Transcript Global Growth Accounting: The Role of Shifting Investment Patterns

Global Growth Accounting: The role of shifting investment patterns
Abdul A Erumban
Robert Inklaar
Klaas de Vries
World KLEMS Conference, May 22-24 2016, Madrid
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The background and objectives
 Investment and capital are important factors in understanding crosscountry income differences
 But there is limited available data, especially on investment by asset
 PWT and TED have investment data for productivity estimation
 But these do not yet reflect SNA 2008 changes
 And PWT and TED have used different data sources
 Main use PWT: capital and productivity growth and levels
 Main use TED: productivity growth and role of ICT assets
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Asset detail
Total GFCF
Total
Constructio
n
Total
Machinery
Transport
Equipment
Other
Machinery
Hardware
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Residenti
al
Constructio
n
Communic
ation
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Other
Products
NonResidential
Constructio
n
Cultivated
Assets
Intellectual
Property
Products
(IPP)
Software
These are further aggregated into ICT and non-ICT
categories in TED
Total GFCF
Non-ICT
Non-ICT
Machinery
ICT
Transport
Equipment
Structures
Residential
Construction
Hardware
Communic
ation
Software
Non-Residential
Construction
 Notes:
 In TED: Non-ICT Machinery consists of ‘Other Machinery (excluding hardware and
communication)’, ‘IPP’ and ‘Cultivated assets’ (whenever available)
 Structures: inclusion of residential structure?
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Approach
1. Total gross fixed capital formation from National Accounts
2. Split by asset based on:
a. National Accounts data
b. WITSA data (ICT)
c. Commodity Flow Method
3. Respect national values and prices
a. Except for ICT assets: harmonized US deflators
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Contrast to current data
1. Coverage of all investment, not only non-residential (TED)
2. Broader country coverage and more use of national accounts
sources (PWT/TED)
3. More sophisticated estimation of ICT investment from WITSA
(TED/PWT)
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Methodological approach
1.
2.
3.
4.
5.
6.
7
Asset-wise real investment data
Depreciation rates
Capital Stock (Perpetual Inventory Method – PIM)
Rate of return (internal) – capital compensation share
Rental prices
Capital services
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Initial capital stock, and Perpetual Inventory Method (PIM)
 Perpetual inventory method, capital stock in any individual asset:
𝑆𝑖𝑡 = 𝑆𝑖𝑡−1 1 − 𝜕𝑖 + 𝐼𝑖𝑡
 Initial capital stock
 steady state assumption (Solow) – Harberger, 1978 approach:
𝐾
𝑌
𝐼
=
0
𝑌
𝜕+𝑔
 assume capital/output ratios in starting year
– More robust approach than steady-state/Harberger (1978) approach (Feenstra
et al., 2015)
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Capital services
 Capital services growth rates are obtained as a weighted average of
individual asset-wise capital stock, the weights being the rental share
of each asset in total capital compensation
S
𝑣𝑖,𝑡
∆𝑙𝑛S𝑖,𝑡
∆𝑙𝑛𝐾𝑡 =
k
S
K=aggregate capital services, 𝑣𝑖,𝑡
=share of asset i in total capital compensation (average
for current and previous year)
 Also aggregate over sub-groups of ICT and non-ICT assets
 Capital compensation for each asset is obtained using rental prices
(user cost of capital) constructed using internal rate of return
 Total capital compensation obtained after subtracting labor
compensation from GDP
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Inclusion of residential construction has important
implications for aggregate capital/output ratio
Current investment/GDP ratio
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Real investment/GDP ratio
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
0.25
0.23
0.21
0.19
0.17
0.15
0.13
0.11
0.09
0.07
