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Does what you export matter?
In search of empirical
guidance for industrial policies
D. Lederman & W. F. Maloney
DECRG & LCRCE, World Bank
April 2011
Pretoria, South Africa
The question, the truth about industrial
policies (IP), and this report
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Question: If we are “condemned to choose,” what
empirical criteria can we use to select products or
industries?
The truth: Everybody does it
What we do in this report
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Discipline our thinking about the desirability of IP in “theory”
Review empirical indicators advocated by various literatures
Some evidence from Latin America, but broader relevance
Our motto: “Give IP a chance” (was it John Lennon?)
Why might price signals be deceptive in
choosing goods and warrant IP?
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Marshallian externalities related to goods
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Local externalities that raise productivity with size of the industry:
e.g., local industry-level knowledge spillovers, input-output
linkages, labor pooling
Not infant industries
Pushing the envelope: inter-industry externalities through
price signals
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Industry growth and private returns to schooling
Volatility externalities and export diversification
Empirical Concerns for Policy Makers
How can we measure these externalities?
Does the world see the same benefits and drive prices
down?
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Look for safe rents, too? e.g., natural resources
Think of demand side (!)
Do externalities necessarily come with a good, or does it
matter how we produce it?
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Heterogeneity of experiences within industries
What if externalities emanate from input use? e.g., knowledge,
human capital
What if externalities come through price co-movement? e.g.,
export diversification
In practice, measurement of MEs is
difficult, so the profession has taken
shortcuts
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Natural resources
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Low productivity (Smith, Matsuyama, Sachs), few externalities
Rent seeking
Volatility
High productivity goods
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Rich country goods (Rodrik, Hausmann)
“High tech” (based on inputs, e.g., Lall) with high inter-industry
ME
Preview
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Introduction
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Part I: What Makes a Good Good?
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Conceptual issues (Marshallian externalities)
Cursed goods
Rich country, high-productivity goods
Smart goods
Part II: Beyond Goods
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Heterogeneity in production: how versus what
Heterogeneity in quality growth and risk
Goods or tasks? (domestic value added)
Export portfolio diversification (cursed goods revisited)
CURSED GOODS: NATURAL
RESOURCES
Heterogeneity in Natural-Resource
Experiences: Net Exporters, 1980-2005
4
NOR
SAU
GAB
2
log Natural Resources Net Exports/ labor force
1980 - 2005
KWT ISL
VEN
DZA SUR
COG
0
MYS
CHL
KAZ
ARG
RUS
ECU
IRN
PNG
NAM
CRI
CIV
BOL
URY
COL
NGA
IDN
ZAF
CMR
PER
MEX
MRT MNG
AZEPRY
ZMB
SLB
HND
YEM
BRA
ZWE
TJK VNM
SYR
BTN
LVA
MWI
GTM
GIN
THA
TGO
GHA
FJI
MDG UGAKEN MOZ
NER
LBR
-2
-4
TTO
NZL AUS
CAN
FIN
ARE
DNK
NLD
IRL
SWE
y = 1.2584x - 12.102
R2 = 0.5593
MLI
GNB
CAF TZA
-6
MDA
ETH
BDI
-8
5
6
7
8
log GDP pc 2005
Source: Lederman & Maloney (2008)
9
10
11
HIGH PRODUCTIVITY GOODS
Does It Matter What We Export? Hausmann,
Hwang, & Rodrik (2007)
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Model: broadly inter-industry spillover
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Country should produce the highest productivity good within its
comparative advantage (!)
Empirics
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PRODY, EXPY
Similar to Lall (2000)
Find higher EXPY (partially) correlated with higher growth.
Caveats
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General equilibrium critique again?
Rents- higher for products already exported by rich
countries?
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Not generally the case
If easy to move into these goods, then barriers to entry and rents
are low
Empirical findings muddy
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Animals, electrical machinery same PRODY
Finding of an impact on growth fragile
Actually, no neat breakdown of
rich/poor country goods
35000
30000
25000
20000
15000
10000
5000
0
PRODYs (with +/- 1 SD*)
Empirically, some support for MODEL
Growth Regressions
Base: HHR Regressions
Log ( initial gdp)
Log (expy)
Including the Export
Herfindahl and the
Investment Share
IV
GMM
IV
GMM
IV
GMM
IV
GMM
-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)
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)
-0.0577***
-0.00566
-0.0431
-0.119
(0.02)
(0.10)
(0.03)
(0.08)
Category Log (expy)
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)
0.0111*
0.0360**
0.00935
0.0566***
(0.01)
(0.02)
(0.01)
(0.02)
0.0551
-0.0381
0.0615
-0.0283
(0.06)
(0.04)
(0.06)
(0.04)
Log (Investment Share)
Root Herfindal Index
Constant
Observations
With Income Average Value
Including the Export
Herfindahl and the
Investment Share
-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)
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
IVs: log population, log land area
Income groups: world deciles
GMM: Blundell-Bond System Estimator
Note: EXPY is n.s. after including either export concentration or investment rate.
