1
Financial globalization and its effects
Kuala Lumpur 2016 - Luis Servén1
Plan
• Gross capital flows
• Global factors and capital flows
• Global imbalances
• The gains from financial globalization
Kuala Lumpur 2016 - Luis Servén2
Gross capital flows.
Traditional focus has been on net flows (= current
account deficits).
But that may miss much of the action:
• The massive increase in gross asset positions in the
globalization period reflects booming gross, not net, flows
• Flows from resident and non-resident investors likely reflect
different factors – e.g., different risks or constraints facing
investors – which get muddled when looking at net flows
• Gross inflows pose stability risks different from those of
saving-investment gaps – related to leverage and the size of
the banking system
Kuala Lumpur 2016 - Luis Servén3
It is more informative to look separately at inflows and
outflows (Broner et al 2013)
• Capital inflows by non-residents [CIF]: net increase
in liabilities to foreign residents
• Capital outflows by residents [COD]: net increase
in foreign assets of domestic residents
[Notice: these are commonly labeled ‘gross flows’ –
but in reality they are net measures]
Net flows = CIF – COD ( = current account deficit)
Total gross flows = CIF + COD
Kuala Lumpur 2016 - Luis Servén4
Kuala Lumpur 2016 - Luis Servén5
05
10
15
20
25
30
05
10
15
20
25
30
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
Advanced Economies: capital inflows and outflows
Kuala Lumpur 2016 - Luis Servén6
-30
36
912
-30
36
912
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
Emerging Economies: capital inflows and outflows
Kuala Lumpur 2016 - Luis Servén7
-6-3
03
69
12
-6-3
03
69
12
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
Developing Economies: capital inflows and outflows
Gross vs net capital flows
This figure shows ellipses that account for the joint distribution of capital flows by foreign and domestic agents. One ellipse for each decade isreported. Each ellipse captures 103 points and each one point represent the average for that decade for a country in the sample.
Globalization
Retrenchment
Net Inflows
Net Outflows
Kuala Lumpur 2016 - Luis Servén8
In developing countries, big drop in gross flows over 1970s-80s.
Since 1980, big rise in gross flows (especially in rich countries – whose net
flows have fallen!)
Gross flows are much more volatile than net flows Kuala Lumpur 2016 - Luis Servén9
How do inflows and outflows vary over the cycle?
• Gross flows are strongly procyclical: in good
times, both CIF and COD rise significantly.
• CIF more procyclical in developing countries;
COD in industrial countries (but the difference is
modest)
• Strong (and increasing) positive correlation
between CIF and COD -- that’s why net flows
are less volatile than gross flows
Kuala Lumpur 2016 - Luis Servén10
Correlation between gross inflows and gross outflows
Kuala Lumpur 2016 - Luis Servén11
Advanced economies: capital inflows and outflows
-50
510
15
-50
510
15
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
United States
-50
510
-50
510
1980 1990 2000 2010
% o
f G
DP
Year
Japan
-10
010
20
30
-10
010
20
30
1980 1990 2000 2010
% o
f G
DP
Year
Germany
-50
050
10
0
-50
050
10
0
1980 1990 2000 2010
% o
f G
DP
Year
United Kingdom
-10
010
20
30
-10
010
20
30
1980 1990 2000 2010
% o
f G
DP
Year
France
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Italy
05
10
15
05
10
15
1980 1990 2000 2010
% o
f G
DP
Year
Canada
-10
010
20
30
-10
010
20
30
1980 1990 2000 2010
% o
f G
DP
Year
Spain
05
10
15
20
05
10
15
20
1980 1990 2000 2010
% o
f G
DP
Year
Australia
Kuala Lumpur 2016 - Luis Servén12
Emerging economies: capital inflows and outflows
-20
24
68
-20
24
68
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
India
-50
510
-50
510
1980 1990 2000 2010
% o
f G
DP
Year
Brazil
-50
510
15
20
-50
510
15
20
1980 1990 2000 2010
% o
f G
DP
Year
Mexico
-10
010
20
-10
010
20
1980 1990 2000 2010
% o
f G
DP
Year
Turkey
-50
510
15
20
-50
510
15
20
1980 1990 2000 2010
% o
f G
DP
Year
Thailand
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
South Africa
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Argentina
-10
010
20
30
-10
010
20
30
1980 1990 2000 2010
% o
f G
DP
Year
Chile
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Colombia
Developing economies: capital inflows and outflows
-50
510
15
-50
510
15
1980 1990 2000 2010
CIF
COD
% o
f G
DP
Year
Nigeria
-20
24
6
-20
24
6
1980 1990 2000 2010
% o
f G
DP
Year
Bangladesh
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Sri Lanka
-10
-50
510
-10
-50
510
1980 1990 2000 2010
% o
f G
DP
Year
Ecuador
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Tunisia
-50
510
-50
510
1980 1990 2000 2010
% o
f G
DP
Year
Dominican Republic
-50
510
15
-50
510
15
1980 1990 2000 2010
% o
f G
DP
Year
Ghana
-50
510
-50
510
1980 1990 2000 2010
% o
f G
DP
Year
Guatemala
-20
020
40
60
-20
020
40
60
1980 1990 2000 2010
% o
f G
DP
Year
Botswana
Financial turbulence (‘crisis’) is associated with a
retrenchment of gross flows in both directions
Both domestic and foreign investors reduce their flows
quite significantly – while net flows change much less
Kuala Lumpur 2016 - Luis Servén15
Kuala Lumpur 2016 - Luis Servén16
The pattern around the 2008 crisis is similar to other crisis: significant
decline in both CIF and CID.
Overall, domestic and global crises have qualitatively similar retrenchment
effects – but quantitatively bigger in the case of global crises.
Kuala Lumpur 2016 - Luis Servén17
Hence in turbulent times both foreign and investors
retrench (or repatriate their assets) – a two-way sudden
stop. In good times both expand.
This suggests the potential presence of shocks with
asymmetric effects on domestic and foreign investors:
• Asymmetric information on domestic vs foreign asset returns
• Differential sovereign and expropriation risk
• Financial constraints – e.g., forced deleveraging
• In contrast, symmetric shocks (e.g., TFP) are not the likely
driving force.
But the CIF-COD correlation may also reflect in part official
intervention to defend the exchange rate when CIF falls – a decline
in reserve accumulation represents a fall in COD.
Kuala Lumpur 2016 - Luis Servén18
Global factors and capital flows
There is growing evidence that gross capital inflows and
outflows, as well as the prices of risky assets around the
world, strongly reflect the action of common factors.
• Flows to different countries are highly correlated.
• Different kinds of flows are highly correlated too.
Portfolio debt and bank-related flows show the highest
commonality.
The commonality refers to gross capital flows – net flows show
much less comovement.
