Structural Transformation

Ricardo HausmannKennedy School of Government and Center for International DevelopmentHarvard University

Development seems to be more than producing more of the same Increasing diversity Changing what you produce

Self-discovery externalities Coordination failures

Progress when progress is easy: quality improvements

Growth collapses

Development entails diversification, not specialization

Source: Imbs and Wacziarg (2003)

Rich countries produce rich-country goods…

Background: Hausmann, Hwang & Rodrik Measuring the revealed ‘sophistication’ of

exports

How sophisticated is a particular product?

Using this measure, how sophisticated is a country’s export basket?

You become what you export: initial level of sophistication and subsequent growth

e( g

row

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p | X

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lexpy1992

Residuals Linear prediction

8.10487 9.83871

.31443

.429625

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PRY

BGD

JAM

ECU

BOL LCA

LKA

COL

HTI

PER

KEN

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BLZ

CHL

DZASAU

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BRA

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AUS

MEX

ESP

KOR

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What you produce is determined by a lot more than “fundamentals” (I)

Partial associations between EXPY and human capital (left panel) and institutional quality (right panel)

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| X,lo

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ln human capital

Residuals Linear prediction

.07236 1.21472

-.508204

.813688

NER

RWASDN

CAF

NGA

PNG

PAK

UGA

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CMR

GAB

TZA

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PER

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URY

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FRAAUT

ARG

GRC

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CYP

RUS

BRB

HKG

KOR

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GBR

CHE

BELISRSWE

FIN

AUS

DNKCAN

NOR

HUN

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+ b*

rule

rule of law

Residuals Linear prediction

-1.20609 1.90945

-.875729

.657409

KEN

RWA

NER

NGA

HND

SDN

CMR

GTMDZA

RUSIDN

PRY

TGOBLR

VENNIC

AZE

COL

BGD

ECU

BIH

PAK

KGZ

ALB

MDGUGA

SLV

NPL

KAZ

CIV

PER

FJI

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BOLIRN

MWI

ARM

MKD

LKA

PNG

BRA

ETH

CHN

TURSEN

PAN

ZAF

LBN

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GUY

TZA

EGY

ARG

IND

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MYS

LVA

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TTO

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BHR

THA

MAR

WSM

POLKOR

CRI

GRCURY

CZE

JORITA

BLZ

HUN

ESTSVN

ISR

PRTCYP

MUSOMN

ESP

BRB

CHL

FRA

NAM

BELHKG

IRLDEU

USA

JPNGBRNLD

AUS

SWECAN

NOR

NZLDNK

ISL

FINSGPAUT

LUXCHE

Problems with structural transformation

Information Externalities: Self-discovery spillovers

Coordination Externalities Public inputs and training externalities

Coordination externalities and the evolution of comparative advantage

Hausmann and Klinger (2007) Every product requires a number of factors of

production that are relatively specific E.g. producing asparagus requires a certain type

of soil, mechanized farming equipment, agribusinesses firms that know the market,

but also such “public goods” such as port infrastructure, road system, cold-storage facilities, phytosanitary regulations, market access agreements, etc.

Implication

The distance from the products in which a country has accumulated its specific human capital to alternative products may affect the speed of its structural transformation

But what do we mean by “distance” and how would we measure it empirically?

Monkeys & the Product Space Our metaphor:

Products are like trees Firms are like monkeys

Growth can happen by: Having more monkeys in the same trees: more of the same Improved quality in the same trees: move up the tree

Hwang 2006 finds rapid and unconditional convergence within trees

Or structural transformation: jumping to more valuable trees HHR (2006) show that this last step drives growth in

a significant fashion

Empirical implementation

Monkeys tend to jump short distances Control for any time-varying national

characteristic Human capital, rule of law, financial conditions

Control for any time-varying product characteristic Price, PRODY

Implementing the model The ‘proximity’ (φ ) of two products captures how

easily the capabilities to produce one can be used to produce the other: measure of the cost of jumping.

φAB = min {P(RCA A | RCA B),P(RCA B | RCA A)}

Proximity of Cotton Undergarments to: Synthetic undergarments: 0.78 Overcoats: 0.51 Centrifuges 0.02

Proximity of CPUs to: Digital central storage units: 0.56 Epoxide resins: 0.50 Unmilled rye: 0

Visual Representation of the Product Space

New Work

“The Product Space and its Consequences for Economic Growth” with Hidalgo, Klinger & Lazlo-Barabasi

How can we map this product space visually?

Could the topography of the export space help explain bimodal income distribution and the lack of convergence?