SMART GOODS
Spillovers and the Education-Expansion
“Problem”
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Social returns to schooling can be higher than private returns
(Krueger & Lindhal 2001 JEL)
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When supply of skilled workers increases, returns decline (common
sense & own estimates)
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Prototypical spillover; economy-wide
The “problem”: private incentives to invest in education decline
… unless demand for skilled workers rises
Do some industries provide higher returns to skills than others?
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If so, IP could help reduce gap between social & private returns by
raising the latter
Dispersion of skill premium across industries
Malcolm Keswell & Laura Poswell (SAJE 2004):
RTS (8-12) b/n 0.33 (quadratic) and 0.15 (cubic)
What explains skill premiums? Countries versus industries
Source: Brambilla, Dix Carneiro, Lederman & Porto (2010)
… and exports …
Source: Brambilla, Dix Carneiro, Lederman & Porto (2010)
IS IT WHAT WE PRODUCE, OR
HOW? BEYOND GOODS
BRA
MYS
ROM
CHL
ESP
JPN
NLD CHE
RUS
BGR
SWE
ISRAUT
AUSHRV
KOR
BEL
CYP
DNK
MEXCHN
NOR
ZAF
THA
IRL
NZL
SAU
HKG
ARG
GRC
UKR
PHLIND
ROM
CHL ISL
COLLUX
SVK
VEN HUN
TTO
BGR
HRV
CYP
KWT
TKM
BHS
VNM
ZWE
PER
ECU
URY
QAT
CZE
POL
MAR
BHS
VNM
ZWE
PRY
IRN
GIN
PRY
BIH
BIH
TTOPHL
IDN
KWT
CRI
BHSBOL
LKA
LVA
MAR
KWT
IRN
QAT
GTM JAM ISL
MDA
DZA
UGA
ATG
NGA
76
1
83
Patents Ratio (ranked)
World
EGY
PAK
SYR
1
1
TWN
USA
JPN
CHN
NLD
DEU
GBR
TWN
MYSMEXHKG
IRL
KOR
THA
FRA
CAN
ITA
BEL
HUN
ESP
CHE
AUS
DNKAUT
CZE
SWE
FIN
ISR
NOR
BRA
IND
PRT
NZL RUS
LUX
POL
GRCZAF
ROM
ARG
TUR
UKR
SVN
HRV BGR
CHL
SAU
COL
PER
LBN BLR
VEN
VEN
PER
PAN
1
1
PAN
TWN
KOR
1
IRN
GIN
FIN
TKM
RUS
ISR
Exports vs Patents in Computers (SIC 357)
ECU
URY
MAR
USA
DEU
FRA
GBR
ITACAN
BRA
MYS
Exports Ratio (ranked)
174
COL
Exports vs Patents in Aircraft
(SIC 372)
BOL
IDN
SAU
PHLIND
POL
BOL
IDN
TUR
PRT
MEXCHN
ZAF
HKG
ARG
UKR
THA
TUR
181
171
Exports vs Patents
in Aircraft,
excl. High-Income
OECD Countries
Producing
and
exporting
without
generating knowledge?
LAC
Patents Ratio (ranked)
Patents Ratio (ranked)
World
LAC
World
LAC
68
Domestic value added: Does China really export
the iPOD?
Table 2 China: 10 Exports with the Lowest Domestic Value Added
Electronic computer
Telecommunication equipment
Cultural and office equipment
Other computer peripheral equipment
Electronic element and device
Radio, television, and communication equipment
Household electric appliances
Plastic products
Generators
Instruments, meters and other measuring equipment
China: 10 Exports with the Highest Domestic Value
Added
Agriculture, forestry, animal husbandry and fishing
machinery
Hemp textiles
Metalworking machinery
Steel pressing
Pottery, china and earthenware
Chemical fertilizers
Fireproof materials
Cement, lime and plaster
Other non-metallic mineral products
Coking
Source: Koopmans, Wang, and Wei (2008).
