Conference Slides

Stages of Development and
Evolving Constraints to Growth
Xuehui Han and Shang-Jin Wei
Asian Development Bank
May 27, 2015
The views expressed in this document are those of the author and do not necessarily reflect the views
and policies of the Asian Development Bank or its Board of Governors or the governments they
represent.
1
Roadmap
• Background, Motivation, and Overview
• Is there a “Middle-Income Trap”?
– Transition matrix and ergodic distribution
• What are the leading constraints to growth at
different stages of development?
– Conditional inference tree (and random forest)
• Conclusions
2
Is there a “Middle-Income Trap”?
Google scholar citations: 126,000
Google citations: 2,650,000
3
What is a trap?
• Merriam-Webster Dictionary: a situation or a
position from which it is difficult or impossible to
escape
• Oxford dictionary: a device or an enclosure …
typically by allowing entry but not exit …
• So a “middle-income trap” should be …?
4
• What has been said to be a middle-income trap?
• Selected discussions on Middle-Income Traps:
– Growth slowdown at $10,000–$11,000 and $15,000–
$16,000 in 2005 PPP (Eichengreen et al. 2013);
– A typical country in the middle-income group has a
higher frequency of deviations from the growth path
(Aiyar et al., 2013);
– Might be a trap in relative term (Robertson and Ye,
2013).
5
Define the thresholds for the income groups
Extremely
Low
Income
LowIncome
$1,096
GDP per
capita
(2005
ppp); $3
per day
WB definitions
LowerMiddleIncome
UpperMiddleIncome
HighIncome
$2,418;
WB’s
cut-off
line
$5,500;
median
point
between
$2,418
and
$15,220
$15,220;
US’s
income
in 1960
$2,418
$10,276
$19,429
GDP per capita
(2005 PPP)
6
11
Figure 1. Income Transition from 1960 to 2011
LUX
NOR
9
TPE
KOR
ROU
BWA
GNQ
THA
CHN
TUN
8
7
LSO
BFA
MLI
ETH
MOZ
BDI
NLDDNK
AUS
AUTBEL
IRL
SWE
CAN
FINDEU
ISL
GBR
FRA
ESP ITA
NZL
MLT
GRC ISR
PRT
TTO BRB
ARG
TURCHL
MYS
MEX
GAB
IRN PAN
CRI
MUS
BRA
DOMPER
COL ZAF
ECU
USACHE
45 Degree Line
URY
VEN
CPVIDN
COD
6
HKG
JPN
CYP
JORNAM
JAM
LKA
FJI
PRY ZWE GTM
BOL
SYR
IND MAR
PHL
HND
MRT COG
GHA
PAK
NGA
ZMB
TCD
CMR
BGD
SEN
CIV
TZA
GMB KEN
BEN
RWA
UGA
NPL
SLV
GIN
TGO
COM GNB
MWI
MDG
CAF
$2,418 (in 2005 PPP)
NER
EGY
6
logIncome2011
10
SGP
Low Income
Middle Income
7
8
$15,220
High Income
9
10
logIncome1960
7
Decade-average transition matrix for 1960–2010 (in %)
Extremely
LowIncome
EL
82
L
3
LM
0
UM
0
H
0
Ergodic distribution
0
LowIncome
LowerMiddleIncome
UpperMiddleIncome
HighIncome
18
72
3
0
0
0
25
68
0
0
0
0
29
70
0
0
0
0
30
100
0
0
0
100
8
• Formally, there is no unconditional “middle
income trap.”
• There is no unconditional “low income trap”
either.