Kuala Lumpur 2016 - Luis Servén19
Source: Rey (2013) Kuala Lumpur 2016 - Luis Servén20
• How big is the role of common factors in capital flows?
• We can use a factor-model setting to quantify their contribution to gross inflows and outflows (Barrot and Servén 2016)
Large panel dataset: 82 countries, annual data for 1979-2014. IMF data on inflows, outflows and net flows.
Kuala Lumpur 2016 - Luis Servén21
Assessing common factors
Multi-level factor model
N countries, M regions / groups with Nm countries each; ∑ Nm= N
– Countries’ group membership known a priori (Ando and Bai 2015 for the unknown case)
– Necessarily arbitrary (e.g., North America; developing vs emerging)
Three types of factors:
– Global factors (rG) – affect all countries in the world
– Regional / group factors (rm) – affect all countries in a group
– Idiosyncratic (or domestic) factors – specific to a country
Kuala Lumpur 2016 - Luis Servén22
Assessing common factors
Basic equation:
𝑦𝑚,𝑖,𝑡 = 𝛾𝑚,𝑖 ′𝐺𝑡 + 𝜆𝑚,𝑖′𝐹𝑚,𝑡 + 𝑢𝑚,𝑖,𝑡
Collecting all countries, groups and years:
𝑌 = 𝐺Γ′ + 𝐹Λ′ + 𝑈
Y (TxN): capital flow measure (scaled by trend GDP). Each column is a country.G (TxrG): global factorsF (Tx∑rm): group factorsU (TxN): idiosyncratic factors (residual)
Γ, Λ: factor loadings – can vary freely across countries
Kuala Lumpur 2016 - Luis Servén23
Assessing common factors
• Factors are unobserved, so both factors and loadings need to be estimated. This requires identifying assumptions:
𝐺’𝐺
𝑇= 𝐼;
𝐹𝑚’𝐹𝑚
𝑇= 𝐼 for all m
𝐹𝑚’𝐺 = 0 for all m: global and group factors are mutually orthogonal
• This suffices to uniquely determine the factors (up to a sign change)– use the loading of a ‘major country’ to set the sign (e.g., USA, Brazil…)
Kuala Lumpur 2016 - Luis Servén24
Assessing common factors
• Classical PC estimation is straightforward in single-level models (Bai 2003), but not in multi-level models.
– Earlier applications of multi-level models typically use Bayesian methods
• Instead we use a recent extension of PC methods to multi-level models
– Breitung and Eickmeier 2015, Choi et al 2015, Wang 2014
• Important: we do not need to assume 𝐹𝑚’𝐹𝑛 = 0 for 𝑚 ≠𝑛, i.e., regional factors can be correlated across regions
– Big difference with earlier (Bayesian) literature imposing (wrongly?) orthogonality – leads to an over-identified model.
Kuala Lumpur 2016 - Luis Servén25
Assessing common factors
• ‘Sequential least squares’min𝐺,𝐹,Γ,Λ
𝑆𝑆𝑅 = 𝑡𝑟 (𝑌 − 𝐺Γ′ − 𝐹Λ′)′(𝑌 − 𝐺Γ′ − 𝐹Λ′)
1. Start from initial estimate of global factors (we use CCA)
2. Compute estimate of regional factors
3. Iterate until convergence
• Information criteria (ICp2,HQ,BIC) to determine the number of factors rG and rm
– In all cases one factor suffices.
Kuala Lumpur 2016 - Luis Servén26
Kuala Lumpur 2016 - Luis Servén27
The three global factors are strongly correlated.They display considerable persistenceThey are significantly correlated with the Chinn-Ito measure of financial openness
Figure 4 Global Factors
-2-1
01
23
4
-2-1
01
23
4
1975 1985 1995 2005 2015
CIF
COD
NET
Financial Openness
Glo
bal F
acto
rs
Year
• All three global factors correlate with global interest rates, risk spreads and (less robustly) global commodity prices
• The correlation with the financial openness measure is quite robust
Kuala Lumpur 2016 - Luis Servén28
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Financial Openness 6.575*** 2.451*** 4.537** 2.457** 2.835*** 6.004*** 3.226*** 6.662*** 3.476*** 4.325*** 5.611*** 1.899** 3.741** 1.879** 2.983***
[4.452] [2.881] [2.372] [2.183] [3.698] [4.160] [4.527] [3.320] [3.082] [5.653] [2.998] [2.501] [2.543] [2.504] [3.317]
1st Lag Global factor 0.630*** 0.603*** 0.746*** 0.582*** 0.508*** 0.365*** 0.623*** 0.354* 0.781*** 0.740*** 0.880*** 0.608***
[6.068] [5.347] [8.417] [5.470] [3.940] [3.280] [4.971] [2.003] [7.866] [6.412] [10.07] [4.106]
Interest rate 0.0726* 0.0995** 0.0622**
[1.896] [2.181] [2.382]
Risk spread -0.423** -0.515** -0.270**
[-2.184] [-2.237] [-2.380]
Real Commodity price 0.00126 0.00386* 0.00388***
[1.175] [1.856] [2.792]
Constant -3.358*** -1.225*** -2.684** -0.248 -1.665*** -3.066*** -1.645*** -3.944*** -0.581 -2.961*** -2.865*** -0.958** -2.237** -0.320 -2.271***
[-5.063] [-3.120] [-2.409] [-0.735] [-3.925] [-4.312] [-4.410] [-3.262] [-1.012] [-4.057] [-3.180] [-2.551] [-2.649] [-0.856] [-3.813]
Observations 35 34 34 34 34 35 34 34 34 34 35 34 34 34 34
Adjusted R-squared 0.554 0.736 0.763 0.791 0.731 0.457 0.604 0.645 0.684 0.625 0.396 0.818 0.837 0.833 0.840
Q (4) 17.15 6.951 6.942 7.039 6.601 10.17 6.100 5.205 7.431 4.898 49.85 1.365 1.460 3.132 3.589
P-value 0.00181 0.139 0.139 0.134 0.159 0.0376 0.192 0.267 0.115 0.298 3.87e-10 0.850 0.834 0.536 0.465
t-statistics in brackets
*** p<0.01, ** p<0.05, * p<0.1
Dependent variableGlobal CIF Global COD Global NET
Table 4 Covariates of the Global Factors
Table 5 Correlation matrix of regional factors, 1979-2013
(a) Gross capital inflows by foreign agents (CIF)
(b) Gross capital outflows by domestic agents (COD)
North
America
Latin America
and Caribbean
Asia and
PacificEurope
Middle East
and North
Africa
Sub-
Saharan
Africa
North America 1.000
Latin America and Caribbean -0.583 1.000
Asia and Pacific -0.547 0.515 1.000
Europe 0.227 -0.065 -0.136 1.000
Middle East and North Africa -0.369 0.560 0.593 -0.048 1.000
Sub-Saharan Africa -0.426 0.502 0.183 -0.441 0.057 1.000
North
America
Latin America
and Caribbean
Asia and
PacificEurope
Middle East
and North
Africa
Sub-
Saharan
Africa
North America 1.000
Latin America and Caribbean -0.164 1.000
Asia and Pacific -0.160 0.409 1.000
Europe 0.199 0.199 0.180 1.000
Middle East and North Africa -0.070 -0.278 -0.054 -0.573 1.000
Sub-Saharan Africa 0.231 0.317 0.042 0.525 -0.536 1.000
Kuala Lumpur 2016 - Luis Servén29
Kuala Lumpur 2016 - Luis Servén30
Table 5 Correlation matrix of regional factors, 1979-2013
(a) Gross capital inflows by foreign agents (CIF)
(b) Gross capital outflows by domestic agents (COD)
Advanced Emerging Developing
Advanced 1.000
Emerging 0.512 1.000
Developing 0.090 0.561 1.000
Advanced Emerging Developing
Advanced 1.000
Emerging 0.611 1.000
Developing 0.635 0.592 1.000
Kuala Lumpur 2016 - Luis Servén31
Table 5 Correlation matrix of regional factors, 1979-2013
(a) Gross capital inflows by foreign agents (CIF)
(b) Gross capital outflows by domestic agents (COD)
Advanced Emerging Developing
Advanced 1.000
Emerging 0.512 1.000
Developing 0.090 0.561 1.000
Advanced Emerging Developing
Advanced 1.000
Emerging 0.611 1.000
Developing 0.635 0.592 1.000
Variance decompositions
• The factor model permits decomposing the variance of capital flows into 3 orthogonal components –global, regional, domestic.
