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Working Paper Series
_______________________________________________________________________________________________________________________
National Centre of Competence in Research Financial Valuation and Risk Management
Working Paper No. 690
Structural Development Accounting
Gino Gancia Andreas Müller
Fabrizio Zilibotti
First version: June 2011
Current version: June 2011
This research has been carried out within the NCCR FINRISK project on “Macro Risk, Systemic Risks and International Finance”
___________________________________________________________________________________________________________
Figure 1: Steady state comparative statics
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 ∞
ALS/ALN
AH
S/A
HN
ξ = 0.1 →
0.5
12
5
0.1
0.5
1
2
5
0.1
0.5
1
2
5
Northh̃S = 0.1h̃S = 0.5h̃S = 0.9
(a) χS/χN = 1.2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 ∞
ALS/ALN
AH
S/A
HN
ξ = 0.1 →
0.5
1
2
5
0.1
0.5
1
2
5
0.1
0.5
1
2
5
Northh̃S = 0.1h̃S = 0.5h̃S = 0.9
(b) χS/χN = 1.5
0 1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
ξ
GD
Ppw
(rel
ativ
eto
the
Nor
th)
h̃S = 0.1h̃S = 0.5h̃S = 0.9
(c) χS/χN = 1.2
0 1 2 3 4 5 6 7 8 9 101.5
2
2.5
3
3.5
4
4.5
ξ
Ski
ll-p
rem
ium
h̃S = 0.1h̃S = 0.5h̃S = 0.9
(d) χS/χN = 1.2
39
Figure 2: Baseline estimation: GDP pw (log-difference from the US)
AUSAUT
BDI
BEL
BEN
BOL
BRB
CAF
CAN
CHL
CMRCOG
COLCRI
CYP
DNK
DOM
DZA
ECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUY
HND
IDN
IND
IRL
IRN
IRQ
ISR
ITA
JAM
JOR
JPN
KEN
KOR
LBR
LBYLKA
LSO
LUX
MAR
MEX
MLT
MRT
MUS
MWI
MYS
NIC
NLDNOR
NPL
PAK
PAN
PERPHL
PNG
PRT
PRY
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SWE
SWZ
SYR
TGO
THA
TTOTUNTUR
TZA UGA
URY
USA
VEN
ZAF
ZMB ZWE−4
−3−2
−10
Mod
el p
redi
ctio
n
−4 −3 −2 −1 0Data
(a) 1970, secondary schooling
AUS
AUT
BDI
BEL
BEN
BOL
BRB
CAF
CAN
CHL
CMRCOG
COL
CRI
CYP
DNK
DOM
DZA
ECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUYHND
IDN
IND
IRL
IRN
IRQ
ISR
ITA
JAM
JOR
JPN
KEN
KOR
LBR
LBYLKA
LSO
LUX
MAR
MEX
MLT
MRT
MUS
MWI
MYS
NIC
NLDNOR
NPL
PAK PAN
PERPHL
PNG
PRT
PRY
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SWE
SWZ
SYR
TGO
THA
TTOTUNTUR
TZA UGA
URY
USA
VEN
ZAF
ZMBZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(b) 1970, tertiary schooling
ALB
AREARG
AUSAUTBEL
BEN
BGD
BGR BHR
BLZBOL
BRABRB BRN
BWA
CANCHE
CHL
CHN
CMR
COL CRICUB
CYP
CZE DNK
DOM
DZA
ECU
EGY
ESP
EST
FIN
FJI
FRA
GBR
GER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDN
IND
IRL
IRN
IRQ
ISL ITA
JAM
JOR
JPN
KAZ
KENKHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPERPHL
PNG
POL
PRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZSYR
THA
TONTTOTUN
TUR
TZA UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(c) 2000, secondary schooling
ALB
AREARG
AUSAUTBEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRB BRN
BWA
CANCHE
CHL
CHN
CMR
COL CRICUB
CYP
CZEDNK
DOM
DZAECU
EGY
ESP
EST
FIN
FJI
FRAGBR
GER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDNIND
IRL
IRN
IRQ
ISL
ITA
JAM
JOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MACMAR
MDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK PANPERPHL
PNG
POL
PRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA
TON
TTOTUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(d) 2000, tertiary schooling
Note: plots l̂og(yS/yUS) against log(yS/yUS) across time and skill categories, ξ varies across OECD, sub-Saharan and other countries.
