Innovation Complementarities,

Management Quality and Export

Composition

W. F. Maloney

Research Department, World Bank

Ankara, December 2013

https://openknowledge.worldbank.org/handle/10986/9371

Ancient Latin American Growth Mystery I:

Same Good, Different Outcomes

4000

5000

30

35

40

45

Copper in Chile, 1870-1950:

Production and % of World Production

Introduction of New Foreign

Technologies

0

1000

2000

3000

0

5

10

15

20

25

30

1870 1880 1890 1900 1910 1920 1930 1940 1950% Prod. Mundial Production

Ancient LA Growth Mystery II: Same

Business Climate, Different Outcomes

Country Year % Immigrant

Directors/Owners

Argentina 1900 80

Percentage of Firms Managed by Immigrants

Argentina 1900 80

Chile 1880 70

Colombia (Baranquilla) 1888 60

Mexico 1935 50

Source: Maloney 2013

Structure of the Presentation

• Part I: Export Composition: is this the missing ingredient?

• Part II: Innovation: the critical agenda

• Part III: Management quality as a missing complement

Export Composition-is this the Export Composition-is this the

critical ingredient?

Why might standard price signals be

deceptive in choosing goods

• Marshallian externalities related to goods

• local industry-level knowledge spillovers, input-output

linkages, and labor pooling.

Volatility externalities: Export diversification?• Volatility externalities: Export diversification?

• Intervention warranted to shift to good with

externalities against price signals.

Empirical concerns of policy

makers about export composition

1. Yes, Externalities dictate that market will not generate optimal basket

2. How do we measure these externalities?

3. Doesn’t the whole world see the same benefit and drive the price

down? (GE)

• Interindustry spillovers, assymetries• Interindustry spillovers, assymetries

• Should we look for safe rents, too? Natural Resources

• More generally, must think of demand side as well

4. Do externalities necessarily come with a good, or does it matter how

we produce it?

• Heterogeneity, Heterogeneity, Heterogeneity

In practice, measurement of MEs is

difficult, so we take shortcuts

• Natural resources

• Low productivity (Smith, Matsuyama, Sachs), few Externalities

• Rent seeking

• High productivity goods

• Rich Country Goods (Rodrik, Hausmann)

• High tech (Lall) high inter-industry MEs

CURSED GOODS: NATURAL RESOURCES

Empirically, there is no resource curse

• In growth regressions

• Minerals are good: Davis (1995), Sala-i-Martin et al. (2004), Stijns

(2005), Brunnschweiler (2008, 2009)

• Conditional on education (above 2 years of schooling): Bravo-Ortega &

De Gregorio (2007)

• Existing resource curse findings fragile: Lederman & Maloney (2007,

2008)2008)

• [Also, Jacob Viner and Douglass North years ago…]

There is lots of heterogeneity in

experiences with NRLog GDP per capita 1990 9

10

Argentin

AustraliAustria

Brazil

Canada

Chile

Cyprus

Denmark

Fiji

FinlandFrance

Gabon

Germany

Greece

Hong Kon

Hungary

Iceland

Iran, Is

IrelandIsrael

ItalyJapan

Korea, RMalaysiaMauritiuMexico

NetherlaNew Zeal

Norway

Poland

Spain

SwedenSwitzerl

Syrian A

Turkey

United K

United S

Uruguay

Venezuel

Leamer Measure: Net Exports of

NR/Worker

Log GDP per capita 1990

Log Natural Resources (Leamer)-11.5041 11.7949

6

7

8

Algeria

Banglade

Benin

Bolivia

Brazil

Burkina Burundi

Cameroon

Cape Ver

Chad

China

Colombia

Comoros

Congo, D

Costa Ri

Dominica

Ecuador

Egypt, A El Salva

Gabon

Gambia,

Ghana

Guatemal

GuineaGuinea-B

Guyana

Honduras

India

Indonesi

Iran, Is

Cote d'I

Jamaica

Jordan

Kenya

Madagasc

MalawiMali

Mauritan

Morocco

Mozambiq

Nicaragu

Nigeria

Pakistan

Panama

Papua Ne

ParaguayPeru

Philippi

Rwanda

Senegal

Sierra L

South Af

Sri Lank

Sudan

Thailand

Togo

Tunisia

Turkey

Uganda

Zambia

Zimbabwe

Resource AbundantResource Scarce

Maloney 2007

Trees can be very high tech!

Innovation policy is key

12/23/2013

Nokia: Site of an early

pulp mill in Finland

Learn how to learn

HIGH PRODUCTIVITY GOODS

Does It Matter What We Export?