0.05
Capital stock/GDP ratio
3.00
2.50
2.00
1.50
All assets
1.00
Excluding Residential Structures
0.50
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2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
0.00
Estimates of ICT using WiTSA and U.S Investment/spending
ratio are quite different from actual national accounts data
ICT investment/GDP ratio, WITSA (Jorgenson and Vu, 2013), EU KLEMS and national accounts (TED & PWT)
3.0
3.5
Germany
Ireland
South Africa
4.0
2.0
1.5
1.0
2.5
ICT investment/GDP
ICT investment/GDP
2.0
1.5
1.0
3.5
3.0
2.5
2.0
1.5
1.0
WiTSA (Kuznets)
TED&PWT (new)
0.0
0.5
WiTSA (Kuznets)
TED&PWT (new) 0.5
TED&PWT (new)
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2015
2012
2009
2006
2003
2000
1997
1994
1991
0.0
1988
2015
2012
2009
2006
2003
2000
1997
1994
1991
1988
1985
0.0
WiTSA (Kuznets)
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
0.5
1985
ICT investment/GDP
4.5
3.0
2.5
11
5.0
ICT investment in EU KLEMS is also different from national
accounts data (SNA 2008) for some countries
6.0
3.5
United Kingdom
France
3.0
2.0
WiTSA (Kuznets)
EU KLEMS
TED&PWT (new)
2.5
2.0
1.5
1.0
WiTSA (Kuznets)
EU KLEMS
TED&PWT (new)
0.5
3.0
2.5
2.0
1.5
1.0
0.5
WiTSA (Kuznets)
EU KLEMS
TED&PWT (new)
0.0
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
0.0
3.5
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1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
4.0
ICT investment/GDP
ICT investment/GDP
ICT investment/GDP
Czech Republic
4.0
0.0
12
4.5
3.0
5.0
1.0
5.0
There is a strong correlation between investment in
equipment capital and GDP growth
NON-ICT EQUIPMENT INVESTMENT/GDP RATIO AND GDP
GROWTH, 1995-2013
NON-ICT EQUIPMENT SHARE IN TOTAL INVESTMENT
12.0%
43.0%
41.0%
MMR
10.0%
39.0%
MOZ
KHM
BIH
ETH
UGAIND
NGA
VNM
ARM
IRQ
GHA BFA
ZMB
GEO
TZA
BGD
SGP
LKA
TTO KAZ
DOM
ALB
UZBBLR
MWISDN PER
MYS
IRL
JOR
ARE
CHLBHR EST KOR
PHL
LTU
CRI
MLI
TJK
IDN
NER
TUR
EGYPOL
TWN
KGZ
TUN
SVK
ISR
BOL
MAR
SEN
LVA
KWT
CMR
PAK
KEN
OMN
GTM
DZA IRN
THA
YEM
ECU
COL
HKG
LUX
AUS
ISL
MLT
RUS
BRA
SAU
ZAF
URY
SRB
NZL
ROU
MDG
SVN CZE
MDA
VEN
MEX
CAN
CIV
MKD
CYP
ARG
FIN
SWE
USA
HRV
COD GBR
NOR
ESPLCA
BGR
HUN
NLD
AUT
CHE
BEL
FRA
BRB
DNK
UKR
DEU
PRT
GRC
ITAJPN
JAM
8.0%
37.0%
GDP GROWTH
35.0%
6.0%
33.0%
31.0%
4.0%
29.0%
2.0%
27.0%
World
Euro Area
13
Advanced
Emerging
BRIC
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2012
2010
2008
2004
2006
2002
2000
1998
1996
1994
1992
1990
1986
1988
1984
1982
1980
25.0%
AGOAZE
CHN
TKM
ZWE
0.0%
-2.0%
0.0%
10.0%
20.0%
30.0%
40.0%
CURRENT INVESTMENT/GDP RATIO - NON-ICT MACHINERY
Equipment investment in general results in higher capital
composition effect, as the share of productive assets increases
CAPITAL COMPOSITION EFFECT AND CHANGE IN EQUIPMENT SHARE, 1995-2013
1.5%
SDN
AZE
CHANGE IN EQUIPMENT SHARE
1.0%
0.5%
NGA
0.0%
ZWE
PAK
CIV
BRB
-0.5%
RUSDZA
BRA
VNM
MWI
IND
KEN MOZ GEO
LVA
ARE
NERBGR
LKAARG
IRQ
VEN
COL
HKG
MAR
ETH
URY
DNK
ZAF GRC SWE
MLI
JOR
SAU
BLR
BFAISR ALB
ZMB
USA
PER
BOL
LTU
MDG
SVN
IRL
EST
CHL
OMN
POL NZL CHN
SRB
TJK
LUX
GTM
PHL
UZB
CODAUS
DEUCAN
NLD
KAZ CMRUGA
TWN
TUR
CZE
IRN KOR
MEX
BIH
JPN
ITA
PRT
FRA
AUT
FINGBRNOR BELCHE
YEM
GHA
BHR SGP
UKR
ECU
HUNESP
SENARM
TTO
THA
DOM
ISL
JAM KGZ
MKD
KHM
MLT
MDA HRV
LCA
TUN
EGY
TKM MYS
CYP
BGD
AGO
ROU
MMR
KWT
SVK
CRI
-1.0%
-1.5%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
CAPITAL COMPOSITION EFFECT
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1.0%
1.5%
2.0%
Increases in the ICT investment share results in higher
capital composition effect
CAPITAL COMPOSITION EFFECT AND REAL ICT INVESTMENT/GDP RATIO , 1995-2013
3.0%
SWE
TWN
JPN
USA
REAL INVESTMENT/GDP RATIO - ICT
2.5%
FRA CZE
AUT
2.0%
CRI
MYS
LTU
1.5%
1.0%
0.5%
0.0%
-1.5%
NGA
-1.0%
ZWE
PAK
GBR
NZL
BEL DNK ISL
CAN AUS
ITASVN
JOR
CHL
SGP FIN PRT
CHN
KEN
EST
POL
DEU
SAU
ESP
UKR
MAR
LUX TUR
HKG
PHL BGRNOR
ROU
HUN
THA
KOR
GRC
ZAF
EGY
MEX
IRL
RUS
LVA
COL
VNM
URY
SRB
TUN
BRA
CYP BGD JAMCMR BOL
ECUPER IRN
ISRARE
VENARG
DZA
MDA
MLT
MKD
LKA
BLR ALB GEO
SEN
-0.5%
