Does It Matter What We

Export? The Quality of Trade

D. Lederman & W. F. Maloney,

DECRG and LAC CEO, World Bank

Why Quality? It is the quality of our work that will please God, and

not the quantity”- Mahatma Gandhi

Translation: X growth, (X+M)/GDP aren’t enough

“Quality is independent of and prior to intellectual

abstractions” Robert Pirsig, Zen and the Art of Motorcycle

Maintenance

Correction: All we are saying is give IP a chance, but

base it on solid analytical principles

“I consider a bad bottle of beer to be a personal insult

to me”- Freddy Heineken

Translation: It ain’t just the what, but the how

Today’s Presentation

The “good” as the unit of analysis

Quality as price heterogeneity within goods

Should a good be the unit of analysis?

Jobs and “brainy” goods

Quality as the basket of exports

THE GOOD AS UNIT OF

ANALYSIS

Why might standard price signals be

deceptive in choosing goods

Marshallian externalities

local externalities that lead productivity to rise with

the size of the industry

local industry level knowledge spillovers, input-

output linkages, and labor pooling, for instance.

Rents

Marshallian Externalities

Intervention warranted to shift to good with

externalities against price signals.

Harrison and Rodriguez-Clare (2007):

Problems

Measurement difficult

Caveat: If Colombia can exploit these, so can/did the

US--P* reflects this.(Rodriguez critique)

Caveat to caveat:

Interindustry spillovers (Tyson-Intel in Israel… )

Assymetries (…vs Silicon valley)

Rents

e.g. Increasing returns to scale (Krugman)

Also, often tough to quantify

Caveat 3:

Gov’t actions could offset gains

Baldwin- what if ME, rents are not intrinsic to

good, but how produced?

Strategy: focus on characteristics

thought correlated with “good” things

High productivity goods

Rich Country Goods (Rodrik, Hausmann)

High tech (Lall) high inter-industry ME

Natural resources

Low productivity (Smith, Matsuyama, Sachs), few

ME

Rent seeking

HIGH PRODUCTIVITY GOODS

Does It Matter What We Exports?

Hausmann, Hwang, Rodrik (2007)

Model- broadly inter-industry spillover

Country should produce the highest productivity

good within its CA

Empirics:

PRODY, EXPY

Similar to Lall (2000)

Find higher EXPY correlated with higher growth.

Caveats

Rodriguez critique?

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

Ditto MNCs permanence

Entrepots: Highest PRODY 2001:

“Asses, mules and hinnies, live”

Actually, no neat breakdown of

rich/poor country goods

0

5000

10000

15000

20000

25000

30000

35000 PRODYs (with +/- 1 SD*)

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 -0.119

(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

A final note on Monkeys and Trees

Being a tree in a dense area is like a ME-

subject to Rodriguez critique

If easy to jump from one tree to others, then

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

rents

Frontier goods (high PRODY) on the edge of the

forest?

Is past a good predictor?

iPhone didn’t exist, Saab already does

Would Chilean forestry produce Saab?

CURSED GOODS: NATURAL

RESOURCES

10/2/2009

NR abundant success stories: “β” countriesLo

g G

DP

per

cap

ita 1

990

Log Natural Resources (Leamer)-11.5041 11.7949

6

7

8

9

10

Algeria

Argentin

AustraliAustria

Banglade

Benin

Bolivia

Brazil

Burkina Burundi

Cameroon

Canada

Cape Ver

Chad

Chile

China

Colombia

Comoros

Congo, D

Costa Ri

Cyprus

Denmark

Dominica

Ecuador

Egypt, A El Salva

Fiji

FinlandFrance

Gabon

Gambia,

Germany

Ghana

Greece

Guatemal

GuineaGuinea-B

Guyana

Honduras

Hong Kon

Hungary

Iceland

India

Indonesi

Iran, Is

IrelandIsrael

Italy

Cote d'I

Jamaica

Japan

Jordan

Kenya

Korea, R

Madagasc

Malawi

Malaysia

Mali

Mauritan

MauritiuMexico

Morocco

Mozambiq

NetherlaNew Zeal

Nicaragu

Nigeria

Norway

Pakistan

Panama

Papua Ne

ParaguayPeru

Philippi

Poland

Rwanda

Senegal

Sierra L

South Af

Spain

Sri Lank

Sudan

SwedenSwitzerl

Syrian A

Thailand

Togo

Tunisia

Turkey

United K

United S

Uganda

Uruguay

Venezuel

Zambia

Zimbabwe

Leamer Measure: Net

Exports of NR/Worker:

Empirically, there is no resource curse

Minerals are good

Davis (1995), Sala-i-Martin et al. (2004), Stijns

(2005), Brunnschweiler (2008, 2009)

Ag has higher TFP growth than manufactures

Bernard & Jones (1996), Martin and Mitra (2001):

(also Jacob Viner and Douglass North)

Lederman & Maloney (2007, 2009)

Sachs and Warner results easily overturned

Trade proxies for endowments not clear

Sparse forest, or Monkeys with low HC?

Conditional curse?

