Economic Development Experts versus

Economics:

World Bank

Monday September 14, 2009

William Easterly (NYU and NBER)

the example of industrial policy

Outline: 2 Negatives on Development Experts & 1 Positive

on Economics

• Negative: The failure of the empirical growth literature

• Negative: How Experts mistake randomness for evidence (with example of industrial policy)

• Positive: How Economics is useful after all.

Reliance on Development Experts was Legacy of the Great

Depression• “the Depression led to conclusion: economic

development was not spontaneous, as in the classical capitalist pattern, but was consciously achieved through state planning.” (UN History of Development Thinking)

• Gunnar Myrdal (1956): “Super-planning HAS to be staged by underdeveloped countries with weak administrative apparatus … the alternative to making the heroic attempt is acquiescence in economic stagnation … which is politically impossible …”

• Strong political motive (as opposed to academic breakthrough) to create a “Development Expert Economics” to achieve faster growth than capitalist rich countries had achieved with just “Economics”

Index of per capita income in developing and developed (based on unweighted average per capita growth)

100

150

200

250

300

350

400

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Ind

ex o

f p

er c

apit

a in

com

e (1

960=

100) developing

developed

One Big (Only Half-Serious)

Stylized Fact

Getting development expert advice

Not getting development expert advice

Failure of empirical growth literature to give expert guidance

• “experience … frustrated the expectations …{that} we had a good fix on the policies that promote growth.” (Rodrik 2007)

• “It is hard to know how the economy will respond to a policy” (World Bank Spence Growth Commission 2008)

• predicted in advance when Levine and Renelt (1992) failed to find any robust determinants of growth

• 145 different variables “significant” in growth regressions (with approx. 100 observations) --Durlauf, Johnson, and Temple 2006

• Ciccone and Jarociński 2008: Bayesian model averaging gave completely different “robust” variables from Doppelhofer, Miller, and Sala-i-Martin 2004 for different equally plausible samples

• Therefore, growth regression evidence on trade & industrial policy is of little value either pro- or con- (e.g. Rodriguez and Rodrik 2000)

Fail to spread high growth from East Asia to other countries

• “At any time, some country is doing well, and … observers … generalize from its choices and recommend the same to all countries. After a decade or two, this country ceases to do so well, some other country using some other policies starts to do well, and becomes the new star that all countries are supposed to follow.” Avinash Dixit (2007)

• Development economists have advised “just be like Korea” for decades, but we do not have any successes at replicating Korea’s growth rates (not even in Korea, where growth has now slowed!)

Mistaking randomness for evidence• Panel regressions of annual growth rates for all

countries show that only 8% of the variance is permanent cross-country differences, the other 92% is transitory (will disappear next year!) deviations from world mean of about 1.8% per capita

• Kahneman and Tversky’s Sarcastically-named “Law of small numbers”– Making too much of a small # of episodes with

too few years to give us “secrets to success”, not sufficiently appreciating that outcomes of a small sample will have a huge random component (as we see growth rates do)

Transitory growth: there is very strong mean reversion in growth

(including East Asia!)

ALB

DZA

AGO

ARGAUS

AUTBHR

BGD

BEL

BLZ

BEN

BTNBOLBWA

BRA

BGR

BFA

BDI

CMR

CANCPVCAF

TCD

CHL

CHN

COL

COM

ZAR

COG

CRI

CIV

DNK

DOM

ECU EGY

SLV

EST

ETH FIN

FRAGAB

GMB

DEU

GHA

GRC

GTM

GNB

GUY

HTI

HND

HKG

HUN

ISL

IND

IDN

IRN

IRL

ISRITA

JAM

JPN

JOR

KENKIR

KOR

LAO

LVA

LSO LUX

MAC

MDGMWI

MYS

MLI

MLT

MHL

MRT

MUS

MEX

MDA

MNGMAR

MOZ

MMR

NAM

NPLNLD

NZL

NIC

NERNGA

NORPAK

PAN

PNG

PRY

PERPHL

PRT

ROMRWA

WSM

SAUSEN

SYC

SLE

SGP

SVK

SLB

ZAF

ESPLKA

VCT

SDN

SUR

SWZ

SWECHE

SYR

THA

TGO

TON

TTO

TUN

TURUGA

ARE

GBRUSA

URYVUTVEN

VNM

ZMB

ZWE

-.0

50

.05

.1.1

5

-.1 -.05 0 .05 .1g8695

dg9605 Fitted values

Change in growth from 1986-95 to 1996-2005

Previous growth rate (1986-95)

Original source: Easterly, Kremer, Pritchett, Summers 1993

Seeing patterns in randomness: episode analysis

• Episode analysis: select maximum growth period in each country, timing flexible, biases episodes to have large transitory element (analogous to a streak of heads if you flip a coin long enough, & then study “heads episode”)

• Example: Spence Commission criteria for success stories: Monte Carlo simulations suggest 37 percent of top successes using their selection criteria would NOT have the top long-term growth rates.

• Selecting maximum decade growth on average out of 45 years will discover a growth experience 2.5 pp above the LT average (Monte Carlo simulations).

• Spence Commission attributes high growth episodes to whatever “leaders” (advised by experts) happen to be in power during the episode (sounds plausible but non-falsifiable)

• Therefore, episode analysis is not reliable guide to whether industrial policy “works.”

Example of Ha Joon Chang case for industrial policy

• With a lot of random variation, easy to find examples to confirm your bias.

• Chang cites high-growth Korea and Taiwan as evidence for industrial policy, but says free-trade high-growth Singapore and Hong Kong were “exceptions,” and he never mentions high-growth free trade Botswana.

