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 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

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? Marshallian externalities related to goods

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 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? 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?

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 Natural resources

Low productivity (Smith, Matsuyama, Sachs), few externalities Rent seeking Volatility

High productivity goods Rich country goods (Rodrik, Hausmann) “High tech” (based on inputs, e.g., Lall) with high inter-industry

ME

Preview Introduction

Conceptual issues (Marshallian externalities)

Part I: What Makes a Good Good? Cursed goods Rich country, high-productivity goods Smart goods

Part II: Beyond Goods 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

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Source: Lederman & Maloney (2008)

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

comparative advantage (!)

Empirics PRODY, EXPY Similar to Lall (2000) Find higher EXPY (partially) correlated with higher growth.

Caveats

General equilibrium critique again? Rents- higher for products already exported by rich

countries? Not generally the case If easy to move into these goods, then barriers to entry and rents

are low

Empirical findings muddy Animals, electrical machinery same PRODY Finding of an impact on growth fragile

Actually, no neat breakdown of rich/poor country goods

Empirically, some support for MODEL Growth 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

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” Social returns to schooling can be higher than private returns

(Krueger & Lindhal 2001 JEL) Prototypical spillover; economy-wide

When supply of skilled workers increases, returns decline (common sense & own estimates) 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? 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

Producing and exporting without generating knowledge?

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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).

Domestic value added: 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)

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

Market failures inhibit diversification Spillovers in product innovation, which is correlated with

diversification Correlation of prices and quantities across products are not

internalized

Export concentration leads to terms of trade volatility poor, small and mining-dependent economies have higher

export-revenue concentration, and terms of trade volatility (Lederman & Xu 2010)

Problems of diversification policy 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

Export concentration and terms of trade volatility, 1980-2005

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0 .2 .4 .6 .8 1Export-Revenue Concentration (Root of Herfindahl)

Where Is South Africa in Export Concentration?

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  (1) (2) (3)

Dependent Variable: Export Concentration Terms-of-Trade VolatilityGDP-per-Capita Growth

Volatility

Export concentration0.351**(0.000)

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)

Terms-of-trade volatility0.310**(0.000)

Observations 101 101 101

Pseudo R-squared 0.541 0.505 0.295

F-stat (p-value) 0.000 0.000 0.000

Adj. R-squared/First Stage 0.519 0.309 0.257

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 “first-stage” 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

Little guidance on what goods are good Even whether we should be focusing on goods

vs. tasks Leads us back to horizontalish policies that

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