No question about political economy

However, Sierra Leon does not negate experience

of Australia, Canada, Finland, Sweden, US…..

But central tendency is not a curse…it’s an issue

Diversification a problem

QUALITY AS PRICE

HETEROGENEITY

Export quality gaining interest in the

trade and development fields Productivity can’t explain export performance at the firm

level Brooks (2006), Hallak and Sivadasan (2009):

Need another dimension/factor (Caliber?)

Unit values rise with GDP/capita Hummels and Klenow (2005)

Variance within HS10 more important than across products-challenge to trade theory? Schott (2004)

Hwang (2007): LDCs grow slowly because they’re at the top of their respective (short) ladders

There is convergence within good categories: Hwang (2007), Krishna and Maloney (2009)

Products may matter for growth Unit Values: Drift and Standard Deviation, Country Fixed Effects, HS-1 1990-2001

woodprods (44-49)

vegetables (06-15)

transp (86-89)

textiles (50-63)

stoneglass (68-71)

plasticrubber (39-40)

misc (90-97)

minerals (25-27)

metals (72-83)

machinelec (84-85)

leatherprods (41-43)

foothead (64-67)

foods (16-24)

chemallied (28-38)

animals (01-05)

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

-0.2 -0.1 0 0.1 0.2 0.3

Standard Deviation (Product Dummies)

Dri

ft (

Pro

du

ct

Du

mm

ies

)

Krishna and Maloney (2009)

Unconditional divergence at country level

Figure 3: Quality Growth by Region 1990-2001

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

OECD

(high-

income)

EASIAP

(high-

income)

LAC MENA EASIAP

(low -

income)

SASIA EUROPE

(non-

OECD)

SSAFRICA CASIA

Region

Med

ian

Qu

ali

ty G

row

th

Krishna and Maloney (2009)

However, how matters as much or

more than what. Figure 4: Quality Growth by Region 1990-2001 (Product Fixed Effects

Included)

-0.014

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

OECD

(high-

income)

LAC MENA EASIAP

(high-

income)

SASIA SSAFRICA EASIAP

(low -

income)

EUROPE

(non-

OECD)

CASIA

Region

Med

ian

Qu

ali

ty G

row

th

Krishna and Maloney (2009)

IS A GOOD A GOOD A GOOD?

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

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).

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”

Goh Keng Swee, Minister of

Finance Singapore (1972)

Technological Sophistication Embodied in

Products: Influential Views

“…Technology intensive exports imply greater development benefits to exporting countries. Therefore there is considerable interest in analyzing the technology structure of exports in developing and developed countries.” Lall, Weiss and Zhang (2005)

“…Ignoring such specialization can cloud our thinking about the responses of wages to globalization. It also interferes with our ability to identify other determinants of production such as cross-country differences in technology.” Schott (AER 2003)

The Global Computer Industry:

Is It “Sophisticated” Everywhere? A Bit of DataCountry Net-Exports Ranking K/L Ranking Skilled-L/L Ranking

Top

Net-exp

orters

China 1.24E+07 1 1.45E+07 51 38.4 37

Malaysia 1.18E+07 2 5.76E+07 27 50.5 25

Singapore 1.05E+07 3 2.03E+08 3 59.1 17

Korea Rep. 9187286 4 2.42E+08 1 75.3 5

Philippines 6350562 5 1.61E+07 48 53.6 23

Ireland 5953102 6 1.04E+08 21 64.1 15

Japan 5000000 7 1.85E+08 5 71.9 8

Mexico 4675278 8 4.48E+07 29 40.3 36

Indonesia 2329506 9 1.61E+07 49 26.8 50

India 33958 10 7649168 58 22.2 56

Costa Rica 21775 11 1.99E+07 44 29.9 44

Top

Net-im

po

rters

Austria -1029426 63 1.65E+08 6 70.1 11

Denmark -1196473 64 1.44E+08 12 68.1 12

U.K. -1200000 65 1.11E+08 20 58.2 18

Sweden -1592865 66 1.32E+08 16 80.3 3

Spain -1613921 67 1.13E+08 18 46.9 30

Switzerland -2773254 68 2.03E+08 2 71 9

Australia -3062108 69 1.48E+08 10 73.4 6

France -3942278 70 1.52E+08 9 55.7 20

Italy -4117605 71 1.53E+08 8 46.7 31

Canada -5744931 72 1.40E+08 14 79.6 4

U.S.A -3.11E+07 73 1.60E+08 7 89.7 1

Skilled-L/L Ranking Net-exports

1-10 Korea Rep.

Japan

U.S.A

Sweden

Canada

Australia

Switzerland

11-20 Ireland

Singapore

Austria

Denmark

U.K.

France

21-30 Philippines

Malaysia

Spain

31-40 Mexico

China

Italy

Source: Cusolito and

Lederman (2009)

Who Are the “Sophisticated” Computer Suppliers?