• Because regional factors can be correlated, we can further decompose each one of them into:
– A component orthogonal to all other regional factors: unambiguously associated with the home region (call it the ‘own region’ component)
– A component correlated with other regional factors: call it the ‘cross region’ component
Kuala Lumpur 2016 - Luis Servén32
Kuala Lumpur 2016 - Luis Servén33
Kuala Lumpur 2016 - Luis Servén34
Figure 6 Variance decomposition of Gross Inflows
(a) World Regions
0 20 40 60 80 100
World
Developing economies
Emerging economies
Advanced economies
Global share Cross-region share Own region share Country share
1979-2013
1990-2013
1979-2013
1990-2013
1979-2013
1990-2013
1979-2013
1990-2013
Variance decompositions
• On average, common factors (global + regional / group) contribute around 40-50% of the variance– There are no big changes over time (puzzling! Due to timing?)
• But the roles of the different components vary a lot across world regions– North America and Europe show the largest contribution of the
global factor and the smallest of the idiosyncratic factor – they are the most financially-integrated regions
– In Europe, the regional factor has become more important over time for CIF – at the expense of the global factor
– Latin America shows the biggest contribution of idiosyncratic factor and the smallest of the global factor
Kuala Lumpur 2016 - Luis Servén35
Kuala Lumpur 2016 - Luis Servén36
Kuala Lumpur 2016 - Luis Servén370 20 40 60 80 100
World
Developing economies
Emerging economies
Advanced economies
Global share Cross-region share Own region share Country share
1979-2013
1990-2013
1979-2013
1990-2013
1979-2013
1990-2013
1979-2013
1990-2013
Figure 7 Variance decomposition of Gross Outflows
(a) World Regions
• Risky asset prices across the world also reflect the action of global factors
• Miranda-Agrippino and Rey (2015)
– Commodity prices
– Corporate bond and stock price indices
Monthly data expressed in log-differences
• Two-level factor model, Bayesian estimation (so group factors are forced to be mutually orthogonal).
Kuala Lumpur 2016 - Luis Servén38
Kuala Lumpur 2016 - Luis Servén39
The global factor is strongly negatively correlated with measures of volatility
Kuala Lumpur 2016 - Luis Servén40
Kuala Lumpur 2016 - Luis Servén41
The global factor is also negatively correlated with measures of risk premia
Kuala Lumpur 2016 - Luis Servén42
Sovereign spreads likewise show a high degree of
comovement across countries – higher than capital flows and
other asset prices.
Longstaff et al (2011) analyze the role of local and global
factors in the CDS spreads of 26 countries over 2000-2010.
• First, compute principal components.
-- almost 2/3 of the variance of monthly CDS changes is accounted
for by the first principal component alone; 80 percent by the first 3.
-- commonality increases in the crisis years (2007-10): the first
principal component accounts for 75% of the variance
-- the first component is highly correlated (.61) with the VIX and the
US stock market return (-.74)
Commonality is weaker for equity returns: 46 percent accounted for
by the first principal component; 58 percent by the first 3.Kuala Lumpur 2016 - Luis Servén43
Kuala Lumpur 2016 - Luis Servén44
To understand what’s behind the commonality, regress spreads
on local and global variables and separate the respective
contribution of each group to the overall variance
• Similar to Albuquerque, Loayza and Servén with FDI (JIE 2005)
• Local variables: stock market return, exchange rate change, foreign
reserve accumulation
• Global variables:
• Returns: U.S. stock market excess return, changes in U.S. 5-year
Treasury yield, corporate and high-yield spreads.
• Risk premia: changes in ‘equity premium’ (P/E), volatility
premium (spread between implied and realized volatility), term
premium
• Investment flows: new flows into equity and bond mutual funds.
• Spreads of other countries (global and regional averages) Kuala Lumpur 2016 - Luis Servén45
Kuala Lumpur 2016 - Luis Servén46
47
How does international financial integration change the role of global factors?
Albuquerque, Loayza and Servén (2005) first assessed this question for FDI flows using a standard model
– Integration should bring the world closer to an equilibrium with perfect risk sharing
– Hence it should raise the relative importance of global factors:
• Local risk can be more easily traded away• In the extreme of complete markets and perfect risk-sharing
all factors would be global
48
Global and local factors• Example: 2 periods and 3 countries subject to productivity
(and endowment) shocks.– Technology shows decreasing returns – Consumers / investors are risk averse
Both forces drive FDI
There are restrictions on asset trade and FDI: – No trade in assets (other than FDI) – Country 1’s consumer can invest in 1, 2; – Country 3’s consumer can invest in 1, 3; – Country 2’s consumer does not invest anywhere.This offers illustrative examples of local and global factors.
