IAOS 2018 - How digitalization and globalization have remapped the global FDI network, T. Elkjaer
1. Financial Institutions Division
IMF Statistics Department
1
How Digitalization and Globalization
have Remapped the Global FDI
Network
Jannick Damgaard, National Bank of Denmark
And
Thomas Elkjaer, IMF
2. 2
How Digitalization and Globalization
have Remapped the Global FDI Network
Since the global financial crisis, FDI has
grown while other investments have been
flat, what are the drivers?
3. Financial Institutions Division
IMF Statistics Department
3
Corporate wealth creation has changed:
The 50 companies that created the most wealth from
1926-2016 (source NYT 09/22/2017)
4. 0
1500
3000
4500
6000
0
30
60
90
120
U.S.
Fortune
500
U.S.
Pharma
Alphabet
(Google)
Amazon Apple Facebook Other U.S.
Tech
Firms*
Alibaba Samsung SAP SE Tencent
Intangibles as a share of total assets "Deemed" intangible income share* Ratio of market capitalization to tangible assets (RHS)
Does it make sense to say some companies are digital?
In percent
4Sources: IMF Fiscal Monitor, April 2018.
Perhaps, but they are not uniquely intensive in the use of intangibles
5. Unlisted companies and intangibles:
Introduce signifcant blur in valuations
5
Source: Damgaard and Elkjaer (2014), Review of Income and Wealth
OFBV
P/B
P/E
FDI in % of GDP
6. Remap FDI: who is investing in whom
Model: Apply FDI pattern of the OECD on the world
SPE propensity
OECD dataset #1:
30 countries report
FDI broken down by
resident SPEs and
non-SPE
Find investors
OECD dataset #2:
12 countries report
ultimate and
immediate investors
Global coverage IMF
Dataset #3:
116 economies report
bilateral pairs
(immediate owner and
no SPE split)
6
7. Model and data :
SPE propensity
OECD dataset #1:
30 countries report
FDI broken down by
resident SPEs and
non-SPE
Find investors
OECD dataset #2:
12 countries report
ultimate and
immediate investors
Global coverage IMF
Dataset #3:
116 economies report
bilateral pairs
(immediate owner and
no SPE split)
7
y= -0.5485x+ 6.706
R² = 0.895
1,5
2,0
2,5
3,0
3,5
4,0
4,5
5,0
3 4 5 6 7 8 9
ln(non-SPEshare)
ln(total inward FDI as percentage ofGDP)
LUX
NLD
HUN
10. 10
Because:
• Shows ultimate source of financing
• And is less sensitive to:
• Group financing/holding activities of MNEs
• SPEs’ relocation decisions
The new network is a better measures of
economic integration and long-term
financial linkages
11. 0
10
20
30
40
50
60
70
80
90
100
Outward FDI that
passes through
SPEs (%)
World average: 37.8%
In total USD 12 trillion
11
Includes non-low-tax economies with GDP greater than USD 300 billion and a ratio of outward FDI to GDP
greater than 3 percent in 2015. Source F&D, June 2018, Damgaard, Elkjaer, and Johannesen
Phantom investments: A worldwide phenomenon
12. Model verification: Ultimate counterpart more uncertain than SPE
adjustments, but still lower discrepancies than reported data
12
Discrepancies between Reported versus Estimated Inward FDI Positions
0
200
400
600
800
1,000
1,200
1,400
1 3 5
Legend (percentiles):
90th
75th
25th
10th
Percent
Total FDI FDI equity FDI debt instruments
Increasing asymmetries
Discrepancies between Reported Outward versus Reported Inward FDI Positions
13. Conclusion: World wide phenomena
13
Corporate phantom investments
USD 12 trillion (1/3 of global FDI
lower-bound estimate)
Traditional “major” economies
become more dominant
but financial centres remain
important
Not only a developed country
phenomenon
… perhaps some ‘near SPE’ are not
just passive holding companies/
…rethink definition of SPEs/FDI?
FDI data dichotomy:
Phantom and “real” investments are mixed
Hampers Economic Analysis Distort Macroeconomic Statistics
FDI may not bring jobs,
infrastructure or know-how
FDI locational decisions not clear
Shifting FDI=>profit shifting =>
trade surplus shifting =>shifting GDP
Editor's Notes
Away from PPE: property, plant and equipment investments. Intangibles (brand, software, and R&D) more scaleable, promote economies of scale, concentration, market power, rising mark-up (extra profits) (Jackson Hole)
Second general issue is the use of intangible assets (IP, software, algorithms to handle big data, etc.) – which is how much of the value is created
It’s again very hard to find good data – see Figure.