• The only unconditional trap is a “high income
trap”
9
# of decades needed for X percent of countries
to be in higher income groups
X=50%
X=90%
ELIC
LIC
LMIC
UMIC
4
14
3
12
3
8
2
7
10
• However, one might get a difference picture if
one looks at incomes relative to the US
11
1.2
Relative Income Transition (in % of US income)
1960–2011
NOR
SGP
HKG
NLD
AUT
SWE
DNK
BEL
CAN
FIN DEU
FRAISLGBR
ITA
0.2
0.20
URY
ZAF
0.4
PER
DOM
COL
0.15
BRB
ECU
TUN
EGY
IDN
CPV
IND
LKA
FJI
PRY
BOL
MAR
JOR
PAK
GTM
SYR
PHL
GHA
NGA
ZWE
TCD
CMR
BGD
LSO
TZA
BEN
GMB KEN
UGA
RWA
SLV
NPL
BFA
MLI MWI
TGO
COMGNB
MOZ ETH
MDG
CAF
NER
BDI
COD
0.6
0.05
0.8
NAM
HND
MRT COG
0.00
0.0
MUS
BRA
THA
CHN
0.00
0.2
0.4
TTO
ARG
TURCHL
ROU MYS
MEX
GAB
PAN
BWA
IRN
CRI
GNQTHA BRA
PER MUS
COL ZMB
CHN DOM
TUN ECU
NAM
JOR
JAM
EGY
LKA
FJI
IDN
PRY
GTM
CPV
BOL
SYR
IND
MAR
PHL
HND
MRT
PAK
GHA ZWE
COG
NGA
TCD
CMR
BGD
LSO
SEN16%
TZA
KEN
CIV
NPL
UGA
RWA
BEN
GMB
BFA
SLV
MLI
TGO
COM
GNB
GIN
of US
MOZ
ETH
MWI
MDG
CAF
BDI
NER
COD
Enlarged Portion of Income below 16%
NZL
GNQ
ISR
0.10
MLT
GRC
PRT
ESP
Income2011RelativeUS
CYP
0.05
0.8
0.6
KOR TPE
USA
[$15,220(1960),$42,646(2011)]
AUS
IRL
JPN
0.0
Income2011RelativeUS
1.0
CHE
VEN
SEN
CIV
GIN
16% of US
0.10
1.0
Income1960RelativeUS
0.15
0.20
1.2
Income1960RelativeUS
12
Decade-average transition matrix for 1960–2010 relative to US (in %)
16% and below
16%–36%
36%–75%
75% and
above
16% and below
92
8
0
0
16%–36%
13
72
15
0
36%–75%
0
4
73
22
75% and above
0
2
19
79
23
13
31
33
Ergodic distribution
13
• A relative income trap is a bit silly, similar to
saying that the human specie has an “infant
age trap” because newborns can never catch
up with their parents in age.
14
Moving beyond transition matrix analysis
• The transition matrix analysis has an important
assumption:
• Every country in a given income group is similar
to all other countries in the same group
• But different countries in a group can have
different fundamentals and policy choices, and
these may matter for growth too.
15
What are the binding constraints to
growth at different stages of
development?
16
What is a Regression Tree?
• Non-parametric Machine Learning method
proposed by Breiman (1984);
• Searches for all possible binary splitting points
for each predictor;
• Newly developed Conditional Inference Tree:
– No structures imposed;
– Can deal with many regressors;
missing data; correlations
between predictors;
– Good at picking up the leading
explanatory variables.
17
Indicators
1. Political Stability: Domestic conflicts indication ;
2. Macroeconomic stability: inflation, government
debt share, and the number of crisis episodes
(currency and banking);
3. Infrastructure: paved roads & railway, power
generating capacity.
4. Human capital: the average years of schooling;
5. Openness: trade/GDP; FDI net inflow/GDP;
6. Good governance: political constraints indices
7. Initial Income level;
8. Demography: share of the 15-64 cohort in
population; growth rate of the 15-64 cohort;
9. Growth rate of the leading economics;
10. Oil exporter indicator.
ADB
Washington Others
Consensus
President
Nakao’s Eight
Key Actions
















18
Tree Growing
•
•
•
•
p ≤ 0.05 for splitting;
Minimum size for splitting: 20 obs.;
Minimum size for ending nodes: 7 obs.;
Using 10-year horizon for growth rate
measurement;
• Most explanatory variables are decade
averages.
