structural transformation ricardo hausmann kennedy school of government and center for international...
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Structural Transformation
Ricardo Hausmann
Kennedy School of Government and
Center for International Development
Harvard 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(
gro
wth
gdp
| X
,lexp
y19
92 )
+ b
*lex
py1
992
lexpy1992
Residuals Linear prediction
8.10487 9.83871
.31443
.429625
MDG
PRY
BGD
JAM
ECU
BOL LCA
LKA
COL
HTI
PER
KEN
IDN
BLZ
CHL
DZASAU
OMNTUR
TTO
IND
GRC
ROM
THA
CYP
CHN
HRV
PRT
MYS
BRA
HUN
AUS
MEX
ESP
KOR
NZL
SGP
NLD
CANUSADNKSWE
DEU
IRL
FINISL
CHE
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)
e(
lexp
y200
3 | X
,logh
l ) +
b*l
oghl
ln human capital
Residuals Linear prediction
.07236 1.21472
-.508204
.813688
NER
RWASDN
CAF
NGA
PNG
PAK
UGA
BGD
TGO
CMR
GAB
TZA
SEN
DZA
MWI
GTM
CIV
IND
KENIRN
TURNAMBRA
HND
SLV
IDN
NIC
PRT
MDG
SYRBOLJOR
MEX
COL
SGP
MUS
PRY
OMN
ZAF
MAR
THA
EGY
GUY
CRI
MYS
VEN
LKAESP
ECU
ROM
PER
CHN
ITA
PAN
URY
CHL
PHL
TTO
FRAAUT
ARG
GRC
FJI
CYP
RUS
BRB
HKG
KOR
ISL
IRLPOL
JPN
DEU
NLD
LUX
GBR
CHE
BEL
ISRSWE
FIN
AUS
DNKCAN
NOR
HUN
USANZL
e(
lexp
y200
3 | X
,rul
e )
+ b
*ru
le
rule of law
Residuals Linear prediction
-1.20609 1.90945
-.875729
.657409
KEN
RWA
NER
NGA
HND
SDN
CMR
GTMDZA
RUSIDN
PRY
TGO
BLR
VENNIC
AZE
COL
BGD
ECU
BIH
PAK
KGZ
ALB
MDGUGA
SLV
NPL
KAZ
CIV
PER
FJI
SYR
PHL
GAB
GEO
MDAMEX
BOLIRN
MWI
ARM
MKD
LKA
PNG
BRA
ETH
CHN
TURSEN
PAN
ZAF
LBN
ROMBGR
GUY
TZA
EGY
ARG
IND
HRVLTU
MYS
LVA
SVK
TTO
MNG
BHR
THA
MAR
WSM
POLKOR
CRI
GRC
URY
CZE
JORITA
BLZ
HUN
ESTSVN
ISR
PRT
CYP
MUSOMN
ESP
BRB
CHL
FRA
NAM
BELHKG
IRL
DEU
USA
JPNGBRNLD
AUS
SWE
CAN
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
BGD
BLRBOL
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
ZWE
01
23
4ln
avg
pat
hs
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
10
Den
sity
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
CZEDEU
DNK
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
LBNLKA
LTU
LVAMAR
MDA
MDG
MEX
MLI MNG
MOZ
MWI
MYS
NERNGA
NIC
NLD
NOR
NPL
NZL
OMN
PAK PANPER
PHL
PNG
POL
PRT
PRY
ROM
RUS
RWA
SAUSDN
SEN
SGP
SLESLV
SVK SWE
SYR
TGO
THA
TJK
TKM
TUR
TZA
UGA
UKR
URY
USA
VEN
ZAF
ZMB
ZWE
11
12
13
14
15
lno
pen_
fore
st1b
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
TU
R
BR
A
HU
N
AR
G
RO
M
CH
L
CH
N
ME
X
PO
L
MY
S
CZ
E
AF
R
LAC
SA
S
MN
A
EC
A
EA
P
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
10
15
20
Fre
que
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
01
02
03
04
05
0F
req
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
10
15
20
25
Fre
que
ncy
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
BHS
BLZ
BMU
BOL
BRA
BRB
BTNBWA
CAF
CAN
CHE
CHLCHN
CIV
CMRCOG
COL
COM
CRICYP DEU
DMA
DNKDOM
DZA
ECUEGYESPFINFJIFRA
GAB
GBR
GHA
GMB
GNB
GRC
GRD
GTM
GUYHKG
HNDHTI
HUN
IDN
INDIRL
IRN
IRQ
ISL
ISR
ITA
JAM
JOR
JPN
KEN
KIR
KNA
KOR
KWT
LBR
LBY
LCA
LKA LSOMAR
MDG
MEXMLI
MLTMMR
MOZMRT MUS
MWI
MYS
NAM
NCL
NER
NGANIC
NLD
NOR
NPL
NZL
OMN
PAKPAN
PER
PHL
PNG
PRT
PRY
PYF
ROM
RWA
SAU
SDN
SEN
SGP
SLB
SLE
SLVSUR
SWESWZ
SYCSYR
TCD
TGO
THATTOTUNTUR
URY
USAVCT
VEN
VUT
WSM
ZAF
ZAR
ZMB
ZWE
196
01
970
198
01
990
200
0
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
CIV
CMR
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
SLB
SLE
SLV
SUR
SYR
TGO
URY
VEN
VUTZAF
ZAR
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
Fal
l of
outp
ut
19601961
19621963
1964
1965
1966
19671968
19691970
1971
1972
19731974
19751976
19771978
197919801981
1982
198319841985
1986 19871988
1989
19901991
1992
1993
1994
1995
19961997
199819992000
20012002
20032004
5.4
5.5
5.6
5.7
LY
PC
LC
UK
4 5 6 7LXPCKUS
The growth collapse in Zambia
Log of real exports per capita
Log
of r
eal G
DP
per
cap
ita
Growth collapse in Bolivia
1974
1975
1976
19771978
1979
1980
1981
1982
1983
1984
1985
1986198719881989
1990
19911992
1993
1994
1995
1996
1997
19981999 200020012002
20032004
3.3
53
.43
.45
3.5
LY
PC
LC
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)