Balance sheets do report ‘intangible assets’ = bars in Figure. But that is acquired IP, not self-developed IP or goodwill (which can be the lion share). The bars suggest several tech companies are not very intensive in intangible assets, relative to F500 or e.g. Pharma
Can look at other ‘indirect’ indicators. Diamonds present two alternatives:
Dark = MC/tangible A (reflection of the value of intangible A) – most tech are higher than F500, but not higher than e.g. Pharma
Light = income in excess of 10 percent of tangible A – again mostly higher than F500, but not all (e.g. Amazon) and not higher than Pharma
Taxation of income from intangibles is in general a major challenge (not only in tech).
Very mobile so can be relocated easily
Often unique so hard to value based on market comparable
E.g., algorithms used to process data and generate value
Intellectual property can be located anywhere and be hard to price.
Jackson Hole: consumer sector: higher mark up driven by productivity gain (online platforms, inventory management). Health care: higher mark up driven by product invasion than can be patent. Software: both. If high mark up, then weaker investmets in physical capital
Digitalization seems to reinforce the existing challenge = it’s just bigger but not a new issue.
Sources: Fortune 500, Bureau van Dijk Orbis, and IMF staff calculations.
Notes: * Calculated as the median of excess of income over 10 percent of value of tangible assets; ** Average Fortune 500 firms matched to ORBIS data; ** Includes companies from multiple NACE sectors, including 2620 (manufacture of computers and peripheral equipment), 5829 (other software publishing), 6201 (computer programming activities), and 6209 (other information technology and computer service activities).
The bilateral asymmetries can stem from differences in applying the macroeconomic statistical methodology and from compilation practices. These differences apply to FDI statistics in general and not just the CDIS data.
To types of valuation: relative and absolut
P/E very volatile and earnings from intangibles booked differently,
P/B in many accounting, for instance US-GAAP, dont book self-produced intangible on balance sheet
OFBV: how to value intangibles
Both choice of valuation principle and estimation method can have a significant impact on data.
Two alternative views of the world
Only show top 40 FDI economies
The network of FDI positions based on the new global FDI estimates, i.e., with SPEs removed and broken down by the UIE,
Explain: nodes, arrows, round-tripping
US still dominate, while the role of the Netherlands and Luxembourg is much smaller compared to the CDIS network.
The substantial presence of SPEs has been removed for the Netherlands and Luxembourg, and other economies' inward FDI from these two countries has been adjusted significantly downwards when moving from the immediate counterpart economy to the UIE.
However, compared to the size of their economies, FDI remains substantial for the Netherlands and Luxembourg.
BVI - Hong Kong –China link
Two alternative views of the world
Only show top 40 FDI economies
The network of FDI positions based on the new global FDI estimates, i.e., with SPEs removed and broken down by the UIE,
Explain: nodes, arrows, round-tripping
US still dominate, while the role of the Netherlands and Luxembourg is much smaller compared to the CDIS network.
The substantial presence of SPEs has been removed for the Netherlands and Luxembourg, and other economies' inward FDI from these two countries has been adjusted significantly downwards when moving from the immediate counterpart economy to the UIE.
However, compared to the size of their economies, FDI remains substantial for the Netherlands and Luxembourg.
BVI - Hong Kong –China link
The purpose of the new project is to develop a novel global FDI dataset, which can be used to cast light on financial and economic links between economies. The data construction builds on the methods used in the DN/IMF working paper "The Global FDI Network – Searching for Ultimate Investors", but adds three important dimensions to the dataset:
the coverage is expanded from 116 economies to all 246 economies, [how???]
the time dimension is expanded from one year (2015) to eight years (2009-2016), and
information about shares of outward FDI to non-resident SPEs (see slide) and inward FDI from non-resident SPEs will be included.
Aligns with Tørslev, Wier and Zucman (2018): 45% of MNE foreign profits are artificially shifted to tax havens
In sample text, using on outside text.
Model FDI estimates based on data from a subset of economies, the reporting OECD countries, will inevitably be uncertain because investment patterns may vary across economies, regions, and economic development levels, and therefore any single data point should not be over-interpreted.
As a model verification, reported and estimated inward detailed FDI positions are compared for each reporting OECD country. The SPE adjustment factors are applied to the CDIS data for the SPE-reporting OECD countries, and the UIE adjustment factors are applied to reported non-SPE data for the UIE-reporting OECD countries.
The tests show that the uncertainties for the SPE estimations are relatively small. The main reason is that only a few OECD countries have a large SPE presence, meaning that the adjustments and the discrepancies are modest in most cases. As an illustration, the SPE adjustment model generates an adjustment factor of 0.8 for an economy with an inward FDI-to-GDP ratio of 70 percent, effectively adjusting down inward FDI by 20 percent, and only seven OECD countries have ratios above that threshold.
The uncertainties for the UIE estimations are somewhat higher because significant adjustments are made for all reporting economies, and investment patterns can vary significantly across economies. For instance, Haberly and Wójcik (2015) show that FDI patterns are influenced by historical and political relationships between economies.
The highest discrepancies are observed for the joint model test, where the UIE estimations are based on estimated SPE data.