19
Low-income group
If unfavorable
demographics,
low inflation +
good
infrastructure
Unfavorable
demographics +
high inflation
If not resource Oil exporter
rich, favorable + favorable
demographics demographics
+ good
20
education
Three groups of countries
• Progressive (annual growth>3%; blue circle);
• Near-stagnant (≤3%),
• Regressive (negative annual growth; red
triangle);
21
Variable combinations
Progressive group
Oil exporter + favorable
demographics
If Not oil exporter, then
favorable demographics
+ good education
If unfavorable
demographics, then low
inflation + good
infrastructure
Regressive
Unfavorable
demographics + high
inflation
Country examples
Growth rates
Indonesia (1970)
Sudan (2000)
India (1990)
Sri Lanka (1980)
8.6%
5.2%
4.2%
4.7%
Cape Verde (1980)
Malaysia (1960)
4.0%
2.0%
Madagascar (1980)
Tanzania (1990)
Tanzania (1980)
Zambia (1990)
–0.7%
–1.0%
–4.5%
–3.0%
22
What is a Forest?
• Many trees (1000 trees in our forest);
• For each tree, use 90% of the sample for tree
growing;
• Count the appearance of the predictors across
all trees and take average;
• Each tree is subject to different errors. By
taking the average of all trees, we have more
robust results than the regression tree.
23
Low-Income
Rank
(1)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Factors
(2)
Population share (15–64)
Paved Road (km/1,000 workers)
FDI share
Power Generating Capacity
(gigawatts/1,000 workers)
Log Initial Income
Population growth (15–64)
Oil Exporter or Not
Years of Schooling
Inflation
Government Debt Share
Global Growth Rate
Log Conflicts Index
Political Constraints
Railway (km/1,000 workers)
Trade Share
No. of Crises
Difference of
Frequencies of
growth between
positive
Average nodes with higher
Total
contributions /
split
/ low value than
frequency
negative
value the splitting value
contributions (in
(decade growth
%)
rate)
(3)
(4)
(5)
(6)
1277
53.77%
2.54
93.89/6.11
1181
1.39
1.21
69.18/30.82
955
1.83%
0.79
65.03/34.97
839
0.12
1.54
78.67/21.33
832
828
740
736
617
603
571
507
480
294
282
278
6.86
0.2
0
3.21
12.83%
41.82%
2%
5.5
0.12
0.39
60.44%
3
-2.47
2.37
2.36
2.02
-2.21
-1.72
0.84
-1.12
1.62
0.29
-0.92
-1.39
9.62/90.38
85.75/14.25
78.65/21.35
89.95/10.05
15.24/84.76
5.14/94.86
71.98/28.02
30.97/69.03
78.96/21.04
53.06/46.94
20.21/79.79
24.82/75.18
24
Middle-income group
If unfavorable
demographics
+ low debt +
low initial
income
Unfavorable
demographics
+ high debt +
low openness
+ low global
growth
If unfavorable
demographics
+ high debt+
high
openness
Favorable
demographics + low
debt
25
Variable combinations
Country examples
Progressive
Favorable demographics + low PRC (2000)
debt
Greece (1960)
Portugal (1990)
Japan (1970)
If unfavorable demographics +
high debt, then high openness
to FDI
If unfavorable demographics,
then low debt + low initial
income
Regressive
Unfavorable demographics +
high debt + low openness +
low global growth
Growth rates
8.1%
7.9%
4.5%
4.1%
Malaysia (1970)
Bolivia (2000)
7.6%
3.5%
Turkey (1960)
Chile (1960)
3.5%
3.4%
Honduras (1980)
Sri Lanka (1970)
–0.7%
–4.5%
26
Middle-Income
Rank
Factors
(1)
1
2
3
4
5
6
7
8
9
10
11
12
13
(2)
Population share (15–64)