49
Global and local factors
Country 1
Country 3
Country 2
– Productivity shocks to 2 orthogonal to 1&3 are local factors (only affect FDI into 2, not into 1)– Productivity shocks to 2 correlated with 1&3 are global factors (affect all FDI flows) – An endowment shock to 2 perfectly (negatively) correlated with the endowment shock to 1 is a local factor– As restrictions on asset trade and FDI are lifted, some local factors may disappear altogether (e.g., the endowment shock can be fully diversified by 1&2) or become global.
50
Empirically, financial integration would be reflected in:
– In a factor model setting, the variance contribution of the common factors should rise
– In a standard regression setting, the contribution of the global variables to the overall explanatory power should rise relative to that of the local variables
51
Implementation
• Local FDI determinants: variables affecting the perceived profitability and risk from investing in the host country:
– local productivity, taxes, macro volatility, expropriation risk…
• Global FDI determinants:
– Worldwide cost of capital (e.g., world interest rates)
– Global forces affecting the local variables -- e.g., world productivity shocks driving local ones [Glick-Rogoff 1995]
52
Empirical Analysis
• Global variables: taken from literature explaining cross-
section of equity returns
G3 interest rate
World stock market return
U.S. credit spread (global risk appetite)
World per capita GDP growth (global productivity)
U.S. yield curve slope (premium on long-term assets)
Add: G3 inflation rate to transform returns to real
terms (but leave its coefficient unconstrained)
53
Empirical Analysis• Local variables: taken from the empirical FDI literature
Per capita GDP growth (local productivity)
Public consumption/GDP (overall tax burden)
Financial depth
RER depreciation (wealth effects; Froot-Stein 1991)
Institutional quality (property rights / exprop. risk)
Trade openness (trade-FDI complementarity)
Volatility of GDP growth
Volatility of ToT
Volatility of RER
54
Empirical Analysis
Sample: annual data 1970-2000
Unbalanced panel with 1,900 observations
94 countries (20 industrial + 74 developing)
Combined and separate estimates
Basic specification:
parameter homogeneity across countries (relaxed later)
observable global factors (relaxed later)
tj
L
tj
LG
t
G
jtj
tj uGDP
FDI FηFη ''0
55
Variable
All
countries
Industrial
countries
Developing
countries
Global Variables
G3 average interest rate -0.1304 ** -0.0928 ** -0.1368 **
0.0286 0.0467 0.0354
World stock market return -0.0019 -0.0008 -0.0022
0.0038 0.0038 0.0050
U.S. yield curve slope -0.3125 ** -0.2612 ** -0.3095 **
0.0518 0.0815 0.0615
U.S. credit spread 0.0106 0.2917 -0.1165
0.1956 0.3614 0.2474
G3 average inflation -0.0570 ** -0.0853 -0.0504
0.0270 0.0665 0.0335
World growth -0.1118 ** -0.1827 ** -0.1062 **
0.0371 0.0731 0.0457
Table 3. Determinants of FDI. Basic Model
56 SSEF – Sept 2009 © Luis Servén
Variable
All
countries
Industrial
countries
Developing
countries
Local Variables
GDP growth 0.0354 ** 0.0653 ** 0.0325 **
0.0135 0.0250 0.0144
Trade openness 0.0099 ** 0.0172 * 0.0090 **
0.0027 0.0096 0.0028
Financial depth 0.0055 * -0.0027 0.0114 **
0.0031 0.0045 0.0047
Government consumption / GDP -0.0716 ** -0.1975 ** -0.0645 **
0.0227 0.0579 0.0249
REER growth -0.0009 -0.0091 0.0000
0.0038 0.0068 0.0042
Institutional quality 0.0012 0.0082 -0.0011
0.0030 0.0055 0.0035
REER volatility -0.0112 -0.0052 -0.0107
0.0071 0.0149 0.0077
GDP growth volatility -0.0933 ** -0.0541 -0.0900 **
0.0204 0.0625 0.0210
ToT volatility -0.0002 0.0267 0.0006
0.0109 0.0204 0.0112
# Observations 1926 482 1444
# Countries 94 20 74
R-squared total 0.4482 0.3921 0.4529
R-squared within 0.1382 0.2303 0.1309
Table 3 (cont). Determinants of FDI. Basic Model
57
Empirical Analysis
Higher returns on international assets reduce FDI
(world growth and interest rates; term premium)
Higher local returns raise FDI (local productivity
growth, low tax burdens)
Higher local volatility reduces FDI (volatility of
productivity growth)
58
A Measure of Globalization
• Definition: Share of explained variance of FDI accounted for by global factors
– Identification problem: local and global factors are not mutually orthogonal
– Solution: attribute the covariance to the global factors --only the orthogonal component of the local factors is truly local (standard assumption but not critical for the results).
– Global factors affect FDI both directly and indirectly (i.e., through their impact on local factors)
59
A Measure of Globalization
Calculation:
• Construct the projection of local on global factors
10;
)'ˆ'ˆ(
)'ˆ(12
LLGG
GG
FFVar
FVar
)'ˆ(
)'ˆ,'ˆ(GG
LLGG
FVar
FFCov
• The globalization measure is computed as
60
A Measure of Globalization
• Implementation: based on parameter estimates (ignoring country fixed effects) from rolling regressions over a moving 16-year window
• Three results:
– Rising trend in globalization: from under 10 percent in 1985 to 60 percent in 2000.
– Globalization is higher for industrial than for developing countries – but the difference has narrowed lately (not significant after 1993)
– Trends in LAC and Asia are similar
61
Share of explained variance of FDI/GDP
accounted for by global factors
62
A Measure of Globalization • Where does the rising importance of global factors come from? • Direct ( ) vs indirect effect: the direct effect dominates, but the
indirect effect also shows a rising trend.0
63
A Measure of Globalization • Where does the rising importance of global factors come
from – their increasing relative variability or their increasing impact on FDI ?
• Further decompose the change in the direct effect into change in slope parameters + change in variance ratio:
where
64
A Measure of Globalization
• Most of the effect comes from the rising regression coefficient on the global factors.
• The variance ratio plays a minimal role – if anything, it works in the opposite direction
65
Global Factors and Liberalization
• Next, check that opening up of capital markets helps explain the larger role of global factors in FDI– Opening up makes it easier to trade away unsystematic risk.
The role of local factors in FDI should decline.
– Deeper integration can turn local factors into global.
• Empirical analysis using indicators of financial openness:– Official liberalization, first sign, investability (from Bekaert et al)
– IMF: AREAR restrictions
• Simple regressions of globalization measure on (GDP-weighted average of) openness indicators
• Strong positive association in every case
66
(robust standard errors and regression R2 under each coefficient)
67
Extensions and robustness checks
• Three sets of extensions:– Additional variables (with limited sample coverage)– Alternative specifications of the global factors (b)– Alternative samples and assumptions on the regression
residual.