0.0%
0.5%
CAPITAL COMPOSITION EFFECT
15
NLD
IND
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1.0%
SVK
KWT
1.5%
2.0%
ICT share in investment has declined after the peak in
early 2000s in most countries
ICT SHARE IN TOTAL INVESTMENT
ICT SHARE IN TOTAL INVESTMENT
16.0%
20.0%
14.0%
18.0%
16.0%
12.0%
14.0%
10.0%
12.0%
8.0%
10.0%
6.0%
8.0%
6.0%
4.0%
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World
Advanced
Euro Area
BRIC
Emerging
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United States
Germany
China (Official)
United Kingdom
India
2012
2010
2008
2006
2004
2000
2002
1998
1996
1994
1992
1990
1988
1986
1984
2012
2008
2010
2006
2004
2002
2000
1998
1996
1994
1990
1992
1988
1986
0.0%
1984
0.0%
1982
2.0%
1980
2.0%
1980
1982
4.0%
Consequently, ICT capital/output ratio has increased
across regions until 2008, since then the speed has slowed
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
World
17
Advanced
Emerging
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EU 28
Euro Area
BRIC
USA
The compositional shift towards more productive assets has
eroded in general in the recent years
Growth rates of Capital Stock, Capital Service and Capital composition Effect
Germany
5.0%
5.0%
4.0%
6.0%
4.0%
3.0%
3.0%
2.0%
2.0%
1.0%
1.0%
Capital service growth
Capital stock growth
Composition effect
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Capital service growth
Capital stock growth
Composition effect
Capital service growth
Capital stock growth
Composition effect
2013
2010
2007
2004
-1.0%
2001
0.0%
1998
2013
2010
2007
2004
2001
1998
1995
1992
1989
1986
1983
2013
2010
2007
2004
2001
1998
1995
1992
1989
1986
1983
-1.0%
1980
0.0%
1980
0.0%
1.0%
1995
2.0%
1992
3.0%
1989
4.0%
1986
5.0%
1983
7.0%
United Kingdom
6.0%
1980
United States
8.0%
Contribution of capital, in general and ICT in particular, to
GDP growth has declined in most advanced economies
1.8
Non-ICT
1.8
ICT
1.6
1.6
1.4
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
US
Germany
UK
France
2000-2006
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US
Germany
2007-2013
UK
France
While contribution of non-ICT capital as increased in
almost all BRIC countries, ICT has declined except in India
7.0
Non-ICT
7.0
ICT
6.0
6.0
5.0
5.0
4.0
4.0
3.0
3.0
2.0
2.0
1.0
1.0
0.0
0.0
Brazil
Russia
India
China
2000-2006
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Brazil
Russia
India
2007-2013
China
Summary
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Remaining issues
 Investment price deflators
 We ignore non-produced assets, such as land and subsoil assets (World
Bank 2006, Caselli and Feyrer, 2007) and inventories
 Intangible assets (Corrado et al., 2009); IPP are included in machinery,
software in ICT
 No data on most indicators after 2013 (in some cases 2014)
 Imputations for 2015, 2016 and 2017
 No ICT price data for the U.S beyond 2014
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Countries with higher labor productivity level also have
higher level of ICT capital per worker
LOG LABOR PRODUCTIVITY LEVEL AND LOG ICT CAPITAL DEEPENING, 2013
4.0
CHE
LOG ICT CAPITAL DEEPENING
3.0
2.0
1.0
SEN
KEN
VNM
BGD
CMR
0.0
USA SAU
TWN
SWE
AUT
JPN NLD
BEL KWT
ARE LUX
FRA
SGP
DNK
CZE CAN
AUS
HKGNOR
ITA
ISL
ESP
IRL
FIN
LTU
JOR
GBR
DEU
EST
MYS
GRC
PRT
CHL
SVN
POL
SVK
TUR
ROURUS
CYP
HUN
IDN CRI
IRN
BGR
EGY
MAR
TUN
MEX
ZAF
UKR
IND
URY
THA
BRA
CHNCOL
DZA ISR
ECU
PHL JAMPER
ARG
VEN
BOL NGA
LKA
-1.0
PAK
-2.0
GEO
ZWE
BLR
-3.0
0.0
1.0
2.0
3.0
LOG LABOR PRODUCTIVITY LEVEL
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4.0
5.0
6.0
To build historical data series on investment by asset types
we depend on multiple sources
National Accounts based data (106 countries)
Country coverage
Source
Availability*
UN National Accounts Statistics, main aggregates and detailed tables, pt.1 (CD-ROM)
80 countries; 1950-1997
UN National Accounts Statistics – Main aggregates database (Total GFCF only)
211 countries; 1970-2014
UN National Accounts Statistics – Official estimates (Total GFCF only)