Step 1: Maximum Spanning Tree

Step 2: Overlay Strong Links

0.4 >

0.4 – 0.55

0.55 – 0.65

0.65 <

Nodes sized according to World Exports, darker links are stronger (red is strongest)

Step 3: Add Products

Nodes sized according to World Exports, darker links are stronger (red is strongest)

Step 3: Add Products

Regions Produce in Different Areas of the Space

Malaysia: 1975-2000

Malaysia 1975

Malaysia 1980

Malaysia 1985

Malaysia 1990

Malaysia 1995

Malaysia 2000

Monkeys jump to nearby trees

Average Paths vs. GDP per capita (logs), 2000

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFA

BGDBLRBOL

BRA

CAF

CAN

CHL

CHN

CIV

CMR

COL

CZEDEU

DNK

DOM

DZA

ECU

EGY

ESP

ETH

FIN

GBR

GEO

GHA

GIN

GRC

GTM

HKG

HND

HRV

HTI

HUNIDN

IND

IRL

IRN

ISR

ITA

JAM

JPN

KAZKEN

KGZ

KOR

LBNLKA

LTU

LVAMARMDA

MDG

MEX

MLI

MOZ

MWI

MYS

NERNGA

NIC

NLD

NOR

NPL

NZL

PAKPER

PHL

PNG

POL

PRT

PRY

ROM

RUS

RWA

SAUSDN

SEN

SGP

SLE

SLV

SVK SWE

SYR

TGO

THA

TJK

TKM

TUR

TZA

UGA

UKR

URY

USA

VEN

ZAF

ZMB

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01

23

4ln

avgp

aths

6 7 8 9 10 11lngdppcppp

Measuring density around a tree

0.5

.6.4

For all the surrounding trees you occupy, add their “proximity” (conditional probability) to the new tree,

0

This is a measure of the ‘density’ around a particular good

’

.5 .3

.5

divided by the total number of ‘roads leading to Rome

We use these pairwise distances to measure how close a country’s entire export basket is to an unoccupied tree: Density

Density for jumps (green) versus non-jumps (brown)

05

10D

ensi

ty

0 .2 .4 .6 .8density1b

Density Density

Does the product space matter? More formally, we estimate:

where X is a vector of country+year and product+year dummies, controlling for all time-varying country and product-level characteristics.

Standard errors clustered at the country level, density normalized into units of standard deviation

Xdensityxx tcitcitci ,,,,1,,

1 standard deviation increase in density associated with 6.2 percentage point increase in the probability of having RCA in that good in the next period

The unconditional probability is 1.27%: almost 5-fold increase

This effect dominates the influence of having RCA in the Leamer or Lall category

(1) (2) xi,c,t+1 xi,c,t+1

xi,c,t 0.657 0.655 (66.27)** (67.44)** densityi,c,t 0.062 0.056 (7.03)** (6.36)** RCA_lall la,c,t 0.004 (7.46)** RCA_leamer le,c,t 0.008

(6.19)** Observations 398362 389092 R-squared 0.56 0.56

The model at the country level

How green is your valley?

Proposition

It is easier for a country to move to a higher EXPY if the unoccupied trees are near and fruity

We need an equivalent measure of “density” at the country level

We call it “open forest”