• Three parts in each extension:– Re-estimation of the FDI equation– Recalculation of the globalization measure– Regression of globalization on liberalization indicators
• Qualitative results do not change
68
69
70
In summary,
• FDI flows reflect an increasing role of global factors
• The higher relevance of global factors is associated with increased financial integration
• But local factors continue to be important– Those considered (growth, openness, government size, macro
volatility) account for 40% of explained FDI variance.
– Unmeasured local factors are likely behind the fixed effects
The growing role of common factors reveals a ‘global financial
cycle’ driven by a few key variables summarizing global
financial conditions (Rey 2013, Bruno and Shin 2014):
• monetary policy in the US
• risk perceptions (as summarized by e.g., VIX).
When monetary policy loosens, the VIX falls, bank leverage
rises and gross credit flows rise as well.
Kuala Lumpur 2016 - Luis Servén71
The global cycle reflects the key role of the U.S. dollar:
• Dollar credit extended to non financial borrowers outside the US is worth
approximately 13% of non US World GDP (McCauley et al. 2014)
• Dollar also widely used around the world by asset managers
• Top 10 global asset management firms have more than $19 trillion in assets under
management
• Important role for global banks, in particular EU based
• Dollar as a funding currency: monetary policy has a direct effect on interest
payments, cash flow and net worth
• Dollar as an investment currency: a change in discount rate has an effect on
valuation of dollar assets, which can be used as collateral
Kuala Lumpur 2016 - Luis Servén72
Kuala Lumpur 2016 - Luis Servén73
Global banks play a central role in the global financial cycle
(Bruno and Shin 2014)
The leverage cycle of global banks is the key transmission
mechanism of financial conditions across borders
• Setting: regional banks borrow from global banks to lend
to local borrowers facing a maturity mismatch
Kuala Lumpur 2016 - Luis Servén74
Kuala Lumpur 2016 - Luis Servén75
• The leverage of global banks drives cross-border bank flows –
and thus the ‘common factor’
• Also, US dollar appreciation is associated with financial
tightening
– Local borrowers become riskier due to their currency mismatch, so
banks cut back on lending
– Consistent with the fact that financial crises are associated with dollar
shortages
• In regression results, cross-border bank flows are significantly
affected by global leverage and USD RER changes
Kuala Lumpur 2016 - Luis Servén76
Kuala Lumpur 2016 - Luis Servén77
Miranda-Agrippino and Rey (2014): assess the role of the ‘global
credit channel’ in the transmission of U.S. monetary policy
They set up a VAR to examine the global effects of U.S. monetary policy.
An increase in the effective fed funds rate has strong effects on:
• the global component of asset prices (-)
• the risk premium (+)
• the volatility of asset prices (+)
• bank leverage in the US and the EU (-)
• global domestic credit (with or without US) and cross border credit (-)
Kuala Lumpur 2016 - Luis Servén78
Kuala Lumpur 2016 - Luis Servén79
Kuala Lumpur 2016 - Luis Servén80
US monetary policy
• affects credit spreads and risk premia globally
• affects leverage and credit flows internationally
Thus it drives, at least in part, the Global Financial Cycle
• Countries may import monetary and financial conditions (even asset price
bubbles!) which do not necessarily fit their economies.
• And they show this applies regardless of countries’ exchange rate regime –
i.e., to Canada, U.K., Sweden,…
So Mundell’s trilemma really is just a dilemma – whether to have
an open capital account or not
Kuala Lumpur 2016 - Luis Servén81
Global imbalances
Until the global crisis, financial globalization was accompanied
by a surge in the current account gaps of major economies
• Deficit: USA, EU periphery
• Surplus: China, Germany, Japan
and also oil exporters
The magnitude of the imbalances has declined post-crisis
Kuala Lumpur 2016 - Luis Servén82
Kuala Lumpur 2016 - Luis Servén83
Persistent global imbalances are hardly a new phenomenon
• Canada and Australia ran 7%+ CA deficits over 1870-
1913 (Taylor 2002)
• Since 1980, current account balances of G-20 countries
have exceeded +/- 4% of GDP 30% of the time
Source: Bracke et al (2008)
Two main novelties of the recent episode:
• Emerging markets (with China and oil exporters at the top)
lending to the center country (unlike in the 1980s – Japan
and the EU were the creditors)
Japan-bashing in the 1980s, vs China-bashing in the latest
episode
• Bigger scale: the U.S. deficits of the 2000s reached above
1.5 percent of global GDP, and exceeded 1 percent of
global GDP for a decade.
Kuala Lumpur 2016 - Luis Servén86
Kuala Lumpur 2016 - Luis Servén87
Kuala Lumpur 2016 - Luis Servén88
Kuala Lumpur 2016 - Luis Servén89
Kuala Lumpur 2016 - Luis Servén90
Where did the imbalances come from?
Changes in the CA balance identically reflect changes in
saving and investment.
These provide an accounting decomposition – not an
explanation.
The big changes relate mostly to saving
1. Trend decline in U.S. saving rate since late 1990s
2. Steady rise in China’s saving since 2000 – to exceed 50% of
GDP at present (even higher than its massive investment!)
3. Recovery in Emerging Asia’s saving after 1997-98 crisis
4. Oil (and commodity) boom of the 2000s – with only modest rise
in oil exporters’ investment
[2+3+4 = Bernanke’s (2005) “global saving glut”]
Kuala Lumpur 2016 - Luis Servén91
Kuala Lumpur 2016 - Luis Servén92
The roots of global imbalances: view from goods markets
• Consumption rise in the U.S.
– Favored by credit growth, financial innovation, and the wealth effect of the housing bubble
• Consumption restraint in China
– Growth model oriented to production, rather than consumption: low real wages, declining social expenditure
– Weak corporate governance that allows big (semi-public) firms to retain their profits
– Limited access to outside financing for private firms and households
Kuala Lumpur 2016 - Luis Servén93
Modeling global imbalances
Most theoretical explanations take an asset-marketperspective: imbalances ultimately arise from international asymmetries in the supply and demand for assets
• Insurance needs vs asset availability in emerging markets:
– Limited supply of safe assets in fast-growing EMs –savers tilt their portfolios to rich-country assets
– Excess precautionary saving in EMs with underdeveloped financial and social protection systems – driven by idiosyncratic risks
– Post-1997 precautionary demand in EMs for foreign assets to self-insure against aggregate risks (‘suddenstops’)
Kuala Lumpur 2016 - Luis Servén94
• Neo-mercantilist policies in support of real undervaluation to promote growth
• Positive externalities from tradable sector development
These forces lead to accumulation of large foreign assetstocks.
• Note that in some explanations the accumulation is driven bygovernment action – Ricardian equivalence must fail(otherwise the private sector could just undo it)
• An overview of alternative explanations is Serven and Nguyen (2013)
Asymmetries in asset supply (Caballero et al 2008)
Real and financial shocks with unequally-developed
financial markets across world regions.
