Managing Trade:
Evidence from China and the US
Nick Bloom, Stanford
Kalina Manova, Stanford and Oxford
Stephen Sun, Peking University
John Van Reenen, LSE
Zhihong Yu, Nottingham

Hitotsubashi Conference: International Trade and FDI
December 13, 2015
Bloom, Manova, Sun, Van Reenen and Yu

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Two Literatures with Open Questions
 Trade and productivity■ Long literature linking export performance to productivity: e.g. Melitz
2003, BEJK 2003, Melitz-Ottaviano 2008, Bernard et al 2007, …
■ Recent focus on product quality: e.g. Verhoogen 2008, KuglerVerhoogen 2012, Khandelwal 2010, Manova 2012, Johnson 2007, …
 Management and productivity■ Even older literature on management, productivity and firm
performance: e.g. Walker 1887, Taylor 1912, Syversson 2011, …
■ Again, newer literature focusing on quality control: e.g. Deming 1950,
Roos et al 1990, Bloom et al 2013, Sutton 2007, …

Bloom, Manova, Sun, Van Reenen and Yu2

This Paper
 Examine the role of management practices for export

performance to shed light on both open questions
 Exploit unique data on plant-level production, plant-level

management practices, and transaction-level trade activity■ Study world’s two largest export economies: China and the US■ Establish consistent patterns in both countries despite different
income levels, institutional quality and market frictions

Bloom, Manova, Sun, Van Reenen and Yu3

Key Results
 Better managed firms are more successful exporters■ Export probability and revenues■ Export product range and destinations
 Better managed firms produce higher quality■ Export product price, quality and quality-adjusted price■ Imported input price, quality and range
 Management more important for trade than domestic activity
 Production complementarity between quality and management
 Management an important, directly measured component ofproductivity
Bloom, Manova, Sun, Van Reenen and Yu

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Related Literature Firm heterogeneity matters for aggregate productivity, welfare and gains
from trade (Hsieh-Klenow 2009, Arkolakis et al 2012, Melitz-Redding 2013)■ Reallocation across firms and productivity upgrading within firms important inadjustment to trade reforms (Pavcnik 2002, Bernard et al 2006, Bustos 2011)
 Poor managerial practices and poor product quality hinder growth andentrepreneurship in developing countries■ Role of trade with developed countries that maintain high quality standards■ Role of imported inputs (e.g. Goldberg et al 2013, Fieler et al 2015…)
…

Bloom, Manova, Sun, Van Reenen and Yu

5Outline
1. Six datasets
2. Empirical results
3. ModelBloom, Manova, Sun, Van Reenen and Yu

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US Data: Production and Trade Production: establishment-level data from Annual Survey ofManufacturers■ ~45,000 plants and >10,000 firms in 2010
■ Shipments, exports, labor, capital, materials, …
 Trade: transaction-level data from Longitudinal Federal Trade

Transaction Database■ ~100 million transactions a year
■ Product, month, source/destination country■ Revenue, units, quantity

Bloom, Manova, Sun, Van Reenen and Yu

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US Data: Management
 Management and Organizational

Practices Survey■ 47,534 manufacturing plants■ Mandatory, 78% response rate
■ 5.6m employees or >50% of USmanufacturing
 3 types of management practices■ targets■ monitoring■ incentives

Bloom, Manova, Sun, Van Reenen and Yu

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Targets
 Design, integration and realism of production targets

Example:Bloom, Manova, Sun, Van Reenen and Yu

9Monitoring Data collection and analysis

Example:Bloom, Manova, Sun, Van Reenen and Yu

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Example of Performance Metrics: Car Plant

Bloom, Manova, Sun, Van Reenen and Yu

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Example of No Performance Metrics: Textile Plant

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Incentives
 Rewarding high performers and improving low performers

Example:Bloom, Manova, Sun, Van Reenen and Yu

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China Data: Production and Trade Production: firm-level data from Annual Survey of Industrial

Enterprises■ >200,000 firms, 1999-2007■ Output, total exports, employment, inputs, ownership, …
 Trade: transaction-level data from Chinese Customs Trade

Statistics■ ~100 million transactions a year
■ Product, month, source/destination country, trade regime■ Revenue, units, quantity

Bloom, Manova, Sun, Van Reenen and Yu

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China Data: Management
 World Management Survey■ 20,000+ firms, 34 countries since 2004
■ 507 companies in China in 2007
 Survey procedure (Bloom and Van Reenen 2007)■ 45min double-blind phone interview of plant managers■ 18 questions on monitoring, targets and incentives