211 countries; 1950-2014
OECD National Accounts Statistics
40 countries; 1950-2015
Eurostat
43 countries; 1950-2015
Annual Macro-Economic Database (European Commission)
42 countries; 1960-2017
Latin America
Economic Commission for Latin America and the Caribbean (ECLAC CEPALstat)
32 countries; 1950-2015
Other
National Statistical Institutes
65 countries; 1950-2015
Global
High-income
Europe
Commodity Flow Method
Country coverage
Global
Source
Availability*
World Bank’s International Comparison Program
168 countries; 1970-2011
UNIDO INDSTAT ISIC rev.2
180 countries; 1963-2003
UN COMTRADE
196 countries; 1962-2011
* Data availability is a rough indication, and can vary between countries. For instance, ECLAC national accounts provides estimates for 1950 for
only half of the countries; as for other countries data series start in later years (e.g. 1977, 1978 etc.).
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Data on ICT assets are obtained from a variety of sources
and using alternate approaches
1. National Accounts based data

Available for about 50 countries

Sources: Eurostat, OECD, country-specific sources
2. Non-official data

Databases constructed by researchers often working together with
national statistical agencies or national policy institutes

KLEMS (EU KLEMS, Asia KLEMS) and related databases

Commodity flow approach (using input-output tables) – e.g. India
3. ICT spending data from WITSA (see next slide)
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Deriving investment data from WITSA ICT spending data
 Discontinued WITSA reports on ICT spending for around 70
countries until 2013
 Business and consumer spending need to be separated manually
 Previously we used ‘Kuznets’ database (Jorgenson and Vu, 2013):
– Constant spending/investment ratio derived from United States data applied to
all countries
 New approach:
– Compile investment spending ratio using national accounts or other sources
(steps 1&2 in the previous slide) and WITSA (using step 3) for all countries for
which both WiTSA and NAS data available
– Use regional averages of spending/investment ratio, based on newly collected
actual investment data
– 5-year moving averages used instead of assuming constant
investment/spending ratio to allow for intertemporal variation
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Non-ICT assets: investment in structures
 National Accounts data whenever available
 Split of residential and non-residential not always available
 For countries where NAS is not available, actual investment in
structures is obtained from at least one ICP benchmark year
 Data for other years are extrapolated using the trend in value added in the
construction industry provided by UN national accounts
 In the past we did NOT include residential construction for countries
where we could obtain that data separately in TED
 This choice has a major implication for capital/output ratios
 Data on this split is lacking for many countries
 Output measure includes imputed rents,
– perhaps it is more appropriate to include residential construction in this database,
also from an international comparability perspective
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Non-ICT assets: investment in machinery and transport
equipment
 National Accounts based data used whenever available
 Split into transport equipment and other machinery not always available
 For remaining countries and years, we rely on data derived from the
Commodity Flow Method (next slide)
 The trend in the CFM derived availability of assets are used to extrapolate
the benchmark estimates of investment in these assets from ICP
– The distribution of this estimates (i.e. the share of each asset in the sum of
structures, machinery and transport) is considered as the final asset distribution.
– This asset distribution is applied to the total GFCF obtained in the first step, to
derive consistent series of asset wise investment.