One region (U, the U.S. mainly) can supply financial
assets to global savers, the other (R, developing countries)
cannot.
R experiences fast growth but adverse financial shocks
(i.e., the crises of the 1990s).
The model generates increasing K-flows to U, falling
world interest rates, and increasing importance of U assets
in global portfolios – as actually observed.
The 3-region of the model adds E (Europe) that can also
supply assets but experiences adverse growth shocks.
Sketch of the model:
Two key parameters in each region
δ = financial development.
g = growth rate
Endowment economies: each period they receive an endowment
(1- δ) Xt, which cannot be capitalized in assets.
Finite-lived consumers á la Blanchard – they die at rate θ. At time of
death, they consume all their wealth, so Ct = θ Wt.
Only one saving vehicle – trees yielding dividend δXt per period. Their
total value is Vt. Hence ΔVt = rtVt – δXt
X grows at rate g.
With these assumptions, asset supply (scaled by output) equals
Vt / Xt = δ /(r – g)
Saving is ΔWt = + (1 – δ) Xt + r Wt – θ Wt ( = non-pledgeable income
+ return on wealth – consumption)
Integrating and letting t →∞, we get long-run (scaled) asset demand:
Wt / Xt = (1 – δ) / (g + θ – r)
Equating asset demand and supply Wt = Vt yields the autarky rate
raut = g + δθ
[equivalently, this follows directly since Wt = Vt implies Wt = Xt / θ,
and thus r = DXt / Xt + δθ = g + δθ.]
The Metzler diagram: Autarky equilibrium
The key ingredients of the model are (1) only a fraction δ < 1 of the
present value of future output can be capitalized in assets, and this is
reflected in asset supply; and (2) δ also affects the demand for assets.
Financial integration between two regions U and R with different raut
leads to a world interest rate that lies in between them.
If the two regions differ only in their respective δ, the resulting r is
just
r = g + θ[δRxR + δU(1 – xR)]
where xR is region R’s share of total output.
The current account is just CA = Δ W – Δ V. Hence
CAU / XU = g (r – rUaut ) / [(r – g) (g + θ – r)]
So the region with high autarky rate runs a (permanent!) CA deficit,
the region with low autarky rate runs a permanent surplus.
At the initial r, there is excess world demand for assets, so r must fall.
At the lower r in the new world equilibrium, R’s demand for assets
exceeds its supply, and conversely in U. U runs a permanent current
account deficit, matched by a permanent flow of saving from R.
A decline in θR (a saving glut – here lower death rate means lower
consumption) has analogous effects.
NFA in U World interest rate
The effects are larger if gR > g
Consider now a three-region world (U, R, E). Assume a collapse in gE
followed by a fall in δR.
The two shocks reinforce each other.
NFA in U, E World interest rate
In this model capital flows out of the high-growth
economies (rather than into them) because of their limited
ability to generate assets for savers.
The model illustrates how the collapse of asset markets in
Japan and the emerging market crashes of the 1990s can
account for the observed pattern of global imbalances and
interest rates.
What could halt these trends ?
• A recovery in Europe / Japan.
• Financial deepening in emerging markets
• A decline in China’s (or in oil exporters’) saving
Asymmetries in asset demand
• Mendoza et al (2009): model of sustainable global
imbalances driven by asymmetric financial market
development.
The driving force is idiosyncratic risk that generates
precautionary saving. The economy with less-developed
markets has higher precautionary saving than the advanced
economy.
Setup with identical consumers (with U’’’>0). One (risky)
productive asset. Two types of idiosyncratic shocks:
endowment shocks and investment shocks. No aggregate
shocks (to sharpen the focus on idiosyncratic risk)
Financial market development is measured by the (source)
country-specific enforcement of financial contracts
(appropriability of returns) – it applies to all assets held by
residents, whether at home or abroad.
Under autarky, the country with lower enforcement has
higher precautionary saving and lower interest rate.
Financial integration of the two economies then makes the
less-developed one hold positive net foreign assets. The
more advanced country holds negative net foreign assets.
Moreover, the advanced economy holds a positive position
in the productive (risky) asset – i.e., it finances risky
foreign investment with foreign debt, and earns a return
differential (a risk premium).
Equilibrium when economy #2 is less financially developed than 1
If enforcement is source-based rather than residence-
based, in equilibrium the country with better enforcement
still is a net debtor – but now its position in risky assets is
negative too.
In summary, financial integration among economies with
different degrees of financial development generates
global imbalances:
• the imbalances are large for reasonable parameter values
• they develop gradually after financial integration –
disappear only in the very long run.
• they are larger the bigger the difference between the
degrees of financial development of the two economies.
Idiosyncratic unemployment risk another way for
precautionary saving to generate global imbalances.
(Carroll and Jeanne 2013)
Unemployment risk (really ‘retirement risk’: the
unemployed never work again) makes individuals hold
precautionary wealth for self-insurance.
The key parameter is the scope of ‘social insurance’: a
government-run unemployment or pay-as-you-go benefit
that provides (partial) insurance. The more generous social
insurance, the lower saving and the lower net foreign
assets.
Empirically, there is evidence that social public spending
has a negative effect on household saving.
Here an increase in growth lowers target precautionary
wealth and thus foreign assets.
But if it is accompanied by a simultaneous increase in
unemployment risk (e.g., sector-wise growth uncertainty)
then the effects are reversed: target precautionary wealth
rises, and NFA rises too.
In a two-country version where countries have different
levels of social insurance, the country with the more
generous system is a net debtor.
Catch-up by the less-generous country in terms of social
insurance eliminates global imbalances. But it also reduces
global saving and raises the real interest rate. This lowers
K/Y and the real wage – and is welfare-reducing in both
countries (except for the current generation of workers that
start receiving the social transfers).
These papers focus on asset demand driven by self-
insurance against idiosyncratic risk.
An alternative explanation focuses on aggregate risk:
foreign asset hoarding to self-insure against sudden stops.
The underlying reason is the lack of international risk-
sharing mechanisms to insure against global financial
turbulence.
In the recent global crisis, countries holding massive
foreign reserve war chests have done better than the rest.
This tends to encourage more reserve hoarding post-crisis.
The preceding explanations of global imbalances focus on the
role of risk for asset demand.
An alternative explanation stresses ‘new mercantilism’: foreign
asset hoarding to sustain undervalued exchange rates and
export-led development.
Undervaluation in essence amounts to postponing consumption
in order to grow faster, so it trades off a present consumption
loss for a future consumption gain (Korinek and Serven 2015).
