗∗∗

(0.02) (0.02) (0.02)Management Experience 0.26∗∗ -0.15 -0.14

(0.11) (0.13) (0.13)Industry Experience 0.75∗∗∗ 0.74∗∗∗ 0.75∗∗∗

(0.07) (0.07) (0.07)Prior Occupations 0.73∗∗∗ 0.43∗∗∗ 0.31∗

(0.14) (0.17) (0.17)Tenure at Firm -0.09∗∗∗ -0.01 -0.01

(0.03) (0.03) (0.03)Observations 476432 476432 476432 476432 476432 476432 476432 476432 476432Number of Firms 132085 132085 132085 132085 132085 132085 132085 132085 132085

31

Outline

1 Data

2 Findings

3 Aggregate Implications

32

Implications for Aggregate Output

What does estimated effect imply for aggregate output?

Present a simple model of heterogeneous firms based on Hsieh and Klenow(2014) where manager education drives firm-level technology

In equilibrium, aggregate technology (TFP) is a function of the distributionof manager education and firm age

Use estimated effect to calibrate model and calculate the effect of changes inmanager education on TFP

33

Moving to U.S. Distribution of Manager Education

Use data from Global Entrepreneurship Monitor to measure distribution ofmanager education in Portugal and U.S. (10 vs 14 average years of schooling)

Assume distribution of firm age is independent of manager education, andequal to the overall distribution in Portugal

ResultsI Moving from distribution in Portugal to that of U.S. would increase TFP by

33%I Accounting for increased physical capital accumulation, output per capita

would rise by 54%, half of the difference between the two countries

34

Conclusion

FindingsI If education drives technology adoption, then aggregate return to education

exceeds individual returnI Micro-level test: does firm growth increase with manager education?I Find strong support using administrative data on the universe of firms and

workers in PortugalI Magnitude suggests manager education can account for large differences in

aggregate output

ImplicationsI Good news: investment in education has a higher return than implied by

individual earningsI Bad news: the return takes a very long time to fully materialize

35