Government Debt Share
No. of Crisis
Log Initial Income
FDI share
Global Growth Rate
Political Constraints
Years of Schooling
Inflation
Population growth (15–64)
Log Conflicts Index
Oil Exporter or Not
Trade Share
Power Generating Capacity
(gigawatts/1,000 workers)
Paved Road (km/1,000
workers)
Railway (km/1,000
workers)
14
15
16
Difference of growth
Frequencies of
between nodes with
positive
Total
Average Split
higher / low value than
contributions /
Frequency
Value
the splitting value
negative
(decade growth rate) contributions (in %)
(3)
(4)
(5)
(6)
2093
59.03%
2.38
96.42/3.58
1690
38.29%
-2.01
2.9/97.1
1414
8
-2.02
4.03/95.97
1152
8.51
-2.37
7.12/92.88
1036
1.84%
2.25
91.89/8.11
974
2%
2.04
97.33/2.67
721
0.28
-0.15
51.87/48.13
606
6.41
0.22
45.87/54.13
603
13.98%
-0.64
32.01/67.99
505
0.18
-0.98
22.57/77.43
488
5.75
0.58
59.84/40.16
458
0
1.92
89.96/10.04
422
62.75%
-1.22
27.49/72.51
286
0.8
-0.46
41.96/58.04
256
4.32
1.09
75.39/24.61
246
0.69
1.19
68.7/31.3
27
Using Regression Tree Results to
Predict Decade Transition Matrix
• Classify countries into three groups:
progressive (annual growth>3%; blue circle),
near-stagnant (≤3%), and regressive (negative
annual growth; red triangle);
• Apply the “end node” predicted growth rates
to each country’s initial income to get the
predicted decade end income;
• Construct the decade transition matrix.
28
Regression-Tree-Simulated Transition Matrix (in %)
obs. in
the
obs.
group /
percentage
total
obs.
Extremely Low-Income Countries
Progressive
12/82
14.63
Near-stagnant 60/82
73.17
Regressive
10/82
12.20
Low-Income Countries
Progressive
32/104
30.77
Near-stagnant 59/104
56.73
Regressive
13/104
12.50
Lower-Middle-Income Countries
Progressive
43/86
50.00
Near-stagnant 19/86
22.09
Regressive
24/86
27.91
Upper-Middle-Income Countries
Progressive
66/97
68.04
Near-stagnant 23/97
23.71
Regressive
8/97
8.25
Extremely
LowIncome
# years # years
for 50% for 90%
moving moving
up
up
LowIncome
LowerMiddleIncome
UpperMiddleIncome
HighIncome
17
85
100
83
15
0
0
0
0
0
0
0
0
0
0
4
43
never
13
142
never
0
0
54
38
92
46
62
8
0
0
0
0
0
0
0
8
79
never
24
261
never
0
0
0
0
0
0
44
95
100
56
5
0
0
0
0
9
129
never
29
426
never
0
0
0
0
0
0
0
0
12
65
96
88
35
4
0
17
156
never
54
518
never
29
Conclusions (1)
• We reject the unconditional notion of a “middleincome trap,” or a “low-income trap”.
• In general, there is no trap for countries in a
progressive group.
• For countries in a regressive group (minority), the
negative expected growth rate implies that they
may do worse than being simply trapped in their
current income status.
• For countries in a near-stagnant group, because
growth is low, they may look like they are being
trapped in their current income status for a long
time.
30
Conclusions (2)
• For low-income countries, favorable
demographics, macroeconomic stability, good
education, and good infrastructure appear to be
the most important variables separating fast
growing and slow growing countries.
• For middle-income countries, favorable
demographics, macroeconomic stability, sound
global economic growth, and openness to FDI
appear to be the key discriminatory variables.
31