Bloom, Manova, Sun, Van Reenen and Yu

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Example Monitoring: how is performance tracked?
Score (1): Measures
tracked do notindicate directlyif overallbusiness
objectives arebeing met.
Certainprocesses aren’t
tracked at all
Bloom, Manova, Sun, Van Reenen and Yu(3): Most key
performanceindicators
are tracked
formally.
Tracking isoverseen by
seniormanagement(5): Performance iscontinuously
tracked andcommunicated,both formally andinformally, to all
staff using a rangeof visualmanagement tools

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Example Incentives: how does promotion work?
Score (1) People arepromoted
primarily upon
the basis oftenure,irrespective ofperformance(ability & effort)

Bloom, Manova, Sun, Van Reenen and Yu(3) People(5) We actively
are promoted identify, develop
primarily
and promote ourupon the
top performers
basis ofperformance17

United StatesJapan
Germany
SwedenCanadaUnited Kingdom
FranceAustralia
Italy
MexicoPolandSingapore
New ZealandPortugal
IrelandChile
Spain
GreeceChina
TurkeyArgentina
BrazilIndia
Vietnam
Colombia
Kenya
Nigeria
Nicaragua
MyanmarZambiaTanzania
Ghana
Ethiopia

3.308
3.230
3.210
3.188
3.142
3.029
3.015
2.997
2.978
2.899
2.887
2.861
2.851
2.826
2.762
2.752
2.748
2.720
2.707
2.706
2.699
2.684
2.6112.608
2.578
2.549
2.516
2.397
2.372
2.316
2.254
2.225
2.221

2

AfricaAsia
Australasia
EuropeLatin America
North America

2.5
3
Average Management Scores, Manufacturing Firms

Bloom, Manova, Sun, Van Reenen and Yu

3.5
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Summary Statistics
 Unconditional exporter management premium■ China: 21%■ US: 146%

China

USExporters

Non-exporters

Exporters

Non-exporters# Observations

1,875

1,358

Management score

-0.27

-0.34

0.12-0.26

Log Gross output

11.72

11.55

10.60

9.55

Log Employment

6.46

6.15

4.76

3.96

Bloom, Manova, Sun, Van Reenen and Yu

19Summary Statistics
ChinaUSN

MeanSt Dev

NMeanSt Dev

Log Exports

2,236

14.80

2.31

13,000

13.79

2.77

# Export Products

2,236

8.65

11.58

13,000

18.94

47.50

# Export Destinations

2,236

12.85

14.99

13,000

12.95

16.72

Log Imports

2,048

13.87

2.97

10,000

13.93

2.96

# Import Products

2,048

33.45

51.43

10,000

19.67

43.09

# Import Origin Countries

2,048

6.30

5.67

10,000

6.20

8.02

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Outline
1. Six datasets
2. Empirical results
3. ModelBloom, Manova, Sun, Van Reenen and Yu

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Empirical Strategy
 Document the conditional correlation between firms’ managerial

practices and trade performance

Trade ft = α + β ⋅ Management f + δ ′ ⋅ Z ft + ϕ p + ϕi + ϕt + ε ft
■ Tradeft : various export and import outcomes■ Managementf : management z-score■ φp, φi, φt : 31 province FE, 82 SIC-3 industry FE, year FE (China)■ φp, φi : 50 state FE, NAICS-6 industry FE (US)■ Zft : firm ownership, productivity, size, skill and capital intensity, age,“noise” in management score■ εft : errors clustered by firm (China) or robust (US)

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Export Activity
China
Dep Variable:

Exporter DummyUS
Log Exports

0.638**(2.14)

0.566*(1.81)

Exporter DummyManagement Score 0.096**(2.30)

0.116***(2.75)

Capital Intensity-0.01(-0.76)0.145(1.43)

Skill Intensity-0.609***(-3.10)-4.231***(-2.64)0.032***(9.34)

0.230***(8.07)

Log Wage

0.041*(1.82)

0.401**(2.17)

0.119***(11.20)1.071***(13.96)Age

0.03(1.53)

0.153(1.01)

0.057***(15.82)0.562***(19.09)Own, Prov, Ind, Year FE; Noise Controls
R-squared# observations# firms

0.41
3,233
485

0.43
3,123465

0.40
2,236
334

Bloom, Manova, Sun, Van Reenen and Yu

0.43
1,935
305

0.042***(13.96)0.032***(10.71)

Log Exports
0.481***(21.38)0.404***(18.11)

State, Ind FE; Noise Controls
0.26
32,0000.27
32,0000.33
13,0000.37
13,000

23Extensive Margin of Exports
China
Dep Variable:

USLog # Dest Log # Prod Log # Dest-Prod Log # Dest Log # Prod Log # Dest-Prod

Management Score 0.451***(2.80)