𝐼𝑖𝑡 = 𝑆𝑖𝑡 ∗ 𝐺𝐹𝐶𝐹 𝑁𝐴
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Filling the gaps: imputing missing investment data using
Commodity Flow Method (CFM)
 Caselli and Wilson (2004), Feenstra, Inklaar and Timmer (2015):
𝐼𝑖𝑡 = 𝑌𝑖𝑡 − 𝑋𝑖𝑡 + 𝑀𝑖𝑡
Y = gross output; X = exports; M = imports
 Implied assumption: change in investment is proportional to change in total
availability of these assets in the economy
– Gross output from UNIDO INDSTAT
– Exports and imports from either United Nations Comtrade database or Feenstra’s
World Trade Flows (only when Comtrade is not available)
– A better approach would be to combine it with IO tables, benchmark year investment
to total availability ratios (and interpolate it between benchmark years), but that is
cumbersome and data demanding at a global scale
 Approximately 10% of total observations had no trade or output data; in those
cases a linear interpolation was applied:
(𝑡−𝑡𝑎 )
;
𝑏 −𝑡𝑎 )
𝑋 = 𝑋𝑎 + (𝑋𝑏 − 𝑋𝑎 ) (𝑡
𝑀 = 𝑀𝑎 + 𝑀𝑏 − 𝑀𝑎
𝑡−𝑡𝑎
(𝑡𝑏 −𝑡𝑎 )
 Missing trade data for earlier years are obtained by extrapolating the trend in total GFCF
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Investment price deflators
 Aggregate and asset-wise investment deflators from NAS whenever
available
 Asset wise investment deflators from: EU KLEMS, India KLEMS,
OECD National Accounts, ECLAC
 Missing countries: for non-ICT assets, aggregate deflators are used
 For ICT assets: US hedonic deflators, corrected for domestic
investment price inflation are used (Schreyer, 2002)
𝐼𝐶𝑇
𝑛𝑜𝑛𝐼𝐶𝑇
𝐼𝐶𝑇
𝑛𝑜𝑛𝐼𝐶𝑇
∆𝑙𝑛𝑃𝑡,𝑖
= ∆𝑙𝑛𝑃𝑡,𝑖
+ ∆𝑙𝑛𝑃𝑡,𝑈𝑆
− ∆𝑙𝑛𝑃𝑡,𝑈𝑆
30
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Depreciation rates differ across assets
Asset
Depreciation rate
Structures (residential and non-residential)
2.0 (1.1 & 3.1)
Transport equipment
18.9
Computers
31.5
Communication equipment
11.5
Software
31.5
Other machinery and assets
12.6
Source: depreciation rates are based on official BEA deprecation rates of Fraumeni (1997).
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Investment by Assets – what is aimed for?
 PWT
1. Investment by broadly defined asset groups (SNA 2008)
a) Current and constant prices
b) ICT and non-ICT assets and ICT hedonic deflators
2. Capital stock by broadly defined asset groups
 TED (in addition to 1 & 2 above)
1. Capital services by ICT and non-ICT
2. Contribution of ICT and non-ICT capital to GDP and labor productivity
growth
3. Total Factor Productivity growth
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Overview of current approaches….
 TED uses non-ICT investment distribution underlying the PWT 6
capital stock data, along with national accounts total GFCF
 For countries where the national accounts data was unavailable, asset
distribution in PWT 6 was created using a linear interpolation
– In some cases negative investment data (Erumban, 2008)
 For ICT assets, TED relied on Jorgenson and Vu (2013) – Kuznet’s
dataset based on WITSA ICT spending data, and EU KLEMS.
 In addition to limited country coverage, EU KLEMS is not up-to-date
 Methodological issues: use of a constant United States’ investment to
spending ratio from WITSA to all countries is less appropriate (de Vries …
Erumban, 2016)
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Improvement to past approach of combining different
sources and methods
 Better availability of data, especially on ICT investment
 More data for several advanced economies
 Improved use of WITSA data to derive investment from spending
data
 More transparent methodology to construct asset distribution for
countries where the data is not available.
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Broad step-by-step overview of our approach – investment
data by assets
1. Total GFCF in national currency in current and constant prices are
collected from UN national accounts
2. Price deflators are constructed
3. GFCF/GDP rates (investment rates) are computed
4. In TED these are applied to PPP converted GDP, to obtain
consistent total GFCF in PPP terms
 In PWT annual PPPs are used.
5. These totals are considered sacred, and the remaining estimation
procedures are used to distribute the total across assets
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