Factor Intensities Depend on Endowments and

IPRs

Industry Regime Factor Intensities Exporting Countries

Global computer

industry

1 capital (1.90E-06) and skill (55.3) Phillipines

2 capital (1.66 E-06) China, Costa Rica, India, Indonesia

3

skill (4.05E+02) and unskill

(1.05E+02)

Ireland, Japan, Korea Rep. Mexico, Malaysia,

Singapore

Final goods computer

industry

1 skill (1.04E+03) China

2

capital (5.33 E-05) and unskill

(7.25E+02) Indonesia, Phillipines

3

capital (3.61E-04) and skill

(7.67E+04) Malaysia, Mexico

4 skill (8.65 E+04) Ireland, Japan, Netherlands, Singapore

Skill=workers with secondary; Capital=capital stock in $; Unskill=workers without secondary.

Source: Cusolito and Lederman (2009)

Country and Industrial Differences in the Skill Premium

(Returns to Schooling) of 5 Million Workers

Source: Brambilla, Carneiro, Lederman & Porto (2009, in progress)

“Brainy” Goods in LCR?

Source: Brambilla, Carneiro, Lederman & Porto (2009, in progress)

For Policy: A Simple Analysis on Countries versus Industries

Source: Brambilla, Carneiro, Lederman & Porto (2009, in

progress)

What’s Behind the Dummies?

Skills, Exports and the Wages of 5 Million Workers

National skill endowments

Income per capita

Industrial exports The incidence of exports (industries versus countries?)

Weak evidence on scope for product differentiation

Some of the

Estimations:

Source: Brambilla, Carneiro, Lederman & Porto (2009, in progress)

Export Portfolios and Volatility

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90 100

Concentration Index

Terms of trade volatility

Source: Gamberoni and Newfarmer, 2009 based on authors calculation

based on World Bank, World Development Indicators, as shown in Canuto

(2009, Presentation at CEIP).

The Role of the Commodity Trade Balance (and

Natural Resources): Some Estimations

(1) (2) (3)

Dependent Variable: Terms-of-Trade Volatility Export Concentration Terms-of-Trade Volatility

Estimator: OLS OLS First Stage Second Stage

Export-revenue

concentration [Root of the

Herfindahl Index] 0.281*** 0.348***

(0.000) (0.000)

Net exports of natural

resources per worker 0.006* 0.031*** 0.004

(0.081) (0.000) (0.157)

Log(Labor Force in 1980) 0.012*** -0.044*** 0.015***

(0.001) (0.000) (0.001)

Log(GDP per capita in

1980) -0.005 -0.071***

(0.415) (0.000)

Observations 102 102 102

Notes: ***, ** and * represent statistical significant at the 1, 5 and 10 percent levels.

P-values appear inside parentheses

The results correspond to cross-sectional estimates for 1980-2005.

Source: Authors' calculations based on data described in the Appendix.

Picking Winners and Export Portfolios:

The Power (Law) of Exports

Easterly, Reshef & Schwenkenberg (2009)

Manufacture exports are highly concentrated

Relationship between the share of each product’s exports in total exports follows a power law:

The probability of hitting it diminishes exponentially with the size of the hit!

Smart and successful old-style IP would yield high export concentration

Not a desirable outcome from the Portfolio View

IP: Back to Drawing Board

Not just the what but the how

Technologically sophisticated goods might be passé IP as technology policy

Bridging the gap between private and social returns to schooling Education is not enough

Further research: Exports-Skills complementarities

Diversification of exports Not your grandma’s IP…

Fin

More on the Determinants of Volatility: Does Malik and

Temple (2009) trump Acemoglu & Zilibotti (1997)? NO.(1) (2) (3)

Dependent Variable:

Terms-of-Trade

Volatility

Export

Concentration

Terms-of-Trade

Volatility

Estimator: OLS OLS First Stage Second Stage

A. Testing Acemoglu & Zilibotti (1997)

Export-revenue concentration [Root of the

Herfindahl Index] 0.281*** 0.348***

(0.000) (0.000)

Net exports of natural resources per worker0.006* 0.031*** 0.004

(0.081) (0.000) (0.157)

Log(Labor Force in 1980) 0.012*** -0.044*** 0.015***

(0.001) (0.000) (0.001)

Log(GDP per capita in 1980) -0.005 -0.071***

(0.415) (0.000)

Observations 102 102 102

Instrument Relevance (partial R-squared) 0.218

A. Testing Acemoglu & Zilibotti (1997) with Malik & Temple’s (2009) Geography

Export-revenue concentration [Root of the

Herfindahl Index] 0.351***

(0.000)

Net exports of natural resources per worker 0.028*** 0.004

(0.000) (0.160)

Log(Labor Force in 1980) -0.057*** 0.015***

(0.000) (0.001)

Log(GDP per capita in 1980) -0.065***

(0.000)

Frankel & Romer’s (1999) Constructed

Trade Share -0.002

(0.132)

Observations 101 101

Instrument Relevance (partial R-squared) 0.236

Sargan Over Identification Test (p-values) 0.838

Notes: ***, ** and * represent statistical significant at the 1, 5 and 10 percent levels.

P-values appear inside parentheses

The results correspond to cross-sectional estimates for 1980-2005.

Source: Authors' calculations based on data described in the Appendix.

Source: Lederman and Xu (2009, in

progress)