Hausmann, Hwang, Rodrik (2007)

• Model- broadly inter-industry spillover

• Country should produce the highest productivity good within its CA

• Empirics:

• PRODY= avg. income of countries producing good

• EXPY= income value of our export basket• EXPY= income value of our export basket

• Similar to Lall (2000)

• Find higher EXPY correlated with higher growth.

Caveats

• GE critique again?

• Rents- higher where rich countries already are?

• Not generally the case- Nokia and TVs

• If easy to move into these goods, then barriers to entry/rents low

• Empirical findings muddy

• Animals, electrical machinery same PRODY• Animals, electrical machinery same PRODY

Again, high degree of heterogeneity

20000

25000

30000

35000 PRODYs (with +/- 1 SD*)

0

5000

10000

15000

Caveats

• GE critique again?

• Rents- higher where rich countries already are?

• Not generally the case- Nokia and TVs

• If easy to move into these goods, then barriers to entry/rents low

• Empirical findings muddy

• Animals, electrical machinery same PRODY• Animals, electrical machinery same PRODY

• Finding of an impact on growth fragile

Empirically, some support for

MODELGrowth Regressions

Base: HHR

Regressions

Including the Export

Herfindahl and the

Investment Share

With Income Average

Value

Including the Export

Herfindahl and the

Investment Share

IV GMM IV GMM IV GMM IV GMM

Log ( initial gdp) -0.0382*** -0.0203** -0.0414* -0.0177 -0.0166* -0.0177 -0.028 0.0215

(0.01) (0.01) (0.02) (0.01) (0.01) (0.04) (0.02) (0.03)

Log (expy) 0.0925*** 0.0532** 0.107 -0.00687 0.102*** 0.0504** 0.124 0.00275

(0.02) (0.02) (0.07) (0.03) (0.02) (0.02) (0.08) (0.03)

Category Log (expy) -0.0577*** -0.00566 -0.0431 -0.119Category Log (expy) -0.0577*** -0.00566 -0.0431 -0.119

(0.02) (0.10) (0.03) (0.08)

Log (primary schooling) 0.00468* 0.00565 0.00271 0.0101 0.00394 0.00582 0.00207 0.00958

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

Log (Investment Share) 0.0111* 0.0360** 0.00935 0.0566***

(0.01) (0.02) (0.01) (0.02)

Root Herfindal Index 0.0551 -0.0381 0.0615 -0.0283

(0.06) (0.04) (0.06) (0.04)

Constant -0.426*** -0.250* -0.572 0.14 -0.186* -0.199 -0.449 0.699

(0.10) (0.13) (0.44) (0.18) (0.10) (0.47) (0.40) (0.46)

Observations 285 285 285 285 285 285 285 285

Number of wbgroup 75 75 75 75

Regressions include decade dummies

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

1b. A digression on Monkeys

• Being a tree in a dense area is like a ME with same

GE concerns

• If easy to jump from one tree to others, then easy to

jump to, i.e, no barriers to entry and rents

• Is past a good predictor?• Is past a good predictor?

• iPhone didn’t exist, Saab already does

• Would Chilean forestry produce Saab?

IS IT WHAT WE PRODUCE, OR HOW?

BEYOND GOODS

Is High Tech Necessarily High Tech?

CAI=1

Fuente: Lederman and Maloney (2012)

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181

Exports Ratio (ranked)

Exports vs Patents in Computers (SIC 357)

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1Exports Ratio (ranked)

1 83Patents Ratio (ranked)

World LAC

Heterogeneity in product quality is also huge (relative unit values, standardized)

Krishna and Maloney 2011

Quality ladders by product and countries (relative unit

values, standardized)

Export Quality Growth

Krishna and Maloney 2011

Growth in Quality Driven by Both

What and How

Source: Krishna and Maloney 2011

GOODS OR TASKS

Table 2 China: 10 Exports with the Lowest Domestic Value Added

Electronic computer 4.6

Telecommunication equipment 14.9

Cultural and office equipment 19.1

Other computer peripheral equipment 19.7

Electronic element and device 22.2

Radio, television, and communication equipment 35.5

Household electric appliances 37.2

Plastic products 37.4

Generators 39.6

Instruments, meters and other measuring equipment 42.2

Goods or Tasks: Does China really

export the iPOD?