Open_forest Open forest measures the value of the option to

move to a higher EXPY It calculates the value of the unoccupied trees, by

weighing their proximity and their PRODY

Take the scaled distance from the tree you occupy to trees you don’t

.60

.3

Multiplied by the ‘fruitiness’ of the potential tree

1,000 x2,000 x

1,600 x

And add that together for the whole export basket

open_forest vs. GDP p.c. (logs), 2000

ALB

ARG

ARM

AUS

AUT

AZE

BDI

BEN

BFA

BGD

BLR

BOL

BRA

CAF

CAN

CHL

CHN

CIV

CMR

COL

CRI

CZEDEUDNK

DOM

DZA

ECU

EGY

ESP

ETH

FIN

GBR

GEO

GHA

GIN

GRC

GTM

HKG

HND

HRV

HTI

HUN

IDNIND

IRL

IRN

ISR

ITA

JAM

JOR

JPN

KAZ

KEN

KGZ

KOR

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LTU

LVAMARMDA

MDG

MEX

MLI MNG

MOZ

MWI

MYS

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NLD

NOR

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NZL

OMN

PAK PANPERPHL

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POL

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PRY

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USA

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1112

1314

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open

_for

est1

b

6 7 8 9 10 11lngdppcppp

Open Forest & EXPY GrowthTable 5: Open_Forest and EXPY Growth, 1985-2000

(1) (2) (3) (4) FE RE FE RE EXPY

growth EXPY

growth EXPY growth

EXPY growth

lnEXPYc,t -0.185 -0.059 -0.229 -0.068 (9.36)** (5.69)** (10.86)** (6.35)** lnGDPpcc,t 0.025 0.010 0.009 0.012 (1.48) (2.75)** (0.53) (3.22)** lnopen_forestc,t 0.027 0.016 (3.67)** (4.14)** lnopen_forest_sizec,t 0.006 0.010 (0.79) (2.38)* lnopen_forest_valuec,t 0.329 0.145 (5.95)** (3.51)** Constant 1.085 0.242 -1.111 -0.865 (5.81)** (4.99)** (2.53)* (2.43)* Observations 1434 1434 1434 1434 Number of countryid 106 106 106 106 R-squared 0.06 0.09 Growth rate is between t and t+1 (annual observations) Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

1-standard deviation in open forest is associated with higher EXPY growth of 1.6 percentage points per year.

Quality improvements and convergence

What happens when countries can upgrade within the same products?Based on Hwang (2007)

There is no unconditional convergence of GDP per capita

But there is unconditional convergence given the within-product quality distance to the frontier (Hwang 2006)

The evolution of within-product quality (Hwang 2006) Quality in any particular product converges to the

frontier at a rate of 5-6% per year This happens unconditionally Countries that are further away from the quality

frontier grow faster When a country develops a new product, it tends to

enter at a lower quality Therefore, the development of new products creates

more room for within-product quality upgrading, and subsequently faster growth

Africa and LAC have the lowest gaps in the products they are in

150%

170%

190%

210%

230%

250%

270%

290%

UR

Y

TUR

BRA

HU

N

ARG

RO

M

CH

L

CH

N

MEX

POL

MYS CZE

AFR

LAC

SAS

MN

A

ECA

EAP

Recent work by Kugler, Stein and Wagner

Does quality matter for jumping to new trees?

R I PNot really a good project !

But height will help you !

Safe landing !

Growth collapses

Based on Hausmann, Rodriguez and Wagner (2006)

Question: How many industrial countries had their highest GDP per capita before 2000

None

1

3

20

05

1015

20Fr

eque

ncy

2000 2001 2002 2003 2004MAXPCTIME

Out of 112 developing countries with data since 1980, how many had their maximum GDP per capita before 2000?

67 (58 percent) had their peak before 2000

2 24

6

17

10

4 42

16

49

010

2030

4050

Freq

uenc

y

1960 1970 1980 1990 2000MAXPCTIME

How deep have recessions been?

Developing countries: peak to trough fall in GDP per capita in long recessions26

14

9

15

6

8

11

6

3

7

12 2

1 12 2

05

1015

2025

Freq

uenc

y

0 .2 .4 .6 .8 1LGAPPCGDP

52 countries in excess of 20 percent21 countries in excess of 40 percent

Implication

Many countries have seen negative per capita growth for a very long time

This has happened in spite of improvements in schooling attainment, life expectancy and global technological possibilities

In fact, most developing countries have seen declines in GDP per capita lasting more than 10 years

Question #1: Why do countries fall into crises? Probit analysis: We study the determinants of

the probability of countries falling into crises. Usual suspects:

Wars Natural disasters Export collapses Sudden Stops

Unusual suspect: Open Forests

AFGAGOANT

ARE

ARG

ATG AUSAUT

BDI

BELBENBFABGDBGRBHR

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BRA

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MDG

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MWI

MYS

NAM

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NGANIC

NLD

NOR

NPL

NZL

OMN

PAKPAN

PER

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PRT

PRY

PYF

ROM

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SYCSYR

TCD

TGO

THATTOTUNTUR

URY

USAVCT

VEN

VUT

WSM

ZAF

ZAR

ZMB

ZWE

1960

1970

1980

1990

2000

1960 1970 1980 1990 2000LOCMAXTIMEX

MAXPCTIME LOCMAXTIMEX

Most growth collapses coincide with export collapses

Date of export collapse

Dat

e of

gro

wth

col

laps

e

AFGANT

ARE

BDI

BHS

BOL

BRB

CAF

CIVCMR

COG

COM

DMA

DOM

DZA

GAB

GHA

GMB

GNB

GRD

GTM

HND

HTI

IDN

IRN

IRQ

ISR

JAM

KEN

KIR

KNA

KWT

LBR

LCA

MDG

MLTMWI

NAM

NCL

NER

NGA

NIC

NPL OMN

PER

PNGPRY

ROM

RWASAU

SEN

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URY

VEN

VUTZAF

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ZMB

ZWE

0.2

.4.6

.81

.2 .4 .6 .8 1GAPXPC

GAPPCGDP GAPXPC

Collapses in exports were typically larger than those in output

Fall of exports

Fall

of o

utpu

t

19601961

19621963

1964

1965

1966

19671968

19691970

1971

19721973

1974

19751976

19771978

197919801981

1982

198319841985

1986 19871988

19891990

1991

19921993

1994

1995

19961997199819992000

200120022003

2004

5.4

5.5

5.6

5.7

LYP

CLC

UK

4 5 6 7LXPCKUS

The growth collapse in Zambia

Log of real exports per capita

Log

of re

al G

DP

per

cap

ita

Growth collapse in Bolivia

1974

1975

1976

19771978

1979

19801981

1982

1983

1984

1985

1986198719881989

1990

199119921993

1994

1995

1996

1997

19981999 200020012002 2003

2004

3.35

3.4

3.45

3.5

LYP

CLC

UK

4.5 5 5.5 6LXPCKUS

Baseline results: Random Effects ProbitTable 4: Random Effects Probit Regressions, All Countries

Dependent Variable: Probability of Falling into a Crisis(1) (2) (3) (4) (5)

Log GDP per Working Age Person -0.017 -0.007 -0.001 0.000 -0.004(1.78)* (0.66) (0.06) (0.01) (0.17)

Log Change in Real Merchandise Exports -0.266 -0.430 -0.422 -0.410(4.03)*** (4.85)*** (3)*** (2.94)***

War 0.732 0.415 0.467(3.66)*** (1.56) (1.81)*

Natural Disaster -0.037 0.063(0.3) (0.37)

Sudden Stop 0.167 0.240 0.229(2.19)** (2.36)** (2.27)**

Log of Inflation 1.017 1.020(3.31)*** (3.35)***

Change in Polity Indicator 0.312 0.362(2.45)** (2.92)***

Open Forest -0.144 -0.158(1.69)* (1.89)*

Democracy -0.002(0.18)

Constant -1.173 -1.301 -1.366 0.370 1.068(11.46)*** (10.82)*** (9.37)*** (0.29) (0.9)

Observations 3344 2785 1872 1054 1062Countries 187 169 145 83 83Percent crises predicted 0.0% 0.0% 1.6% 5.1% 6.1%Pseudo-R^2 2.3% 3.8% 5.5% 7.4% 7.9%

Question 2: What determines how long a country stays in a crisis? None of the usual suspects

Wars Natural disasters Export collapses Sudden Stops

…but the impact of open forest is very robust

Duration analysis

Two types of specifications. Parametric with frailty (Weibull + others).

Cox with corrected variance (models for multiple spells).

Parametric may be more adequate to precisely estimate the hazard function.

)exp()|( 0 XvhXth iti

)exp()()|( XthXthi

Table 12: Duration Regressions, Weibull Specification with FrailtyDependent Variable: Years in crisis. (1) (2) (3) (4) (5) (6) (7)Representation, Hazard rate with Region and Decade dummies (not shown)Log GDP per Working Age Person 0.024 0.030 0.056 0.057 0.046 0.049 0.055

(1.2) (1.4) (1.21) (1.15) (0.75) (1.07) (1.1)Openforest 0.533 0.558 0.712 0.438 0.494

(3.61)*** (3.13)*** (3.36)*** (2.49)** (2.36)**Democracy (Polity Index) 0.031 0.028 0.032

(1.48) (1.07) (1.35)Sudden Stop -0.092 -0.223

(0.45) (1)Log Change in Real Merchandise Exports 0.287 -1.648 -1.345

(0.83) (0.64) (0.38)War -0.584

(1.11)Natural Disaster 0.055

(0.14)Log of Inflation -0.262

(0.4)Change in Polity Indicator -0.186

(0.65)Change in Exports*Open Forest 0.136 0.104

(0.66) (0.38)Polity*Change in Merchandise Exports -0.003

(0.06)Polity*Sudden Stops -0.003

(0.12)Constant -1.679 -0.796 -8.117 -8.982 -10.640 -6.859 -8.138

(11.74)*** (2.64)*** (3.89)*** (3.63)*** (3.3)*** (2.81)*** (2.82)***N 535 535 233 191 175 230 191

Conclusion: a common cause of protracted growth collapses Adverse shock to the earning capacity of

exports …in a country with low open forest

(connectedness)