40
Figure 3: No barriers to technology adoption: GDP pw (log-difference from the US)
AUSAUT
BDI
BEL
BEN
BOL
BRB
CAF
CAN
CHL
CMR
COG
COLCRI
CYP
DNK
DOM
DZA
ECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUY
HND
IDNIND
IRL
IRN
IRQ
ISR ITAJAM
JOR
JPN
KEN
KOR
LBR
LBYLKA
LSO
LUX
MAR
MEXMLT
MRT
MUS
MWI
MYS
NIC
NLDNOR
NPL
PAK
PAN PER
PHL
PNG
PRT
PRY
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SWE
SWZSYRTGO
THA
TTO
TUN
TUR
TZA UGA
URY
USA
VEN
ZAFZMB ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(a) 1970, secondary schooling
AUSAUT
BDI
BEL
BEN
BOL
BRB
CAF
CANCHL
CMR
COG
COLCRI
CYP
DNK
DOM
DZA
ECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUY
HND
IDNIND
IRL
IRN
IRQ
ISRITA
JAM
JOR
JPN
KEN
KOR
LBR
LBY
LKA
LSO
LUX
MAR
MEXMLT
MRT
MUS
MWI
MYS
NIC
NLDNOR
NPL
PAK
PANPER
PHLPNG
PRT
PRY
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SWE
SWZSYRTGO
THA
TTO
TUN
TUR
TZAUGA
URY
USAVEN
ZAFZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(b) 1970, tertiary schooling
ALB
AREARG
AUSAUTBEL
BEN
BGD
BGR
BHR
BLZ
BOL
BRA
BRB BRN
BWA
CANCHE
CHL
CHN
CMR
COL
CRICUB
CYPCZE DNK
DOM
DZAECU
EGY
ESPEST FIN
FJI
FRA
GBR
GER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDN
IND
IRL
IRN
IRQ
ISL ITA
JAM
JOR
JPN
KAZ
KENKHM
KOR
LAOLBR
LBY
LKA
LSO
LTU
LUX
LVA
MAC
MAR
MDAMEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPER
PHL
PNG
POL
PRT
PRY
QATROM
RWA
SAU
SENSLE
SLV
SVK SVN
SWE
SWZ
SYR
THA
TONTTO
TUNTUR
TZAUGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(c) 2000, secondary schooling
ALB
AREARG
AUSAUTBEL
BENBGD
BGR BHRBLZ
BOL
BRA
BRB BRN
BWA
CANCHE
CHL
CHN
CMR
COL
CRI
CUB
CYP
CZE
DNK
DOMDZAECU
EGY
ESPEST
FIN
FJI
FRAGBR
GER
GHAGMB
GRC
GUY
HKG
HND
HRV
HUN
IDNIND
IRL
IRNIRQ
ISL ITA
JAM
JOR
JPN
KAZ
KEN
KHM
KOR
LAOLBR
LBY
LKALSO
LTU
LUX
LVA
MAC
MARMDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPER
PHL
PNG
POL
PRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA TONTTO
TUNTUR
TZA UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
(d) 2000, tertiary schooling
Note: plots l̂og(yS/yUS) against log(yS/yUS) across time and skill categories, ξ → ∞ for all countries.
41
Figure 4: Sectoral productivities (relative to the US)
AUS
AUT
BDI
BEL
BEN
BOL
BRB
CAF
CAN
CHL
CMRCOG
COL
CRICYP
DNK
DOMDZAECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUYHND
IDN
IND
IRL
IRN
IRQ
ISR
ITA
JAMJOR
JPN
KEN
KOR
LBR
LBY
LKA
LSO
LUX
MEX
MLT
MRT
MUS
MWI
MYSNIC
NLD
NOR
NPL
PAK
PAN
PER
PHL
PNG
PRT
PRY
RWA
SAU
SDNSEN
SGP
SLE
SLV
SWE
SWZ
SYR
TGO
THA
TTOTUN
TUR
TZA UGA
URY
USA
VEN
ZAF
ZMBZWE
0.4
.81.
2Pr
oduc
tivity
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
(a) 1970, high-skill sector
AUS
AUT
BDI
BEL
BEN
BOL
BRB
CAF
CAN
CHL
CMR
COG
COL
CRICYP
DNK
DOM
DZA
ECU
EGY
FIN
FJI
GBR
GHA
GMB
GRC
GUY
HND
IDN
IND
IRLIRN
IRQ ISR
ITA
JAM
JOR
JPN
KEN
KOR
LBR
LBY
LKA
LSO
LUXMAR
MEX
MLT
MRT
MUS
MWI
MYS
NIC
NLD
NOR
NPL
PAK
PAN
PER
PHL
PNG
PRT
PRY
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SWE
SWZ
SYR
TGO
THA
TTO
TUN
TUR
TZAUGA
URY
USA
VEN
ZAF
ZMB ZWE
0.4
.81.