“..the electronic components we

make in Singapore require less

skill than that required by

barbers or cooks, involving

mostly repetitive manual

operations” Instruments, meters and other measuring equipment 42.2

China: 10 Exports with the Highest Domestic Value

Added

Agriculture, forestry, animal husbandry and fishing

machinery 81.8

Hemp textiles 82.7

Metalworking machinery 83.4

Steel pressing 83.4

Pottery, china and earthenware 83.4

Chemical fertilizers 84.0

Fireproof materials 84.7

Cement, lime and plaster 86.4

Other non-metallic mineral products 86.4

Coking 91.6

Source: Koopmans, Wang, and Wei (2008).

operations”

Goh Keng Swee, Minister of

Finance Singapore (1972)

Innovation: The Critical Agenda

Weak innovative capacity meant LA

missed the 2nd Industrial RevolutionInnovative Capacity vs Income 1900

Source: Maloney yValencia (2013)

Current literature focusing on R&D

is not credible...

� US firm level/industry data- social returns

� Grilliches and Lichtenberg (1984) 71%

� Terleckyj 1980, Scherer (1982 ) >100%

� Griffith, Redding, Van Reenen (2004) 57%

Estimated returns to R&D are very highN

� Jones and Williams (1998) 28%

� X country

� Coe and Helpman (1995) G7 123%

� Van Pottlesberghe and Lichtenberg (2001) G7 68%

� …And imply social rates of return far above private

� Jones and Williams (1998): US should quadruple investment in RD

…and get higher with distance

from the frontier

� Two Faces of R&D (Cohen and Levinthal 1989)

� Invention

� Learning\Catch-up

� Poor countries should have much greater returns

� Griffith, Redding, Van Reenen (2004)� Griffith, Redding, Van Reenen (2004)

� Dist. Frontier RoR R&D

� USA -.18 57%

� UK -.53 77%

� Italy -.73 88%

� What should the rate of return be for Korea (-1.33),

Malaysia (-2.28), Turkey (-2.5), Indonesia (-3.74)? 200%? 300%?

But poor countries do generally less

R&D than rich countries…Why?

R&D/GDP vs Level of Development

2

21

&

+=CAP

GDP

CAP

GDP

GDP

DRββ

Innovation Superstars?

3.0%

3.5%

4.0%

4.5%

5.0%

Predicted & Observed R&D/GDP Israel

Finland

2

21

&

+=CAP

GDP

CAP

GDP

GDP

DRββ

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

4 5 6 7 8 9 10 11

Log GDP per Capita

Predicted & Observed R&D/GDP

IndiaArgentina

China

Korea

Mexico

Source: Goni, Lederman, Maloney 2006

Rates of Return suggest missing

complementary factors

AR

G(1

97

1-1

97

5)

20

00

)

AU

S(1

97

6-1

98

0)

AU

S(1

99

6-2

00

0)

AU

T(1

97

6-1

98

0)

AU

T(1

99

6-2

00

0)

BO

L(1

99

6-2

00

0)

BR

A(1

97

6-1

98

0) BR

A(1

99

6-2

00

0)

CH

E(1

97

1-1

97

5)

20

00

)

CH

L(1

98

1-1

98

5)

CH

L(1

99

6-2

00

0)

CH

N(1

99

6-2

00

0)

CO

L(1

97

6-1

98

0)

CO

L(1

98

1-1

98

5)

19

80

)

CR

I(1

99

6-2

00

0)

DE

U(1

99

6-2

00

0)

DN

K(1

97

6-1

98

0)

DN

K(1

99

6-2

00

0)

EC

U(1

97

6-1

98

0)

EC

U(1

99

6-2

00

0)

19

85

)

ES

P(1

97

6-1

98

0)

ES

P(1

99

6-2

00

0)

FIN

(19

71

-19

75

)

FIN

(19

96

-20

00

)

GR

C(1

98

1-1

98

5)

GR

C(1

99

6-2

00

0)

19

90

)

HU

N(1

97

6-1

98

0)

HU

N(1

99

6-2

00

0)

IDN

(19

96

-20

00

)

-19

80

)

ISL(

19

71

-19

75

)

ISL(

19

96

-20

00

)

ISR

(19

71

-19

75

)IT

A(1

97

1-1

97

5)

ITA

(19

96

-20

00

)

JAM

(19

86

-19

90

)

19

85

)

JOR

(19

86

-19

90

)

JPN

(19

71

-19

75

)

KO

R(1

97

1-1

97

5)

KO

R(1

99

6-2

00

0)

ME

X(1

97

1-1

97

5)

ME

X(1

99

6-2

00

0)

19

90

)

MY

S(1

99

1-1

99

5)

MY

S(1

99

6-2

00

0)

NLD

(19

76

-19

80

)

19

80

)

PA

N(1

99

1-1

99

5)

PE

R(1

97

6-1

98

0)

PE

R(1

99

6-2

00

0)