Minimal setting:
2 intermediate goods, traded and nontraded. Both use
capital and labor; tradables are (broad) K-intensive
(think of industry vs services)
Final good is produced using both intermediates.
Small open economy; capital account closed to private
agents.
1
1
1
Intermediate goods sectors:
Tradables
Non-tradables
Final goods sector
: > Tradables are relatively capital-intensive
: by suitabl
T T T
N N N
Z
T K A L
N K A L
Z A T N
Assumption 1
Assumption 2 e choice of units, T NA A K
Tradables are intensive in capital that yields economy-wide
productivity gains – a positive externality.
The externality is ignored by private agents, so the private
return on capital R falls short of its social return A*:
The decentralized equilibrium yields too much consumption
and too little investment (and thus growth is too slow).
This creates a divergence between the growth rate (standard
AK-style) that results in the decentralized equilibrium (DE)
and the social optimum (SP).
*; 0 1R A
1/
1/
1 [ (1 )]
1 [ (1 * )] 1
DE
SP DE
R
A
The socially-optimal growth rate is higher – driven by faster
capital accumulation and productivity growth.
Subsidizing capital yields a first-best (has only a second-order
cost vs a first-order benefit). The optimal subsidy rate is
Equivalently, one could provide an investment tax credit, or a
production subsidy to the tradable sector.
But there may be targeting problems:
• selective subsidies favor rent-seeking and leakage (as in the EU
linen scam)
• major information requirements for government to know which
sectors / goods to subsidize
• WTO rules prohibit direct or indirect export promotion
* (1 ) *Ks A
A second-best alternative is to ‘outsource’ the targeting
problem: ‘lending’ to foreigners (= hoarding foreign assets)
who only purchase tradable goods.
This reduces the local availability of tradables and raises their
relative price (= RER) and thus the return on capital.
Hence investment and growth rise too.
This raises future consumption, but at the cost of present
consumption – static loss vs dynamic gain.
Note the importance of the closed capital account – otherwise
private agents can undo the government’s asset hoarding.
Under reasonable parameterizations the net welfare effect
is small and in most cases negative.
When is the welfare effect more likely positive?
-- the bigger the gap in sectoral capital intensities
-- the more patient consumers are
-- the higher the elasticity of intertemporal substitution
Note: if foreign asset hoarding is welfare-improving, then
foreign aid (= asset gifts from abroad) must be welfare-
reducing.
Likewise, resource windfalls must reduce welfare (the
‘resource curse’).
• Should we still worry about large current account gaps (= net
capital flows) in the globalization era (Obstfeld 2011) ?
• The data show two facts:
(i) Gross flows far exceed net flows (and both show little
correlation).
(ii) Changes in net foreign asset positions are dominated by
valuation effects, not by current account imbalances.
So is the current account still a policy-relevant magnitude?
However, in the long run, the level of net foreign asset
positions tracks the cumulative CA fairly closely.
• the U.S. appears to be the main exception (also Switzerland)
In other words, capital gains and losses on assets and
liabilities cannot systematically offset the external wealth
effects of the current account.
• Although the strength of this conclusion depends on the
quality of the (noisy!) valuation data…
Hence the current account remains a (rough) guide to
long-term external sustainability.
Kuala Lumpur 2016 - Luis Servén122
Kuala Lumpur 2016 - Luis Servén123
The gains from financial integration
In theory, integration should be welfare-enhancing – through two
channels:
1. Growth: financial integration allows access to capital at
lower cost. In capital-scarce countries, this speeds up
investment and income growth, and hence consumption.
• Possibly also: productivity: financial integration may bring
improvements in TFP – from new technologies, better organization
of production, better corporate governance, financial
development…(e.g., Bonfiglioli 2009)
Kuala Lumpur 2016 - Luis Servén126
2. Risk: financial integration allows idiosyncratic risk to be
diversified away. Consumers can stabilize their consumption
streams and raise their welfare.
• The downside: the economy may become more vulnerable to financial
turbulence and crises arising from world markets. Thus even if
idiosyncratic risk falls, overall consumption volatility might rise!
The theoretical presumption is that financial integration opens
new markets / allows new transactions (= removes distortions)
But with otherwise incomplete markets (= other distortions
remain), it is not necessarily welfare-increasing
Kuala Lumpur 2016 - Luis Servén127
Massive literature (mostly reduced-form empirics) attempting to
quantify the effects of integration on key outcome variables:
growth, volatility…[Kose et al (2009) is a good survey]
Few robust results.
Endogeneity – integration is not a random occurrence
Omitted variables – integration often happens along with other measures
Heterogeneity – structural features and initial conditions vary a lot across
countries…
Kuala Lumpur 2016 - Luis Servén128
Consider the capital scarcity effect: in a capital-scarce
economy opening up to capital flows lowers the cost of capital
to world levels.
In the neoclassical (Solow) benchmark model, this speeds up
convergence: the economy jumps to the steady-state k/l ratio
without having to sacrifice current consumption.
At the time of opening-up, there is a surge in capital inflows and
investment. Output growth must rise too.
But this is just a transitional effect: the growth acceleration
eventually tapers off (unless productivity growth is affected).
Kuala Lumpur 2016 - Luis Servén129
How big is the welfare gain for a small country?
In the Solow model, it depends only on the gap between the cost
of capital under financial autarky, and the world real interest rate
(the cost of capital under financial integration).
Gourinchas and Jeanne (2006), Henry (2007)…
Let R = (gross) cost of capital under autarky, given by the
marginal productivity of capital – declining over time
R* = (gross) cost of capital under financial integration
Along the autarky growth path, R gradually falls towards R*
Kuala Lumpur 2016 - Luis Servén130
The welfare gain depends only on the gap between the cost of
capital under financial autarky, and the world real interest rate
For a marginal increase in capital inflows (=opening up), the
equivalent consumption variation can be shown to equal
1
0
ln ( *)
where is the ratio of capital flows to consumption, and
(1 )
is the 'permanent' cost of capital under autarky
t
t
t
d C R R
R R
So everything depends on how quickly R would have
converged to R*
Kuala Lumpur 2016 - Luis Servén131
Equivalently, the result depends on how far k is from its long-
run value (= how scarce capital is before opening up).
With standard parameters, the welfare gain from the capital
scarcity effect is quite small – unless capital is very scarce.
Kuala Lumpur 2016 - Luis Servén132
But these calculations assume that the autarky R converges very
quickly to the world R* -- by using rates of convergence derived
from the canonical growth model, around 5-6% per year.
Observed rates of convergence are much slower – around 2%
per annum.
This means that in autarky R would remain above R* for a very
long time – which makes a big difference for the welfare gain:
The equivalent consumption variation then rises from 1.7% of
annual consumption (Gourinchas and Jeanne 2006) to 7% !