2Pr
oduc
tivity
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
(b) 1970, low-skill sector
ALB
ARE
ARG
AUS
AUTBEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRBBRN
BWA
CAN
CHE
CHL
CHN
CMR
COL
CRICUB
CYP
CZE DNK
DOM
DZAECU
EGY
ESP
EST
FIN
FJI
FRAGBRGER
GHAGMB
GRC
GUY
HKG
HND HRV
HUN
IDN
IND
IRL
IRN
IRQ
ISL
ITA
JAMJOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBYLKA
LSO
LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLT
MNG
MOZ MRT
MUS
MWI
MYS
NAMNER
NIC
NLD
NOR
NPL
NZL
PAK
PAN
PER
PHL
PNG
POL
PRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA
TON
TTO
TUN
TUR
TZA UGA
URY
USA
VENVNM
YEMZAF
ZMB ZWE
0.4
.81.
2Pr
oduc
tivity
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
(c) 2000, high-skill sector
ALB
ARE
ARG
AUSAUTBEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRB BRN
BWA
CAN
CHE
CHL
CHN
CMR
COL
CRI
CUB
CYP
CZEDNK
DOM
DZA
ECU
EGY
ESP
EST
FIN
FJI
FRAGBRGER
GHA
GMB
GRC
GUY
HKG
HND HRV
HUNIDN
IND
IRL
IRN
IRQ
ISL
ITA
JAMJOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAMNER
NIC
NLD
NOR
NPL
NZL
PAK
PAN
PER
PHL
PNG
POLPRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA
TON
TTO
TUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEMZAF
ZMBZWE
0.4
.81.
2Pr
oduc
tivity
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
(d) 2000, low-skill sector
Note: plots AHS/AH,US and ALS/AL,US against log(yS/yUS) across time for the tertiary skill category. ξvaries across OECD, sub-Saharan and other countries.
42
Figure 5: Counterfactual GDP pw (log-difference from the US)
ALBAREARG
AUSAUT
BEL
BENBGD
BGR
BHR
BLZ
BOL
BRABRB
BRNBWA
CANCHE
CHL
CHN
CMR
COLCRI
CUB
CYPCZE DNK
DOM
DZAECU
EGY
ESP
EST
FIN
FJI
FRAGBR
GER
GHAGMB
GRC
GUY
HKGHND HRV
HUN
IDNIND
IRL
IRN
IRQ
ISLITA
JAM
JOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBRLBY
LKA
LSO LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLTMNG
MOZ
MRT MUSMWI
MYS
NAMNER
NICNLDNOR
NPL
NZL
PAK
PAN PERPHL
PNGPOL
PRT
PRYQAT
ROM
RWA
SAU
SEN
SLE
SLVSVK
SVNSWE
SWZSYR
THA
TON TTO
TUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB ZWE
−3−2
−10
1Co
unte
rfact
ual
−4 −3 −2 −1 0GDP pw (log−difference from the US, Model)
(a) No barriers to technology adoption
ALB
ARE
ARG
AUS
AUT
BEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRBBRN
BWA
CANCHE
CHL
CHN
CMR
COL
CRI
CUB
CYP
CZE
DNK
DOM
DZAECU
EGY
ESP
EST
FIN
FJI
FRAGBR
GER
GHA
GMB
GRC
GUY
HKG
HND HRV
HUN
IDN
IND
IRL
IRN
IRQISL ITA
JAM
JOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBYLKA
LSO
LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUSMWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAKPAN
PER
PHL
PNG
POLPRT
PRY
QAT
ROM
RWA
SAU
SENSLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA
TON
TTOTUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMBZWE
−4−3
−2−1
0Co
unte
rfact
ual
−4 −3 −2 −1 0GDP pw (log−difference from the US, Model)
(b) Free trade
ALBARE
ARG
AUSAUTBEL
BEN BGD
BGR
BHR
BLZ
BOL
BRA
BRB
BRNBWA
CAN
CHE
CHLCHN
CMR
COL
CRI
CUB
CYPCZE
DNK
DOM
DZA
ECU
EGY
ESP
EST
FIN
FJI
FRA
GBR
GER
GHAGMB
GRC
GUY
HKGHND
HRV
HUN
IDN
IND
IRLIRN
IRQ
ISL ITA
JAM
JOR
JPN
KAZKEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MAC
MAR
MDA
MEX
MLI
MLTMNG
MOZ
MRT MUSMWI
MYS
NAMNER
NIC
NLD
NOR
NPL
NZL
PAK
PAN PER
PHLPNG
POL
PRT
PRY
QAT
ROM
RWA
SAU
SEN
SLE
SLV
SVKSVN
SWE
SWZ
SYR
THA
TONTTO
TUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB ZWE
−1.5
−1−.