0.404***(3.33)

0.524***(2.89)

Own, Prov, Ind, Year FE;
Noise + Firm Controls
R-squared# observations# firms

0.44
1,935
305

0.42
1,935305

Bloom, Manova, Sun, Van Reenen and Yu

0.40
1,935
305

0.143***(14.05)0.176***(16.30)0.209***(16.19)State, Ind FE; Noise + Firm Controls
0.37
13,0000.32
13,0000.36
13,00024

Intensive Margin of Exports

Dep Variable:
Management Score

R-squared# observations# firms

ChinaUSLog Avg Exports Log Exports
per Dest-Prod Top Dest-Prod

Log Avg Exports Log Exports
per Dest-Prod Top Dest-Prod

0.042(0.20)

0.478*(1.74)

0.194***(13.94)0.348***(17.38)Own, Prov, Ind, Year FE;
Noise + Firm Controls

State, Ind FE;
Noise + Firm Controls

0.45
1,9353050.30
13,000Bloom, Manova, Sun, Van Reenen and Yu

0.43
1,936305

0.43
13,00025Economic Magnitudes
 Improving management by 1 standard deviation associated with■■■■■

5% higher probability of exporting
27% higher exports
19% more destinations
17% more export products
22% more destination-products

Bloom, Manova, Sun, Van Reenen and Yu

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Is Management Same as Productivity ?
 Standard trade theory: firm productivity determines export

performance (e.g. Melitz 2003)■ Noise and endogeneity of measured TFPR (e.g. Hsieh-Klenow
2009, Bartelsman et al 2013, De Loecker 2011)■ Multiple firm attributes may matter (e.g. Hallak-Sivadasan 2013)
 Management: productivity vs. input vs. second attribute■ direct measure of TFPQ
■ more productive firms endogenously adopt better management■ management ┴ other factor
 Less overlap in management distributions for exporters and non-exporters than TFPR distributions
Bloom, Manova, Sun, Van Reenen and Yu

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Management vs. TFPR
Dep Variable:

TFPR

Exporter
Dummy

China

Log Exports Log # Dest Log # Prod

Log # Dest- Log Avg Exports
Prodper Dest-Prod

Own, Prov, Ind, Year FE; Noise + Firm Controls

Management Score 0.211*(1.69)

0.138***(2.96)

0.593*(1.87)

0.484***(2.92)

0.456***(3.69)

0.586***(3.19)

0.007(0.03)TFPR (Lev-Pet)-0.010(-0.82)0.257***(3.35)

0.146***(3.73)

0.055(1.61)

0.139***(3.29)

0.118*(1.94)

USState, Ind FE; Noise + Firm Controls

Management Score 0.046***(6.39)

0.031***(10.24)0.392***(17.60)0.137***(13.49)0.171***(15.87)0.202***(15.66)0.190***(13.59)TFPR (Lev-Pet)

0.023***(6.19)

0.254***(8.24)

0.134***(9.75)

0.110***(7.37)

0.166***(9.39)

0.088***(4.43)

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Management: Efficiency or Quality ?
 Good management may increase production efficiency■ Assembling inputs more cheaply Producing high-quality goods may require effective management■ Sourcing high-quality inputs■ Ensuring quality control■ Assembling complex products

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Management: Efficiency or Quality ?

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Management: Efficiency or Quality ?

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Management: Efficiency or Quality ?
 Identifying efficiency and quality channels■ output prices■ variation across sectors with different scope for quality differentiationand intensity in relationship-specific investments■ theory-based proxies for output quality and quality-adjusted prices■ input characteristics

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Export Product Quality
 Better managed firms produce higher-quality products, more efficiently■

Model-consistent measure of export quality (Khandelwal 2010) : σ p + q

China
Log Export Unit ValueDep Variable:
Management Score

0.335**(2.16)

Management Score
x Adv & RD Intensity

0.180(0.98)-0.221(-0.68)

US
Estimated Export Quality

1.218*(1.95)

0.433*(1.73)

Management Score
x Relation Specificity

0.402(0.51)-1.234(-0.90)0.92
58,102303

0.944*(1.79)

0.92
57,814302

0.003(0.77)

0.047**(2.55)-0.043***(-2.82)2.067*(1.89)
4.054*(1.87)

Own, Prov, Ind, Year FE; Noise + Firm Controls
R-squared# observations# firmsLog EX Estim EX EX QualityUnit Value Quality
Adj Price

0.92
57,817302

Bloom, Manova, Sun, Van Reenen and Yu

0.92
58,102303

0.9257,814302

State, Ind FE; Noise + Firm Controls
0.92
57,817302

0.97
290,000

0.96
290,000

0.95
290,000

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Imported Inputs: Quality
 Better managed exporters use more imported inputs, more expensive

and higher-quality inputs, from richer countries of origin■■

China: foreign inputs from advanced countries are higher quality
US: fixed costs to offshoring inputs and assembly