• Small Numbers Problems: Mexico has had only 1.8 percent per capita growth from 1994 to 2002 after NAFTA, so Chang concludes NAFTA isn’t working.

Another heuristic bias --Attributing intentional skill to random outcomes

• An experiment in which subjects observed two people executing a task, rigged so that the two persons’ performance was equal.

• The subjects told that one would receive a large payment and choice which one would be random.

• The subjects then asked to describe the performance of the two agents.

• Despite subjects’ knowledge that payment was random, gave superior marks on performance attributes to the agent who received the payment.

• A lot of industrial policy “case studies” of success are like this – we give too much credit to those geniuses in Korea and Taiwan for industrial policy, or to Spence’s “leaders” for high growth episodes.

Yet another randomness fallacy: Confusing conditional probabilities

• Kahneman et al. experiments show we confuse Prob(X|Y) with Prob(Y|X)

• (A) Probability(If you win big in Vegas|You bet a large sum at long odds) is high.

• (B) While Probability(If you bet a large sum at long odds|You win big in Vegas) is low.

• You get in trouble if you decide whether to make such a bet based on A and not B!

• do we really ever make this obvious mistake?

Example: Dani Rodrik on industrial policy

• Dani Rodrik:“the countries that have produced steady, long-term growth during the last six decades are those that …promoted… diversification into manufactured … goods”

• So Dani concludes that developing countries will have to get busy with “real industrial policies.”

Yes, we do reverse conditional probabilities

• Dani is calculating (A) probability(If successful/Then have industrial policy)

• But this is wrong probability, just like in Vegas!• We want to know (B) Probability (If have industrial

policy/Then successful)• There are many examples of failed industrial policy

around Africa ($6 billion Nigerian Ajaokuta Steel Mill that never produced steel?), Middle East, Former Soviet Union, and Latin America, so probability (B), the right probability, seems too low to get enthusiastic about industrial policy.

• So “success story” evidence for industrial policy is just reflecting cognitive biases on how we mishandle random variables and probabilities.

Alternative explanations of data

• (A) Industrial policy worked in Korea and Taiwan, but not elsewhere, thanks to some unknown factor.

• (B) Industrial policy didn’t even work in Korea and Taiwan, which succeeded due to other reasons.

• (C) Industrial policy worked in other places too, but other factors made for poor growth outcomes.

• We don’t have a reliable aggregate empirical methodology (growth regressions or case studies) to distinguish (A), (B), or (C)

Why economists can say something useful after all

Optimal response to “law of small numbers”

• Get more numbers!• Empirical literature has shifted towards explaining income

levels rather than growth rates• Log of per capita income is the sum of all previous percent

growth rates – that takes us away from misleading “law of small numbers” to the true power of “law of large numbers.”

• Long-term evidence provides little support for industrial policy, lots of support for Econ 101 principles: Entrepreneurship in Markets, Division of Labor, Gains From Specialization, Comparative Advantage, Gains from Trade; No such LT evidence for industrial policy.

• Moreover, we are not starting from scratch as economists, the ideas that made it into Econ 101 are those that have survived the test of time by many previous generations of economists testing these ideas.

Long-run evidence on trade and per capita income

• Levels studies summarized by Harrison and Rodriguez-Clare (2009) suggestive evidence that trade causes prosperity.

• Stylized Facts: Over the last two centuries, divergence between (1) Europe and North America (with a lot of trade) and (2) rest of world (with a lot less trade because of poor infrastructure and geographic distance).

• Common sense: a small, poor economy can’t make most goods for itself, it desperately needs trade to get access to valuable goods (Computers? Cars? Antibiotics?). Industrial policy is second-order compared to this.

Applying common sense of comparative advantage to industrial

policy• Corrupt Indian civil servants give drivers’

licenses to people who can’t drive (Bertrand, Djankov, Hanna, Mullanaithan 2008) and we expect them to do industrial policy?

• M.A. Thomas (2009) & Pritchett (2009) on limited capacity of poor governments.

• Comparative advantage depends on government too.

• A corrupt, low-skilled, poorly-funded government does not have a comparative advantage in finding the country’s comparative advantage.

The Leroy Smith Principle: Success is a Surprise

• Who predicted cut flowers in Kenya (40% of European market), or women’s cotton suits in Fiji (42% of the US market), or bathroom ceramics in Egypt? (30 percent of manf exports,93% goes to Italy).

• Countries specialize to a remarkable degree by both product (out of 2985 6-digit manf. possibilities) and destination (217 possibilities). Out of 647,745 manf. possibilities, Top 1% of nonzero entries account for 52% of manufacturing exports (Easterly, Reshef, and Schwenkenberg 2009)

• Who will do better finding Big Hits: public officials with limited capacity & information and ambiguous incentives, or decentralized search by entrepreneurs with specialized skills, strong incentives and much more information?

Friedrich Hayek on why the Leroy Smith principle is itself an argument

for Econ 101

• “It is because every individual knows so little and… because we rarely know which of us knows best that we trust the independent and competitive efforts of many to induce the emergence of what we shall want when we see it."

Summary

• Empirical growth literature has failed to produce useful expert knowledge

• Mistaking randomness for evidence led to the wrong approach to development – “industrial policy” which requires “expert knowledge,” which has little or no evidence base.

• But economists can say something useful about “big picture” development after all, using – (1) long run evidence, – (2) common sense economics that has stood the test

of time, – (3) following Principles of Economics that produce

prosperity even when the “development experts” can’t produce exact answers.