50
.5Co
unte
rfact
ual
−4 −3 −2 −1 0GDP pw (log−difference from the US, Model)
(c) Free trade and perfect IPR protection
Note: plots l̂og(ycountS /ycount
US ) against l̂og(yS/yUS) in 2000 for the tertiary schooling category. ξ variesacross OECD, sub-Saharan and other countries.
43
Figure 6: Change in skill premium: benchmark to no barrier counterfactual
ALB
ARE
ARG
AUSAUT
BEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRB
BRN
BWA
CANCHE
CHL
CHN
CMR
COLCRI
CUB
CYPCZEDNK
DOMDZA
ECU
EGY
ESPEST FIN
FJI
FRAGBRGER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDN
IND
IRL
IRNIRQ
ISLITA
JAMJOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MACMAR
MDA
MEX
MLI
MLTMNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPER
PHL
PNG
POLPRT
PRY
QAT
ROM
RWA
SAU
SEN
SLE
SLV
SVK
SVN
SWE
SWZ
SYR
THA TON
TTOTUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
0.2
.4.6
Chan
ge in
log
skill−
prem
ium
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
Note: plots l̂ogw̃nobarrJ − l̂ogw̃J in 2000 for the tertiary schooling
category. ξ varies across OECD, sub-Saharan and other countries.
Figure 7: Change in skill premium: benchmark to free trade counterfactual
ALB
ARE
ARG
AUS
AUT
BEL
BEN
BGD
BGR
BHRBLZ
BOL
BRA
BRB
BRN
BWA
CAN
CHE
CHL
CHN
CMR
COL
CRI
CUB
CYP
CZE
DNK
DOMDZA
ECU
EGY
ESPEST
FIN
FJI
FRAGBRGER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDN
IND
IRL
IRNIRQ
ISL
ITAJAMJOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTULUX
LVA
MACMAR
MDA
MEX
MLI
MLTMNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPER
PHL
PNG
POL
PRTPRY
QAT
ROM
RWA
SAU
SEN
SLE
SLVSVK SVN
SWE
SWZ
SYR
THA TON
TTOTUN
TUR
TZA
UGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−2−1
01
2Ch
ange
in lo
g Sk
ill−Pr
emiu
m
−4 −3 −2 −1 0GDP pw (log−difference from the US, Data)
Note: plots l̂ogw̃tradeJ − l̂ogw̃J in 2000 for the tertiary schooling
category. ξ varies across OECD, sub-Saharan and other countries.
44
Figure 8: Open Economy Estimation: GDP pw (log-difference from the US)
ALB
AREARG
AUS
AUT
BEL
BEN
BGD
BGR
BHRBLZ
BOL
BRABRB BRN
BWA
CANCHE
CHL
CHN
CMR
COL
CRI
CUB
CYPCZE
DNK
DOMDZAECU
EGY
ESP
EST
FIN
FJI
FRAGBR
GER
GHA
GMB
GRC
GUY
HKG
HND
HRV
HUN
IDNIND
IRL
IRNIRQ
ISLITA
JAM
JOR
JPN
KAZ
KEN
KHM
KOR
LAO
LBR
LBY
LKA
LSO
LTU
LUX
LVA
MAC
MARMDA
MEX
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM
NER
NIC
NLDNOR
NPL
NZL
PAK
PANPERPHL
PNG
POL
PRT
PRY
QATROM
RWA
SAU
SENSLE
SLV
SVKSVN
SWE
SWZ
SYR
THA
TON
TTOTUN
TUR
TZAUGA
URY
USA
VEN
VNM
YEM
ZAF
ZMB
ZWE
−4−3
−2−1
0M
odel
pre
dict
ion
−4 −3 −2 −1 0Data
Note: plots l̂og(yS/yUS) against log(yS/yUS) for the open economyestimation in 2000, ξ varies across OECD, sub-Saharan and othercountries.
45