Kuala Lumpur 2016 - Luis Servén133
The capital scarcity effect is typically analyzed in a partial
equilibrium setting:
• Integration of the capital-scarce country(ies) has no effect on
the world interest rate – does this work for China?
• The role of risk is ignored
Taking instead a general equilibrium view changes the
conclusions (Coeurdacier, Rey and Winant 2015):
• The steady-state itself is modified by financial integration in
the presence of risk.
• The capital-scarce country may not gain the most
Kuala Lumpur 2016 - Luis Servén134
General equilibrium setting
• Integration through risk-free bond only (important!)
• Stochastic neoclassical framework with two production
economies
• An emerging (risky) country (5% volatility of productivity
shocks)
• A relatively safer developed country (2.5% volatility)
• Emerging country starts with 50% of the capital of developed
country.
Kuala Lumpur 2016 - Luis Servén135
Questions
• What is the growth impact of financial integration?
• What are the patterns of capital flows?
• How big are the gains from financial integration?
• Who benefits the most?
Kuala Lumpur 2016 - Luis Servén136
Kuala Lumpur 2016 - Luis Servén137
Kuala Lumpur 2016 - Luis Servén138
Kuala Lumpur 2016 - Luis Servén139
Kuala Lumpur 2016 - Luis Servén140
Kuala Lumpur 2016 - Luis Servén141
Kuala Lumpur 2016 - Luis Servén142
Kuala Lumpur 2016 - Luis Servén143
Kuala Lumpur 2016 - Luis Servén144
Kuala Lumpur 2016 - Luis Servén145
Kuala Lumpur 2016 - Luis Servén146
Kuala Lumpur 2016 - Luis Servén147
Kuala Lumpur 2016 - Luis Servén148
Kuala Lumpur 2016 - Luis Servén149
Kuala Lumpur 2016 - Luis Servén150
Kuala Lumpur 2016 - Luis Servén151
Kuala Lumpur 2016 - Luis Servén152
Kuala Lumpur 2016 - Luis Servén153
Kuala Lumpur 2016 - Luis Servén154
Kuala Lumpur 2016 - Luis Servén155
Globalization and risk sharing
Under optimal international diversification with complete
markets, consumers in each country can fully diversify country-
specific risk.
Representative consumers in all countries would reach the same
portfolio allocation (in terms of wealth shares) – a sort of “world
mutual fund” whose assets are claims on world output
each country’s income flow is a constant fraction (given by its
share in the world mutual fund) of world income.
Kuala Lumpur 2016 - Luis Servén156
Each country’s consumption is proportional to world
consumption
All countries share the same consumption growth rate.
consumption growth is perfectly correlated across countries
[whatever the correlation of their respective GDP growth rates]
In reality consumption growth is only weakly correlated across
countries. It is much more highly correlated with local GDP
growth.
Have these correlations changed with financial globalization?
Kuala Lumpur 2016 - Luis Servén157
Kuala Lumpur 2016 - Luis Servén158
Consumption correlations appear to have risen for rich countries but not forthe rest. Even for rich countries they remain pretty low (under 0.5).
Kuala Lumpur 2016 - Luis Servén159
One popular way to test the null hypothesis of perfect risk-
sharing is by running the regression
If the slope coefficient is significantly different from zero, the
null is rejected – which is the most frequent result.
Many papers go on to take as a measure of risk-sharing (1–
minus) the slope coefficient – although there is no solid
theoretical basis for this interpretation (more on this later).
The usual strategy is to compare slope estimates across
country groups and time periods (e.g., Kose et al 2009).
Kuala Lumpur 2016 - Luis Servén160
Cross-sectional regressions
Kuala Lumpur 2016 - Luis Servén161
Regressions over rolling windows (group medians)
Kuala Lumpur 2016 - Luis Servén162
• Overall, the extent of ‘risk sharing’ (the slope coefficients)
does not seem higher in the 1990s-2000s than in the 1970s.
• There is some evidence of an upturn after the 1980s, but only
for advanced countries
Another popular exercise is to add financial integration FO to
the regression interacted with the output term:
Heuristically, the sign of the new coefficient should be negative.
Kuala Lumpur 2016 - Luis Servén163
Kose et al do this using various measures of de jure and de facto
integration.
The interacted term is significant only for rich countries –
whether integration is measured by external assets, liabilities or
their sum.
This suggests possible ‘threshold effects’:• Integration only helps if you are highly integrated (like OECD)
• Integration only helps with highly developed financial markets and
institutions (like OECD?)
Kuala Lumpur 2016 - Luis Servén164
The weakness of this approach is that once perfect risk sharing is
rejected, the slope coefficients of these regressions are
meaningless.
To derive an empirical measure of risk sharing one needs to
specify an insurance arrangement – as in e.g., Crucini (1999):
• Prior to any income realization, permanent-income consumers /
countries contribute a fraction of their income to an income pool
• After the realization, they get the same fraction of whatever the pool’s
total income turns out to be
But empirical implementations of this setting assume that all
agents contribute the same fraction of income – so it is no suited
for a situation with heterogeneous agents / countries.
Kuala Lumpur 2016 - Luis Servén165
The obvious solution is to extend Crucini’s framework to the
case of agents / countries engaging in different extents of risk-
sharing.
• Hevia and Servén (2016): different agents / countries may contribute
different fractions of their income to the pool
• Their respective contributions can be interpreted as their degree of
risk-sharing
• Now the growth of each agent’s consumption depends on the
innovations to the permanent income of all agents
• The weights on the various innovations are nonlinear functions of the
income shares contributed by all agents.
• This leads to a highly nonlinear empirical model – a system with
nonlinear parameter constraints across equations (in contrast with the
simple OLS regressions of earlier applications)
Kuala Lumpur 2016 - Luis Servén166
In this setting, we can estimate the degree of risk-sharing of each
of 50 countries that make up the ‘world economy’.
Two-step approach:
i. Estimate the innovations to each country’s permanent
income – using alternative specifications
ii. Estimate the risk-sharing coefficients -- which theoretically
should lie between 0 and 1 but are not restricted to do so
Kuala Lumpur 2016 - Luis Servén167
Three main results emerge:
1. The estimated degree of consumption risk-sharing is higher
in advanced countries
2. It has been on the rise since the 90s –but mainly in rich
countries, so the gap has widened
3. Countries with more open capital accounts exhibit higher
degrees of risk-sharing
Kuala Lumpur 2016 - Luis Servén168
Kuala Lumpur 2016 - Luis Servén169
Kuala Lumpur 2016 - Luis Servén170
Kuala Lumpur 2016 - Luis Servén171
Kuala Lumpur 2016 - Luis Servén172
Kuala Lumpur 2016 - Luis Servén173
Kuala Lumpur 2016 - Luis Servén174