4.6
14.9
19.1
19.7
22.2
35.5
37.2
37.4
39.6
42.2
“..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)
81.8
82.7
83.4
83.4
83.4
84.0
84.7
86.4
86.4
91.6
HETEROGENEITY IN
QUALITY OF EXPORTS (UNIT
VALUES)
Quality ladders by product and countries
(relative unit values, standardized)
But high growth is risky (Brazil on the edge of the cloud, p62)
DIVERSIFICATION OF THE
EXPORT BASKET
Diversification
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Market failures inhibit diversification
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Export concentration leads to terms of trade volatility
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Spillovers in product innovation, which is correlated with
diversification
Correlation of prices and quantities across products are not
internalized
poor, small and mining-dependent economies have higher
export-revenue concentration, and terms of trade volatility
(Lederman & Xu 2010)
Problems of diversification policy
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Big hits are rare and associated with high concentration of
(manufacturing) exports (Easterly et al. 2009)
Never really know where the next product comes from
.4
Export concentration and terms of trade
volatility, 1980-2005
.3
IRN
RWA
NIC
.2
SDN
BIH
GEO
CMR
UGA
CIV
ZMB
AZE
GNB
COM
TJK
KAZ
SYR
GHA
RUS
TZA MWI
ETH
CPV
KGZ
UKR
GMB MOZ
ECU
VNM
BEN
IND PAK
TGO
MDG
ARM
BFA
PER
EGY
NAM
SLV
DOM
BLRLVA
JORIDNKEN
CAF
BOL
PNG
CHL ERI
SEN
BRAARG LKA
ZWE
MEX
COL
LSO
MLI
CZE
CRI
ALB
JPN
GUY
GTM
BGR URYPHL
HND
ZAF
ESP
GIN
THA MAR
PRY
SVK
TUR
MYS MDA
ROM
MKD
MUS
KOR
POL
CHN
NZL
ITA
LTU
LBN
DEU
CHE
PRT
USA
FRA
KHM
FIN
GRC
SVN
HUN
IRL TUNISR PAN
EST
SWE
SWZ
GBR
DNK
BEL
HKG
AUT
NLD HRV
BTN DJI
TKM
0
.1
YUG
0
BGD
MRT
BWA
YEM
.2
.4
.6
.8
Export-Revenue Concentration (Root of Herfindahl)
1
Where Is South Africa in Export
Concentration?
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
ITA
USA
CHN
BRA
ARG
MEX
ZAF
BWA
SAU
VEN
South Africa: Output Composition
Source: Ryan Hawthorne, Reena Das Nair & Keith Bowen, TIPS Trade and Industry Monitor, vol. 37, 2006, p. 101.
South Africa: Composition of Manufactures
Source: Ryan Hawthorne, Reena Das Nair & Keith Bowen, TIPS Trade and Industry Monitor, vol. 37, 2006, p. 102 (?).
The three robust determinants of export
concentration and volatility, ToT & GDP volatility
Dependent Variable:
(1)
(2)
(3)
Export Concentration
Terms-of-Trade Volatility
GDP-per-Capita Growth
Volatility
0.351**
(0.000)
Export concentration
Net exports of energy and mining per
worker
0.040**
(0.000)
0.004
(0.170)
-0.003
(0.154)
Net exports of agriculture per worker
-0.036*
(0.022)
-0.000
(0.941)
-0.002
(0.456)
Labor force (log, initial)
-0.058**
(0.000)
0.015**
(0.000)
-0.005**
(0.000)
GDP per capita (log, initial)
-0.065**
(0.000)
Geographic trade over GDP
-0.002*
(0.030)
101
0.505
0.000
0.309
0.310**
(0.000)
101
0.295
0.000
0.257
Terms-of-trade volatility
Observations
Pseudo R-squared
F-stat (p-value)
Adj. R-squared/First Stage
101
0.541
0.000
0.519
Notes: ** and * represent statistical significant at the 1 and 5 percent levels. Cross-equation error correlations are assumed to be unstructured. All
explanatory variables, except the dependent variables (export concentration, terms of trade volatility, and GDP-per-capita growth volatility) are
assumed to be exogenous. Volatility is measured by the standard deviation of the annual growth rate of each variable during 1980-2005. The “firststage” estimates are not reported. P-values appear inside parentheses and correspond to standard errors adjusted for degrees of freedom due to
finite-sample assumptions. “Initial” means that the observation is from 1980; the results correspond to cross-sectional estimates for 1980-2005.
Intercepts are not reported.
Source: Lederman and Xu (2010).
Doing IP blindfolded
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Little guidance on what goods are good
Even whether we should be focusing on goods
vs. tasks
Leads us back to horizontalish policies that
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Resolve market failures related to innovation in old
and new goods
Other barriers to the emergence of new goods and
improvement of old
Strategic coordination policies
Risk taking (entrepreneurship, finance)
Fin / Einde