PH

L(1

97

1-1

97

5)

PO

L(1

99

6-2

00

0)

PR

T(1

97

6-1

98

0)

20

00

)

RO

M(1

99

1-1

99

5)

20

00

) SW

E(1

97

1-1

97

5)

TH

A(1

98

1-1

98

5)

20

00

)

TU

R(1

99

6-2

00

0)

UR

Y(1

97

1-1

97

5) U

RY

(19

96

-20

00

)

20

00

)

VE

N(1

97

6-1

98

0)

VE

N(1

99

6-2

00

0)ZA

F(1

98

6-1

99

0)

ZA

F(1

99

1-1

99

5)

1,0

2,0

3,0

4,0

Retirns to R&D

AR

G(1

99

6-2

00

0)

AU

S(1

97

6

AU

S(1

99

6

AU

T(1

97

6

AU

T(1

99

6

BE

L(1

97

1-1

97

5)

BO

L(1

99

6

CH

E(1

97

1

CH

E(1

99

6-2

00

0)

CO

L(1

97

6

CO

L(1

98

1

CR

I(1

97

6-1

98

0)

DE

U(1

99

6

DN

K(1

99

6

EC

U(1

97

6

EC

U(1

99

6

EG

Y(1

98

1-1

98

5)

EG

Y(1

99

6-2

00

0)

ES

P(1

99

6

FIN

(19

96

FR

A(1

97

1-1

97

5)

FR

A(1

99

6-2

00

0)

GB

R(1

97

6-1

98

0)

GB

R(1

99

6-2

00

0)

GT

M(1

97

1-1

97

5)

GT

M(1

98

6- 1

99

0)

IDN

(19

96

IRL(

19

76

-

ISL(

19

96

ITA

(19

96

JOR

(19

81

-19

85

)

JPN

(19

71

JPN

(19

96

-20

00

)

KO

R(1

97

1

MU

S(1

98

6-1

99

0)

MY

S(1

99

1

NLD

(19

76

NLD

(19

96

-20

00

)

NO

R(1

97

1-1

97

5)

NO

R(1

99

6-2

00

0)

NZ

L(1

97

6- 1

98

0)

NZ

L(1

99

6-2

00

0)

PE

R(1

99

6

PH

L(1

99

1-1

99

5)

PR

T(1

99

6-2

00

0)

RO

M(1

99

1

RO

M(1

99

6-2

00

0)

SLV

(19

91

-19

95

)S

LV(1

99

6-2

00

0)

SW

E(1

99

6-2

00

0)

TH

A(1

99

6-2

00

0)

TU

N(1

99

6-2

00

0)

TU

R(1

97

6-1

98

0)

TU

R(1

99

6

US

A(1

97

1-1

97

5)

US

A(1

99

6-2

00

0)

-4,0

-3,0

-2,0

-1,0

0,0

-4,0 -3,5 -3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0

Retirns to R&D

Distance to the economic frontier

Source: Goni and Maloney 2012

Universities/

Think tanks/CTsThe firm

Barriers to Demand

Macro Context

Innovation

“supply”Demand Side

A

Accumulation

Barriers to AccumulationBarriers to Innovation

K

Innovation Ecosystem

Macro Context

Trade Regime

International Marketing

Externalities

Competitive Structure

Entrepreneurship

Barriers to Accumulation

Credit

Entry/Exit barriers

Business/Regulatory

Climate

Market Failures (&IP)

Seed/Venture capital

Poorly articulated S&T

system (including

discovery, oversight)

Labor regulation

Deficient human capital

Perceived Quality of Research

Institutions

AUS

AUT

BEL

CAN

CHE

CRI

DEUDNK

FINFRA

GBR

HUN

IND

IRL

ISL

ISR

JPN

KOR

MEX

MYS

NLD

NOR

NZL

POL

PRT

SWE

TUN

USA

5,0

6,0

7,0

ARGBRA

CHLCHN

ECU

EGY

ESP

GRC

IDN ITA

MEX

PAN

PER

POL

ROM

SEN

SLV

TUN

TURUGA

URY

VEN

0,0

1,0

2,0

3,0

4,0

-5,5 -5,0 -4,5 -4,0 -3,5 -3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0

Quality

Distance to the economic frontier (1996-2000)

University-Industry Collaboration

AUSAUT

BEL

BRA

CAN

CHE

CHLCHNCRI

DEUDNK

FIN

GBR

HUN

IND

IRL ISL

ISRJPN

KORMYS

NLD

NOR

NZLPRT

SWE

USA

4,0

5,0

6,0

7,0

Collaboration

ARG

BRA

ECU

EGY

ESPFRA

GRC

IDNIND

ITA

MEXPAN

PER POL

ROM

SEN

SLV

TUNTURUGA

URY

VEN

0,0

1,0

2,0

3,0

4,0

-5,5 -5,0 -4,5 -4,0 -3,5 -3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0