China
Dep Variable:

Log (IM /Log Imports
Inputs)Management Score 0.543*(1.86)

R-squared# observations# firms

1.341***(4.32)USLog AvgOrigin
IncomeLog Import
Unit Value

Log Avg
Log Imports Origin
IncomeLog Import
Unit Value

0.113**(2.14)

0.245**(2.53)

0.376***(12.83)-0.001(-0.21)

0.041***(4.27)

Own, Prov, Ind, Year FE; Noise + Firm Controls

State, Ind FE; Noise + Firm Controls

Orig-Prod FE

Orig-Prod FE

0.50
1,778
290

0.56
1,778
290

0.38
1,780
290

Bloom, Manova, Sun, Van Reenen and Yu

0.81
76,626290

0.29
10,0000.21
10,0000.97
140,00034Imported Inputs: Complexity Better managed exporters use wider range of imported inputs, from morecountries of origin■■

More complex products require more distinct inputs
Robust to controlling for number of export products

China
Dep Variable:
Management Score

Log # Origins
0.435***(4.47)USLog # Import Log # OriginLog # Import Log # OriginLog # Origins
Prod
Prod
Prod
Prod
0.415**(2.55)

0.467***(2.76)

Own, Prov, Ind, Year FE;
Noise + Firm Controls
R-squared# observations# firms

0.52
1,778
290

0.58
1,780290

Bloom, Manova, Sun, Van Reenen and Yu

0.60
1,780290

0.141***(14.99)0.187***(13.8)

0.209***(14.15)

State, Ind FE; Noise + Firm Controls
0.33
10,0000.30
10,0000.32
10,00035

Trade vs. Domestic Activity
 Management is disproportionately more important for exporting

than domestic activity
Dep Variable:

Log Dom
Sales

Exporter
Dummy

China

Log Exports Log # Dest Log # Prod

Log # Dest- Log Avg Exports
Prodper Dest-Prod

Own, Prov, Ind, Year FE; Noise + Firm Controls

Management Score 0.747***(5.30)

0.140***(3.32)

0.611*(1.96)

0.446***(2.78)

0.409***(3.36)

0.533***(2.96)

0.078(0.37)Log Dom Sales-0.025***(-7.33)-0.035(-1.46)0.005(0.40)-0.004(-0.41)-0.007(-0.43)-0.028(-1.50)USState, Ind FE; Noise + Firm Controls

Management Score 0.374***(35.38)0.022***(7.00)

0.171***(7.65)

0.061***(5.87)

0.053***(4.92)

0.074***(5.70)

0.097***(6.78)Log Dom Sales

0.030***(10.52)

0.632***(35.64)0.225***(28.93)0.334***(38.88)0.368***(35.38)0.264***(23.63)Bloom, Manova, Sun, Van Reenen and Yu

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(Export) Profits Better managed exporters have higher profits, even controlling fordomestic sales■ Consistent with higher export profits
China

Log Profits

Management Score

Baseline

Controls

Domestic Sales

1.309***(6.98)

0.928***(5.70)

0.865***(5.43)Log Domestic Sales

0.097***(5.85)

Province FE, Industry FE, Year FE, Own FE, Noise Controls
R-squared# observations# firms

0.45
2,520
467

Bloom, Manova, Sun, Van Reenen and Yu

0.55
2,438
448

0.57
2,43844837

Outline
1. Six datasets
2. Empirical results
3. ModelBloom, Manova, Sun, Van Reenen and Yu

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Model Ingredients
 Heterogeneous-firm trade model with endogenous quality choice à laKugler-Verhoogen (2012) Complementarity between management and product quality

■
■
■
■

Management ≈ productivity ≈ production technology
Output quality = input quality + production technology
Output complexity = input complexity + production technology
Output quantity = input quantity + production technology

 Two alternatives for management■ Exogenous productivity draw
■ Fixed-cost technology chosen endogenously based on exogenous
productivity draw

Bloom, Manova, Sun, Van Reenen and Yu

39Conclusions
 Good management practices enhance export performance byenabling more efficient production of more sophisticated products■ Consistent results for China and the US■ Informs sources of firm heterogeneity and management mechanisms■ Suggests management know-how and access to quality inputs shape
impact of export reforms
 Future work: How does management affect …

■ response to shocks (2008-2009 crisis, SARS epidemic, XR shocks)■ position in global value chains■ multinational activityBloom, Manova, Sun, Van Reenen and Yu

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