Collaboration

Distance to the economic frontier (1996-2000)

Business Share in R&D

AUS

BEL

CAN

CHE

CHN

DEU

ESP

FIN

FRA

GBR

HUN

IRL

ISL

ISR

JPN

KOR

MYS

NLD

ROM

USA

50,0

60,0

70,0

80,0

90,0

perform

ed by Business enterprise %

(year=

2000)

ARG

BRA

CRI

HUN

IDN

IND

ITA

MEX

PER

POL

PRT

TUN

TUR

URY

0,0

10,0

20,0

30,0

40,0

-5,5 -5,0 -4,5 -4,0 -3,5 -3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0

GERD-perform

ed by Business enterprise %

(year=

2000)

Distance to the economic frontier (1996-2000)

Managing Risky Ventures

Return

Krishna and Maloney 2011Risk

Ability to manage risk

Export Quality Growth and Risk

Krishna and Maloney 2011

So, China: Waste or Wisdom?

3.0%

3.5%

4.0%

4.5%

5.0%

Predicted & Observed R&D/GDP Israel

Finland

2

21

&

+=CAP

GDP

CAP

GDP

GDP

DRββ

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

4 5 6 7 8 9 10 11

Log GDP per Capita

Predicted & Observed R&D/GDP

IndiaArgentina

China

Korea

Mexico

Who’s doing R&D? Patents granted by the

USPTO to inventors based in China

Branstetter 2012

China differs from Taiwan and Korea in the

composition of their innovation surges

Branstetter (2012)

Pros and Cons

• MNCs providing eco-system: complementary factors

• Most patents owned by MNCs

• 50% are co-patented

• But will it maintain an autonomous innovative capacity?• But will it maintain an autonomous innovative capacity?

Management Quality: A Missing Management Quality: A Missing

Complement?

Management Quality and

Productivity

Argentina

Brazil

France

Germany

Great BritainGreece

Italy

Japan

Mexico

Northern Ireland

Poland

Portugal

Sweden

US

45

6

Log of Sales/Employees

China

India

23

4

Log of Sales/Employees

2.6 2.8 3 3.2 3.4Overall Management Scores

.

Bloom and Van Reenen (2010) Sarrias and Maloney (2013)

Management Quality and Productivity

Argentina

France

Germany

Great BritainGreece

Italy

Japan

Northern Ireland

Poland

Portugal

Sweden

US56

Log of Sales/Employees

• Chile

Brazil

China

India

Mexico

23

4Log of Sales/Employees

2.6 2.8 3 3.2 3.4Overall Management Scores

.

Fuente: Bloom et al. 2010+LAC, Sarrias and Maloney (2013)

• Colombia

China: Unprepared for indigenous

Innovation

Source: Maloney 2013

Convergence to the management

frontier-not just more competition

Source: Maloney and Sarrias 2013

Direct interventions in management

quality?

• Japan, Korea, Singapore: All employed management

promotion programs for SMEs

• Korea: The Small and Medium Industries Promotion program

• Singapore: Local Industry Upgrading Program (LIUP)Singapore: Local Industry Upgrading Program (LIUP)

• India

• Colombia

• Lays the foundation for progressively more adoption and

invention of new technologies.

India-Successful Management

Intervention .5

.6

Treatment plants

(●)

Control

plants (♦)

Sh

are

of

ke

y t

ext

ile

ma

na

ge

me

nt

pra

ctic

es

ad

op

ted

11

%

pro

du

ctivid

ad

.2.3

.4

-10 -8 -6 -4 -2 0 2 4 6 8 10 12Months after the diagnostic phase

plants (♦)

Sh

are

of

ke

y t

ext

ile

ma

na

ge

me

nt

pra

ctic

es

ad

op

ted

Source: Bloom, et al 2013

pro

du

ctivid

ad

Colombia

• Technological Extension pilot-Autoparts

• RCT 180 firms

• Individual company intervention

• Group intervention- Lower cost, more dynamism?

• Current plans to scale up to whole sector • Current plans to scale up to whole sector

Thank You

Distribution relative to the US

Sarrias and Maloney (2012

Decomposition: Manager having a

degree, important. OtherwiseN?

Do Chinese managers know

what they don’t know?Self Percieved vs. Actual Management Ability

Sarrias and Maloney (2012